repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
SATHVIKRAJU/Inferential_Statistics | Hospital_readmit.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import bokeh.plotting as bkp
from mpl_toolkits.axes_grid1 import make_axes_locatable
# read in readmissions data provided
hospital_read_df = pd.read_csv('data/cms_hospital_readmissions.csv')
hospital_read_df.head()
"""
Explanat... |
jdossgollin/CWC_ANN | Week02/03-MNIST_CNN.ipynb | mit | import numpy as np
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
"""
Explanation: MNIST CNN
You should already have gone through the GettingStartedSequentialModels notebook -- if not you'll be lost here!
End of explanation
"""
import keras
from keras.datasets import mnist # load up t... |
phoebe-project/phoebe2-docs | development/tutorials/alternate_backends.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
import phoebe
from phoebe import u # units
logger = phoebe.logger()
b = phoebe.default_binary()
"""
Explanation: Advanced: Alternate Backends
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook sessi... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/matching_engine/two-tower-model-introduction.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
! pip3 install {... |
landmanbester/fundamentals_of_interferometry | 3_Positional_Astronomy/3_3_Horizontal_Coordinates.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Outline
Glossary
Positional Astronomy
Previous: 3.2 Hour Angle (HA) and Local Sidereal Time (LST)
Next: 3.4 Direction Cosine Coordinates ($l,m,n$)
... |
benvanwerkhoven/kernel_tuner | tutorial/convolution.ipynb | apache-2.0 | %%writefile convolution_naive.cu
__global__ void convolution_kernel(float *output, float *input, float *filter) {
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int i, j;
float sum = 0.0;
if (y < image_height && x < image_width) {
for (j = 0... |
CalPolyPat/phys202-2015-work | assignments/midterm/InteractEx06.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image
from IPython.html.widgets import interact, interactive, fixed
"""
Explanation: Interact Exercise 6
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
End of explan... |
mathinmse/mathinmse.github.io | Lecture-15-Separation-of-Variables.ipynb | mit | %matplotlib notebook
import sympy as sp
# You may have to comment out the init_sesison() line if you have not used my session.py file.
sp.init_session(quiet=True)
sp.init_printing()
dum = sp.symbols('dum')
def initial_condition(x):
return x
def b_m_amplitudes(m, funToProject, center=0.5, width=1.0):
return (2... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/machine_learning_in_the_enterprise/solutions/sdk-custom-image-classification-batch.ipynb | apache-2.0 | # Setup your dependencies
import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = ... |
JerryKurata/MachineLearningWithPython | Notebooks/Pima-Prediction-with-reload.ipynb | gpl-3.0 | import pandas as pd # pandas is a dataframe library
import matplotlib.pyplot as plt # matplotlib.pyplot plots data
%matplotlib inline
"""
Explanation: Pima Indian Diabetes Prediction
### Update History
2021-04-15 Added bypass of imputation of Num of Pregnancies field, switched to using only trans... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/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... |
tensorflow/docs-l10n | site/ja/r1/tutorials/load_data/tf_records.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... |
anshbansal/anshbansal.github.io | udacity_data_science_notes/Data_Wrangling_with_MongoDB/lesson_01/Lesson1.ipynb | mit | import os
DATA_FILE_CSV = "beatles-diskography.csv"
def parse_file(data_file):
data = []
row_count = 0
with open(data_file) as f:
header = f.readline().split(',')
for line in f:
if row_count >= 10:
break
fields = line.strip().split('... |
icrtiou/coursera-ML | ex8-anomaly detection and recommendation/1- Anomaly detection.ipynb | mit | %reload_ext autoreload
%autoreload 2
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(context="notebook", style="white", palette=sns.color_palette("RdBu"))
import numpy as np
import pandas as pd
import scipy.io as sio
from scipy import stats
import sys
sys.path.append('..')
