repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-mm/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-mm', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-MM
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radi... |
xpharry/Udacity-DLFoudation | tutorials/sentiment_network/.ipynb_checkpoints/Sentiment Classification - Project 1 Solution-checkpoint.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
quietcoolwu/python-playground | notebooks/Module_Inspect.ipynb | mit | #coding: UTF-8
import sys # 模块，sys指向这个模块对象
def foo(): pass # 函数，foo指向这个函数对象
class Cat(object): # 类，Cat指向这个类对象
def __init__(self, name='kitty'):
self.name = name
def sayHi(self): # 实例方法，sayHi指向这个方法对象，使用类或实例.sayHi访问
print self.name, 'says Hi!' # 访问名为name的字段，使用实例.name访问
cat = Cat() # cat是Cat类... |
gururajl/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)
len(source_text)
"""
Explanation: Language Translation
In this pro... |
AllenDowney/ThinkStats2 | code/chap13ex.ipynb | gpl-3.0 | from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats2/raw/master/code/th... |
antoniomezzacapo/qiskit-tutorial | qiskit/ignis/relaxation_and_decoherence.ipynb | apache-2.0 | import qiskit as qk
import numpy as np
from scipy.optimize import curve_fit
from qiskit.tools.qcvv.fitters import exp_fit_fun, osc_fit_fun, plot_coherence
from qiskit.wrapper.jupyter import *
# Load saved IBMQ accounts
qk.IBMQ.load_accounts()
# backend and token settings
backend = qk.IBMQ.get_backend('ibmq_16_melbour... |
GoogleCloudPlatform/mlops-with-vertex-ai | 07-prediction-serving.ipynb | apache-2.0 | import os
from datetime import datetime
import tensorflow as tf
from google.cloud import aiplatform as vertex_ai
"""
Explanation: 07 - Prediction Serving
The purpose of the notebook is to show how to use the deployed model for online and batch prediction.
The notebook covers the following tasks:
1. Test the endpoints... |
IS-ENES-Data/submission_forms | test/Templates/DKRZ_CDP_submission_form.ipynb | apache-2.0 | from dkrz_forms import form_widgets
form_widgets.show_status('form-submission')
"""
Explanation: Generic DKRZ CMIP Data Pool (CDP) ingest form
This form is intended to request data to be made locally available in the DKRZ national data archive.
If the requested data is available via ESGF please use the specific ESGF r... |
abhishekraok/GraphMap | notebook/Getting_Started.ipynb | apache-2.0 | %pylab inline
import sys
import os
sys.path.insert(0,'..')
import graphmap
"""
Explanation: GraphMap
Getting Started
This notebook shows how to get started using GraphMap
End of explanation
"""
from graphmap.graphmap_main import GraphMap
from graphmap.memory_persistence import MemoryPersistence
G = GraphMap(MemoryPe... |
pfschus/fission_bicorrelation | methods/build_det_df_angles_pairs.ipynb | mit | %%javascript
$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')
"""
Explanation: <h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
End of explanation
"""
%%html
<img src="fig/setup.png",width=80%,height=80%>
"""
Explanation: Chi-Nu Array Detector Angles
Author:... |
transcranial/keras-js | notebooks/layers/convolutional/UpSampling3D.ipynb | mit | data_in_shape = (2, 2, 2, 3)
L = UpSampling3D(size=(2, 2, 2), data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(260)
data_in = 2 * np.random.random(data_in_shape) -... |
gkvoelkl/python-sonic | python-sonic.ipynb | mit | from psonic import *
"""
Explanation: python-sonic - Programming Music with Python, Sonic Pi or Supercollider
Python-Sonic is a simple Python interface for Sonic Pi, which is a real great music software created by Sam Aaron (http://sonic-pi.net).
