repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
karlstroetmann/Algorithms | Python/Chapter-08/Heapsort.ipynb | gpl-2.0 | import graphviz as gv
"""
Explanation: This notebook implements an array-based version of Heapsort.
Graphical Representation of Heaps as Trees
End of explanation
"""
def toDot(A, f, g):
n = len(A)
dot = gv.Digraph(node_attr={'shape': 'record'})
for k, p in enumerate(A):
if k < f:
do... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_eco/TD2A_eco_API_SNCF.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.eco - Exercice API SNCF
Manipulation d'une API REST, celle de la SNCF est prise comme exemple. Exercices.
End of explanation
"""
import os
from pyquickhelper.loghelper import get_password
key = get_password("sncf", "ensae_teaching_cs... |
google-research/tensorflow-coder | TF-Coder_Colab.ipynb | apache-2.0 | #@title Run this cell after making your choices.
allow_data_collection = True #@param {type: "boolean"}
include_in_dataset = True #@param {type: "boolean"}
if allow_data_collection:
if include_in_dataset:
print('Usage data may be collected and released in a public dataset.')
else:
print('Usage data may ... |
IBM-Bluemix/BigInsights-on-Apache-Hadoop | examples/notebooks/WebHDFS.ipynb | apache-2.0 | # Cluster number, e.g. 100000
cluster = ''
# Cluster username
username = ''
# Cluster password
password = ''
# file path in HDFS
webhdfs_filepath = 'yourpath/yourfile.txt'
# where to save the file in the spark service file system
local_filepath = 'yourfile.txt'
"""
Explanation: Also see: https://datascience.ibm.... |
AllenDowney/ProbablyOverthinkingIt | red_dice.ipynb | mit | from __future__ import print_function, division
from thinkbayes2 import Pmf, Suite
from thinkbayes2 import MakeMixture
from fractions import Fraction
"""
Explanation: A dice problem
This notebook demonstrates the use of Pmf and Suite objects, and explores a dice-rolling problem that turns out to be more complicated ... |
tensorflow/workshops | extras/tensorflow_lattice/02_advanced_lattice_estimators.ipynb | apache-2.0 | # import libraries
!pip install tensorflow_lattice
import pandas as pd
import tensorflow as tf
import tensorflow_lattice as tfl
import tempfile
import urllib
import os
def download_if_not_exists(train_data, test_data):
"""Maybe downloads training data and returns train and test file names."""
train_file_name =... |
BrainIntensive/OnlineBrainIntensive | resources/matplotlib/Examples/scatterplots.ipynb | mit | %load_ext watermark
%watermark -u -v -d -p matplotlib,numpy
"""
Explanation: Sebastian Raschka
back to the matplotlib-gallery at https://github.com/rasbt/matplotlib-gallery
End of explanation
"""
%matplotlib inline
"""
Explanation: <font size="1.5em">More info about the %watermark extension</font>
End of explanati... |
rickiepark/tfk-notebooks | first-contact-with-tensorflow/chapter2_regression.py.ipynb | mit | import numpy as np
"""
Explanation: numpy 패키지를 임포트합니다.
End of explanation
"""
num_points = 1000
vectors_set = []
for i in range(num_points):
x1 = np.random.normal(0.0, 0.55)
y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
vectors_set.append([x1, y1])
"""
Explanation: 1000개의 데이터 포인트를 만듭니... |
ccasotto/rmtk | rmtk/vulnerability/derivation_fragility/NLTHA_on_SDOF/NLTHA_on_SDOF.ipynb | agpl-3.0 | import NLTHA_on_SDOF
from rmtk.vulnerability.common import utils
%matplotlib inline
"""
Explanation: Non-Linear Time History Analysis (NLTHA) for Single Degree of Freedom (SDOF) Oscillators
In this method, a single degree of freedom (SDOF) model of each structure is subjected to non-linear time history analysis (NLTHA... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n05_day56_model_choosing_close_feat_all_syms_equal.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
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (20.0, 10... |
flsantos/startup_acquisition_forecast | exploratory_code/3_dataset_exploration.ipynb | mit | import pandas as pd
startups = pd.read_csv('data/startups_2.csv', index_col=0)
startups[:3]
"""
Explanation: Dataset Exploration
Here we'll be exploring how each of the features we have so far relates to the target variable "status"
Importing the dataset
End of explanation
"""
import seaborn as sns
import matplotlib... |
tiagoantao/biopython-notebook | notebooks/17 - Graphics including GenomeDiagram.ipynb | mit | #Lets load notebook's Image
from IPython.core.display import Image
from reportlab.lib import colors
from reportlab.lib.units import cm
from Bio.Graphics import GenomeDiagram
from Bio import SeqIO
"""
Explanation: Source of the materials: Biopython cookbook (adapted)
End of explanation
"""
record = SeqIO.read("data... |
oyamad/theory16 | support_enumeration/support_enumeration_py.ipynb | mit | import numpy as np
"""
Explanation: Implementing Support Enumeration in Python: Solutions
Exercises on NumPy arrays
First follow the lecture on NumPy.
