repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
mne-tools/mne-tools.github.io | 0.18/_downloads/4eb6243ca7f447169baac6cdad977ee8/plot_stats_spatio_temporal_cluster_sensors.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mne.viz import plot_topomap
import mne
from mne.stats import spatio_... |
dato-code/tutorials | notebooks/introduction_to_sframes.ipynb | apache-2.0 | import graphlab as gl
"""
Explanation: Introduction to SFrames
What is an SFrame?
Note: This notebook uses GraphLab Create 1.7.
An SFrame is a tabular data structure. If you are familiar with R or the pandas python package, SFrames behave similarly to the dataframes available in those frameworks. SFrames act like a ... |
wdbm/Psychedelic_Machine_Learning_in_the_Cenozoic_Era | TensorFlow_introduction.ipynb | gpl-3.0 | import tensorflow as tf
print('TensorFlow version:', tf.__version__)
"""
Explanation: TensorFlow introduction: the art of the sesh
This introduction seeks to broach a few basic topics in TensorFlow: what it is, how operations and data are defined for its computational graphs and how its operations are visualized. In... |
QFinancier/blog | give_me_data_or_death/give_me_good_data_or_give_me_death.ipynb | mit | import pandas as pd
import numpy as np
#create sizeable dataset
n_obs = 1000000
idx = pd.date_range('2015-01-01', periods=n_obs, freq='L')
df = pd.DataFrame(np.random.randn(n_obs,4), index=idx,
columns=["Open", "High", "Low", "Close"])
df.head()
"""
Explanation: Give me good data, or give me death
... |
eds-uga/csci1360-fa16 | assignments/A1/A1_Q5.ipynb | mit | import numpy as np
def magic():
return np.random.randint(0, 10)
def how_many_loops(stop_val):
loops = 0
### BEGIN SOLUTION
### END SOLUTION
return loops
np.random.seed(3849)
s1 = 5
l1 = 6
assert l1 == how_many_loops(s1)
np.random.seed(895768)
s2 = 3
l2 = 20
assert l2 == how_many_l... |
rgarcia-herrera/sistemas-dinamicos | human_immune.ipynb | gpl-3.0 | # Para hacer experimentos numéricos importamos numpy
import numpy as np
# y biblioteca para plotear
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
# cómputo simbólico con sympy
from sympy import *
# init_printing(use_latex='matplotlib') # en emacs
init_printing()
"""
Explanation: Human immune ... |
arviz-devs/arviz | doc/source/user_guide/numpyro_refitting_xr_lik.ipynb | apache-2.0 | import arviz as az
import numpyro
import numpyro.distributions as dist
import jax.random as random
from numpyro.infer import MCMC, NUTS
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
import xarray as xr
numpyro.set_host_device_count(4)
"""
Explanation: Refitting NumPyro models with Arv... |
wiheto/teneto | docs/tutorial/tctc.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from teneto.communitydetection import tctc
import pandas as pd
data = np.array([[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 1, 2, 1],
[0, 0, 0, 0, 1, 1, 1, 0, 2, 2, 2, 2, 1],
[1, 0, 1, 1, 1, 1, 1, 1, 2, 2, 1, 0, 0], [-1, 0, 1, 1, 0, -1, 0, -1, 0, 2,... |
pybel/pybel-notebooks | BEL to Natural Language.ipynb | apache-2.0 | import sys
import time
import indra
import indra.util.get_version
import ndex2
import pybel
from indra.assemblers.english_assembler import EnglishAssembler
from indra.sources.bel.bel_api import process_pybel_graph
from pybel.examples import sialic_acid_graph
from pybel_tools.visualization import to_jupyter
"""
Expl... |
tensorflow/tensorboard | tensorboard/plugins/mesh/Mesh_Plugin_Tensorboard.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... |
johnpfay/environ859 | 07_DataWrangling/notebooks/00-Intro-to-NumPy.ipynb | gpl-3.0 | #Create a list of heights and weights
height = [1.73, 1.68, 1.17, 1.89, 1.79]
weight = [65.4, 59.2, 63.6, 88.4, 68.7]
print height
print weight
"""
Explanation: Intro to NumPy
This notebook demonstrates the limitations of Python's built-in data types in executing some scientific analyses.
