repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
imatge-upc/activitynet-2016-cvprw | notebooks/17 Visualization of Results with Feedback.ipynb | mit | import random
import os
import numpy as np
from work.dataset.activitynet import ActivityNetDataset
dataset = ActivityNetDataset(
videos_path='../dataset/videos.json',
labels_path='../dataset/labels.txt'
)
videos = dataset.get_subset_videos('validation')
videos = random.sample(videos, 8)
examples = []
for v in... |
Astrohackers-TW/IANCUPythonAdventure | notebooks/notebooks4beginners/01_python_tutorial_basics1.ipynb | mit | # ←此為Python的註解符號，在這之後的文字不會被當作程式碼執行
# Python不用宣告變數型態，在指定變數的值時即會動態決定其型態
n_solar_mass = 10 # 整數
MASS_SUN = 1.99 * 10 ** 30 # 浮點數
z = complex(3., -1.) # 複數
unit = "kg" ... |
FFroehlich/AMICI | python/examples/example_steadystate/ExampleSteadystate.ipynb | bsd-2-clause | # SBML model we want to import
sbml_file = 'model_steadystate_scaled_without_observables.xml'
# Name of the model that will also be the name of the python module
model_name = 'model_steadystate_scaled'
# Directory to which the generated model code is written
model_output_dir = model_name
import libsbml
import importli... |
VictorQuintana91/Thesis | notebooks/005_filtering_nouns.ipynb | mit | import pandas as pd
# For monitoring duration of pandas processes
from tqdm import tqdm, tqdm_pandas
# To avoid RuntimeError: Set changed size during iteration
tqdm.monitor_interval = 0
# Register `pandas.progress_apply` and `pandas.Series.map_apply` with `tqdm`
# (can use `tqdm_gui`, `tqdm_notebook`, optional kwarg... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/generic_mle.ipynb | bsd-3-clause | import numpy as np
from scipy import stats
import statsmodels.api as sm
from statsmodels.base.model import GenericLikelihoodModel
"""
Explanation: Maximum Likelihood Estimation (Generic models)
This tutorial explains how to quickly implement new maximum likelihood models in statsmodels. We give two examples:
Probit ... |
masve/saav-deliveries | app/notebooks/2d.ipynb | mit | data_path = '../../SFPD_Incidents_-_from_1_January_2003.csv'
data = pd.read_csv(data_path)
"""
Explanation: Creating datasets for 2D
We begin by reading the csv file, into a data frame. This makes it easier to create.
End of explanation
"""
mask = (data.Category == 'PROSTITUTION') & (data.Y != 90)
filterByCat = da... |
metpy/MetPy | v1.0/_downloads/8532b75251585046a16f04a9afaef079/Advanced_Sounding.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units
"""
Explanation: Advanced Sounding
Plot a sounding using MetPy with more advanced features.
Beyond just plotting data, this ... |
VadimMalykh/courses | deeplearning1/my/redux/Dogs vs Cats redux.ipynb | apache-2.0 | import zipfile
import tempfile
import os
tmp_dir = tempfile.mkdtemp()
tmp_dir
zf = zipfile.ZipFile("../data/redux/train.zip")
zf.extractall(tmp_dir)
zf.close
zf = zipfile.ZipFile("../data/redux/test.zip")
zf.extractall(tmp_dir)
zf.close
import sys
sys.path.append('../../nbs')
import utils
from utils import *
impor... |
tensorflow/federated | docs/tutorials/federated_learning_for_text_generation.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... |
duttashi/Data-Analysis-Visualization | scripts/general/Taarifa_Regression.ipynb | mit | import pandas as pd # for data import and dissection
import numpy as np # for data analysis
import statsmodels.formula.api as smf
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: Load the relevant libraries
End of explanation
"""
plt.interactive(F... |
pfschus/fission_bicorrelation | methods/slices_bhp_by_t.ipynb | mit | %%javascript
$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')
"""
Explanation: <div id="toc"></div>
End of explanation
"""
import numpy as np
import scipy.io as sio
import os
import sys
import matplotlib.pyplot as plt
import matplotlib.colors
from matplotlib.pyplot import c... |
graphistry/pygraphistry | demos/demos_databases_apis/umap_learn/umap_learn.ipynb | bsd-3-clause | # Already installed in Graphistry & RAPIDS distros
# ! pip install --user umap-learn
# ! pip install --user graphistry
import graphistry, pandas as pd, umap
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server='hub.graphistry.com')
# For ... |
gsentveld/lunch_and_learn | notebooks/Data_Exploration_Sample_Child.ipynb | mit | import os
from dotenv import load_dotenv, find_dotenv
# find .env automagically by walking up directories until it's found
dotenv_path = find_dotenv()
# load up the entries as environment variables
load_dotenv(dotenv_path)
"""
Explanation: Exploring the files with Pandas
Many statistical Python packages can deal wit... |
phnmnl/workflow-demo | Jupyter/DeleteCvJobs.ipynb | apache-2.0 | control=input()
"""
Explanation: Delete CV jobs at once
Deleting multiple jobs using the Chonos UI may be tedious. Run this script to delete all of the CV jobs at once.