Import NumPy:
End of explanation
"""
n = 5
a = np.arange(n)
a
"""
Explanation: Consider the following 1-dimensional array:
End of explanation
"""
a[-1]
"""
Expla... |
vangj/py-bbn | jupyter/generate-bbn.ipynb | apache-2.0 | import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import warnings
from pybbn.generator.bbngenerator import generate_singly_bbn, generate_multi_bbn, convert_for_exact_inference
from pybbn.generator.bbngenerator import convert_for_drawing
from pybbn.pptc.inferencecontroller import InferenceControl... |
gcgruen/homework | foundations-homework/08/homework-08-gruen-dataset3-refugees.ipynb | mit | # datasource: https://data.humdata.org/dataset/unhcr-refugee-pop-stats/resource/fbacbba3-1b20-4331-931b-6a21a4cb80f5
# dataset is labeled "Group of concern to UNHCR"
import pandas as pd
import matplotlib.pyplot as plt
% matplotlib inline
df = pd.read_csv('refugee_data.csv')
df.head(20)
"""
Explanation: Homework 8: ... |
hektor-monteiro/curso-python | listas-arrays.ipynb | gpl-2.0 | lista = [2, 3.4, 'teste'] # sintaxe para definir uma lista
print lista
x = 3.0
y = 5.3
lista = [x**2, x+y, x**y, 2*x+5*y] # usando expressões para definir listas
print lista
"""
Explanation: Listas e arrays (vetores)
até agora vimos variáveis que representam valores unicos de um dado tipo
em física é comum querer... |
gtrichards/PHYS_T480 | MixtureModel.ipynb | mit | from IPython.display import YouTubeVideo
YouTubeVideo("B36fzChfyGU")
"""
Explanation: Gaussian Mixture Models (GMM)
KDE centers each bin (or kernel rather) at each point. In a mixture model we don't use a kernel for each data point, but rather we fit for the locations of the kernels--in addition to the width. So a m... |
danresende/deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
BrownDwarf/ApJdataFrames | notebooks/Wilking2005.ipynb | mit | import warnings
warnings.filterwarnings("ignore")
from astropy.io import ascii
import pandas as pd
"""
Explanation: ApJdataFrames Wilking2005
Title: Optical Spectroscopy of the Surface Population of the ρ Ophiuchi Molecular Cloud: The First Wave of Star Formation
Authors: Wilking et al.
Data is from this paper:
http... |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/predict.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
plt.rc("figure", figsize=(16, 8))
plt.rc("font", size=14)
"""
Explanation: Prediction (out of sample)
End of explanation
"""
nsample = 50
sig = 0.25
x1 = np.linspace(0, 20, nsample)
X = np.column_stack((x1, np.sin(x... |
mne-tools/mne-tools.github.io | dev/_downloads/8ea2bfc401dbdff70c284d271d62fa8c/label_from_stc.ipynb | bsd-3-clause | # Author: Luke Bloy <luke.bloy@gmail.com>
# Alex Gramfort <alexandre.gramfort@inria.fr>
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.datasets import sample
print(__doc__)
data_path = sample.da... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/sandbox-1/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-1', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: SANDBOX-1
Topic: Landice
Sub-Topic... |
mit-eicu/eicu-code | notebooks/diagnosis.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import getpass
import pdvega
# for configuring connection
from configobj import ConfigObj
import os
%matplotlib inline
# Create a database connection using settings from config file
config='../db/config.ini'
#... |
SubhankarGhosh/NetworkX | 6. Network Statistical Inference (Student).ipynb | mit | # Read in the data.
# Note from above that we have to skip the first two rows, and that there's no header column,and that the edges are
# delimited by spaces in between the nodes. Hence the syntax below:
G = cf.load_propro_network()
"""
Explanation: Introduction
In this notebook, we will walk through a hacker's approa... |
Mashimo/datascience | 02-Classification/isomap-pca.ipynb | apache-2.0 | import pandas as pd
#
# Load up the dataset into a variable called X.
#
X = pd.read_csv("../datasets/wheat.data", index_col='id')
X.head()
"""
Explanation: Variance and eigenvalues
We will tackle the first four steps of a principal component analysis: standardizing the data, constructing the covariance matrix, obta... |
tensorflow/docs-l10n | site/ja/lite/performance/post_training_integer_quant_16x8.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... |
cydcowley/Imperial-Visualizations | visuals_maths/3D Transformations (Cube)/CubeRotation.ipynb | mit | import plotly
import plotly.offline as py
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
import numpy as np
from plotly.graph_objs import Surface
import json
"""
Explanation: 3D Transformations
Consider a point P, which, in cartesian coordinates is given by P(x,y,z).