Source: https://campus.dataca... |
Housebeer/Natural-Gas-Model | .ipynb_checkpoints/Matching Market-checkpoint.ipynb | mit | import random as rnd
class Supplier():
def __init__(self):
self.wta = []
# the supplier has n quantities that they can sell
# they may be willing to sell this quantity anywhere from a lower price of l
# to a higher price of u
def set_quantity(self,n,l,u):
for i in range(n):
... |
darothen/py-mie | tutorials/Tutorial.ipynb | mit | import mie
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import seaborn as sns
rc = {
"figure.figsize": (12,6),
"xtick.major.size": 12.0,
"xtick.minor.size": 8.0,
"ytick.major.size": 12.0,
"ytick.minor.size": 8.0,
"axes.linewidth": 1.75,
"xtick.color"... |
liganega/Gongsu-DataSci | notebooks/GongSu17-Pandas-tutorial-03.ipynb | gpl-3.0 | import pandas as pd
import matplotlib.pyplot as plt
import numpy.random as np
# 쥬피터 노트북에서 그래프를 직접 나타내기 위해 사용하는 코드
# 파이썬 전문 에디터에서는 사용하지 않음
%matplotlib inline
"""
Explanation: pandas 3
자료 안내:
pandas 라이브러리 튜토리얼에
있는 Lessons for new pandas users의 03-Lesson 내용을 담고 있다.
익명함수(lambda 함수), GroupBy, apply, transform에 대한 설명... |
christophebertrand/ada-epfl | HW01-Intro_to_Pandas/Intro to Pandas.ipynb | mit | import pandas as pd
import numpy as np
pd.options.mode.chained_assignment = None # default='warn'
"""
Explanation: Table of Contents
<p><div class="lev1"><a href="#Introduction-to-Pandas"><span class="toc-item-num">1 </span>Introduction to Pandas</a></div><div class="lev2"><a href="#Pandas-Data-Structures"... |
jamesfolberth/NGC_STEM_camp_AWS | notebooks/data8_notebooks/lab10/lab10.ipynb | bsd-3-clause | # Run this cell to set up the notebook, but please don't change it.
# These lines import the Numpy and Datascience modules.
import numpy as np
from datascience import *
# These lines do some fancy plotting magic.
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
imp... |
allandieguez/teaching | Matplotlib e Seaborn/Modulo 2 - Scatter Plot + Text.ipynb | gpl-3.0 | import numpy as np
import os
import pandas as pd
""" habilitando plots no notebook """
%matplotlib inline
""" plot libs """
import matplotlib.pyplot as plt
import seaborn as sns
""" Configurando o Matplotlib para o modo manual """
plt.interactive(False)
"""
Explanation: Módulo 2: Scatter Plot + Text
Tutorial
Impor... |
shead-custom-design/pipecat | docs/gps-receivers.ipynb | gpl-3.0 | # nbconvert: hide
from __future__ import absolute_import, division, print_function
import sys
sys.path.append("../features/steps")
import test
socket = test.mock_module("socket")
path = "../data/gps"
client = "172.10.0.20"
socket.socket().recvfrom.side_effect = test.recvfrom_file(path=path, client=client, stop=6)
... |
vinitsamel/udacitydeeplearning | first-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
mne-tools/mne-tools.github.io | 0.24/_downloads/81e58e463fcd949fd4ab7ab7ab8ef317/left_cerebellum_volume_source.ipynb | bsd-3-clause | # Author: Alan Leggitt <alan.leggitt@ucsf.edu>
#
# License: BSD-3-Clause
import os.path as op
import mne
from mne import setup_source_space, setup_volume_source_space
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
subject = 'sample'
aseg_f... |
mne-tools/mne-tools.github.io | 0.19/_downloads/e5c0288e15772e4fb31189b766e9d7be/plot_metadata_epochs.ipynb | bsd-3-clause | # Authors: Chris Holdgraf <choldgraf@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import mne
import numpy as np
import matplotlib.pyplot as plt
# Load the data from the internet
path = mne.datasets.kiloword.data_path() ... |
yingchi/fastai-notes | deeplearning1/rnn/rnn-modu.ipynb | apache-2.0 | from theano.sandbox import cuda
cuda.use('gpu1')
%matplotlib inline
import utils;
from utils import *
from keras.layers import TimeDistributed, Activation
from keras.callbacks import ModelCheckpoint
from numpy.random import choice
"""
Explanation: Auto Generate Text for <<默读>>
End of explanation
"""
path = 'text/mo... |
tpin3694/tpin3694.github.io | python/pandas_select_rows_when_column_has_certain_values.ipynb | mit | # Import modules
import pandas as pd
# Set ipython's max row display
pd.set_option('display.max_row', 1000)
# Set iPython's max column width to 50
pd.set_option('display.max_columns', 50)
"""
Explanation: Title: Select Rows When Columns Contain Certain Values
Slug: pandas_select_rows_when_column_has_certain_values
S... |
InsightLab/data-science-cookbook | 2019/12-spark/12-spark-intro/Actions.ipynb | mit | data = sc.parallelize(range(1, 11))
def summation(a, b): return a + b
def max(a, b): return a if a > b else b
# reduce to find the sum
print (data.reduce(summation))
# reduce to find the max
print (data.reduce(max))
"""
Explanation: reduce(func)
Agrega os elementos do RDD usando uma função func (que leva dois argume... |
keras-team/autokeras | docs/ipynb/load.ipynb | apache-2.0 | dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz" # noqa: E501
local_file_path = tf.keras.utils.get_file(
origin=dataset_url, fname="image_data", extract=True
)