Prerequisites
Instert your control node address
End of explanation
"""
import getpass
password=getpass.getpass()
"""
Explanation: Insert your admi... |
wgong/open_source_learning | projects/Open_Food/open-food-5k.ipynb | apache-2.0 | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: Motivation
<br>
<font color=red size=+3>Know what you eat, </font>
<font color=green size=+3> Gain insight into food.</font>
<a href=https://world.openfoodfacts.org/>
<img src=https://static.openfoodfacts.org/images/misc/openfoodfacts-log... |
quantumlib/OpenFermion | docs/fqe/tutorials/diagonal_coulomb_evolution.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... |
dereneaton/ipyrad | tests/ipyparallel-tutorial.ipynb | gpl-3.0 | ## conda install ipyrad -c ipyrad
"""
Explanation: Parallelization in ipyrad using ipyparallel
One of the real strenghts of ipyrad is the advanced parallelization methods that it uses to distribute work across arbitrarily large computing clusters, and to be able to do so when working interactively and remotely. This i... |
ypeleg/Deep-Learning-Keras-Tensorflow-PyCon-Israel-2017 | 2.3 Deep Convolutional Neural Networks.ipynb | mit | from keras.applications import VGG16
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
import os
# -- Jupyter/IPython way to see documentation
# please focus on parameters (e.g. include top)
VGG16??
vgg16 = VGG16(include_top=True, weights='imagenet')
"""
Explanation: Deep CNN Models
... |
ereodeereigeo/dataTritiumWS22 | numero_de_datos_perdidos.ipynb | gpl-2.0 | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Número de datos obtenidos y perdidos
Importamos las librerías necesarias
End of explanation
"""
import ext_datos as ext
import procesar as pro
import time_plot as tplt
"""
Explanation: Importamos las librerías creadas para tra... |
msampathkumar/data_science_sessions | Session-2-Hands-Experience-for-ML/DataScience_Presentation2.ipynb | mit | import numpy as np
"""
Explanation: Data Science Workshop
Goal: To learn, how to start implementing ML.
Recap
Machine Learning is a sub feild of Artificial Intelligence, which is focused on self learning.
Data Science is not single step process
Model Building: Linear Models, Support Vector Machines, Random Forest M... |
mikekestemont/leyden-workshop | Digital Text Analysis.ipynb | mit | text = 'It is a truth, universally acknowledged.'
"""
Explanation: Digital Text Analysis
Present-day society is flooded with digital texts: never before, humankind has produced more text than now. To efficiently cope with the vast amounts of text that are published nowadays, industry and academia alike increasingly tu... |
arsenovic/clifford | docs/tutorials/apollonius-cga-augmented.ipynb | bsd-3-clause | from clifford import ConformalLayout, BasisVectorIds, MultiVector, transformations
class OurCustomLayout(ConformalLayout):
def __init__(self, ndims):
self.ndims = ndims
euclidean_vectors = [str(i + 1) for i in range(ndims)]
conformal_vectors = ['m2', 'm1']
# Construct our ... |
mrcinv/matpy | 02a_zaporedja.ipynb | gpl-2.0 | # zaporedje definiramo kot funkcijo
a = lambda n: n**10/2**n
for n in range(10):
print("%f" % a(n))
from matplotlib import pyplot as plt
%matplotlib inline
n = range(30)
plt.plot(n,[a(k) for k in n],'*')
#plt.semilogy(n,[a(k) for k in n],'*')
plt.title("prvih %d členov zaporedja" % len(n))
plt.show()
"""
Explanat... |
edjdavid/adventures | python/rpy2 DataFrames.ipynb | mit | try:
base.summary(df)
except NotImplementedError as e:
print(e)
"""
Explanation: rpy2 doesn't convert pd.DataFrames by default
End of explanation
"""
pd.DataFrame(r_df)
"""