It can also be represented by... |
weinbe58/QuSpin | examples/notebooks/BHM.ipynb | bsd-3-clause | from quspin.operators import hamiltonian # Hamiltonians and operators
from quspin.basis import boson_basis_1d # Hilbert space boson basis
import numpy as np # generic math functions
"""
Explanation: Coding the Bose-Hubbard Hamiltonian with QuSpin
The purpose of this tutorial is to teach the interested user to construc... |
RazerM/orbital | examples/ipython_notebook/maneuvers.ipynb | mit | %matplotlib inline
from scipy.constants import kilo
from orbital import earth, KeplerianElements, Maneuver, plot
"""
Explanation: Maneuvers
End of explanation
"""
orbit1 = KeplerianElements.with_altitude(1000 * kilo, body=earth)
man1 = Maneuver.set_apocenter_altitude_to(10000 * kilo)
plot(orbit1, title='Maneuver ... |
timothydmorton/usrp-sciprog | day3/Astropy-Demo.ipynb | mit | kpc_to_km = 3.086E16
distance = 1. # kpc
distance * kpc_to_km
type(distance)
import astropy.units as u
distance_q = 1 * u.kpc
type(distance_q)
distance_q.to(u.km)
distance_q.to(u.jupiterRad)
distance_M = 1 * u.Mpc
distance_q + distance_M
"""
Explanation: Using variables with units
We'll use astropy to unburden us... |
tuanavu/coursera-university-of-washington | machine_learning/1_machine_learning_foundations/assignment/week6/Deep Features for Image Retrieval.ipynb | mit | import graphlab
"""
Explanation: Building an image retrieval system with deep features
Fire up GraphLab Create
End of explanation
"""
image_train = graphlab.SFrame('image_train_data/')
"""
Explanation: Load the CIFAR-10 dataset
We will use a popular benchmark dataset in computer vision called CIFAR-10.
(We've red... |
mnschmit/LMU-Syntax-nat-rlicher-Sprachen | 09-notebook-solution.ipynb | apache-2.0 | from nltk.featstruct import FeatStruct
"""
Explanation: Übungsblatt 9
Präsenzaufgaben
Aufgabe 1 Unifikation I
End of explanation
"""
f1 = FeatStruct(
'[Vorname=Max, Nachname=Mustermann,' +
'Privat=[Strasse=Hauptstrasse, Ort=[Muenchen]]]'
)
f2 = FeatStruct(
'[Arbeit=[Strasse="Oettingen... |
bburan/psiexperiment | examples/notebooks/stimulus generation.ipynb | mit | from psi.controller.calibration.api import FlatCalibration
"""
Explanation: Calibration
Several types of calibrations are supported. The simplest calibration assumes that frequency response is "flat". In other words, if you send a 1V RMS tone to the speaker, it will always produce the same output level regardless of f... |
maptime/boulder | geopandas/plotting_with_geoplot.ipynb | bsd-2-clause | import geopandas
path = geopandas.datasets.get_path('naturalearth_lowres')
df = geopandas.read_file(path)
# Add a column we'll use later
df['gdp_pp'] = df['gdp_md_est'] / df['pop_est']
boroughs = geopandas.read_file(geopandas.datasets.get_path('nybb')).to_crs(epsg='4326')
injurious_collisions = geopandas.read_file(
... |
statsmodels/statsmodels | examples/notebooks/statespace_arma_0.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.graphics.api import qqplot
"""
Explanation: Autoregressive Moving Average (ARMA): Sunspots data
This notebook replicates the existing ARMA notebook using th... |
tensorflow/decision-forests | documentation/tutorials/advanced_colab.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... |
SamLau95/nbinteract | docs/notebooks/examples/examples_sampling_from_a_population.ipynb | bsd-3-clause | united = Table.read_table('http://inferentialthinking.com/notebooks/united_summer2015.csv')
united
"""
Explanation: Sampling from a Population
The law of averages also holds when the random sample is drawn from individuals in a large population.
As an example, we will study a population of flight delay times. The tabl... |
UOADataScience/machine-learning-tutorial | numpy_demo/tutorial_draft.ipynb | mit | import numpy as np
my_vector = np.asarray([1,2,3])
my_matrix = np.asarray([[1,2,3],[10,10,10]])
print(my_matrix*my_vector)
"""
Explanation: Welcome to the Tutorial!