# The file is extracted in the same directory as the downloaded file.
local_dir_path = os.path.dirna... |
hetaodie/hetaodie.github.io | assets/media/uda-ml/supervisedlearning/jc/为慈善机构寻找捐助者/charity_finish/charity/titanic_survival_exploration/titanic_survival_exploration.ipynb | mit | # 检查你的Python版本
from sys import version_info
if version_info.major != 2 and version_info.minor != 7:
raise Exception('请使用Python 2.7来完成此项目')
import numpy as np
import pandas as pd
# 数据可视化代码
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# 加载数据集
in_file = 't... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session13/Day2/02-Fast-GPs.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
from matplotlib import rcParams
rcParams["figure.dpi"] = 100
rcParams["figure.figsize"] = 12, 4
"""
Explanation: Fast GP implementations
End of explanation
"""
import numpy as np
np.random.seed(0)
t = np.linspace(0, 10, 10000)
y = np.random.randn(1... |
yevheniyc/Python | 1m_ML_Security/notebooks/day_1/Worksheet 1 - Working with One Dimensional Data.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Worksheet 1: Working with One Dimensional Data
This worksheet covers concepts covered in the first half of Module 1 - Exploratory Data Analysis in One Dimension.
There are many ways to accomplish the tasks that you are presented w... |
jsignell/MpalaTower | inspection/.ipynb_checkpoints/inspect_raw_netcdf-checkpoint.ipynb | mit | usr = 'Julia'
FILEDIR = 'C:/Users/%s/Dropbox (PE)/KenyaLab/Data/Tower/TowerData/'%usr
NETCDFLOC = FILEDIR + 'raw_netcdf_output/'
DATALOC = 'F:/towerdata/'
"""
Explanation: Inspect Raw Netcdf
Playing around with efficient ways to merge and view netcdf data from the tower. This ipython notebook depends on the python sc... |
muatik/dm | SVM-comparision.ipynb | mit | from sklearn import svm, linear_model, neighbors, ensemble
from sklearn import cross_validation, grid_search
from sklearn import datasets
import numpy as npes
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pylab as plt
import time
from IPython.display import YouTubeVideo
%matplotlib... |
WNoxchi/Kaukasos | FADL1/lesson3-rossman-Copy1-old.ipynb | mit | %matplotlib inline
%reload_ext autoreload
%autoreload 2
from fastai.structured import *
from fastai.column_data import *
np.set_printoptions(threshold=50, edgeitems=20)
PATH='data/rossmann/'
"""
Explanation: Structured and time series data
This notebook contains an implementation of the third place result in the Ros... |
yhilpisch/dx | 07_dx_portfolio_risk.ipynb | agpl-3.0 | import dx
import datetime as dt
import time
import numpy as np
"""
Explanation: <img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="45%" align="right" border="4">
Derivatives Portfolio Risk Statistics
From a risk management perspective it is important to know how sensitive derivatives portfolios... |
liquidSVM/liquidSVM | bindings/python/demo.ipynb | agpl-3.0 | from liquidSVM import *
"""
Explanation: liquidSVM for Python
We give a demonstration of the capabilities of liquidSVM from a Python viewpoint.
More information can be found in the help (e.g. ?mcSVM).
Disclaimer: liquidSVM and the Python-bindings are in general quite stable and well tested by several people.
However,... |
srnas/barnaba | examples/example_03_annotate.ipynb | gpl-3.0 | import barnaba as bb
# annotate
pdb = "../test/data/SARCIN.pdb"
stackings, pairings, res = bb.annotate(pdb)
# list base pairings
print("BASE-PAIRS")
for p in range(len(pairings[0][0])):
res1 = res[pairings[0][0][p][0]]
res2 = res[pairings[0][0][p][1]]
interaction = pairings[0][1][p]
print("%10s %10s ... |
numenta/nupic.research | projects/archive/dynamic_sparse/notebooks/ExperimentAnalysis-TestRestoration.ipynb | agpl-3.0 | %load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
from nupic.research.frameworks.dynamic... |
bureaucratic-labs/yargy | docs/index.ipynb | mit | from yargy import Parser, rule, and_
from yargy.predicates import gram, is_capitalized, dictionary
GEO = rule(
and_(
gram('ADJF'), # так помечается прилагательное, остальные пометки описаны в
# http://pymorphy2.readthedocs.io/en/latest/user/grammemes.html
is_capitalized()
... |
jdhp-docs/python-notebooks | photography_fr.ipynb | mit | %matplotlib inline
import math
import numpy as np
import matplotlib.pyplot as plt
import ipywidgets
from ipywidgets import interact
"""
Explanation: Photographie
TODO
* ...
