repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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mne-tools/mne-tools.github.io | 0.14/_downloads/plot_stockwell.ipynb | bsd-3-clause | # Authors: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.time_frequency import tfr_stockwell
from mne.datasets import somato
print(__doc__)
"""
Explanation: Time frequency with St... |
dalonlobo/GL-Mini-Projects | TweetAnalysis/Final/Q4/Dalon_4_RTD_MiniPro_Tweepy_Q4_3.ipynb | mit | import logging # python logging module
# basic format for logging
logFormat = "%(asctime)s - [%(levelname)s] (%(funcName)s:%(lineno)d) %(message)s"
# logs will be stored in tweepy.log
logging.basicConfig(filename='tweepyloc.log', level=logging.INFO,
format=logFormat, datefmt="%Y-%m-%d %H:%M:%S")
... |
mathinmse/mathinmse.github.io | Lecture-18-Implicit-Finite-Difference.ipynb | mit | import sympy as sp
sp.init_session(quiet=True)
var('U_LHS U_RHS')
"""
Explanation: Lecture 18: Numerical Solutions to the Diffusion Equation
(Implicit Methods)
Sections
Introduction
Learning Goals
On Your Own
In Class
Revisiting the Discrete Version of Fick's Law
A Linear System for Diffusion
An Implicit Numerical So... |
khalido/algorithims | quicksort.ipynb | gpl-3.0 | import random
import numpy as np
random_data = [random.randint(0,100) for i in range(10)]
random_data[:10]
def quicksort(data):
if len(data) < 2:
return data
else:
pivot = data[0]
less = [i for i in data[1:] if i <= pivot]
more = [i for i in data[1:] if i > pivot]
ret... |
ceos-seo/data_cube_notebooks | notebooks/landslides/Landslide_Identification_SLIP.ipynb | apache-2.0 | import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
import numpy as np
import xarray as xr
import pandas as pd
import matplotlib.pyplot as plt
from utils.data_cube_utilities.dc_display_map import display_map
from utils.data_cube_utilities.clean_mask import landsat_clean_mask_full
# landsat_qa_cl... |
ContinualAI/avalanche | notebooks/from-zero-to-hero-tutorial/05_evaluation.ipynb | mit | !pip install avalanche-lib==0.2.0
"""
Explanation: description: Automatic Evaluation with Pre-implemented Metrics
Evaluation
Welcome to the "Evaluation" tutorial of the "From Zero to Hero" series. In this part we will present the functionalities offered by the evaluation module.
End of explanation
"""
import torch
f... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/.ipynb_checkpoints/n10_dyna_q_with_predictor_full_training-checkpoint.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
import pickle
%matplotlib inline
%pylab inli... |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/trimDataset/dataset_info.ipynb | mit | %load_ext rpy2.ipython
%%R
workDir = '/home/nick/notebook/SIPSim/dev/fullCyc/'
physeqDir = '/home/nick/notebook/SIPSim/dev/fullCyc_trim/'
physeqBulkCore = 'bulk-core_trm'
physeqSIP = 'SIP-core_unk_trm'
ampFragFile = '/home/nick/notebook/SIPSim/dev/bac_genome1147/validation/ampFrags_kde.pkl'
"""
Explanation: General... |
amlanlimaye/yelp-dataset-challenge | notebooks/reports/3.1-technical-report.ipynb | mit | ### Link to requirements.txt on github
"""
Explanation: Discovering Abstract Topics in Yelp Reviews - Technical Report
1. Background
Yelp is an American multinational corporation headquartered in San Francisco, California. It develops, hosts and markets Yelp.com and the Yelp mobile app, which publish crowd-sourced rev... |
rnoxy/cifar10-cnn | Classification_using_CNN_codes.ipynb | mit | !ls features/
"""
Explanation: CIFAR10 classification using CNN codes
Here we are going to build linear models to classify CNN codes of CIFAR10 images.