Explanation: Do not use pd.DataFrame on R DataFrame, the results are transposed and not indexed correctly
End of explanation
"""
with... |
pfschus/fission_bicorrelation | methods/singles_correction.ipynb | mit | import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import imageio
import pandas as pd
import seaborn as sns
sns.set(style='ticks')
sys.path.append('../scripts/')
import bicorr as bicorr
import bicorr_e as bicorr_e
import bicorr_plot as bicorr_plot
import bicorr_sums as bicorr_sums
import bicorr... |
gaufung/PythonStandardLibrary | FileSystem/Path.ipynb | mit | import os.path
PATHS = [
'/one/two/three',
'/one/two/three/',
'/',
'.',
'',
]
for path in PATHS:
print('{!r:>17} : {}'.format(path, os.path.split(path)))
for path in PATHS:
print('{!r:>17}:{}'.format(path, os.path.basename(path)))
for path in PATHS:
print('{!r:>17}:{}'.format(path, o... |
keras-team/keras-io | examples/vision/ipynb/super_resolution_sub_pixel.ipynb | apache-2.0 | import tensorflow as tf
import os
import math
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import array_to_img
from tensorflow.keras.preprocessing.image import img_to_array
from t... |
jhillairet/scikit-rf | doc/source/tutorials/Networks.ipynb | bsd-3-clause | import skrf as rf
from pylab import *
"""
Explanation: Networks
Introduction
This tutorial gives an overview of the microwave network analysis
features of skrf. For this tutorial, and the rest of the scikit-rf documentation, it is assumed that skrf has been imported as rf. Whether or not you follow this convention ... |
brandoncgay/deep-learning | batch-norm/Batch_Normalization_Exercises.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
darkomen/TFG | ipython_notebooks/01_pid_extrusora/.ipynb_checkpoints/modelado-checkpoint.ipynb | cc0-1.0 | #Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Mostr... |
DOV-Vlaanderen/pydov | docs/notebooks/search_informele_hydrostratigrafie.ipynb | mit | %matplotlib inline
import inspect, sys
# check pydov path
import pydov
"""
Explanation: Example of DOV search methods for interpretations (informele hydrogeologische stratigrafie)
Use cases explained below
Get 'informele hydrogeologische stratigrafie' in a bounding box
Get 'informele hydrogeologische stratigrafie' ... |
chengsoonong/mclass-sky | projects/jakub/kernel_density/kde.ipynb | bsd-3-clause | DATA_PATH = '~/Desktop/sdss_dr7_photometry_source.csv.gz'
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.neighbors
%matplotlib inline
PSF_COLS = ('psfMag_u', 'psfMag_g', 'psfMag_r', 'psfMag_i', 'psfMag_z')
"""
Explanation: Careful, these constants may be diff... |
lakshmanok/nexradaws | nexrad_sample.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy.ma as ma
import numpy as np
import pyart.graph
import tempfile
import pyart.io
import boto
"""
Explanation: <h2> How to read and display Nexrad on AWS using Python </h2>
<h4> Valliappa Lakshmanan, The Climate Corporation, lak@climate.com </h4>
Amazon We... |
tensorflow/federated | docs/tutorials/simulations.ipynb | apache-2.0 | #@test {"skip": true}
!pip install --quiet --upgrade tensorflow-federated
!pip install --quiet --upgrade nest-asyncio
import nest_asyncio
nest_asyncio.apply()
import collections
import time
import tensorflow as tf
import tensorflow_federated as tff
source, _ = tff.simulation.datasets.emnist.load_data()
def map_f... |
eford/rebound | ipython_examples/CloseEncounters.ipynb | gpl-3.0 | import rebound
import numpy as np
def setupSimulation():
sim = rebound.Simulation()
sim.integrator = "ias15" # IAS15 is the default integrator, so we don't need this line
sim.add(m=1.)
sim.add(m=1e-3,a=1.)