At the moment Python-Sonic works with Sonic Pi. It is planned, that it ... |
sarvex/PythonMachineLearning | Chapter 2/EstimatorCV Objects.ipynb | isc | from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
feature_elimination... |
pycomlink/pycomlink | notebooks/Blackout gap detection examples.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pycomlink as pycml
import xarray as xr
from tqdm import tqdm
import urllib.request
import io
import pycomlink.processing.blackout_gap_detection as blackout_detection
# Do show xarray.Dataset representation as text because gitlab/github
# do not (yet) render the... |
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Phosphorylation Chemical Tests - MLP-checkpoint.ipynb | mit | from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
"""
Explanation: Template for test
End of explanation
"""
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
print("y", i)
y = Predictor()
y.load_data(file="Data/Trainin... |
privong/pythonclub | sessions/01-introduction/Software I.ipynb | gpl-3.0 | import numpy as np
x = np.array([1, 5, 3, 4, 2])
x
"""
Explanation: Software I : Anaconda, AstroPy, and libraries
We will make extensive use of Python and various associated libraries and so the first thing we need to ensure is that we all have a common setup and are using the same software. The Python distribution th... |
PyDataMadrid2016/Conference-Info | talks_materials/20160410_1215_Whoosh_a_fast_pure_Python_search_engine_library/whooshNotebook.ipynb | mit | from IPython.display import Image
Image(filename='files/screenshot.png')
from IPython.display import Image
Image(filename='files/whoosh.jpg')
"""
Explanation: Whoosh: a fast pure-Python search engine library
Pydata Madrid
2016.04.10
Who am I?
Claudia Guirao Fernández
@claudiaguirao
Background: Double degree in L... |
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Phosphorylation Sequence Tests -MLP -dbptm+ELM-checkpoint.ipynb | mit | from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
"""
Explanation: Template for test
End of explanation
"""
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
print("y", i)
y = Predictor()
y.load_data(file="Data/Trainin... |
roatienza/Deep-Learning-Experiments | versions/2020/cnn/code/cnn-functional.ipynb | mit | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow
from tensorflow.keras.layers import Dense, Dropout, Input
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten
from tensorflow.keras.models import Model
from te... |
newsapps/public-notebooks | Monthly homicides and shootings report.ipynb | mit | import os
import requests
def get_table_data(table_name):
url = '%stable/json/%s' % (os.environ['NEWSROOMDB_URL'], table_name)
try:
r = requests.get(url)
return r.json()
except:
print 'doh'
return get_table_data(table_name)
homicides = get_table_data('homicides')
shootings ... |
lfsimoes/beam_paco__gtoc5 | usage_demos.ipynb | mit | # https://esa.github.io/pykep/
# https://github.com/esa/pykep
# https://pypi.python.org/pypi/pykep/
import PyKEP as pk
import numpy as np
from tqdm import tqdm, trange
import matplotlib.pylab as plt
%matplotlib inline
import seaborn as sns
plt.rcParams['figure.figsize'] = 10, 8
from gtoc5 import *
from gtoc5.multio... |
PollyP/CAPE-ratios | notebooks/Using CAPE ratios to make investment decisions.ipynb | mit | %matplotlib inline
import IPython.html.widgets as widgets
import IPython.display as display
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import pandas as pd
pd.set_option('display.float_format', lambda x: '%.4f' % x)
pylab.rcParams['figure.figsize'] = 14, 8
pd.set_option('display.width', 400)
# lo... |
phoebe-project/phoebe2-docs | 2.3/tutorials/nelder_mead.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger('error')
"""
Explanation: Advanced: Nelder-Mead Optimizer
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook sessi... |
wcmckee/signinlca | .ipynb_checkpoints/pggNumAdd-checkpoint.ipynb | mit | #for ronum in ranumlis:
# print ronum
randict = dict()
othgues = []
othlow = 0
othhigh = 9
for ranez in range(10):
randxz = random.randint(othlow, othhigh)
othgues.append(randxz)
othlow = (othlow + 10)