End of explanation
"""
CAPTEUR_DICT = {"APS-C Canon (15x23 mm)": (14.9, 22.3),
"Full frame (24x36 mm)": (24, 36)}
"""
Explanat... |
gschivley/ERCOT_power | Group classification/Group classification.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import sklearn as sk
from cluster import Clusters
import os
filename = 'Cluster_Data_2.csv'
path = '../Clean Data'
fullpath = os.path.join(path, filename)
cluster = Clusters(fullpath)
cluster.mak... |
leoferres/prograUDD | clases/09-Iteradores.ipynb | mit | for i in range(10):
print(i, end=' ')
"""
Explanation: Iteradores
Una de las cosas más maravillosas de las compus es que podemos repetir un mismo cálculo para muchos valores de forma automática. Ya hemos visto al menos un iterator (iterador), que no es una lista... es otro objeto.
End of explanation
"""
for valu... |
google-research/google-research | privacy_poison/svm_pois_mi.ipynb | apache-2.0 | import sklearn
import numpy as np
from sklearn import svm
from tensorflow.keras.datasets import fashion_mnist
(trn_x, trn_y), (tst_x, tst_y) = fashion_mnist.load_data()
twoclass_inds = np.where(trn_y<=1)[0]
trn_x, trn_y = trn_x[twoclass_inds], trn_y[twoclass_inds]
trn_x = trn_x.reshape((trn_x.shape[0], -1))/255.0 - .5... |
snth/split-apply-combine | The Split-Apply-Combine Pattern in Data Science and Python.ipynb | mit | import os
import gzip
import ujson as json
directory = 'data/github_archive'
filename = '2015-01-29-16.json.gz'
path = os.path.join(directory, filename)
with gzip.open(path) as f:
events = [json.loads(line) for line in f]
#print json.dumps(events[0], indent=4)
"""
Explanation: The Split-Apply-Combine Pattern... |
trungdong/datasets-provanalytics-dmkd | Cross Validation Code.ipynb | mit | # The 'combined' list has all the 22 metrics
feature_names_combined = (
'entities', 'agents', 'activities', # PROV types (for nodes)
'nodes', 'edges', 'diameter', 'assortativity', # standard metrics
'acc', 'acc_e', 'acc_a', 'acc_ag', # average clustering coefficients
'mfd_e_e', 'mfd_e_a', 'mfd_e_ag',... |
bspalding/research_public | lectures/linear_regression/Linear Regression.ipynb | apache-2.0 | # Import libraries
import numpy as np
from statsmodels import regression
import statsmodels.api as sm
import matplotlib.pyplot as plt
import math
"""
Explanation: Linear Regression
By Evgenia "Jenny" Nitishinskaya and Delaney Granizo-Mackenzie with example algorithms by David Edwards
Part of the Quantopian Lecture Ser... |
GoogleCloudPlatform/ai-platform-samples | notebooks/samples/pytorch/text_classification/text_classification_using_pytorch_and_ai_platform.ipynb | apache-2.0 | import sys
# If you are running this notebook in Colab, run this cell and follow the
# instructions to authenticate your GCP account. This provides access to your
# Cloud Storage bucket and lets you submit training jobs and prediction
# requests.
if 'google.colab' in sys.modules:
from google.colab import auth as go... |
gobabiertoAR/datasets-portal | estructura-organica-pen/Cleaner estructura organica.ipynb | mit | from __future__ import unicode_literals
from __future__ import print_function
from data_cleaner import DataCleaner
import pandas as pd
input_path = "estructura-organica-raw.csv"
output_path = "estructura-organica-clean.csv"
dc = DataCleaner(input_path)
"""
Explanation: Limpieza de Estructura Organica del PEN
Se util... |
swirlingsand/deep-learning-foundations | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import numpy as np
import time
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that ta... |
g-weatherill/catalogue_toolkit | notebooks/Homogenisation.ipynb | agpl-3.0 | parser = ISFReader("inputs/isc_test_catalogue_isf.txt",
selected_origin_agencies=["ISC", "GCMT", "HRVD", "NEIC", "EHB", "BJI"],
selected_magnitude_agencies=["ISC", "GCMT", "HRVD", "NEIC", "BJI"])
catalogue = parser.read_file("ISC_DB1", "ISC Global M >= 5")
print("Catalogue contains... |
jeicher/cobrapy | documentation_builder/getting_started.ipynb | lgpl-2.1 | from __future__ import print_function
import cobra.test
# "ecoli" and "salmonella" are also valid arguments
model = cobra.test.create_test_model("textbook")
"""
Explanation: Getting Started
To begin with, cobrapy comes with bundled models for Salmonella and E. coli, as well as a "textbook" model of E. coli core metab... |
chapmanbe/utah_highschool_airquality | windrose/make_WindRose.ipynb | apache-2.0 | %matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
from datetime import datetime
import json
from urllib.request import urlopen
# Confirm that `pm25rose.py` is in your directory
from pm25rose import WindroseAxes
import mesowest
"""
Explanation: P... |
AhmetHamzaEmra/Deep-Learning-Specialization-Coursera | Sequence Models/Emojify+-+v2.ipynb | mit | import numpy as np
from emo_utils import *
import emoji
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Emojify!
Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier.
Have you ever wanted to make your text messages more expressive? You... |
dennys-bd/Udacity-Deep-Learning | 3 - Convolutional Neural Net/Scripts/Simple_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)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
probml/pyprobml | notebooks/misc/elegy_intro.ipynb | mit | %%capture
!pip install git+https://github.com/deepmind/dm-haiku
#!pip install -q clu ml-collections git+https://github.com/google/flax
%%capture
! pip install --upgrade pip
! pip install elegy datasets matplotlib
"""
Explanation: <a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/note... |
M0nica/python-foundations-hw | 08/08.ipynb | mit | # workon dataanalysis - my virtual environment
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# df = pd.read_table('34933-0001-Data.tsv')
odf = pd.read_csv('accreditation_2016_03.csv')
odf.head()
odf.columns
odf['Campus_City'].value_counts().head(10)
top_cities = odf['Campus_City'].value_co... |
bbfamily/abu | abupy_lecture/23-美股UMP决策(ABU量化使用文档).ipynb | gpl-3.0 | # 基础库导入
from __future__ import print_function
from __future__ import division
import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import ipywidgets
%matplotlib inline
import os
import sys
# 使用insert 0即只使用github，避免交叉... |
scikit-optimize/scikit-optimize.github.io | 0.8/notebooks/auto_examples/plots/partial-dependence-plot-with-categorical.ipynb | bsd-3-clause | print(__doc__)
import sys
from skopt.plots import plot_objective
from skopt import forest_minimize
import numpy as np
np.random.seed(123)
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.eliminate_state.ipynb | gpl-3.0 | import vcsn
"""
Explanation: automaton.eliminate_state(state = -1)
In the Brzozowski-McCluskey procedure, remove one state.
Preconditions:
- The labelset is oneset (i.e., the automaton is spontaneous).
- The weightset is expressionset (i.e., the weights are expressions).
- The _state_ is indeed a state of the automato... |
atcemgil/notes | HamiltonianDynamics.ipynb | mit | %matplotlib inline
import scipy as sc
import numpy as np
import scipy.linalg as la
import matplotlib.pyplot as plt
A = np.mat('[0,1;-1,0]')
dt = 0.05
T = 100
z = np.mat(np.zeros((2,T)))
H = la.expm(dt*A)
z[:,0] = np.mat('[2.4;0]')
for i in range(1,T):
z[:,i] = H*z[:,i-1]
plt.plot(z[0,:], z[1,:],'.-r')
ax... |
tanmay987/deepLearning | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that takes in a sequence of letters, an... |
nathawkins/PHY451_FS_2017 | Diode Laser Spectroscopy/20171003_morning/Interference with SAS no Dopple/Interferometer with SAS No Doppler Analysis.ipynb | gpl-3.0 | get_peak_data(ch2, [0.025, 0.030]);
get_peak_data(ch2, [0.030, 0.035]);
get_peak_data(ch2, [0.0350,0.045]);
get_peak_data(ch2, [0.049, 0.0517]);
maximum_time_positions = [0.028124, 0.03266, 0.042744, 0.05052]
maximum_voltage_positions = [0.738, 0.53, 0.716, 0.48]
# Two subplots, unpack the axes array immediately
f... |
OSGeo-live/CesiumWidget | Examples/CesiumWidget Interact-Example.ipynb | apache-2.0 | from CesiumWidget import CesiumWidget
from IPython import display
from czml_example import simple_czml, complex_czml
"""
Explanation: Cesium Widget Example
This is an example notebook to sow how to bind the Cesiumjs with the IPython interactive widget system.
End of explanation
"""
cesiumExample = CesiumWidget(width... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/homework_assignments/function_tutorial.ipynb | agpl-3.0 | def print_hello():
print("hello!")
# call the function and store its output. You don't really have to have the output= part if you don't want to.
output=print_hello()
print("output of print_hello() is:", output)
"""
Explanation: Python functions - some examples
This notebook demonstrates how to work with python... |
trungdong/datasets-provanalytics-dmkd | Extra 2.1 - Unbalanced Data - Application 1.ipynb | mit | import pandas as pd
df = pd.read_csv("provstore/data.csv")
df.head()
df.describe()
# The number of each label in the dataset
df.label.value_counts()
"""
Explanation: Extra 2.1 - Unbalanced Data - Application 1: ProvStore Documents
Identifying owners of provenance documents from their provenance network metrics.