sim.add(m=5e-3,a=1.25)
sim.move_to_com()
return sim
"""
Explanation: Catching close e... |
MehtapIsik/assaytools | examples/competition-fluorescence-assay/3-Competition-Assay-Data-Plotting.ipynb | lgpl-2.1 | #import needed libraries
import re
import os
from lxml import etree
import pandas as pd
import pymc
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: Competition assay analysis and thoughts
Here we will analyze two competition assay co... |
pierreg/tensorflow | tensorflow/tools/docker/notebooks/3_mnist_from_scratch.ipynb | apache-2.0 | from __future__ import print_function
from IPython.display import Image
import base64
Image(data=base64.decodestring("iVBORw0KGgoAAAANSUhEUgAAAMYAAABFCAYAAAARv5krAAAYl0lEQVR4Ae3dV4wc1bYG4D3YYJucc8455yCSSIYrBAi4EjriAZHECyAk3rAID1gCIXGRgIvASIQr8UTmgDA5imByPpicTcYGY+yrbx+tOUWpu2e6u7qnZ7qXVFPVVbv2Xutfce+q7hlasmTJktSAXrnn8... |
adolfoguimaraes/machinelearning | UnsupervisedLearning/Exercicio01_ClusterizacaoDocumentos.ipynb | mit | # Imports necessários para este exercício
from __future__ import print_function
import nltk
import re
import pandas as pd
from sklearn.cluster import KMeans
from imdbpie import Imdb
from nltk.stem.snowball import SnowballStemmer
from sklearn.externals import joblib
from IPython.display import YouTubeVideo, Image
"""
E... |
ewulczyn/talk_page_abuse | src/analysis/Prevalence and Efficacy of Moderation (paper).ipynb | apache-2.0 | # Load scored diffs and moderation event data
d = load_diffs()
df_block_events, df_blocked_user_text = load_block_events_and_users()
df_warn_events, df_warned_user_text = load_warn_events_and_users()
moderated_users = [('warned', df_warned_user_text),
('blocked', df_blocked_user_text),
... |
HaebinShin/tensorflow | tensorflow/examples/tutorials/deepdream/deepdream.ipynb | apache-2.0 | # boilerplate code
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
from IPython.display import clear_output, Image, display, HTML
from __future__ import print_function
import tensorflow as tf
"""
Explanation: DeepDreaming with TensorFlow
Loading and displaying the m... |
queirozfcom/python-sandbox | python3/notebooks/pandas-pivot/pivot-stack-unstack-melt.ipynb | mit | columns = pd.MultiIndex.from_tuples([
('A', 'cat', 'long'), ('B', 'cat', 'long'),
('A', 'dog', 'short'), ('B', 'dog', 'short')
],
names=['exp', 'animal', 'hair_length']
)
df = pd.DataFrame(np.random.randn(4, 4), columns=columns)
df
df.columns
stacked = df.stack(level=['exp'... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MOHC
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Conce... |
modin-project/modin | examples/spreadsheet/tutorial.ipynb | apache-2.0 | # Please install the required packages using `pip install -r requirements.txt` in the current directory
# For all ways to install Modin see official documentation at:
# https://modin.readthedocs.io/en/latest/installation.html
import modin.pandas as pd
import modin.spreadsheet as mss
"""
Explanation: modin.spreadsheet
... |
stsouko/CGRtools | doc/tutorial/5_transformation_rules.ipynb | lgpl-3.0 | import pkg_resources
if pkg_resources.get_distribution('CGRtools').version.split('.')[:2] != ['4', '0']:
print('WARNING. Tutorial was tested on 4.0 version of CGRtools')
else:
print('Welcome!')
# load data for tutorial
from pickle import load
from traceback import format_exc
with open('molecules.dat', 'rb') a... |
tritemio/PyBroMo | notebooks/PyBroMo - B.2 Disk-single-core - Generate smFRET data files.ipynb | gpl-2.0 | %matplotlib inline
from pathlib import Path
import numpy as np
import tables
import matplotlib.pyplot as plt
import seaborn as sns
import pybromo as pbm
print('Numpy version:', np.__version__)
print('PyTables version:', tables.__version__)
print('PyBroMo version:', pbm.__version__)
"""
Explanation: PyBroMo - B.2 Disk-... |
hrjn/ISLR_reading_group | notebooks/chap_2_knn.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distance import euclidean
from tqdm import tqdm
from time import sleep
%matplotlib inline
LARGE_SIZE = (12,8)
"""
Explanation: The K-nearest neighbor algorithm
In this notebook we focus on reproducing the result of Fig. 2.15.