First I'll introduce the theory behind neural nets. then we will implement one from scratch in numpy, (which is installed on the uni computers) - just typ... |
pycrystem/pycrystem | doc/demos/07 Azimuthal Integration Using pyFAI Detector.ipynb | gpl-3.0 | %matplotlib inline
import hyperspy.api as hs
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Azimuthal Integral
Introduction
This tutorial demonstrates how to acquire an azimuthal integral profile from a multidimensional data set in pyXem.
The data set is a 10x10x256x256 data set of a polycrystall... |
johnhw/summerschool2015 | CrashCourse_Lecture2.ipynb | mit | # standard imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.preprocessing, sklearn.linear_model, sklearn.datasets
import sklearn.svm, sklearn.cross_validation, sklearn.grid_search, sklearn.metrics, sklearn.decomposition
import scipy.spatial
import ipy_table
import seaborn
im... |
dtamayo/reboundx | ipython_examples/EccAndIncDamping.ipynb | gpl-3.0 | import rebound
import reboundx
import numpy as np
sim = rebound.Simulation()
ainner = 1.
aouter = 10.
e0 = 0.1
inc0 = 0.1
sim.add(m=1.)
sim.add(m=1e-6,a=ainner,e=e0, inc=inc0)
sim.add(m=1e-6,a=aouter,e=e0, inc=inc0)
sim.move_to_com() # Moves to the center of momentum frame
ps = sim.particles
"""
Explanation: Eccentri... |
hanhanwu/Hanhan_Data_Science_Practice | Better4Industry/explore_features_fastai.ipynb | mit | %matplotlib inline
from fastai.imports import *
from fastai.structured import *
from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from IPython.display import display
from sklearn import metrics
from sklearn.preprocessing import LabelEncoder
from skle... |
fujii-team/GPinv | notebooks/Stochastic_approximation_demo.ipynb | apache-2.0 | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
# Import GPinv
import GPinv
# Import GPflow as comparison
import GPflow
# import graphical things
import seaborn as sns
sns.set_context('poster')
sns.set_style('ticks')
import matplotlib
fontprop = matplotlib.font_manager.F... |
jArumugam/python-notes | P13Interactive Widgets.ipynb | mit | # Start with some imports!
from __future__ import print_function
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
# Very basic function
def f(x):
return x
help(interact)
"""
Explanation: Interactive Widgets
Using interact
Source Link
End of explanation
"""
# Generate a slider t... |
dipanjank/ml | statistics/hypothesis_testing_intro.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
%pylab inline
pylab.style.use('ggplot')
data = np.array([7.65, 7.60, 7.65, 7.70, 7.55, 7.55, 7.40, 7.40, 7.50, 7.50])
"""
Explanation: The general idea of hypothesis testing involves:
Making an initial assumption.
Collecting evidence (data).
Based on the available evidence (dat... |
sertansenturk/tomato | demos/score_conversion_demo.ipynb | agpl-3.0 | xml_output, ly_output, svg_output, txt_ly_mapping = symbTrConverter.convert(
txt_filename, mu2_filename, symbtr_name=symbtr_name, mbid=work_mbid,
render_metadata=render_metadata, xml_out=xml_filename, ly_out=ly_filename,
svg_out=svg_filename_template, svg_paper_size=svg_paper_size)
"""
Explanation: You ... |
kubeflow/examples | demos/recurring/recurring.ipynb | apache-2.0 | import kfp
host = 'http://localhost:3000'
client = kfp.Client(host=host)
"""
Explanation: Recurring runs with the KFP SDK
If you're running on a local cluster, expose the GUI and API, respectively, with
the following commands:
kubectl port-forward -n kubeflow svc/ml-pipeline-ui 8080:80
kubectl port-forward -n kubeflo... |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/sandbox-3/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'sandbox-3', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: CCCMA
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topics: Transport, Emission... |
SimonBiggs/electroninserts_bundle | Update model data.ipynb | agpl-3.0 | import os
import dicom
from glob import glob
import zipfile
import time
import shutil
import datetime
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.core.display import display, Markdown
from bokeh.io import output_notebook, output_file, reset_outpu... |
weichetaru/weichetaru.github.com | notebook/machine-learning/deep_learning-neural-network-gradient-decent-part1.ipynb | mit | # Python imports
import numpy as np # Matrix and vector computation package
import matplotlib.pyplot as plt # Plotting library
from matplotlib.colors import colorConverter, ListedColormap # some plotting functions
from mpl_toolkits.mplot3d import Axes3D # 3D plots
from matplotlib import cm # Colormaps
# Allow matplot... |
jayhetee/dask | notebooks/Using dask and anaconda-cluster to analyze data on an EC2 cluster.ipynb | bsd-3-clause | ### cut me out
import boto
bucket = boto.connect_s3().get_bucket('githubarchive-data') # this is a public bucket
from fnmatch import fnmatchcase
keys = bucket.list()
def data_size(key_str):
"""Check the size of all the keys in our bucket that match pattern
that can contain ? and * for matching.