We assume that we already have all the codes extracted by the scripts in the following notebooks:
- Feature_extraction_using_keras.ipynb
- Feature_extraction_using_Inc... |
LorenzoBi/courses | TSAADS/tutorial 2/TSA2_LORENZO_BIASI__JULIUS_VERNIE.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
from sklearn import datasets, linear_model
%matplotlib inline
def set_data(p, x):
temp = x.flatten()
n = len(temp[p:])
x_T = temp[p:].reshape((n, 1))
X_p = np.ones((n, p + 1))
for i in range(1, p + 1):
X_p[:, i] = tem... |
Raag079/self-driving-car | Term01-Computer-Vision-and-Deep-Learning/P2-Traffic-Sign-Classifier/Traffic_Sign_Classifier.ipynb | mit | # Load pickled data
import pickle
# TODO: Fill this in based on where you saved the training and testing data
training_file = 'train.p'
testing_file = 'test.p'
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(testing_file, mode='rb') as f:
test = pickle.load(f)
X_train, y_train ... |
walkon302/CDIPS_Recommender | notebooks/Exploring_Data.ipynb | apache-2.0 | import sys
import os
sys.path.append(os.getcwd()+'/../')
# other
import numpy as np
import glob
import pandas as pd
import ntpath
#keras
from keras.preprocessing import image
# plotting
import seaborn as sns
sns.set_style('white')
import matplotlib.pyplot as plt
%matplotlib inline
# debuggin
from IPython.core.debu... |
bhargavvader/pycobra | docs/notebooks/visualise.ipynb | mit | %matplotlib inline
import numpy as np
from pycobra.cobra import Cobra
from pycobra.ewa import Ewa
from pycobra.visualisation import Visualisation
from pycobra.diagnostics import Diagnostics
# setting up our random data-set
rng = np.random.RandomState(42)
# D1 = train machines; D2 = create COBRA; D3 = calibrate epsilo... |
cavestruz/MLPipeline | notebooks/anomaly_detection/sample_anomaly_detection_stueber.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
%matplotlib inline
"""
Explanation: Let us first explore an example that falls under novelty detection. Here, we train a model on data with some distribution and no outliers. The test data, has some "novel" subset of data that does not follow... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/ml_ops/stage4/get_started_with_model_evaluation.ipynb | apache-2.0 | import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be installed with '--user'
USER_FLAG = ... |
mjbrodzik/ipython_notebooks | modice/sii_monthly_for_modice.ipynb | apache-2.0 | monthly.shape
monthly = monthly[monthly['hemisphere'] == 'N']
monthly.shape
monthly.loc[:,'date'] = pd.to_datetime(monthly['month'])
# Set the month column to the DataFrame index
monthly.set_index('date', inplace=True, verify_integrity=True, drop=True)
monthly = monthly[monthly.index > '1998-12-31']
monthly.colum... |
xoolive/scientificpython | labs/02_making_maps.ipynb | mit | shapefile_path = "./data/CNTR_2014_03M_SH/Data/CNTR_RG_03M_2014.shp"
"""
Explanation: Cartes du monde, cartes de France
Planisphères et projections
L'objectif de cette séance est de se familiariser avec un format courant de description de contours, le format shapefile, et avec différentes projections couramment utilis... |
yugangzhang/CHX_Pipelines | 2019_1/Template/XPCS_Single_2019_V2.ipynb | bsd-3-clause | from pyCHX.chx_packages import *
%matplotlib notebook
plt.rcParams.update({'figure.max_open_warning': 0})
plt.rcParams.update({ 'image.origin': 'lower' })
plt.rcParams.update({ 'image.interpolation': 'none' })
import pickle as cpk
from pyCHX.chx_xpcs_xsvs_jupyter_V1 import *
import itertools
#from pyCHX.XPCS_SAXS i... |
JohannesEH/time-series-analysis | Fremont Bridge Analysis.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt;
from jubiiworkflow.data import get_data
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture
plt.style.use('seaborn');
"""
Explanation: Analysis of Seattle Fremont Bridge Bike Traffic
End of expla... |
ozorich/phys202-2015-work | assignments/assignment05/InteractEx03.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 3
Imports
End of explanation
"""
def soliton(x, t, c, a):
"""Return phi(x, t) for a soliton wave with co... |
materialsvirtuallab/matgenb | notebooks/2017-03-02-Getting data from Materials Project.ipynb | bsd-3-clause | from pymatgen.ext.matproj import MPRester
from pymatgen.core import Composition
import re
import pprint
# Make sure that you have the Materials API key. Put the key in the call to
# MPRester if needed, e.g, MPRester("MY_API_KEY")
mpr = MPRester()
"""
Explanation: Introduction
This notebook demonstrates how you can ob... |
besser82/shogun | doc/ipython-notebooks/ica/bss_image.ipynb | bsd-3-clause | # change to the shogun-data directory
import os
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
os.chdir(os.path.join(SHOGUN_DATA_DIR, 'ica'))
from PIL import Image
import numpy as np
# Load Images as grayscale images and convert to numpy arrays
s1 = np.asarray(Image.open("lena.jpg").convert('... |
amitkaps/machine-learning | time_series/4-Explore.ipynb | mit | # Import the library we need, which is Pandas
import pandas as pd
"""
Explanation: 4. Explore the Data
"I don't know, what I don't know"
We want to first visually explore the data to see if we can confirm some of our initial hypotheses as well as make new hypothesis about the problem we are trying to solve.