End of explanation
"""... |
ML4DS/ML4all | U1.KMeans/KMeans_professor.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from scipy.spatial.distance import cdist
from fig_code import plot_kmeans_interactive
from sklearn.datasets import make_blobs, load_digits, load_sample_image
from sklearn.decomposition import PCA
from sklearn.metrics import c... |
Merinorus/adaisawesome | Homework/02 - Data from the Web/Question 2.ipynb | gpl-3.0 | # Requests : make http requests to websites
import requests
# BeautifulSoup : parser to manipulate easily html content
from bs4 import BeautifulSoup
# Regular expressions
import re
# Aren't pandas awesome ?
import pandas as pd
"""
Explanation: Obtain all the data for the Master students, starting from 2007. Compute ho... |
liuhanfei0615/liupengyuan.github.io | chapter2/homework/computer/middle/201611680433.ipynb | mit | def dayin(m,n):
for i in range(n):
print(' '*(n-i-1)+(m+' ')*(i+1))
m=input('请给定符号:')
n=int(input('请给定行数:'))
dayin(m,n)
"""
Explanation: 1、写函数，给定符号和行数，如’*’，5，可打印相应行数的如下图形：
End of explanation
"""
for i in range(1, 10):
for j in range(1,10):
if j<=i:
print('{}*{}={:2}'.format(i,j,... |
mne-tools/mne-tools.github.io | 0.13/_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: 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, baseline=(None, 0), proj=... |
SJSlavin/phys202-2015-work | days/day08/Display.ipynb | mit | class Ball(object):
pass
b = Ball()
b.__repr__()
print(b)
"""
Explanation: Display of Rich Output
In Python, objects can declare their textual representation using the __repr__ method.
End of explanation
"""
class Ball(object):
def __repr__(self):
return 'TEST'
b = Ball()
print(b)
"""
Explanatio... |
ricklupton/sankeyview | docs/cookbook/us-energy-consumption.ipynb | mit | from floweaver import *
"""
Explanation: US energy consumption
This example is based on the Sankey diagrams of US energy consumption from the Lawrence Livermore National Laboratory (thanks to John Muth for the suggestion and transcribing the data). We jump straight to the final result – for more explanation of the ste... |
mne-tools/mne-tools.github.io | dev/_downloads/9619fd95b952a0c715b83d0e6b37c416/10_epochs_overview.ipynb | bsd-3-clause | import os
import mne
"""
Explanation: The Epochs data structure: discontinuous data
This tutorial covers the basics of creating and working with :term:epoched
<epochs> data. It introduces the :class:~mne.Epochs data structure in
detail, including how to load, query, subselect, export, and plot data from an
:clas... |
garciparedes/python-examples | numerical/math/stats/stochastic_processes/entrega-01.ipynb | mpl-2.0 | transition_ruiz = np.array([[0.0, 1.0, 0.0, 0.0, 0.0],
[0.3, 0.0, 0.7, 0.0, 0.0],
[0.3, 0.0, 0.0, 0.7, 0.0],
[0.3, 0.0, 0.0, 0.0, 0.7],
[1.0, 0.0, 0.0, 0.0, 0.0]])
"""
Explanation: Exercise: Ruiz Family
La f... |
tensorflow/datasets | docs/keras_example.ipynb | apache-2.0 | import tensorflow as tf
import tensorflow_datasets as tfds
"""
Explanation: Training a neural network on MNIST with Keras
This simple example demonstrates how to plug TensorFlow Datasets (TFDS) into a Keras model.
Copyright 2020 The TensorFlow Datasets Authors, Licensed under the Apache License, Version 2.0
<table cla... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Propertie... |
napjon/krisk | notebooks/legend-title-toolbox.ipynb | bsd-3-clause | df = pd.read_csv('../krisk/tests/data/gapminderDataFiveYear.txt',sep='\t')
p = kk.bar(df,'year',y='pop',how='mean',c='continent')
p.set_size(width=800)
p.set_title('GapMinder Average Population Across Continent')
p.set_toolbox(save_format='png',restore=True)
"""
Explanation: Before we added talk about each of these f... |
AC209ConsumerConfidence/AC209ConsumerConfidence.github.io | ARIMAmodel_BaselineFinal.ipynb | gpl-3.0 | fig = plt.figure(figsize = (15, 15))
ax1 = fig.add_subplot(2, 1, 1)
ax1 = plt.plot(df)
ax1 = plt.title('Consumer Confidence Index \n Monthly Score')
ax1 = plt.xlabel('Date')
ax1 = plt.ylabel('CCI')
ax1 = fig.add_subplot(2, 1, 2)
ax1 = plt.plot(df.diff())
ax1 = plt.title('Consumer Confidence Index \n Monthly Score Dif... |
tensorflow/lattice | docs/tutorials/aggregate_function_models.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... |
gururajl/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... |
ALEXKIRNAS/DataScience | Coursera/Machine-learning-data-analysis/Course 2/Week_02/OverfittingTask.ipynb | mit | import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Практическое задание к уроку 1 (2 неделя).