from help... |
tensorflow/federated | docs/tutorials/building_your_own_federated_learning_algorithm.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/asl-ml-immersion | notebooks/building_production_ml_systems/labs/1_training_at_scale.ipynb | apache-2.0 | from google import api_core
from google.cloud import bigquery
"""
Explanation: Training at scale with AI Platform Training Service
Learning Objectives:
1. Learn how to organize your training code into a Python package
1. Train your model using cloud infrastructure via Google Cloud AI Platform Training Service
1.... |
ajgpitch/qutip-notebooks | docs/guide/Parallel.ipynb | lgpl-3.0 | import numpy as np
from qutip import *
"""
Explanation: Running Computations in Parallel
Contents
Introduction
Single-Variable Functions
Multi-Variable Functions
IPython Based Parallel Processing
End of explanation
"""
def func1(x):
return x, x**2, x**3
a, b, c = parfor(func1, range(10))
print(a)
print(b)
pri... |
saketkc/notebooks | python/Kneedle Algorithm.ipynb | bsd-2-clause | %matplotlib inline
import numpy as np
import scipy as sp
import seaborn as sns
from scipy.interpolate import UnivariateSpline
import matplotlib.pyplot as plt
sns.set_style('white')
np.random.seed(42)
def draw_plot(X, Y, knee_point=None):
plt.plot(X, Y)
if knee_point:
plt.axvline(x=knee_point, color='k... |
chongyangma/python-machine-learning-book | code/bonus/softmax-regression.ipynb | mit | %load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p matplotlib,numpy,scipy
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
%matplotlib inline
"""
Explanation: Sebastian Raschka, 2016
https://github.com/rasbt/p... |
jbpoline/cnv_analysis | CNV_guillaume_huguet.ipynb | artistic-2.0 | CWD = osp.join(osp.expanduser('~'), 'documents','grants_projects','roberto_projects', \
'guillaume_huguet_CNV','File_OK')
filename = 'Imagen_QC_CIA_MMAP_V2_Annotation.tsv'
fullfname = osp.join(CWD, filename)
arr = np.loadtxt(fullfname, dtype='str', comments=None, delimiter='\Tab',
conv... |
myuuuuun/RepeatedMatrixGame | PrisonersDilemma/.ipynb_checkpoints/ts_length-checkpoint.ipynb | mit | #-*- encoding: utf-8 -*-
%matplotlib inline
# 日本語対応
import matplotlib
matplotlib.rcParams['font.family'] = 'Osaka'
import math
import numpy as np
import pandas as pd
import functools
import matplotlib.pyplot as plt
import scipy.stats as stats
np.set_printoptions(precision=3)
np.set_printoptions(linewidth=400)
np.set_pr... |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/talks/DataWeekends/SparkMLDeployment/DataWeekends-Mar182017-SparkMLDeployment.ipynb | apache-2.0 | # You may need to Reconnect (more than Restart) the Kernel to pick up changes to these sett
import os
master = '--master spark://spark-master-2-1-0:7077'
conf = '--conf spark.cores.max=1 --conf spark.executor.memory=512m'
packages = '--packages com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.1'
jars ... |
cathalmccabe/PYNQ | boards/Pynq-Z1/base/notebooks/microblaze/microblaze_programming.ipynb | bsd-3-clause | from pynq.overlays.base import BaseOverlay
base = BaseOverlay('base.bit')
"""
Explanation: Programming Microblaze Subsystems from Jupyter
In the Base I/O overlays that accompany the PYNQ release Microblazes are used to control peripherals attached to the various connectors. These can either be programmed with existin... |
LukasMosser/Jupetro | notebooks/Casing Seat Depth Determination - Part 1.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Simple Bottom Up Casing Seat Design - Part 1
Created by Lukas Mosser, 2016
Introduction
We will look at how we can use python and related libraries to estimate casing setting depths.