othhigh = (othhigh + 10)
#print othgues
tenlis = ['zero', 'ten', 'twenty', 'thirty',... |
AndysDeepAbstractions/deep-learning | image-classification/dlnd_image_classification.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'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
highb/deep-learning | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
mne-tools/mne-tools.github.io | 0.24/_downloads/548b4fc45f1ed79527138879cd79d3c8/muscle_detection.ipynb | bsd-3-clause | # Authors: Adonay Nunes <adonay.s.nunes@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# License: BSD-3-Clause
import os.path as op
import matplotlib.pyplot as plt
import numpy as np
from mne.datasets.brainstorm import bst_auditory
from mne.io import read_raw_ctf
from mne.preprocessing import annotate_muscle_zs... |
vinutah/UGAN | 02_code/Warmup To GANs.ipynb | mit | import numpy as np
a = np.zeros((2,2))
a
a.shape
np.reshape(a, (1,4))
b = np.ones((2,2))
b
np.sum(b, axis=1)
"""
Explanation: Thinking Axes for ML-Packages
Model Specification or
Writing a configuration file
Choice of Grammer
JSON
Caffe
Google DistBelief
CNTK
Programmatic generation
Writing Code
Choi... |
ampl/amplpy | notebooks/gspread.ipynb | bsd-3-clause | from google.colab import auth
auth.authenticate_user()
"""
Explanation: AMPLPY: Using Google Sheets
Documentation: http://amplpy.readthedocs.io
GitHub Repository: https://github.com/ampl/amplpy
PyPI Repository: https://pypi.python.org/pypi/amplpy
Jupyter Notebooks: https://github.com/ampl/amplpy/tree/master/notebooks... |
mne-tools/mne-tools.github.io | 0.20/_downloads/aa221dc65413caee3ba4b18802f88d21/plot_topo_compare_conditions.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.viz import plot_evoked_topo
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
Explanation:... |
JustinShenk/genre-melodies | create_dataset.ipynb | mit | import os
import shutil
import spotipy
import pickle
import pandas as pd
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from collections import Counter
if not os.path.exists('clean_midi'):
# Dow... |
SJSlavin/phys202-2015-work | assignments/assignment05/InteractEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 2
Imports
End of explanation
"""
# YOUR CODE HERE
def plot_sin1(a, b):
x = np.linspace(0, 4*np.pi, 200)
... |
tensorflow/docs-l10n | site/ja/guide/migrate.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... |
lalonica/PhD | vehicles/VehiclesTimeCycles-sample.ipynb | gpl-3.0 | %matplotlib inline
from pandas import Series, DataFrame
import pandas as pd
from itertools import *
import itertools
import numpy as np
import csv
import math
import matplotlib.pyplot as plt
from matplotlib import pylab
from scipy.signal import hilbert, chirp
import scipy
import networkx as nx
"""
Explanation: Loadin... |
econ-ark/HARK | examples/HowWeSolveIndShockConsumerType/HowWeSolveIndShockConsumerType.ipynb | apache-2.0 | from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, init_lifecycle
import numpy as np
import matplotlib.pyplot as plt
LifecycleExample = IndShockConsumerType(**init_lifecycle)
LifecycleExample.cycles = 1 # Make this consumer live a sequence of periods exactly once
LifecycleExample.solve()
"""
Ex... |
ES-DOC/esdoc-jupyterhub | notebooks/pcmdi/cmip6/models/pcmdi-test-1-0/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'pcmdi-test-1-0', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: PCMDI
Source ID: PCMDI-TEST-1-0
Topic: Seaice
Sub-Topics: Dynamics, Thermody... |
amkatrutsa/MIPT-Opt | Spring2022/hb_acc_grad.ipynb | mit | import liboptpy.base_optimizer as base
import numpy as np
import liboptpy.unconstr_solvers.fo as fo
import liboptpy.step_size as ss
import matplotlib.pyplot as plt
%matplotlib inline
plt.rc("text", usetex=True)
class HeavyBall(base.LineSearchOptimizer):
def __init__(self, f, grad, step_size, beta, **kwargs):
... |
mne-tools/mne-tools.github.io | 0.20/_downloads/eea7e38645d4176f944e2f8d02a34fde/plot_run_ica.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.preprocessing import ICA, create_ecg_epochs
from mne.datasets import sample
print(__doc__)
"""
Explanation: Compute ICA components on epochs
ICA is fit to MEG raw data.