For this... |
Caranarq/01_Dmine | Datasets/SEPOMEX/SEPOMEX.ipynb | gpl-3.0 | # Librerias utilizadas
import pandas as pd
import sys
import os
import csv
import urllib
# Descarga de archivos a carpeta local
fuente = r'https://github.com/redrbrt/sepomex-zip-codes/raw/master/sepomex_abril-2016.csv'
destino = r'D:\PCCS\00_RawData\01_CSV\SEPOMEX\sepomex_abril-2016.csv'
urllib.request.urlretrieve(fue... |
SunPower/pvfactors | docs/tutorials/Create_discretized_pvarray.ipynb | bsd-3-clause | # Import external libraries
import matplotlib.pyplot as plt
# Settings
%matplotlib inline
"""
Explanation: Discretize PV row sides and indexing
In this section, we will learn how to:
create a PV array with discretized PV row sides
understand the indices of the timeseries surfaces of a PV array
plot a PV array with i... |
AMICI-developer/AMICI | documentation/GettingStarted.ipynb | bsd-2-clause | import amici
sbml_importer = amici.SbmlImporter('model_steadystate_scaled.xml')
"""
Explanation: Getting Started in AMICI
This notebook is a brief tutorial for new users that explains the first steps necessary for model simulation in AMICI, including pointers to documentation and more advanced notebooks.
Model Compila... |
bambinos/bambi | docs/notebooks/alternative_links_binary.ipynb | mit | import arviz as az
import bambi as bmb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.special import expit as invlogit
from scipy.stats import norm
az.style.use("arviz-darkgrid")
np.random.seed(1234)
"""
Explanation: Regression for Binary responses: Alternative link functions
In th... |
tolaoniyangi/dmc | notebooks/week-4/02-tensorflow ANN for classification.ipynb | apache-2.0 | %matplotlib inline
import math
import random
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
'''Since this is a classification problem, we will need to
represent our targets as one-hot encoding vectors (see previous lab).
To do this we will use sciki... |
beralt85/current_cumulants | example.ipynb | mit | # inline plotting/interaction
%pylab inline
# replace the line above with the line below for command line scripts:
# from pylab import *
from sympy import * # symbolic python
init_printing() # pretty printing
import numpy as np # numeric python
import time # timing, for performance monitoring
# activate latex text ... |
jsgreenwell/teaching-python | tutorial_files/presentations/list_comp_example.ipynb | mit | class vector_math:
'''
This is the base class for vector math - which allows for initialization with two vectors.
'''
def __init__(self, vectors = [[1,2,2],[3,4,3]]):
self.vect1 = vectors[0]
self.vect2 = vectors[1]
def set_vects(self, vectors):
self.vect1 = vect... |
moonbury/pythonanywhere | RegressionAnalysisWithPython/Chap_6 - Achieving Generalization.ipynb | gpl-3.0 | import pandas as pd
from sklearn.datasets import load_boston
boston = load_boston()
dataset = pd.DataFrame(boston.data, columns=boston.feature_names)
dataset['target'] = boston.target
observations = len(dataset)
variables = dataset.columns[:-1]
X = dataset.ix[:,:-1]
y = dataset['target'].values
from sklearn.cross_val... |
ferasz/LCCM | Example/LCCM code example.ipynb | bsd-3-clause | import lccm
import numpy as np
import pandas as pd
import pylogit
import warnings
from collections import OrderedDict
"""
Explanation: LCCM Code Walk-through Example
The following notebook demonstrates how this latent class choice model code works. We will be using an example dataset (Qualtrics data long format.csv) t... |
garibaldu/multicauseRBM | Max/RBM-ORBM-Single-Models.ipynb | mit | from scipy.special import expit
from rbmpy.rbm import RBM
from rbmpy.sampler import VanillaSampler, PartitionedSampler, ApproximatedSampler, LayerWiseApproxSampler,ApproximatedMulDimSampler, ContinuousSampler
from rbmpy.trainer import VanillaTrainier
from rbmpy.performance import Result
import numpy as np
import rbmpy.... |
mne-tools/mne-tools.github.io | stable/_downloads/e41b6a898e7a75f8a9f1a6c00ca73857/20_visualize_epochs.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_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False).crop(tmax=120)
"""
Explanation: Visualizing epo... |
keskarnitish/Recipes | examples/ImageNet Pretrained Network (VGG_S).ipynb | mit | !wget https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg_cnn_s.pkl
"""
Explanation: Introduction
This example demonstrates using a network pretrained on ImageNet for classification. The model used was converted from the VGG_CNN_S model (http://arxiv.org/abs/1405.3531) in Caffe's Model Zoo.