We will derive a simplified methodology to determ... |
Hvass-Labs/TensorFlow-Tutorials | 13B_Visual_Analysis_MNIST.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix
import math
# Use TensorFlow v.2 with this old v.1 code.
# E.g. placeholder variables and sessions have changed in TF2.
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
"""
Explanation: TensorF... |
mne-tools/mne-tools.github.io | stable/_downloads/6d98b103d247000f4433763dd76607c0/25_background_filtering.ipynb | bsd-3-clause | import numpy as np
from numpy.fft import fft, fftfreq
from scipy import signal
import matplotlib.pyplot as plt
from mne.time_frequency.tfr import morlet
from mne.viz import plot_filter, plot_ideal_filter
import mne
sfreq = 1000.
f_p = 40.
flim = (1., sfreq / 2.) # limits for plotting
"""
Explanation: Background in... |
ageron/ml-notebooks | extra_capsnets.ipynb | apache-2.0 | from IPython.display import IFrame
IFrame(src="https://www.youtube.com/embed/pPN8d0E3900", width=560, height=315, frameborder=0, allowfullscreen=True)
"""
Explanation: Capsule Networks (CapsNets)
Based on the paper: Dynamic Routing Between Capsules, by Sara Sabour, Nicholas Frosst and Geoffrey E. Hinton (NIPS 2017).
I... |
awadalaa/DataSciencePractice | practice/04.model_evaluation.ipynb | mit |
# read in the iris data
from sklearn.datasets import load_iris
iris = load_iris()
# create X (features) and y (response)
X = iris.data
y = iris.target
"""
Explanation: Comparing machine learning models in scikit-learn
Agenda
How do I choose which model to use for my supervised learning task?
How do I choose the bes... |
ehrlinger/MachineLearningSamples-PredictiveMaintenance | Code/3_model_building.ipynb | mit | # import the libraries
import os
import glob
import time
# for creating pipelines and model
from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler, VectorIndexer
from pyspark.ml import Pipeline, PipelineModel
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.classificat... |
google/neural-tangents | notebooks/phase_diagram.ipynb | apache-2.0 | !pip install --upgrade pip
!pip install --upgrade jax[cuda11_cudnn805] -f https://storage.googleapis.com/jax-releases/jax_releases.html
!pip install -q git+https://www.github.com/google/neural-tangents
import jax.numpy as np
from jax.example_libraries import optimizers
from jax import grad, jit, vmap
from jax import ... |
opensanca/trilha-python | 02-python-oo/aula-02/Aula 02.ipynb | mit | class Cão:
qtd_patas = 4
carnívoro = True
nervoso = False
def __init__(self, nome):
self.nome = nome
def latir(self, vezes=1):
""" Latir do cão. Quanto mais nervoso mais late. """
vezes += self.nervoso * vezes
latido = 'Au! ' * vezes
pri... |
bumblebeefr/poppy_rate | [startup] Poppy primitives.ipynb | gpl-2.0 | from pprint import pprint
#imports and initilaize virutal poppy using vrep
from pypot.vrep import from_vrep
from poppy.creatures import PoppyHumanoid
robot = PoppyHumanoid(simulator='vrep')
#import and initialize physical poppy
from poppy.creatures import PoppyHumanoid
robot = PoppyHumanoid()
#Rend le robot mou et ... |
mnishida/RII_Pandas | docs/notebooks/03_Material.ipynb | mit | import riip
rid = riip.RiiDataFrame()
water = riip.Material({'id': 428}, rid)
print(f"{water.catalog['book']} {water.catalog['page']}")
print(f"{water.catalog['wl_min']} <= λ <= {water.catalog['wl_max']}")
"""
Explanation: Material
Material(params: Dict, rid: RiiDataFrame)
This class provides the dielectric function f... |
datactive/bigbang | examples/experimental_notebooks/List Correlation.ipynb | mit | %matplotlib inline
"""
Explanation: TO DO:
move most of this to the libraries
order groups reliably by time
in data
in plot
summarize groups
convolve the time series with a declining 'response time'
End of explanation
"""
from bigbang.archive import Archive