We assume that the non-stationary EOG artifacts... |
phoebe-project/phoebe2-docs | 2.2/tutorials/ecc.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Eccentricity (Volume Conservation)
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matp... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/Statements Assessment Test - Solutions-checkpoint.ipynb | apache-2.0 | st = 'Print only the words that start with s in this sentence'
for word in st.split():
if word[0] == 's':
print word
"""
Explanation: Statements Assessment Solutions
Use for, split(), and if to create a Statement that will print out words that start with 's':
End of explanation
"""
range(0,11,2)
"""
E... |
samuelsinayoko/kaggle-housing-prices | ols/regression_full.ipynb | mit | dfnum = pd.read_csv('transformed_numerical_dataset_imputed.csv', index_col=['Dataset','Id'])
dfnum.head()
dfcat = pd.read_csv('cleaned_categorical_vars_with_colz_sorted_by_goodness.csv', index_col=['Dataset','Id'])
dfcat.head()
dfcat.head()
df = pd.concat([dfnum, dfcat.iloc[:, :ncat]], axis=1)
df.shape
"""
Explana... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_lcmv_beamformer_volume.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.beamformer import make_lcmv, apply_lcmv
from nilearn.plotting import plot_stat_map
from nilearn.image import index_... |
eneskemalergin/OldBlog | _oldnotebooks/Introduction_to_Pandas-2.ipynb | mit | import pandas as pd
SpotCrudePrices_2013_Data= { 'U.K. Brent' :
{'2013-Q1':112.9, '2013-Q2':103.0,
'2013-Q3':110.1, '2013-Q4':109.4},
'Dubai':
{'2013-Q1':108.1, '2013-Q2':100.8,
... |
biof-309-python/BIOF309-2016-Fall | Week_02/Week 2 - 04 - Homework.ipynb | mit | # This sequence is the first 100 nucleotides of the Influenza H1N1 Virus segment 8
flu_ns1_seq = 'GTGACAAAGACATAATGGATCCAAACACTGTGTCAAGCTTTCAGGTAGATTGCTTTCTTTGGCATGTCCGCAAACGAGTTGCAGACCAAGAACTAGGTGA'
"""
Explanation: Week 2 Homework
We have seen this week how to print and manipulate text string in python. Lets use th... |
Naereen/notebooks | Février 2021 un mini challenge arithmético-algorithmique.ipynb | mit | // ceci est du code Java 9 et pas Python !
// On a besoin des dépendances suivantes :
import java.util.Calendar; // pour Calendar.FEBRUARY, .YEAR, .MONDAY
import java.util.GregorianCalendar; // pour
import java.util.stream.IntStream; // pour cet IntStream
// ceci est du code Java 9 et pas Python !
IntStr... |
mne-tools/mne-tools.github.io | stable/_downloads/1537c1215a3e40187a4513e0b5f1d03d/eeg_csd.ipynb | bsd-3-clause | # Authors: Alex Rockhill <aprockhill@mailbox.org>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Transform EEG data using current source density (CSD)
This script shows an example... |
ppham27/MLaPP-solutions | chap04/17.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
# benchmark sklearn implementations, these are much faster
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscr... |
ernestyalumni/CompPhys | crack/crack.ipynb | apache-2.0 | def fibonacci(n):
if (n == 0):
return 0
elif (n == 1):
return 1
elif (n==2):
return 1
return fibonacci(n-1) + fibonacci(n-2)
[fibonacci(n) for n in range(15)]
"""
Explanation: cf. Gayle Laakmann McDowell. Cracking the Coding Interview: 189 Programming Questions and Solutions.... |
rochelleterman/scrape-interwebz | 3_Beautiful_Soup/1_bs_workbook.ipynb | mit | # import required modules
import requests
from bs4 import BeautifulSoup
from datetime import datetime
import time
import re
import sys
"""
Explanation: Webscraping with Beautiful Soup
Intro
In this tutorial, we'll be scraping information on the state senators of Illinois, available here, as well as the list of bills ... |
vikashvverma/machine-learning | mlbasic/UnSupervised/Project/customer_segments.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the wholesale custo... |
jealdana/Open-Notebooks | PythonWars/Python Wars.ipynb | gpl-3.0 | import webbrowser
import requests
import bs4
import csv
import pandas as pd
res = requests.get('http://mytowntutors.com/2014/04/star-wars-the-clone-wars/')
print(res.status_code == requests.codes.ok)
print("Number of lines in te downloaded page: %i"%len(res.text))
print("The first 20 thousand lines for a quick assesme... |
cslab-org/cslab | static/teaching/pattern/PR_Your_Name.ipynb | mit | %matplotlib inline
# code to sample a random number between 0 & 1
# Try running this multiple times by pressing Ctrl-Enter
import numpy as np
import matplotlib.pyplot as plt
print np.random.random()
"""
Explanation: Your Name:
Roll Number:
1. Linear Discriminant Analysis
In this part, we will do lda on a synthetic d... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n09_dyna_q_learner.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... |
amolsharma99/UdacityDeepLearningClass | 1_notmnist.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.line... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/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', 'dwd', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
jArumugam/python-notes | P11Iterators and Generators Homework.ipynb | mit | def gensquares(N):
for num in xrange(1,N):
yield num**2
for x in gensquares(10):
print x
"""
Explanation: Iterators and Generators Homework
Problem 1
Create a generator that generates the squares of numbers up to some number N.