For details o... |
ConnectedSystems/veneer-py | doc/training/5_Running_Iteratively.ipynb | isc | import veneer
v = veneer.Veneer(port=9876)
"""
Explanation: Session 5: Running Iteratively
Running Source models from Python becomes more compelling when you start running the model multiple times, modifying something (parameters, inputs, structure) about the model between runs.
At the same time, the number of possibl... |
chengsoonong/mclass-sky | projects/david/lab/experiment_log_regression.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.optimize as opt
from scipy.special import expit # The logistic sigmoid function
%matplotlib inline
"""
Explanation: Classification
COMP4670/8600 - Introduction to Statistical Machine Learning - Tutorial 3
$\newcommand{\trace}[1]{\ope... |
tensorflow/quantum | docs/tutorials/qcnn.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... |
google/jax-md | notebooks/nve_neighbor_list.ipynb | apache-2.0 | #@title Imports & Utils
!pip install jax-md
import numpy as onp
from jax.config import config ; config.update('jax_enable_x64', True)
import jax.numpy as np
from jax import random
from jax import jit
from jax import lax
import time
from jax_md import space
from jax_md import smap
from jax_md import energy
from jax... |
sorter43/PR2017LSBOLP | BaseClass/Porazdelitve.ipynb | apache-2.0 | % matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt ('../ratingSAMPLE.csv', delimiter=",", skiprows=0)
"""
Explanation: Porazdelitev
End of explanation
"""
ratingsNum=list()
for number in np.arange(1,10):
ratingsNum.append(len(data[data[:,2]==number,2]))
plt.figure()
plt.ba... |
chunweixu/Deep-Learning | language-translation/.ipynb_checkpoints/dlnd_language_translation-checkpoint.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... |
amitkaps/hackermath | Module_3a_linear_algebra_eigenvectors.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (10, 6)
def vector_plot (vector):
X,Y,U,V = zip(*vector)
C = [1,1,2,2]
plt.figure()
ax = plt.gca()
ax.quiver(X,Y,U,V,C, angles='xy',scale_units='xy',scale=1)
... |
google/compass | packages/propensity/12.cleanup.ipynb | apache-2.0 | # Add custom utils module to Python environment.
import os
import sys
sys.path.append(os.path.abspath(os.pardir))
from google.cloud import bigquery
from utils import helpers
"""
Explanation: 12. Cleanup BigQuery artifacts
This notebook helps to clean up interim tables generated while executing notebooks from 01 to 09... |
QuantEcon/QuantEcon.notebooks | ddp_ex_MF_7_6_5_py.ipynb | bsd-3-clause | %matplotlib inline
import itertools
import numpy as np
from scipy import sparse
import matplotlib.pyplot as plt
from quantecon.markov import DiscreteDP
maxcap = 30
n = maxcap + 1 # Number of states
m = n # Number of actions
a1, b1 = 14, 0.8
a2, b2 = 10, 0.4
F = lambda x: a1 * x**b1 # Benefit from irrigat... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/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', 'inpe', 'sandbox-1', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: INPE
Source ID: SANDBOX-1
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties:... |
ltiao/project-euler | problem-7-10001st-prime.ipynb | unlicense | from itertools import count, islice
from collections import defaultdict
def _sieve_of_eratosthenes():
factors = defaultdict(set)
for n in count(2):
if factors[n]:
for m in factors.pop(n):
factors[n+m].add(m)
else:
factors[n*n].add(n)
yield n
... |
tensorflow/docs-l10n | site/ja/guide/saved_model.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... |
ajhenrikson/phys202-2015-work | assignments/project/NeuralNetworks.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from IPython.html.widgets import interact
from sklearn.datasets import load_digits
digits = load_digits()
print(digits.data.shape)
def show_digit(i):
plt.matshow(digits.images[i]);
interact(show_digit, i=(0,100));
"""
Explanation: Neural Networks
This project w... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/cmip6/models/sandbox-2/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-2', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, Concent... |
guruucsd/EigenfaceDemo | python/PCA Demo.ipynb | mit | from numpy.random import standard_normal # Gaussian variables
N = 1000; P = 5
X = standard_normal((N, P))
W = X - X.mean(axis=0,keepdims=True)
print(dot(W[:,0], W[:,1]))
"""
Explanation: PCA and EigenFaces Demo
In this demo, we will go through the basic concepts behind the principal component analysis (PCA). We will... |
phockett/ePSproc | notebooks/utilDev/zenodo_data_download_tests_200720.ipynb | gpl-3.0 | import requests
# From doi
urlDOI = 'http://dx.doi.org/10.5281/zenodo.3629721'
r = requests.get(urlDOI)
r.ok
dir(r)