In ... |
tpin3694/tpin3694.github.io | neural-networks/mnist_nn.ipynb | mit | %matplotlib inline
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
import numpy as np
import matplotlib.pyplot as plt
# Load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print(X_train.shape)
print(y_train.shape)... |
msampathkumar/datadriven_pumpit | pumpit/save/BenchMarkSeed_0.8118.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
np.random.seed(69572)
%matplotlib inline
%load_ext writeandexecute
# plt.figure(figsize=(120,10))
small = (4,3)
mid = (10, 8)
large = (12, 8)
"""
Explanation: PUMP IT
Using data from Taarifa and ... |
jorisvandenbossche/DS-python-data-analysis | notebooks/pandas_08_reshaping_data.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: <p><font size="6"><b>07 - Pandas: Tidy data and reshaping</b></font></p>
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvandenbos&... |
char-lie/physical-informatics | Lab1/lab1.ipynb | mit | MU = 3.9
N = int(10E4)
INITIAL = 0.5
MIN_SIZE = 2
MAX_SIZE = 26
BITS_RANGE = array(list(range(MIN_SIZE, MAX_SIZE + 1)))
def generate(x, mu, n):
current = x
for _ in range(n):
yield current
current = mu * current * (1 - current)
def bin_to_dec(sequence, bits):
aligned_sequence = sequence.fl... |
probml/pyprobml | notebooks/book2/17/gp_spectral_mixture.ipynb | mit | %%capture
import jax
import jax.numpy as jnp
import numpy as np
import matplotlib.pyplot as plt
try:
import tinygp
except ModuleNotFoundError:
%pip install -qq tinygp
import tinygp
try:
import optax
except ModuleNotFoundError:
%pip install -qq optax
import optax
try:
import probml_utils a... |
Danghor/Algorithms | Python/Chapter-07/.ipynb_checkpoints/Heap-checkpoint.ipynb | gpl-2.0 | class Heap:
sNodeCount = 0
def __init__(self):
Heap.sNodeCount += 1
self.mID = str(Heap.sNodeCount)
def getID(self):
return self.mID # used only by graphviz
"""
Explanation: Implementing Priority Queues as Heaps
Ths notebook presents <em style="color:blue">heaps</em>.... |
deeplearningsp/5_meetup | src/perceptron.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import inspect
import matplotlib.pyplot as plt
from perceptron import Perceptron
plt.style.use('ggplot')
print inspect.getsource(Perceptron)
inputs = np.array([0.2, 12.2,0.98])
pc = Perceptron(len(inputs), 0.5)
"""
Explanation: Modeling a Perceptron
To show ... |
atulsingh0/MachineLearning | python_DC/Learning_pandas_DataFrame_#2.ipynb | gpl-3.0 | state = ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada']
year = [2000, 2001, 2002, 2001, 2002]
pop = [1.5, 1.7, 3.6, 2.4, 2.9]
print(type(state), type(year), type(pop))
# creating dataframe
df = pd.DataFrame({'state':state, 'year':year, 'pop':pop})
print(df.info())
print(df)
sdata = {'state':state, 'year':year, 'pop':p... |
JAmarel/Phys202 | Integration/IntegrationEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy import integrate
"""
Explanation: Integration Exercise 2
Imports
End of explanation
"""
def integrand(x, a):
return 1.0/(x**2 + a**2)
def integral_approx(a):
# Use the args keyword argument to feed extra a... |
kowey/attelo | doc/tut_parser.ipynb | gpl-3.0 | from __future__ import print_function
from os import path as fp
from attelo.io import (load_multipack)
CORPUS_DIR = 'example-corpus'
PREFIX = fp.join(CORPUS_DIR, 'tiny')
# load the data into a multipack
mpack = load_multipack(PREFIX + '.edus',
PREFIX + '.pairings',
PREFI... |
muratcemkose/cy-rest-python | cytoscape-js/CytoscapeJs_and_igraph.ipynb | mit | from py2cytoscape.cytoscapejs import viewer as cyjs
from py2cytoscape import util
import json
import igraph as ig
"""
Explanation: Network analysis and visualization with py2cytoscape and igraph
What is Cytoscape?
- An open source platform for graph analysis and visualization
- Free! (for both academic and commercial... |
dadavidson/Python_Lab | Complete-Python-Bootcamp/Print Formatting.ipynb | mit | print 'This is a string'
"""
Explanation: Print Formatting
In this lecture we will briefly cover the various ways to format your print statements. As you code more and more, you will probably want to have print statements that can take in a variable into a printed string statement.