"""
ma... |
mzohaib10/datascience-starters | visualization-in-matplotlib/visualization-in-matplotlib.ipynb | gpl-3.0 | # Download data, unzip, etc.
import pandas as pd
import urllib
import tempfile
import shutil
import zipfile
temp_dir = tempfile.mkdtemp()
data_source = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip'
zipname = temp_dir + '/Bike-Sharing-Dataset.zip'
urllib.urlretrieve(data_sour... |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/statespace_fixed_params.ipynb | bsd-3-clause | %matplotlib inline
from importlib import reload
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
"""
Explanation: Estimating or specifying parameters in state space models
In this notebook we show how to fix specific val... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_visualize_evoked.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
"""
Explanation: .. _tut_viz_evoked:
Visualize Evoked data
End of explanation
"""
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
evoked = mne.read_evokeds(fname, basel... |
evangelistalab/forte | tutorials/Tutorial_01.04_forte_cc.ipynb | lgpl-3.0 | import itertools
import functools
import time
import psi4
import forte
import forte.utils
"""
Explanation: Forte Tutorial 1.04: Implementing coupled cluster theory with Forte's sparse operator class
In this tutorial we will implement coupled cluster theory using Forte's SparseOperator and StateVector classes.
We wil... |
dostrebel/working_place_ds_17 | 06_Python_Rückblick/01+Rückblick+02+For-Loop-Übungen+-Copy1.ipynb | mit | primzweibissieben = [2, 3, 5, 7]
for prime in primzweibissieben:
print(prime)
"""
Explanation: 10 For-Loop-Rückblick-Übungen
In den Teilen der folgenden Übungen habe ich den Code mit "XXX" ausgewechselt. Es gilt in allen Übungen, den korrekten Code auszuführen und die Zelle dann auszuführen.
1.Drucke alle diese P... |
tkzeng/molecular-design-toolkit | moldesign/_notebooks/Example 1. Build and simulate DNA.ipynb | apache-2.0 | %matplotlib inline
from matplotlib.pyplot import *
try: import seaborn #optional, makes plots nicer
except ImportError: pass
import moldesign as mdt
from moldesign import units as u
"""
Explanation: <span style="float:right"><a href="http://moldesign.bionano.autodesk.com/" target="_blank" title="About">About</a>&nbs... |
radajin/datascience | PCA/vectorspace.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_formats = {'png', 'retina'}
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
"""
Explanation: Vector Space
End of explanation
"""
import numpy as np
a = np.array([1, 1])
np.linalg.norm(a)
"""
Explanation: 1. vector len... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/structured/solutions/4c_keras_wide_and_deep_babyweight.ipynb | apache-2.0 | import datetime
import os
import shutil
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
print(tf.__version__)
"""
Explanation: LAB 4c: Create Keras Wide and Deep model.
Learning Objectives
Set CSV Columns, label column, and column defaults
Make dataset of features and label from CSV files
... |
oscarmore2/deep-learning-study | sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:200]
#labels[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a re... |
ueapy/enveast_python_course_materials | Day_2/17-Pandas-Intro.ipynb | mit | import pandas as pd
"""
Explanation: Introduction to pandas
pandas is a Python package providing convenient data structures to work with labelled data.
pandas is perfectly suited for observational / statistical data sets, having many similarities with Excel spreadsheets.
Key features:
easy handling of missing d... |
park-python/course | lectures/04_OS_Debug_Tests_Threads_Async/notebook.ipynb | bsd-3-clause | import os
os.mkdir("/tmp/park-python")
try:
os.rmdir("/tmp/park-python")
except IOError as err:
print(err)
path = "/tmp/park-python/lectures/04"
if not os.path.exists(path):
os.makedirs(path)
os.rmdir("/tmp/park-python")
import shutil
shutil.rmtree("/tmp/park-python")
import pprint
pprint.pprint(list(... |
whitead/numerical_stats | unit_5/hw_2018/Homework_5_Key.ipynb | gpl-3.0 | #2.1
#enter data
Q = [-3, 0, 2, 3, 6]
P = [1/10, 4/10, 1/10, 2/10, 2/10]
#initialize psum
psum = 0
#iterate over index i
for i in range(len(Q)):
#add up probabilities
psum += P[i]
#print result
print(psum)
#2.2
#initialize expectation value
ex = 0
#iterate over index i
for i in range(len(Q)):
#add up pr... |
mathemage/h2o-3 | examples/deeplearning/notebooks/deeplearning_cifar10_vgg.ipynb | apache-2.0 | %matplotlib inline
import matplotlib
import scipy.io
import matplotlib.pyplot as plt
import cPickle
import numpy as np
from scipy.misc import imsave
from IPython.display import Image, display, HTML
"""
Explanation: Training on Cifar 10 Using MXNet and H2O
https://www.cs.toronto.edu/~kriz/cifar.html
The CIFAR-10 data... |
jdsanch1/SimRC | 02. Parte 2/12. Clase 12/12Class NB.ipynb | mit | #importar los paquetes que se van a usar
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import datetime
from datetime import datetime
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#algunas opciones para Python
pd.set_option('display.not... |
PMEAL/OpenPNM | examples/getting_started/quick_plotting_networks.ipynb | mit | import warnings
import scipy as sp
import numpy as np
import openpnm as op
%config InlineBackend.figure_formats = ['svg']
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(10)
ws = op.Workspace()
ws.settings['loglevel'] = 40
np.set_printoptions(precision=4)
net = op.network.Cubic(shape=[5, 5, 1])
"""
E... |
phoebe-project/phoebe2-docs | 2.3/tutorials/distribution_propagation.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()
"""
Explanation: Advanced: Distribution Propagation
NOTE: support for distribution propagation was improved in the 2.3.25 release. Please make sure you have at least 2.3.25 installed.