# r.json() Throws an error, not sure why!
# import json
# json.loads(r.text) # Ah, same error - seems to be formatting issue?
# JSONDecodeError: Expecting value: line 2 colum... |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/Day1_fullDataset/rep10_noPCR.ipynb | mit | import os
import glob
import re
import nestly
%load_ext rpy2.ipython
%load_ext pushnote
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(phyloseq)
## BD for G+C of 0 or 100
BD.GCp0 = 0 * 0.098 + 1.66
BD.GCp100 = 1 * 0.098 + 1.66
"""
Explanation: TODO: rerun; DBL default changed
Goal
Ex... |
google/earthengine-community | tutorials/time-series-visualization-with-altair/index.ipynb | apache-2.0 | import ee
ee.Authenticate()
ee.Initialize()
"""
Explanation: Time Series Visualization with Altair
Author: jdbcode
This tutorial provides methods for generating time series data in Earth Engine and visualizing it with the Altair library using drought and vegetation response as an example.
Topics include:
Time series ... |
econ-ark/HARK | examples/ConsIndShockModel/KinkedRconsumerType.ipynb | apache-2.0 | # Initial imports and notebook setup, click arrow to show
import matplotlib.pyplot as plt
import numpy as np
from HARK.ConsumptionSaving.ConsIndShockModel import KinkedRconsumerType
from HARK.utilities import plot_funcs_der, plot_funcs
mystr = lambda number: "{:.4f}".format(number)
"""
Explanation: KinkedRconsumerT... |
mne-tools/mne-tools.github.io | 0.19/_downloads/d52b5321a00f5cf4d4be975019fb541b/plot_morph_surface_stc.ipynb | bsd-3-clause | # Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import mne
from mne.datasets import sample
print(__doc__)
"""
Explanation: Morph surface source estimate
This example demonstrates how to morph an individual subject's
:class:mne.SourceEstimate to a common reference space. We a... |
ajgpitch/qutip-notebooks | examples/qip-optpulseprocessor.ipynb | lgpl-3.0 | from numpy import pi
from qutip.qip.device import OptPulseProcessor
from qutip.qip.circuit import QubitCircuit
from qutip.qip.operations import expand_operator, toffoli
from qutip.operators import sigmaz, sigmax, identity
from qutip.states import basis
from qutip.metrics import fidelity
from qutip.tensor import tensor
... |
ijstokes/bokeh-blaze-tutorial | solutions/1.6 Layout (solution).ipynb | mit | # Import the functions from your file
from viz import climate_map, legend, timeseries
# Create your plots with your new functions
climate_map = climate_map()
legend = legend()
timeseries = timeseries()
# Test the visualizations in the notebook
from bokeh.plotting import show, output_notebook
output_notebook()
show... |
yttty/python3-scraper-tutorial | Python_Spider_Tutorial_06.ipynb | gpl-3.0 | from urllib.request import urlopen
from bs4 import BeautifulSoup
html = urlopen("https://en.wikipedia.org/wiki/Python_(programming_language)")
bsObj = BeautifulSoup(html.read(), "html.parser")
for link in bsObj.findAll("a"):
if 'href' in link.attrs:
print(link.attrs['href'])
"""
Explanation: 用Python 3开发网络... |
jay-johnson/sci-pype | examples/ML-IRIS-Extract-Models-From-Cache.ipynb | apache-2.0 | # Setup the Sci-pype environment
import sys, os
# Only redis is needed for this notebook:
os.environ["ENV_DEPLOYMENT_TYPE"] = "JustRedis"
# Load the Sci-pype PyCore as a named-object called "core" and environment variables
from src.common.load_ipython_env import *
"""
Explanation: Extracting the IRIS Models from Cac... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_introduction.ipynb | bsd-3-clause | import mne
"""
Explanation: .. _intro_tutorial:
Basic MEG and EEG data processing
MNE-Python reimplements most of MNE-C's (the original MNE command line utils)
functionality and offers transparent scripting.