The most basic example of a print st... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/ukesm1-0-mmh/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'ukesm1-0-mmh', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NERC
Source ID: UKESM1-0-MMH
Topic: Ocnbgchem
Sub-Topics: Tracers.
Prope... |
hetaodie/hetaodie.github.io | assets/media/uda-ml/deep/azjc/卷积神经网络的例子/dog/dog_app_zh.ipynb | mit | from sklearn.datasets import load_files
from keras.utils import np_utils
import numpy as np
from glob import glob
# define function to load train, test, and validation datasets
def load_dataset(path):
data = load_files(path)
dog_files = np.array(data['filenames'])
dog_targets = np_utils.to_categoric... |
khalibartan/pgmpy | examples/Gaussian Bayesian Networks (GBNs).ipynb | mit | # from pgmpy.factors.continuous import LinearGaussianCPD
import sys
import numpy as np
import pgmpy
sys.path.insert(0, "../pgmpy/")
from pgmpy.factors.continuous import LinearGaussianCPD
mu = np.array([7, 13])
sigma = np.array([[4 , 3],
[3 , 6]])
cpd = LinearGaussianCPD('Y', evidence_mean = mu, ... |
mne-tools/mne-tools.github.io | 0.20/_downloads/6684371ec2bc8e72513b3bdbec0d3a9f/plot_20_events_from_raw.ipynb | bsd-3-clause | import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(tmax=60).load_data()
"""
Explanati... |
jhconning/Dev-II | notebooks/lognormal.ipynb | bsd-3-clause | mystring = 'economics'
"""
Explanation: Equilibrium Size Distribution of Farms
Like many of these notebooks this one was written quickly.
Indeterminacy of size distribution with constant returns to scale technology
In an earlier analysis we described the optimal consumption and production allocations of a farm househo... |
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Phosphorylation Sequence Tests -MLP -dbptm+ELM-filterBenchmark-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... |
GoogleCloudPlatform/gcp-getting-started-lab-jp | machine_learning/cloud_ai_platform/bigquery_ml.ipynb | apache-2.0 | from google.colab import auth
auth.authenticate_user()
print('認証されました。')
"""
Explanation: <a href="https://colab.research.google.com/github/GoogleCloudPlatform/gcp-getting-started-lab-jp/blob/master/machine_learning/cloud_ai_platform/bigquery_ml.ipynb" target="_parent"><img src="https://colab.research.google.com/asset... |
mne-tools/mne-tools.github.io | 0.23/_downloads/e23ed246a9a354f899dfb3ce3b06e194/10_overview.ipynb | bsd-3-clause | import os
import numpy as np
import mne
"""
Explanation: Overview of MEG/EEG analysis with MNE-Python
This tutorial covers the basic EEG/MEG pipeline for event-related analysis:
loading data, epoching, averaging, plotting, and estimating cortical activity
from sensor data. It introduces the core MNE-Python data struct... |
aufziehvogel/kaggle | quora-question-pairs/notebooks/1.0-sk-initial-overview.ipynb | mit | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
df = pd.read_csv('../data/raw/train.csv')
df.head()
"""
Explanation: Initial Overview
First we want to have a look at the data.
End of explanation
"""
questions = pd.concat([df['question1'], df['questio... |
ioannispartalas/Kaggle | WhatsCooking/whats_cooking.ipynb | gpl-3.0 | train = pd.read_json("train.json")
matplotlib.style.use('ggplot')
cuisine_group = train.groupby('cuisine')
cuisine_group.size().sort_values(ascending=True).plot.barh()
plt.show()
"""
Explanation: Let's do a quick inspection of the data by plotting the distribution of the different types of cuisines in the dataset.
E... |
mne-tools/mne-tools.github.io | dev/_downloads/51cca4c9f4bd40623cb6bfa890e2eb4b/20_erp_stats.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind
import mne
from mne.channels import find_ch_adjacency, make_1020_channel_selections
from mne.stats import spatio_temporal_cluster_test
np.random.seed(0)
# Load the data
path = mne.datasets.kiloword.data_path() / 'kword_metadata-epo.... |
wrightaprilm/squamates | ExploratoryNotebooks/heatmap.ipynb | mit | import pandas as pd
from pandas import *
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: This script generates a heatmap from data indicating the probability of oviparity as the root state of squamates as a function of model parameters.
End of explanation
"""
data = pd.read_csv("../Data/Heatmap/... |
jaimefrio/pydatabcn2017 | taking_numpy_in_stride/Taking NumPy In Stride - Student Version.ipynb | unlicense | a = np.arange(3)
type(a)
"""
Explanation: Array views and slicing
A NumPy array is an object of numpy.ndarray type:
End of explanation
"""
a = np.arange(3)
a.base is None
a[:].base is None
"""
Explanation: All ndarrays have a .base attribute.