Линейная регрессия: переобучение и регуляризация
В этом задании мы на примерах увидим, как переобучаются линейные модели, разберем, почему так происходит, и выясним... |
Vvkmnn/books | ThinkBayes/02_Computational_Statistics.ipynb | gpl-3.0 | import sys
sys.path.insert(0, './code')
# Go into the subdirectory
from thinkbayes import Pmf
# Grab the thinkbayes script
"""
Explanation: Computational Statistics
Distributions
In statistics a <span>distribution</span> is a set of values and
their corresponding probabilities.
For example, if you roll a six-sided d... |
Meena-Mani/SECOM_class_imbalance | secomdata_rf.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from sklearn.preprocessing import Imputer
from sklearn.model_selection import train_test_split as tts # sklearn 0.18.1
from sklearn.model_selection import GridSearchCV # sklearn 0.18.1
from sklearn.ensemble... |
evanmiltenburg/python-for-text-analysis | Assignments/ASSIGNMENT-4a.ipynb | apache-2.0 | def read_csv(input_file, delimiter=","):
# your code here
# test your function here
filename = "../Data/csv_data/trump_facebook.tsv"
status_updates = read_csv(filename, delimiter="\t")
status_updates[0:2]
"""
Explanation: Assignment 4a: Data structures (CSV/TSV and JSON)
Deadline for Assignment 4a+b: Friday, Oc... |
jasag/Phytoliths-recognition-system | code/notebooks/Phytoliths_Classifier/Phytoliths_Recognition.ipynb | bsd-3-clause | # Imports
import pickle
%matplotlib inline
#para dibujar en el propio notebook
import numpy as np #numpy como np
import matplotlib.pyplot as plt #matplotlib como plot
from skimage import io
from skimage.transform import rescale
from skimage.color import rgb2gray
from skimage.io import imshow
from skimage.feature i... |
jinntrance/MOOC | coursera/ml-regression/assignments/week-4-ridge-regression-assignment-1-blank.ipynb | cc0-1.0 | import graphlab
"""
Explanation: Regression Week 4: Ridge Regression (interpretation)
In this notebook, we will run ridge regression multiple times with different L2 penalties to see which one produces the best fit. We will revisit the example of polynomial regression as a means to see the effect of L2 regularization.... |
seanjh/venmovac | match_instagram/VenmoTransAndInstagram.ipynb | mit | import os
import string
from datetime import datetime, date, timedelta
import unicodedata
import pymongo
from instagram.client import InstagramAPI
from instagram.bind import InstagramAPIError
from nltk.corpus import stopwords
from nltk.metrics import edit_distance
from nltk.corpus import wordnet as wn
from gensim im... |
mne-tools/mne-tools.github.io | dev/_downloads/89667e881398db43faecc03a232e53a5/40_whitened.ipynb | bsd-3-clause | import mne
from mne.datasets import sample
"""
Explanation: Plotting whitened data
This tutorial demonstrates how to plot :term:whitened <whitening>
evoked data.
Data are whitened for many processes, including dipole fitting, source
localization and some decoding algorithms. Viewing whitened data thus gives
a di... |
xiongzhenggang/xiongzhenggang.github.io | data-science/00-matalib几种基本图形.ipynb | gpl-3.0 | # 导入绘图模块
import matplotlib.pyplot as plt
# 构建数据
GDP = [12406.8,13908.57,9386.87,9143.64]
# 中文乱码的处理
plt.rcParams['font.sans-serif'] =['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False
# 绘图
plt.bar(range(4),GDP, align = 'center',color='steelblue', alpha = 0.8)
# 添加轴标签
plt.ylabel('GDP')
# 添加标题
plt.title('四个直... |
mne-tools/mne-tools.github.io | 0.20/_downloads/c569084177bc9cce4e0419ab10cfd45d/plot_dipole_fit.ipynb | bsd-3-clause | from os import path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked
from nilearn.plotting import plot_anat
from nilearn.datasets import load_mni152_template
data_path = mne... |
tuanavu/coursera-university-of-washington | machine_learning/3_classification/assigment/week2/module-3-linear-classifier-learning-assignment-blank-graphlab.ipynb | mit | import graphlab
"""
Explanation: Implementing logistic regression from scratch
The goal of this notebook is to implement your own logistic regression classifier. You will:
Extract features from Amazon product reviews.
Convert an SFrame into a NumPy array.
Implement the link function for logistic regression.