On top of that it extends MNE-C's functionality considerably
(customize events, compute contrasts, group statis... |
pyemma/deeplearning | assignment2/BatchNormalization.ipynb | gpl-3.0 | # As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver import Solver
%matplotlib inline
... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-mh/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-mh', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-MH
Topic: Atmoschem
Sub-Topics: Transport, Emissions ... |
chongxi/spiketag | notebooks/LMNN.ipynb | bsd-3-clause | %pylab inline
x = numpy.array([[0,0],[-1,0.1],[0.3,-0.05],[0.7,0.3],[-0.2,-0.6],[-0.15,-0.63],[-0.25,0.55],[-0.28,0.67]])
y = numpy.array([0,0,0,0,1,1,2,2])
"""
Explanation: Metric Learning with the Shogun Machine Learning Toolbox
Building up the intuition to understand LMNN
First of all, let us introduce LMNN throug... |
maliyngh/LTPython | LightTools_Data_Examples.ipynb | apache-2.0 | # Import the packages/libraries you typically use
import clr
import System
import numpy as np
import matplotlib.pyplot as plt
#This forces plots inline in the Spyder/Python Command Console
%matplotlib inline
#In the line below, make sure the path matches your installation!
LTCOM64Path="C:\\Program Files\\Optical Resea... |
tensorflow/docs-l10n | site/ja/tensorboard/graphs.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... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/launching_into_ml/solutions/decision_trees_and_random_Forests_in_Python.ipynb | apache-2.0 | # Scikit-learn is a free machine learning library for Python.
# It features various algorithms like random forests, and k-neighbours.
# It also supports Python numerical and scientific libraries like NumPy and SciPy.
!pip install scikit-learn==0.22.2
"""
Explanation: Decision Trees and Random Forests in Python
Learnin... |
amirziai/learning | algorithms/Merge-Sort.ipynb | mit | import random
random.seed(0)
from resources.utils import run_tests
"""
Explanation: Merge Sort
Known to John von Neumann in 1945, 70+ years ago
Step 0- Testing utilities
Take a look at resources/utils.py if you're curious.
End of explanation
"""
def split(input_list):
"""
Splits a list into two pieces
:p... |
NYUDataBootcamp/Projects | UG_S17/Wang-VIX.ipynb | mit | # Setup
import sys # system module
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
import seaborn as sns # seaborn graphics module
import os ... |
networks-lab/mkD3 | .ipynb_checkpoints/INTEG 120-checkpoint.ipynb | gpl-3.0 | # Only run this the VERY first time
!pip install metaknowledge
!pip install networkx
!pip install pandas
!pip install python-louvain
# Run this before you do anything else
import metaknowledge as mk
import networkx as nx
import pandas
import community
import webbrowser
"""
Explanation: <center> <img src="http://netwo... |
ledeprogram/algorithms | class7/homework/wang_zhizhou_7.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
from sklearn import datasets
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import tree
iris = datasets.load_iris()
x = iris.data[:,2:]
y = iris.target
plt.figure(2, figsize=(8, 6))
plt.scatter(x[:, 0], x[:, 1], c=y, cmap=plt.cm... |
mne-tools/mne-tools.github.io | dev/_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... |
miqlar/PyFME | examples/examples-notebook/example_001.ipynb | mit | # -*- coding: utf-8 -*-
"""
Explanation: EXAMPLE 001
This is the first example o PyFME. The main purpose of this example is to check if the aircraft trimmed in a given state maintains the trimmed flight condition.
The aircraft used is a Cessna 310, ISA1976 integrated with Flat Earth (euler angles).