arx = Archive("ipython-dev",archive_dir="../archives")
"... |
agicquel/dut | M4107C/TP1.ipynb | gpl-3.0 | import numpy as np
import keras
from keras.datasets import mnist
# fix random seed for reproducibility
np.random.seed(7)
# load data
(x_train,y_train), (x_test,y_test) = mnist.load_data()
# show the training size, test size, number of class
print("train size : ",x_train.shape)
print("test size : ",x_test.shape)
prin... |
smousavi05/EQTransformer | EQTransformerviaGoogleColab.ipynb | mit | !pip install obspy
"""
Explanation: <a href="https://colab.research.google.com/github/maihao14/EQTransformer/blob/colab/EQTransformerviaGoogleColab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
EQTransformer via Google Colab
Author: Hao Mai<br>
Da... |
sameersingh/ml-discussions | week7/ensembles_and_boosting.ipynb | apache-2.0 | # Import all required libraries
from __future__ import division # For python 2.*
import numpy as np
import matplotlib.pyplot as plt
import mltools as ml
np.random.seed(0)
%matplotlib inline
"""
Explanation: In this discussion notebook we will cover the material from lecture 12 abour ensambles and boosting. For consi... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_linear_model_patterns.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Romain Trachel <trachelr@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.datasets import sample
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
# impor... |
ocefpaf/secoora | notebooks/timeSeries/sst/01-skill_score.ipynb | mit | import os
try:
import cPickle as pickle
except ImportError:
import pickle
run_name = '2014-07-07'
fname = os.path.join(run_name, 'config.pkl')
with open(fname, 'rb') as f:
config = pickle.load(f)
import numpy as np
from pandas import DataFrame, read_csv
from utilities import (load_secoora_ncs, to_html,
... |
JeffAbrahamson/MLWeek | practicum/02_pandas/pandas.ipynb | gpl-3.0 | import matplotlib
%matplotlib inline
"""
Explanation: Introduction à Python pour Data Science et ML
Ceci suppose que vous avez déjà fait un tutoriel sur les principes de base de la langue. Nous allons parler ici de pandas.
Les données ne servent à rien si on ne peut pas les lire. Heureusement, on a déjà pensé à ça.... |
mne-tools/mne-tools.github.io | 0.19/_downloads/99730d9046b115c3e40e7e01cb975635/plot_50_artifact_correction_ssp.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.preprocessing import (create_eog_epochs, create_ecg_epochs,
compute_proj_ecg, compute_proj_eog)
"""
Explanation: Repairing artifacts with SSP
This tutorial covers the basics of signal-space projection (SSP) ... |
FederatedAI/FATE | doc/tutorial/pipeline/pipeline_tutorial_hetero_sbt.ipynb | apache-2.0 | !pipeline --help
"""
Explanation: Pipeline Tutorial with HeteroSecureBoost
install
Pipeline is distributed along with fate_client.
bash
pip install fate_client
To use Pipeline, we need to first specify which FATE Flow Service to connect to. Once fate_client installed, one can find an cmd enterpoint name pipeline:
End ... |
gmckercher/2015-TTU-SWC | pythonic-data-analysis/pythonic-data-analysis.ipynb | mit | from netCDF4 import Dataset, num2date
data = Dataset('data/model-gfs.nc', 'r')
# Convert the array of time numbers to datetimes
time_var = data['time']
time = num2date(time_var[:], time_var.units).squeeze()
"""
Explanation: Pythonic Data Analysis
One of the nice aspects of Python is that it contains many powerful too... |
vatsan/gp_jupyter_notebook_templates | notebooks/00_database_connectivity_setup.ipynb | apache-2.0 | %matplotlib inline
import psycopg2
import pandas as pd
import pandas.io.sql as psql
import ConfigParser
import os
import IPython
from IPython.display import display
from IPython.display import HTML
#Maximum height of a result set
pd.set_option('display.max_rows', 500)
#Maximum number of rows to display in a cell.