Setup
Let... |
ivannz/study_notes | year_14_15/spring_2015/netwrok_analysis/notebooks/labs/prediction.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
import networkx as nx
%matplotlib inline
"""
Explanation: Node Label Prediction \ Link Prediction
End of explanation
"""
g = nx.read_gml('./data/ppi.CC.gml.txt')
cc = list(nx.connected_components(g))
g = nx.subgraph(g,cc[0])
g = nx.relabel.convert... |
slac207/cs207project | cs207rbtree/demo_RedBlackTree.ipynb | mit | import timeseries, TimeseriesDB, Similarity
import cs207rbtree.RedBlackTree as Database
dir(Database)
"""
Explanation: Package Installation
At the console, within the cs207project directory, you can use
python
pip install -e .
or equivalently
python
python setup.py install
At this point, four subpackages will be ava... |
ericmjl/bokeh | examples/howto/server_embed/notebook_embed.ipynb | bsd-3-clause | import yaml
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, Slider
from bokeh.plotting import figure
from bokeh.themes import Theme
from bokeh.io import show, output_notebook
from bokeh.sampledata.sea_surface_temperature import sea_surface_temperature
output_notebook()
"""
Explanation: E... |
mne-tools/mne-tools.github.io | 0.19/_downloads/96cdfef13f5475e2f32b862fa94904b7/plot_visualize_stc.ipynb | bsd-3-clause | import os
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse, read_inverse_operator
from mne import read_evokeds
data_path = sample.data_path()
sample_dir = os.path.join(data_path, 'MEG', 'sample')
subjects_dir = os.path.join(data_path, 'subjects')
fname_evoked = data_path + '/MEG/... |
IanHawke/ET-NumericalMethods-2016 | slides/02-horizon-finding.ipynb | mit | import numpy
from matplotlib import pyplot
%matplotlib inline
def horizon_RHS(H, theta, z_singularities):
"""
The RHS function for the apparent horizon problem.
Parameters
----------
H : array
vector [h, dh/dtheta]
theta : double
angle
z_singularities : array
... |
mliesenberg/tf-bcgdv | SimpleNetworkLab.ipynb | mit | %matplotlib inline
import os
from urllib import urlretrieve
import math
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from zipfile ... |
DOV-Vlaanderen/pydov | docs/notebooks/search_sonderingen.ipynb | mit | %matplotlib inline
import inspect, sys
import pydov
"""
Explanation: Example of DOV search methods for CPT measurements (sonderingen)
Use cases explained below
Get CPT measurements in a bounding box
Get CPT measurements with specific properties
Get CPT measurements in a bounding box based on specific properties
Sel... |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/canesm5/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'canesm5', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CCCMA
Source ID: CANESM5
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
amccaugh/phidl | docs/tutorials/quickstart.ipynb | mit | from phidl import Device
from phidl import quickplot as qp # Rename "quickplot()" to the easier "qp()"
import phidl.geometry as pg
"""
Explanation: Quick start
PHIDL allows you to create complex designs from simple shapes, and can output the result as GDSII files. The basic element of PHIDL is the Device, which is jus... |
ipython/ipywidgets | docs/source/examples/Widget Asynchronous.ipynb | bsd-3-clause | %gui asyncio
"""
Explanation: Index - Back
Asynchronous Widgets
This notebook covers two scenarios where we'd like widget-related code to run without blocking the kernel from acting on other execution requests:
Pausing code to wait for user interaction with a widget in the frontend
Updating a widget in the background... |
KasperPRasmussen/bokeh | examples/howto/charts/heatmap.ipynb | bsd-3-clause | df.head()
"""
Explanation: Auto MPG Data
End of explanation
"""
hm = HeatMap(df, x=bins('mpg'), y=bins('displ'))
show(hm)
"""
Explanation: 2D Binning
Default behavior for 2d binning is to bin the dimensions provided, then count the rows that fall into each bin. This is visualizing how the source data represents all... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session11/Day4/VisualizingImages.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from astropy.io import fits
%matplotlib notebook
"""
Explanation: An Introduction to Visualizing Astronomical Images
Version 0.1
This session has focused on image processing, with particular attention paid to how we make standard measurements (su... |