Write a f... |
bMzi/ML_in_Finance | 0208_LDA-QDA.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
plt.rcParams['font.size'] = 14
plt.style.use('seaborn-whitegrid')
# Default data set is not available online. Data was extracted from R package "ISLR"
df = pd.read_csv('Data/Default.csv', sep=',')
# F... |
stefanthaler/tf-spikes | vampprior/AES deep template attack.ipynb | apache-2.0 | import tensorflow as tf
assert(tf.__version__=="1.2.0") # make sure we have the right tensorflow version
import numpy as np
import os
import logging
import library.helper as h
from IPython.display import Image # displaying images in ipython
# configure numpy
np.set_printoptions(precision=2)
np.random.seed(0)
# con... |
ThyrixYang/LearningNotes | MOOC/stanford_cnn_cs231n/assignment2/BatchNormalization.ipynb | gpl-3.0 | # As usual, a bit of setup
from __future__ import print_function
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.solv... |
zlpure/CS231n | assignment2/Dropout.ipynb | mit | # 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
... |
tensorflow/docs-l10n | site/zh-cn/hub/tutorials/cord_19_embeddings.ipynb | apache-2.0 | # Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
opesci/tutorial-hands-on | 02b_scipy_optimize.ipynb | mit | #NBVAL_IGNORE_OUTPUT
from examples.seismic import Model, demo_model
import numpy as np
# Define the grid parameters
def get_grid():
shape = (101, 101) # Number of grid point (nx, nz)
spacing = (10., 10.) # Grid spacing in m. The domain size is now 1km by 1km
origin = (0., 0.) # Need origin to defin... |
opesci/devito | examples/seismic/tutorials/09_viscoelastic.ipynb | mit | # Required imports:
import numpy as np
import sympy as sp
from devito import *
from examples.seismic.source import RickerSource, TimeAxis
from examples.seismic import ModelViscoelastic, plot_image
"""
Explanation: Viscoelastic wave equation implementation on a staggered grid
This is a first attempt at implementing th... |
wem3/gems_vs_bomb | rez/.ipynb_checkpoints/all_bandits-checkpoint.ipynb | mit | # imports / display plots in cell output
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as ss
import pandas as pd
import seaborn as sns
import statsmodels
"""
Explanation: Reinforcement Learning Models of Social Group Preferences
Bandit Experiments 1-7
End of explanation
"""
... |
gonmolina/CCE_ProblemasResueltos | ProbsVVEE/Python Control Notebook/.ipynb_checkpoints/rlocus_test-checkpoint.ipynb | mit | sys1 = ctrl.tf([1, 1], [1, 10, 1])
print(sys1)
r, k = ctrl.rlocus(sys1)
plt.show()
r, k = ctrl.rlocus(sys1, grid=True)
"""
Explanation: Simple example that is not OK
End of explanation
"""
r, k = ctrl.rlocus(sys1, grid=True, ylim=[-10, 10])
"""
Explanation: However, when I plot the grid the figure looks not so goo... |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/sandbox-2/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'sandbox-2', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CCCMA
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turb... |
c22n/ion-channel-ABC | docs/examples/human-atrial/standardised_isus.ipynb | gpl-3.0 | import os, tempfile
import logging
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from ionchannelABC import theoretical_population_size
from ionchannelABC import IonChannelDistance, EfficientMultivariateNormalTransition, IonChannelAcceptor
from ionchannelABC.experimen... |
mne-tools/mne-tools.github.io | 0.18/_downloads/fc5b371c8954994307927cbc590118e1/plot_mne_inverse_envelope_correlation.ipynb | bsd-3-clause | # sphinx_gallery_thumbnail_number = 2
# Authors: Eric Larson <larson.eric.d@gmail.com>
# Sheraz Khan <sheraz@khansheraz.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.connectiv... |
cavalcantetreinamentos/curso_python | Primeiros_passos_Google_Colab.ipynb | apache-2.0 | print('Olá seja bem vindo!!')