pd.se... |
adrianstaniec/deep-learning | 11_sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../02_sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../02_sentiment-network/labels.txt', 'r') as f:
labels = f.read()
(type(reviews), type(labels))
reviews[:2000]
labels[:200]
"""
Explanation: Sentiment Analysis with an RNN
In... |
axbaretto/beam | examples/notebooks/documentation/transforms/python/elementwise/tostring-py.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License")
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you u... |
ioggstream/python-course | connexion-101/notebooks/04-02-connexion-writing-operationid.ipynb | agpl-3.0 | # Use this cell to test the output
!curl http://localhost:5000/datetime/v1/echo -vk
"""
Explanation: A new path: /echo
Now we're going to write a new path: /echo.
Our goal is the following:
when invoking /datetime/v1/echo the API will return
the current datetime;
the response is enveloped in the following example ... |
tensorflow/docs-l10n | site/zh-cn/tensorboard/dataframe_api.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... |
JannesKlaas/MLiFC | Week 1/Ch. 6 - Minibatch Gradient Descent.ipynb | mit | # Package imports
# Matplotlib is a matlab like plotting library
import matplotlib
import matplotlib.pyplot as plt
# Numpy handles matrix operations
import numpy as np
# SciKitLearn is a useful machine learning utilities library
import sklearn
# The sklearn dataset module helps generating datasets
import sklearn.datase... |
muneebalam/scrapenhl2 | examples/YTD and ROY CF%.ipynb | mit | import pandas as pd
from pylab import *
%matplotlib inline
from pyarrow import ArrowIOError
from scrapenhl2.scrape import teams, team_info, schedules
from scrapenhl2.manipulate import manipulate as manip
generate = False
fname = '/Users/muneebalam/Desktop/team_game_data.csv'
if generate:
team_dfs = []
for se... |
tritemio/multispot_paper | realtime kinetics/out_notebooks/Simulated Kinetic Curve Fit - Template-out-multi-spot-t0_novary.ipynb | mit | sigma = 0.016
time_window = 30
time_step = 5
time_start = -900
time_stop = 900
decimation = 20
t0_vary = True
true_params = dict(
tau = 60, # time constant
init_value = 0.3, # initial value (for t < t0)
final_value = 0.8, # final value (for t -> +inf)
t0 = 0) # time o... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_read_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname = data_path
fname += '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
inv = read_... |
f-guitart/data_mining | notes/05 - From Raw to Technically Correct Data.ipynb | gpl-3.0 | fname = "../data/people.csv"
with open(fname) as f:
content = f.readlines()
print(content[:5])
"""
Explanation: From Raw to Technically Correct Data
(from de Jonge van der Loo)
A data set is a collection of data that describes attribute values (variables) of a number of real-world objects (units). With data that a... |
esa-as/2016-ml-contest | esaTeam/esa_Submission01.ipynb | apache-2.0 | # Import
from __future__ import division
get_ipython().magic(u'matplotlib inline')
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['figure.figsize'] = (20.0, 10.0)
inline_rc = dict(mpl.rcParams)
from classification_utilities import make_facies_log_plot
import pandas as pd
import numpy as np
impo... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/deep-learning/theano-tutorial/theano_mlp/theano_mlp.ipynb | mit | import numpy
import theano
from theano import tensor
# Set lower precision float, otherwise the notebook will take too long to run
theano.config.floatX = 'float32'
class HiddenLayer(object):
def __init__(self, rng, input, n_in, n_out, W=None, b=None,
activation=tensor.tanh):
"""
... |
bjodah/chempy | examples/interactive_kinetic_modelling.ipynb | bsd-2-clause | from collections import defaultdict
from ipywidgets import interact, FloatSlider
from chempy import ReactionSystem, Substance
from chempy.kinetics.ode import get_odesys
from chempy.kinetics._native import get_native