End of explanation
"""
import random
random.randint(1,10)
def rand_num... |
clumdee/clumdee.github.io | assets/img/twitterBNK48/code_with_pyspark.ipynb | mit | from pyspark.sql import SparkSession
from pyspark.sql.functions import lower, split, explode, substring, count
from datetime import datetime
# create SparkSession
spark = SparkSession.builder.appName('streamTwitterTags').getOrCreate()
# connect and get tweets
tweets = spark.readStream.format("socket").option("host", ... |
computational-class/cjc2016 | code/tba/Introduction-to-Non-Personalized-Recommenders.ipynb | mit | from IPython.core.display import Image
Image(filename='/Users/chengjun/GitHub/cjc2016/figure/recsys_arch.png')
"""
Explanation: Introduction to Non-Personalized Recommenders
The recommendation problem
Recommenders have been around since at least 1992. Today we see different flavours of recommenders, deployed across d... |
flohorovicic/pynoddy | docs/notebooks/1-Simulation.ipynb | gpl-2.0 | from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
%matplotlib inline
# Basic settings
import sys, os
import subprocess
sys.path.append("../..")
# Now import pynoddy
import pynoddy
import importlib
importlib.reload(pynoddy)
import pynoddy.output
import pynoddy.history
#... |
NekuSakuraba/my_capstone_research | subjects/diffusion maps/Diffusion Maps 02.ipynb | mit | n = 3
X, y = make_blobs(n_samples=n, cluster_std=.1, centers=[[1,1]])
X
"""
Explanation: Diffusion Distance <br />
A distance function between any two points based on the random walk on the graph [1].
Diffusion map <br />
Low dimensional description of the data by the first few eigenvectors [1].
End of explanation
""... |
mayank-johri/LearnSeleniumUsingPython | Section 2 - Advance Python/Chapter S2.01 - Functional Programming/04_functools.ipynb | gpl-3.0 | def power(base, exponent):
return base ** exponent
def square(base):
return power(base, 2)
def cube(base):
return power(base, 3)
"""
Explanation: functools
The functools module is for higher-order functions: functions that act on or return other functions. In general, any callable object can be treated ... |
Imperial-College-Data-Science-Society/Scientific-Python | notebooks/Scientific-Python.ipynb | mit | import numpy as np
"""
Explanation: Introduction to scientific Python
Numpy
Exercise difficulty rating:
[1] easy. ~1 min
[2] medium. ~2-3 minutes
[3] difficult. Suitable for a standalone project.
Numpy is imported and aliased to np by convention
End of explanation
"""
a = np.array([0, 1, 2]) # a rank 1 array of int... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch6-Example_6-06.ipynb | unlicense | %pylab notebook
"""
Explanation: Electric Machinery Fundamentals 5th edition
Chapter 6 (Code examples)
Example 6-6:
Creates and plot of the torque-speed curve of an induction motor with a double-cage rotor design as depicted in Figure 6-29.
Note: You should first click on "Cell → Run All" in order that the plots... |
gwsb-istm-6212-fall-2016/syllabus-and-schedule | lectures/week-11/20161122-lecture-notes.ipynb | cc0-1.0 | sc
"""
Explanation: A brief tour of Spark
Apache Spark is "a fast and general engine for large-scale data processing." It comes from the broader Hadoop ecosystem but can be used in a near-standalone mode, which we'll use here.