"""
Explanation: <a href="https://colab.research.google.com/github/cavalcantetreinamentos/curso_python/blob/master/Primeiros_passos_Google_Colab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Aprendendo Google Colab - ... |
SalikWarsi/data-512-a2 | hcds-a2-bias_demo.ipynb | mit | ## getting the data from the CSV files and converting into a list
import csv
import pandas as pd
data = []
with open('page_data.csv', encoding='utf8') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
data.append([row[0],row[1],row[2]])
"""
Explanation: Bias on Wikipedia
The aim of this expe... |
gzuidhof/nn-transfer | example.ipynb | mit | import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
... |
zhaojijet/UdacityDeepLearningProject | examples/DCGAN.ipynb | apache-2.0 | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
swails/mdtraj | examples/centroids.ipynb | lgpl-2.1 | from __future__ import print_function
%matplotlib inline
import mdtraj as md
import numpy as np
"""
Explanation: Finding centroids
In this example, we're going to find a "centroid" (representitive structure) for a group of conformations. This group might potentially come from clustering, using method like Ward hierarc... |
Upward-Spiral-Science/spect-team | Code/Assignment-11/AdvancedFeatureSelection.ipynb | apache-2.0 | # Standard
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
# Dimensionality reduction and Clustering
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn import manifold, datasets
from itertools import cycle
# Plotting tools and classifiers
fr... |
JonasWallin/BayesFlow | script/running_bayesflow_starcluster.ipynb | gpl-2.0 | %%bash
. ~/.bashrc
pip install --upgrade git+https://git@github.com/JonasWallin/linkingEC2
from linkingEC2 import LinkingHandler
from ConfigParser import ConfigParser
config = ConfigParser()
starfigconfig_folder = "/Users/jonaswallin/.starcluster/"
config.read(starfigconfig_folder + "config")
acess_key_id = con... |
sergpolly/FluUtils | FluDB_coding_aln/getting_loci_interest.ipynb | mit | %matplotlib inline
import os
import sys
from Bio import SeqRecord
from Bio import AlignIO
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: We'll try to desribe our loci of interest procedure with details and illustrations here.
Let's start with some modules:
End of explanation
""... |
cristhro/Machine-Learning | ejercicio 2/Ejercicio_2.ipynb | gpl-3.0 | import sys #only needed to determine Python version number
# Handle table-like data and matrices
import numpy as np
import pandas as pd
# Visualisation
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import seaborn as sns
# Enable inline plotting
%matplotlib inline
# Modelo... |
atulsingh0/MachineLearning | python_DC/Data_Wrangling_#1.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
df1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],
'data1': range(7)})
df2 = pd.DataFrame({'key': ['a', 'b', 'd'],
'data2': range(3)})
print(df1, "\n\n", df2)
pd.merge(df1, df2)
pd.merge(df1, df2, on='key')
# if column name are d... |
gfrias/udacity | 1_lines/P1.ipynb | mit | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dimesions:', im... |
Ensembl/cttv024 | tests/__reports__/postgap.20180817.asthma.txt.gz.REPORT.20190110105809.ipynb | apache-2.0 | from reports import helpers
helpers.calc_run_str()
# pg = pd.read_csv(filename, sep='\t', na_values=['None'])
pg = helpers.load_file(filename)
"""
Explanation: POSTGAP Report
This notebook was automatically generated as a summary of POSTGAP output.
Setup
Note that for command line usage (python reporter.py <filen... |
dsavransky/MAE2030 | Notebooks/Moment of Inertia of a Crane.ipynb | mit | from miscpy.utils.sympyhelpers import *
init_printing()
M,h,m1,m2,th1,th2,b,l1,l2 = \
symbols('M,h,m_1,m_2,theta_1,theta_2,beta,l_1,l_2')
"""
Explanation: Preamble stuff (can ignore)
End of explanation
"""
I_O_cab = M*2/3*h**2/4*eye(3); I_O_cab
"""
Explanation: Model the Cab as a Cube : $\left[\mathbb{I}O^\textrm{... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/emac-2-53-vol/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'emac-2-53-vol', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: EMAC-2-53-VOL
Topic: Atmos
Sub-Topics: D... |
drvinceknight/gt | nbs/chapters/07-Prisoners-Dilemma.ipynb | mit | %matplotlib inline
import axelrod as axl
axl.seed(0) # Make this reproducible
players = [
axl.TitForTat(),
axl.FirstByTidemanAndChieruzzi(),
axl.FirstByNydegger(),
axl.FirstByGrofman(),
axl.FirstByShubik(),
axl.FirstBySteinAndRapoport(),
axl.Grudger(),
axl.FirstByDavis(),
axl.Firs... |
kit-cel/wt | mloc/ch4_Autoencoders/Autoencoder_PolicyGradient_AWGN_AdHovReceiver.ipynb | gpl-2.0 | import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from ipywidgets import interactive
import ipywidgets as widgets
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print("We are using the following device for learning:",device)
""... |
Jackie789/JupyterNotebooks | Testing Classifier Models.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pandas as pd
import scipy
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from s... |
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