Automating-GIS-processes/2017 | source/codes/Lesson3-spatial-join.ipynb | mit | import geopandas as gpd
# Filepath
fp = r"/home/geo/Pop15/Vaestotietoruudukko_2015.shp"
# Read the data
pop = gpd.read_file(fp)
# See the first rows
print(pop.head())
"""
Explanation: Spatial join
Spatial join is yet another classic GIS problem. Getting attributes from one layer and transferring them into another l... |
DTOcean/dtocean-core | notebooks/DTOcean Environmental Impact Example.ipynb | gpl-3.0 | %matplotlib inline
from IPython.display import display, HTML
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (14.0, 8.0)
import numpy as np
from dtocean_core import start_logging
from dtocean_core.core import Core
from dtocean_core.menu import ModuleMenu, ProjectMenu, ThemeMenu
from dtocean_core.pi... |
JeanClaudeR/zikaViz | ShowOff_slides.ipynb | mit | Image("img/init.png")
"""
Explanation: Practical use of Jupyter notebook
Second motivation : learning Python by web scraping
Scraping data from the WHO
End of explanation
"""
Image("img/target_result.png")
"""
Explanation: Expected results
End of explanation
"""
# FOR WEB SCRAPING
from lxml import html
import req... |
tuanavu/coursera-university-of-washington | machine_learning/1_machine_learning_foundations/assignment/week2/Predicting house prices.ipynb | mit | import graphlab
"""
Explanation: Fire up graphlab create
End of explanation
"""
sales = graphlab.SFrame('home_data.gl/')
sales
"""
Explanation: Load some house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
End of explanation
"""
graphlab.canvas.set_ta... |
cojacoo/testcases_echoRD | echoRD2D_tests_prep_py3-Copy1.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
%pylab inline
import scipy as sp
import matplotlib.pyplot as plt
import os, sys
#connect echoRD Tools
pathdir='../' #path to echoRD
lib_path = os.path.abspath(pathdir)
sys.path.append(lib_path)
import vG_conv as vG
from hydro_tools import plotparticles_t,hydroprofile,plotparticl... |
chanceraine/nupic.research | projects/sdr_paper/sdr_math_neuron_paper.ipynb | agpl-3.0 | oxp = Symbol("Omega_x'")
b = Symbol("b")
n = Symbol("n")
theta = Symbol("theta")
s = Symbol("s")
a = Symbol("a")
subsampledOmega = (binomial(s, b) * binomial(n - s, a - b)) / binomial(n, a)
subsampledFpF = Sum(subsampledOmega, (b, theta, s))
subsampledOmegaSlow = (binomial(s, b) * binomial(n - s, a - b))
subsampledFp... |
dariox2/CADL | session-3/.ipynb_checkpoints/lecture-3-checkpoint.ipynb | apache-2.0 | # imports
%matplotlib inline
# %pylab osx
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
# Some additional libraries which we'll use just
# to produce some visualizations of our training
from libs.utils import montage
from libs i... |
gVallverdu/cookbook | puits_infini.ipynb | gpl-2.0 | import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.constants import h, m_e, elementary_charge
%matplotlib inline
"""
Explanation: Particule dans une boîte uni-dimensionnelle
Germain Salvato Vallverdu germain.vallverdu@univ-pau.fr
End of explanation
"""
def phi(x, p=1, L=1):
re... |
gtzan/mir_book | Bayes Classification.ipynb | cc0-1.0 | %matplotlib inline
import matplotlib.pyplot as plt
import pickle
import numpy as np
# load some lyrics bag of words data, binarize, separate matrix rows by genre
data = np.load('data/data.npz')
a = data['arr_0']
a[a > 0] = 1
labels = np.load('data/labels.npz')
labels = labels['arr_0']
dictionary = pickle.load(open... |
berkeley-dsc/purchasing | script/andrew/Pricing_Error.ipynb | isc | %pylab inline
import pandas as pd
import numpy as np
#Read in the data
RawData = pd.read_csv('/home/analyst/Desktop/invoices/all.csv', low_memory=False, thousands=',',
converters={'Invoice System Created Date': lambda x: pd.to_datetime(x)}
)
#The UCSF file has different hea... |
dpiparo/ROOTaaS | NoteBooks/Howto-inherit-C++-classes-from-python.ipynb | lgpl-3.0 | from ROOTaaS.iPyROOT import ROOT
"""
Explanation: Inheriting C++ classes from Python
In this exercise we would like to demostrate the inheritance of a C++ class from Python. The base class in C++ is Base and we want to have an specialization of if in Python PyDerived
End of explanation
"""
%%dcl
#include "TObject.h"... |
oxpeter/library_bioinformatics_service | Jupyter/Jupyter_Notebook_Demo.ipynb | apache-2.0 | # this is a code cell with no output