This is a Jupyter notebook with PySpark enabled. To enable PySpark, you need to have Spark... |
musketeer191/job_analytics | parse_title.ipynb | gpl-3.0 | df = pd.read_csv(DATA_DIR + 'doc_index_filter.csv')
titles = list(df['title'].unique())
n_title = len(titles)
print('# unique titles: %d' % n_title)
title_stats = pd.read_csv(DATA_DIR + 'stats_job_titles.csv')
"""
Explanation: Data loading:
End of explanation
"""
def parseBatch(b, start=None, end=None):
'''
... |
intel-analytics/analytics-zoo | apps/tfnet/image_classification_inference.ipynb | apache-2.0 | from zoo.common.nncontext import *
sc = init_nncontext("Tfnet Example")
import sys
import tensorflow as tf
sys.path
slim_path = "/path/to/yourdownload/models/research/slim" # Please set this to the directory where you clone the tensorflow models repository
sys.path.append(slim_path)
"""
Explanation: Image classificati... |
quantopian/research_public | notebooks/lectures/Estimating_Covariance_Matrices/notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
from sklearn import covariance
"""
Explanation: Estimation of Covariance Matrices
By Christopher van Hoecke and Max Margenot
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
gith... |
mne-tools/mne-tools.github.io | 0.23/_downloads/4a4a8e5bd5ae7cafea93a04d8c0a0d00/psf_ctf_vertices_lcmv.ipynb | bsd-3-clause | # Author: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.beamformer import make_lcmv, make_lcmv_resolution_matrix
from mne.minimum_norm import get_cross_talk
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects/'
... |
feststelltaste/software-analytics | notebooks/Mining performance HotSpots with JProfiler, jQAssistant, Neo4j and Pandas.ipynb | gpl-3.0 | with open (r'input/spring-petclinic/JDBC_Probe_Hot_Spots_jmeter_test.xml') as log:
[print(line[:97] + "...") for line in log.readlines()[:10]]
"""
Explanation: Mining performance hotspots with JProfiler, jQAssistant, Neo4j and Pandas
TL;DR I show how I determine the parts of an application that trigger unnecessary... |
shirishr/My-Progress-at-Machine-Learning | Udacity_Machine_Learning/finding_donors/finding_donors.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualization code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Loa... |
julienchastang/unidata-python-workshop | notebooks/MetPy_Advanced/Isentropic Analysis.ipynb | mit | from siphon.catalog import TDSCatalog
cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/'
'NCEP/GFS/Global_0p5deg/catalog.xml')
best = cat.datasets['Best GFS Half Degree Forecast Time Series']
"""
Explanation: <a name="top"></a>
<div style="width:1000 px">
<div style="float:right; width... |
brian-rose/env-415-site | notes/TransientWarming.ipynb | mit | %matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import climlab
import netCDF4 as nc
"""
Explanation: ENV / ATM 415: Climate Laboratory
Exploring the rate of climate change
Tuesday April 11, 2016
End of explanation
"""
# Need the ozone data again for our Radiative-Convective model
ozone_filen... |
aaai2018-paperid-62/aaai2018-paperid-62 | parameter_figures.ipynb | mit | import pandas as pd
file = 'data/evaluations.csv'
conversion_dict = {'research_type': lambda x: int(x == 'E')}
evaluation_data = pd.read_csv(file, sep=',', header=0, index_col=0, converters=conversion_dict)
print('Samples per conference\n{}'.format(evaluation_data.groupby('conference').size()))
"""
Explanation: Figu... |
DJCordhose/ai | notebooks/tensorflow/fashion_mnist_tpu.ipynb | mit | import tensorflow as tf
import numpy as np
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
# add empty color dimension
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
"""
Explanation: View in Colaboratory
Fashion MNIST with Keras and TPUs
Let's try out usi... |
anhaidgroup/py_entitymatching | notebooks/guides/end_to_end_em_guides/Basic EM Workflow.ipynb | bsd-3-clause | import sys
sys.path.append('/Users/pradap/Documents/Research/Python-Package/anhaid/py_entitymatching/')
import py_entitymatching as em
import pandas as pd
import os
# Display the versions
print('python version: ' + sys.version )
print('pandas version: ' + pd.__version__ )
print('magellan version: ' + em.__version__ )... |
ihmeuw/dismod_mr | examples/consistent_data_from_vivarium_artifact.ipynb | agpl-3.0 | np.random.seed(123456)
# if dismod_mr is not installed, it should possible to use
# !conda install --yes pymc
# !pip install dismod_mr
import dismod_mr
# you also need one more pip installable package
# !pip install vivarium_public_health
import vivarium_public_health
"""
Explanation: Consistent models in DisMod-M... |
Olsthoorn/IHE-python-course-2017 | exercises/Apr18/TimeSeriesSampling.ipynb | gpl-2.0 | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: <figure>
<IMG SRC="../../logo/logo.png" WIDTH=250 ALIGN="right">
</figure>
IHE Python course, 2017
Time series manipulation
T.N.Olsthoorn,
April 18, 2017
Most scientists and engineers, including hydrologists, physisists, electr... |
ctralie/TUMTopoTimeSeries2016 | Image Patches.ipynb | apache-2.0 | import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from ripser import ripser
from persim import plot_diagrams
from GeomUtils import getGreedyPerm
import sys
sys.path.append("DREiMac/dreimac")
from P... |
mtat76/atm-py | examples/instruments_POPS_housekeeping.ipynb | mit | from atmPy.instruments.POPS import housekeeping
%matplotlib inline
"""
Explanation: Introduction
This module is in charge of reading the POPS housekeeping file and converting it to a TimeSeries instance.