from chempy.units import SI_base_registry, default_units as u
import matplotlib.pyplot as plt
%matplotli... |
Applied-Groundwater-Modeling-2nd-Ed/Chapter_5_problems-1 | P5.1_Flopy_Island_Recharge_grid_sensitivity.ipynb | gpl-2.0 | %matplotlib inline
import sys
import os
import shutil
import numpy as np
from subprocess import check_output
# Import flopy
import flopy
"""
Explanation: <img src="AW&H2015.tiff" style="float: left">
<img src="flopylogo.png" style="float: center">
Problem P5.1 Island Recharge Grid Sensitivity
In Problem P5.1 from pag... |
jphall663/GWU_data_mining | 10_model_interpretability/src/sensitivity_analysis.ipynb | apache-2.0 | # imports
import h2o
import numpy as np
import pandas as pd
from h2o.estimators.gbm import H2OGradientBoostingEstimator
# start h2o
h2o.init()
h2o.remove_all()
"""
Explanation: License
Copyright 2017 J. Patrick Hall, jphall@gwu.edu
Permission is hereby granted, free of charge, to any person obtaining a copy of this... |
dnc1994/MachineLearning-UW | ml-foundations/backup/image-retrieval/Deep Features for Image Retrieval.ipynb | mit | import graphlab
"""
Explanation: Building an image retrieval system with deep features
Fire up GraphLab Create
End of explanation
"""
image_train = graphlab.SFrame('image_train_data/')
"""
Explanation: Load the CIFAR-10 dataset
We will use a popular benchmark dataset in computer vision called CIFAR-10.
(We've red... |
cggh/scikit-allel | notebooks/sandbox/dask_bcolz.ipynb | mit | callset = h5py.File('/data/coluzzi/ag1000g/data/phase1/release/AR3/variation/main/hdf5/ag1000g.phase1.ar3.pass.h5', mode='r')
callset
genotype = allel.model.chunked.GenotypeChunkedArray(callset['3L/calldata/genotype'])
genotype
"""
Explanation: Let's use some real data...
End of explanation
"""
g = genotype.copy(st... |
catalyst-cooperative/pudl | notebooks/work-in-progress/explore-CEMS.ipynb | mit | %load_ext autoreload
%autoreload 2
# Standard libraries
import logging
import sys
import os
import pathlib
# 3rd party libraries
import geopandas as gpd
import dask.dataframe as dd
from dask.distributed import Client
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pandas as pd
impor... |
OpenAstronomy/workshop_sunpy_astropy | 04-Units/Units_Quantities.ipynb | mit | from astropy import units as u
# Define a quantity length
# print it
# Type of quantity
# Type of unit
# Quantity
# value
# unit
# information
"""
Explanation: Units and Quantities
Objectives
Use units
Create functions that accept quantities as arguments
Create new units
Basics
How do we define a Quan... |
TobiasLe/python-MD | 01_python_intro/python_intro.ipynb | gpl-3.0 | 1 + 1
"""
Explanation: What python is
Python is an easy to learn, yet powerful programming language. In general, programming languages can be divided into low-level and high-level languages.
In a low-level language you have to tell the computer very detailed and specific what to do. These very specific commands can be... |
a-slide/pycoQC | docs/Barcode_split/API_usage.ipynb | gpl-3.0 | # Run cell with Ctrl + Enter
# Import main pycoQC module
from pycoQC.Barcode_split import Barcode_split
# Import helper functions from pycoQC
from pycoQC.common import jhelp, head, ls
"""
Explanation: Barcode_split API Usage
Running Jupyter notebook
If you want to run pycoQC interactively in Jupyter you need to inst... |
jdhp-docs/python_notebooks | nb_sci_ai/ai_ml_perceptron_fr.ipynb | mit | %matplotlib inline
#x = np.linspace(-5, 5, 300)
#y = np.array([-1 if xi < 0 else 1 for xi in x])
#plt.plot(x, y)
plt.hlines(y=-1, xmin=-5, xmax=0, color='red')
plt.hlines(y=1, xmin=0, xmax=5, color='red')
plt.hlines(y=0, xmin=-5, xmax=5, color='gray', linestyles='dotted')
plt.vlines(x=0, ymin=-2, ymax=2, color='gray... |
xtr33me/deep-learning | sentiment-rnn/Sentiment_RNN_Solution.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_dfnp/td1a_correction_session_10.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.data - DataFrame et Matrice (correction)
Correction.