a=120
# this is a code cell with output
# all output to stdout / stderr will be displayed below the cell.
print(a)
"""
Explanation: Library Bioinformatics Service
Jupyter Notebook Tutorial
This tutorial was built in a Jupyter notebook!
Various formats of this tutorial can be acces... |
shngli/Data-Mining-Python | RSS feed/RSS Feed Parser.ipynb | gpl-3.0 | # -*-coding: utf-8 -*-
import feedparser
import re
import string
# Create the regular expressions
reg1 = re.compile(r'<br />') #Regex to replace <br /> with \n (see reg1.sub)
reg2 = re.compile(r'(<!--.*?-->|<[^>]*>)') #Regex to clean all html tags (anything with <something>)
#alternative reg2
#reg2 = re.compile(r'<[^... |
Kaggle/learntools | notebooks/machine_learning/raw/ex4.ipynb | apache-2.0 | # Code you have previously used to load data
import pandas as pd
from sklearn.tree import DecisionTreeRegressor
# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'
home_data = pd.read_csv(iowa_file_path)
y = home_data.SalePrice
feature_columns = ['LotArea', 'YearBuilt', '1stFlrSF'... |
ajrader/timeseries | notebooks/Prophet_retail_2.ipynb | apache-2.0 | retail_datafile = '../datasets/example_retail_sales.csv'
from fbprophet import Prophet
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize']=(20,10)
plt.style.use('ggplot')
sales_df = pd.read_csv(retail_datafile)
sales_df['y_orig'] = sales_df['y'] # t... |
NathanYee/ThinkBayes2 | code/.ipynb_checkpoints/chap02mine-checkpoint.ipynb | gpl-2.0 | % matplotlib inline
from thinkbayes2 import Hist, Pmf, Suite
"""
Explanation: Think Bayes: Chapter 2
This notebook presents example code and exercise solutions for Think Bayes.
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
End of explanation
"""
pmf = Pmf()
for x in [1,2,3,4,5,6]:
... |
jorisvandenbossche/geopandas | doc/source/gallery/plotting_with_geoplot.ipynb | bsd-3-clause | import geopandas
import geoplot
world = geopandas.read_file(
geopandas.datasets.get_path('naturalearth_lowres')
)
boroughs = geopandas.read_file(
geoplot.datasets.get_path('nyc_boroughs')
)
collisions = geopandas.read_file(
geoplot.datasets.get_path('nyc_injurious_collisions')
)
"""
Explanation: Plotting ... |
google-research/google-research | representation_similarity/Demo.ipynb | apache-2.0 | import numpy as np
def gram_linear(x):
"""Compute Gram (kernel) matrix for a linear kernel.
Args:
x: A num_examples x num_features matrix of features.
Returns:
A num_examples x num_examples Gram matrix of examples.
"""
return x.dot(x.T)
def gram_rbf(x, threshold=1.0):
"""Compute Gram (kernel) m... |
dib-lab/SSUsearch | notebooks-pc-linux/ssu-search.ipynb | bsd-3-clause | cd ~/Desktop/SSUsearch/
mkdir -p ./workdir
#check seqfile files to process in data directory (make sure you still remember the data directory)
!ls ./data/test/data
"""
Explanation: Set up working directory
End of explanation
"""
Seqfile='./data/test/data/1c.fa'
"""
Explanation: README
This part of pipeline search... |
tpin3694/tpin3694.github.io | machine-learning/make_simulated_data_for_classification.ipynb | mit | from sklearn.datasets import make_classification
import pandas as pd
"""
Explanation: Title: Make Simulated Data For Classification
Slug: make_simulated_data_for_classification
Summary: Make a simulated dataset for classification using scikit-learn.
Date: 2017-01-16 12:00
Category: Machine Learning
Tags: Basics
Author... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/01_bigquery/labs/b_bqml.ipynb | apache-2.0 | PROJECT = "cloud-training-demos" # Replace with your PROJECT
REGION = "us-central1" # Choose an available region for Cloud MLE
import os
os.environ["PROJECT"] = PROJECT
os.environ["REGION"] = REGION
!pip freeze | grep google-cloud-bigquery==1.21.0 || pip install google-cloud-bigquery==1.21.0
%load_ext go... |
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