Imports
End of explanation
"""
filename = './data/POPS_housekeeping.csv'
hk = housekeeping.read_csv(filename)
""... |
milancurcic/lunch-bytes | Fall_2018/LB22/NeuralNetDemo.ipynb | cc0-1.0 | import numpy as np
import pandas as pd
from sklearn import model_selection
def xfer(wsum):
out = 1.0 / (1.0 + np.exp(-wsum))
return out
def ErrHid(output, weights, outerr):
dt = np.dot(weights, outerr)
ErrorHid = output * (1.0 - output) * dt
return ErrorHid
def ErrOut(output, targets):
ErrorO... |
AllenDowney/ThinkStats2 | workshop/effect_size.ipynb | gpl-3.0 | %matplotlib inline
import numpy
import scipy.stats
import matplotlib.pyplot as plt
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
# seed the random number generator so we all get the same results
numpy.random.seed(17)
"""
Explanation: Effect Size
Examples and exercises for a tutor... |
astrojhgu/mcupy | example/bimodal/README.ipynb | bsd-3-clause | %matplotlib inline
from mcupy.graph import *
from mcupy.utils import *
from mcupy.nodes import *
from mcupy.jagsparser import *
import scipy
import seaborn
import pylab
"""
Explanation: A bimodal example
This is a sample to infer the parameters of a bimodal model, which is a mixture of two Normal distribution componen... |
hktxt/MachineLearning | Backpropagation.ipynb | gpl-3.0 | %run "readonly/BackpropModule.ipynb"
# PACKAGE
import numpy as np
import matplotlib.pyplot as plt
# PACKAGE
# First load the worksheet dependencies.
# Here is the activation function and its derivative.
sigma = lambda z : 1 / (1 + np.exp(-z))
d_sigma = lambda z : np.cosh(z/2)**(-2) / 4
# This function initialises t... |
turbomanage/training-data-analyst | quests/serverlessml/07_caip/solution/export_data.ipynb | apache-2.0 | %%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
import os
PROJECT = "your-gcp-project-here" # REPLACE WITH YOUR PROJECT NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ[... |
ThomasProctor/Slide-Rule-Data-Intensive | UdacityMachineLearning/Lessons 1-3 mini-projects.ipynb | mit | import sklearn.naive_bayes as nb
"""
Explanation: Naive Bayes
End of explanation
"""
alabels_test=np.array(labels_test)
alabels_test.shape
features_test.shape
"""
Explanation: My own exploration of the data
End of explanation
"""
features_test[:10].sum(axis=1)
features_test.sum()/features_test.shape[0]
"""
E... |
lithiumdenis/MLSchool | 3. Гоблины, гули и призраки.ipynb | mit | train, test = pd.read_csv(
'data/HelloKaggle/train.csv'
# путь к вашему файлу train
), pd.read_csv(
'data/HelloKaggle/test.csv'
# путь к вашему файлу test
)
train.head()
X = train.drop(['id', 'type'], axis=1)
y = train['type']
"""
Explanation: Зайдите на http://www.kaggle.com и зарегистрируйтесь.
Д... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch1-Problem_1-12.ipynb | unlicense | %pylab notebook
%precision 4
from scipy import constants as c # we like to use some constants
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 1
Problem 1-12
End of explanation
"""
N1 = 600
N2 = 200
i1 = 0.5 # A
i2 = 1.0 # A
"""
Explanation: Description
The core shown in Fig... |
paulovn/ml-vm-notebook | vmfiles/IPNB/Examples/g Misc/10 ipywidgets.ipynb | bsd-3-clause | from __future__ import print_function
from ipywidgets import widgets
"""
Explanation: Ipywidgets
ipywidgets is a Python package providing interactive widgets for Jupyter notebooks.