Example with trimme... |
DTOcean/dtocean-core | notebooks/DTOcean Tidal Hydrodynamics + Database 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 DataMenu, ModuleMenu, ProjectMenu
from dtocean_core.pip... |
citxx/sis-python | crash-course/strings.ipynb | mit | s1 = "Строки можно задавать в двойных кавычках"
s2 = 'А можно в одинарных'
print(s1, type(s1))
print(s2, type(s2))
"""
Explanation: <h1>Содержание<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Спецсимволы" data-toc-modified-id="Спецсимволы-1">Спецсимволы</a></span></li><li... |
zomansud/coursera | ml-classification/week-3/module-5-decision-tree-assignment-1-blank.ipynb | mit | import graphlab
graphlab.canvas.set_target('ipynb')
"""
Explanation: Identifying safe loans with decision trees
The LendingClub is a peer-to-peer leading company that directly connects borrowers and potential lenders/investors. In this notebook, you will build a classification model to predict whether or not a loan pr... |
knowledgeanyhow/notebooks | noaa/hdtadash/weather_dashboard.ipynb | mit | %matplotlib inline
import os
import struct
import glob
import pandas as pd
import numpy as np
import datetime as dt
import matplotlib.pyplot as plt
import seaborn as sns
import folium
from IPython.display import HTML
from IPython.display import Javascript, display
"""
Explanation: NOAA Weather Analysis
Frequency of D... |
google/tf-quant-finance | tf_quant_finance/examples/jupyter_notebooks/Black_Scholes_Price_and_Implied_Vol.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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, sof... |
jamesjia94/BIDMach | tutorials/MLscalePart1.ipynb | bsd-3-clause | import BIDMat.{CMat,CSMat,DMat,Dict,IDict,Image,FMat,FND,GDMat,GMat,GIMat,GSDMat,GSMat,HMat,IMat,Mat,SMat,SBMat,SDMat}
import BIDMat.MatFunctions._
import BIDMat.SciFunctions._
import BIDMat.Solvers._
import BIDMat.JPlotting._
import BIDMach.Learner
import BIDMach.models.{FM,GLM,KMeans,KMeansw,ICA,LDA,LDAgibbs,Model,NM... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/miroc6/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc6', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC6
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance, ... |
romeokienzler/uhack | projects/bosch/ETLPython.ipynb | apache-2.0 |
import ibmos2spark
# @hidden_cell
credentials = {
'auth_url': 'https://identity.open.softlayer.com',
'project_id': '6aaf54352357483486ee2d4981f8ef15',
'region': 'dallas',
'user_id': 'b160340071b3407ca50c6b9a46b0bb25',
'username': 'member_b092a5c6f5c11f819059a83dfbd5d922b8a2299b',
'password': '... |
QuantumDamage/AQIP | workspace/03-api.ipynb | apache-2.0 | %matplotlib inline
import requests
from pandas.io.json import json_normalize
import pandas as pd
"""
Explanation: Official documentation:
http://powietrze.gios.gov.pl/pjp/content/api#
End of explanation
"""
r = requests.get('http://api.gios.gov.pl/pjp-api/rest/station/findAll')
allStations = json_normalize(r.json(... |
LucaCanali/Miscellaneous | Spark_Physics/ATLAS_Higgs_opendata/H_ZZ_4l_analysis_basic_experiment_data.ipynb | apache-2.0 | # Run this if you need to install Apache Spark (PySpark)
# !pip install pyspark
# Install sparkhistogram
# Note: if you cannot install the package, create the computeHistogram
# function as detailed at the end of this notebook.
!pip install sparkhistogram
# Run this to download the dataset
# It is a small file (20... |
buckleylab/Buckley_Lab_SIP_project_protocols | sequence_analysis_walkthrough/PIPITS_Fungal_ITS_Pipeline.ipynb | mit | import os
# Provide the directory for your index and read files
ITS = '/home/roli/FORESTs_BHAVYA/WoodsLake/raw_seq/ITS/'
# Provide
datasets = [['ITS',ITS,'ITS.metadata.pipits.Woods.tsv']]
# Ensure your reads files are named accordingly (or modify to suit your needs)
readFile1 = 'read1.fq.gz'
readFile2 = 'read2.fq.g... |
cleuton/datascience | datavisualization/data_visualization_python_2_english.ipynb | apache-2.0 | import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d, Axes3D # Objects for 3D charts
%matplotlib inline
df = pd.read_csv('../datasets/evasao.csv') # School dropout data I collected
df.head()
"""
Explanation: Data visualization with Python
2 - Data with more than 2 dimensions
Cleu... |
jamesorr/mocsy | notebooks/mocsy_errors.ipynb | mit | %%bash
pwd
mkdir code
cd code
git clone https://github.com/jamesorr/mocsy.git
cd mocsy
make
pwd
"""
Explanation: Examples of propagating uncertainties in mocsy
<hr>
James Orr - 11 November 2018<br>
<img align="left" width="60%" src="http://www.lsce.ipsl.fr/Css/img/banniere_LSCE_75.png" ><br><br>
LSCE/IPSL, CEA-CNRS-UV... |
mclaughlin6464/pearce | notebooks/Compute Shape Noise.ipynb | mit | from matplotlib import pyplot as plt
%matplotlib inline
#import seaborn as sns
#sns.set()
import matplotlib.colors as colors
import numpy as np