If this attribute is not None, then the array is a view of some other ob... |
the-deep-learners/study-group | nn-from-scratch/MNIST-nn-SGD-flex_arch.ipynb | mit | # Import libraries
import numpy as np
import matplotlib.pyplot as plt
import math
from sklearn.metrics import accuracy_score
import pickle
import sys
"""
Explanation: A neural network from first principles
The code below was adpated from the code supplied in Andrew Ng's Coursera course on machine learning. The origina... |
ccasotto/rmtk | rmtk/vulnerability/mdof_to_sdof/first_mode/first_mode.ipynb | agpl-3.0 | %matplotlib inline
from rmtk.vulnerability.common import utils
from rmtk.vulnerability.mdof_to_sdof.first_mode import first_mode
pushover_file = "../../../../../rmtk_data/capacity_curves_Vb-dfloor.csv"
idealised_type = 'quadrilinear'; # 'bilinear', 'quadrilinear' or 'none'
capacity_curves = utils.read_capacity_curves... |
william-gray/data-science-python | ML-regression/PredictingHousePrices.ipynb | mit | import os
from urllib import urlretrieve
import graphlab
# Limit number of worker processes. This preserves system memory, which prevents hosted notebooks from crashing.
graphlab.set_runtime_config('GRAPHLAB_DEFAULT_NUM_PYLAMBDA_WORKERS', 4)
URL = 'https://d396qusza40orc.cloudfront.net/phoenixassets/home_data.csv'
d... |
ledeprogram/algorithms | class10/donow/radhikapc_DoNow_10.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import dateutil.parser
import pg8000
from pandas import DataFrame
from sklearn.externals.six import StringIO
import pydotplus
from sklearn import tree
from sklearn.cross_validation import train_test_split
from sklearn import metri... |
mrcinv/matpy | 01a_enacbe.ipynb | gpl-2.0 | import sympy as sym
x = sym.symbols("x") # spremenljivka x je matematični simbol
"""
Explanation: << nazaj: Uvod
Enačbe in neenačbe
V tem delu si bomo ogledali različne pristope, kako se spopademo z enačbami. Spoznali bomo nekaj dodatnih knjižnic za python: SymPy, matplotlib in SciPy.
Simbolično reševanje s SymPy
Simb... |
mayankjohri/LetsExplorePython | Section 1 - Core Python/Chapter 12 - Introspection/Chapter14_Introspection.ipynb | gpl-3.0 | trospection or reflection is the ability of software to identify and report their own internal structures, such as types, variabl# Getting some information
# about global objects in the program
from types import ModuleType
def info(n_obj):
# Create a referênce to the object
obj = globals()[n_obj]
# Show... |
postBG/DL_project | intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
hktxt/MachineLearning | ML/Week 3 Assessment Orthogonal Projections.ipynb | gpl-3.0 | # PACKAGE: DO NOT EDIT
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import numpy as np
from sklearn.datasets import fetch_olivetti_faces, fetch_lfw_people
from ipywidgets import interact
%matplotlib inline
image_shape = (64, 64)
# Load faces data
dataset = fe... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/kernel_density.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.distributions.mixture_rvs import mixture_rvs
"""
Explanation: Kernel Density Estimation
Kernel density estimation is the process of estimating an unknown probability density funct... |
googledatalab/notebooks | tutorials/BigQuery/BigQuery Magic Commands and DML.ipynb | apache-2.0 | %%bq query --name UniqueNames2013
WITH UniqueNames2013 AS
(SELECT DISTINCT name
FROM `bigquery-public-data.usa_names.usa_1910_2013`
WHERE Year = 2013)
SELECT * FROM UniqueNames2013
"""
Explanation: BigQuery Magic Commands and DML
The examples in this notebook introduce features of BigQuery Standard SQL and BigQuer... |
pastas/pastas | examples/notebooks/09_calibration_options.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
import pastas as ps
ps.show_versions()
ps.set_log_level("ERROR")
"""
Explanation: Calibrating Pastas models
R.A. Collenteur, University of Graz
After a model is constructed, the model parameters can be estimated using the ml.solve method. It can (and will) happen th... |
iutzeler/Introduction-to-Python-for-Data-Sciences | 4-2_Supervised_Learning.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
%matplotlib inline
# we create 40 separable points in R^2 around 2 centers (random_state=6 is a seed so that the set is separable)
X, y = make_blobs(n_samples=40, n_features=2, centers=2 , random_state=6)
print(X[:5,:],y[:5]) #... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/launching_into_ml/labs/3_repeatable_splitting.ipynb | apache-2.0 | from google.cloud import bigquery
"""
Explanation: Repeatable splitting
Learrning Objectives
* explore the impact of different ways of creating train/valid/test splits
Overview
Repeatability is important in machine learning. If you do the same thing now and 5 minutes from now and get different answers, then it makes ... |
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