* ipywidgets installation
* A small tutorial: interactive dashboards on Jupyter
End of explanation
"""
from IPython.display import displ... |
arcyfelix/Courses | 18-05-28-Complete-Guide-to-Tensorflow-for-Deep-Learning-with-Python/01-Introduction-to-Neural-Networks/01_Neural Network From Scratch.ipynb | apache-2.0 | class SimpleClass():
def __init__(self, str_input):
print("SIMPLE" + str_input)
class ExtendedClass(SimpleClass):
def __init__(self):
print('EXTENDED')
"""
Explanation: Manual Neural Network
In this notebook we will manually build out a neural network that mimics the TensorFlow API. This will ... |
ethen8181/machine-learning | networkx/page_rank.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
# ... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-1/cmip6/models/sandbox-2/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-2', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: TEST-INSTITUTE-1
Source ID: SANDBOX-2
Sub-Topics: Radiative Forcin... |
jvbalen/cover_id | learn.ipynb | mit | n_patches, patch_len = 8, 64
# train, test, validation split
ratio = (50,20,30)
clique_dict, _ = SHS_data.read_cliques()
train_cliques, test_cliques_big, _ = util.split_train_test_validation(clique_dict, ratio=ratio)
# preload training data to memory (just about doable)
print('Preloading training data...')
train_uris... |
BrainIntensive/OnlineBrainIntensive | resources/nipype/nipype_tutorial/notebooks/basic_joinnodes.ipynb | mit | from nipype import Node, JoinNode, Workflow
# Specify fake input node A
a = Node(interface=A(), name="a")
# Iterate over fake node B's input 'in_file?
b = Node(interface=B(), name="b")
b.iterables = ('in_file', [file1, file2])
# Pass results on to fake node C
c = Node(interface=C(), name="c")
# Join forked executio... |
gfeiden/Notebook | Daily/20150820_A_star_models.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Surface Boundary Conditions on the Sub-Giant Branch
In particular, exploring how surface boundary conditions can affect the morphology of the sub-giant branch in relation to the "retired A-star" debate in the literature.
End of expl... |
CommonClimate/teaching_notebooks | research/SEA_high_internal_variability.ipynb | mit | %load_ext autoreload
%autoreload 2
%matplotlib inline
import LMRt
import os
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats.mstats import mquantiles
import xarray as xr
from matplotlib import gridspec
from scipy.signal import find_peaks
import pandas as pd
import pickle
from t... |
sidazhang/udacity-dlnd | sentiment-network/Sentiment_Classification_Projects.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
recepkabatas/Spark | 1_notmnist.ipynb | apache-2.0 | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import matplotlib.pyplot as plt
import numpy as np
import os
import tarfile
import urllib
from urllib.request import urlretrieve
from IPython.display import display, Image
from scipy import ndimage
from sklearn... |
YuriyGuts/kaggle-quora-question-pairs | notebooks/feature-3rdparty-dasolmar-whq.ipynb | mit | from pygoose import *
"""
Explanation: Feature: "Jaccard with WHQ" (@dasolmar)
Based on the kernel XGB with whq jaccard by David Solis.
Imports
This utility package imports numpy, pandas, matplotlib and a helper kg module into the root namespace.
End of explanation
"""
import nltk
from collections import Counter
fr... |
Aggieyixin/cjc2016 | code/16&17networkx.ipynb | mit | %matplotlib inline
import networkx as nx
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import networkx as nx
G=nx.Graph() # G = nx.DiGraph() # 有向网络
# 添加（孤立）节点
G.add_node("spam")
# 添加节点和链接
G.add_edge(1,2)
print(G.nodes())
print(G.edges())
# 绘制网络
nx.draw(G, with_labels = True)
"""
Explanation: 网络科学理论简介... |
npo-poms/pyapi | changes_demo.ipynb | gpl-3.0 | objects = ijson.items(client.changes(stream=True, limit=100000), 'changes.item')
data = {}
"""
Explanation: Receive (streamingly) the latest 100000 changes.
End of explanation
"""
count = 0
print("Iterating all results, and collecting some data")
for o in objects:
if count % 20000 == 0:
sys.stdout.w... |
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