#from nbodykit.source.catalog.halos import HaloCatalog
#from nbodykit.source.catalog.file import HDFCatalog
#from nbodykit.cosmology import Cosmology
#from nbodykit.algorithms... |
pdh21/XID_plus | docs/notebooks/examples/XID+_example_pyvo_prior.ipynb | mit | fields = ['AKARI-NEP',
'AKARI-SEP',
'Bootes',
'CDFS-SWIRE',
'COSMOS',
'EGS',
'ELAIS-N1',
'ELAIS-N2',
'ELAIS-S1',
'GAMA-09',
'GAMA-12',
'GAMA-15',
'HDF-N',
'Herschel-Stripe-82',
'Lockman-SWIRE',
'NGP',
'SA13',
'SGP',
'SPIRE-NEP',
'SSDF',
'XMM-13hr',
'XMM-LSS',
'xFLS']
field_use = fields[6]
print(f... |
jseabold/statsmodels | examples/notebooks/theta-model.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import pandas_datareader as pdr
import matplotlib.pyplot as plt
import seaborn as sns
plt.rc("figure",figsize=(16,8))
plt.rc("font",size=15)
plt.rc("lines",linewidth=3)
sns.set_style("darkgrid")
"""
Explanation: The Theta Model
The Theta model of Assimakopoulos & Nikolopoulos (20... |
bwinkel/cygrid | notebooks/04_sightline_gridding.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
"""
Explanation: Sightline gridding
We demonstrate the gridding of selected sightlines with cygrid. This can be particularly useful if you have some high-resolution data such as QSO absorption spectra and want to get a... |
geography-munich/sciprog | material/sub/jrjohansson/Lecture-7-Revision-Control-Software.ipynb | apache-2.0 | from IPython.display import Image
"""
Explanation: Revision control software
J.R. Johansson (jrjohansson at gmail.com)
The latest version of this IPython notebook lecture is available at http://github.com/jrjohansson/scientific-python-lectures.
The other notebooks in this lecture series are indexed at http://jrjohanss... |
geilerloui/deep-learning | autoencoder/Convolutional_Autoencoder.ipynb | mit | %matplotlib inline
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', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
ibm-cds-labs/pixiedust | notebook/Intro to PixieDust.ipynb | apache-2.0 | #!pip install --user --upgrade pixiedust
"""
Explanation: Hello PixieDust!
This sample notebook provides you with an introduction to many features included in PixieDust. You can find more information about PixieDust at https://pixiedust.github.io/pixiedust/. To ensure you are running the latest version of PixieDust un... |
jldinh/multicell | examples/05 - Gierer-Meinhardt.ipynb | mit | %matplotlib notebook
"""
Explanation: In this example, we will use Multicell to simulate the self-organization of a geometrical Turing pattern (Turing 1952; Note about other proposals and ways to produce spatial patterns), based on equations developed by Gierer and Meinhardt (Gierer and Meinhardt 1972). These equation... |
atlury/deep-opencl | DL0110EN/4.3.3mist1layerassignmnt.ipynb | lgpl-3.0 | import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as dsets
import torch.nn.functional as F
import matplotlib.pylab as plt
import numpy as np
"""
Explanation: <div class="alert alert-block alert-info" style="margin-top: 20px">
<a href="http://cocl.us/pytorch_li... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/launching_into_ml/solutions/improve_data_quality.ipynb | apache-2.0 | # Use the chown command to change the ownership of the repository to user
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
"""
Explanation: Improving Data Quality
Learning Objectives
Resolve missing values
Convert the Date feature column to a datetime format
Rename a feature column, remove a value f... |
tensorflow/tensorflow | tensorflow/lite/g3doc/models/convert/metadata_writer_tutorial.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... |
vsporeddy/bigbang | examples/Plot Activity.ipynb | gpl-2.0 | %matplotlib inline
"""
Explanation: This notebook shows how BigBang can help you explore a mailing list archive.
First, use this IPython magic to tell the notebook to display matplotlib graphics inline. This is a nice way to display results.
End of explanation
"""
import bigbang.mailman as mailman
import bigbang.gra... |
qutip/qutip-notebooks | examples/energy-levels.ipynb | lgpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from numpy import pi
from qutip import *
"""
Explanation: QuTiP example: Energy-levels of a quantum systems as a function of a single parameter
J.R. Johansson and P.D. Nation
For more information about QuTiP see http://qutip.org
End of explanation... |
fastai/fastai | dev_nbs/course/lesson3-planet.ipynb | apache-2.0 | %matplotlib inline
from fastai.vision.all import *
from nbdev.showdoc import *
"""
Explanation: Multi-label prediction with Planet Amazon dataset
End of explanation
"""
# ! {sys.executable} -m pip install kaggle --upgrade
"""
Explanation: Getting the data
The planet dataset isn't available on the fastai dataset pa... |
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