End of explanation
"""
from pyquickhelper.loghelper import get_url_content
import pandas, io
from ensae_teaching_cs.data import donnees_enquete_2003_television
text = donnees_enque... |
mimoralea/applied-reinforcement-learning | notebooks/02-dynamic-programming.ipynb | mit | def fib(n):
if n < 2:
return n
return fib(n-2) + fib(n-1)
%time fib(30)
"""
Explanation: Recursion, Memoization and Dynamic Programming
Remember how we talk about using recursion and dynamic programming. One interesting thing to do is to implement the solution to a common problem called Fibonnaci numb... |
ColeLab/informationtransfermapping | MasterScripts/Manuscript7_PercentFROM_Region_ITE.ipynb | gpl-3.0 | import sys
sys.path.append('utils/')
import numpy as np
import loadGlasser as lg
import scipy.stats as stats
import matplotlib.pyplot as plt
import statsmodels.sandbox.stats.multicomp as mc
import sys
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import nibabel as nib
import os
import permutation... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160516월_3일차_기초 선형 대수 1 - 행렬의 정의와 연산 Basic Linear Algebra(NumPy)/4.NumPy를 활용한 선형대수 입문.ipynb | mit | x = np.array([1, 2, 3, 4])
x, np.shape(x)
x = np.array([[1], [2], [3], [4]])
x, np.shape(x)
"""
Explanation: NumPy를 활용한 선형대수 입문
선형대수(linear algebra)는 데이터 분석에 필요한 각종 계산을 위한 기본적인 학문이다.
데이터 분석을 하기 위해서는 실제로 수많은 숫자의 계산이 필요하다. 하나의 데이터 레코드(record)가 수십개에서 수천개의 숫자로 이루어져 있을 수도 있고 수십개에서 수백만개의 이러한 데이터 레코드를 조합하여 계산하는 과정이 필요할 수 있... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
P... |
xdnian/pyml | code/ch13/ch13.ipynb | mit | %load_ext watermark
%watermark -a '' -u -d -v -p numpy,matplotlib,theano,keras
"""
Explanation: Copyright (c) 2015, 2016 Sebastian Raschka
Li-Yi Wei
https://github.com/1iyiwei/pyml
MIT License
Python Machine Learning - Code Examples
Chapter 13 - Parallelizing Neural Network Training with Theano
We have seen how to wri... |
muneebalam/scrapenhl2 | examples/5v5 TOI for teams and players/5v5 TOI for teams and players.ipynb | mit | # Update data
# autoupdate.autoupdate() # Comment in if needed, and loop if needed
# manip.get_5v5_player_log(2017, force_create) # Comment in if needed, and loop if needed
log = pd.concat([manip.get_5v5_player_log(season).assign(Season=season) for season in range(2012, 2018)])
sch = pd.concat([schedules.get_season_s... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/Mean_Reversion_on_Futures/notebook.ipynb | unlicense | import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.tsa.stattools import coint, adfuller
import matplotlib.pyplot as plt
from quantopian.research.experimental import continuous_future, history
"""
Explanation: Mean Reversion on Futures
by Rob Reider and Maxwell Margenot
Part of the Qu... |
computational-class/cjc2016 | code/04.PythonCrawler_netease_music.ipynb | mit | import requests
from bs4 import BeautifulSoup
headers = {
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
'Accept-Encoding': 'gzip, deflate, sdch',
'Accept-Language': 'zh-CN,zh;q=0.8,en;q=0.6',
'Cache-Control': 'no-cache',
'Connection': 'keep-alive',
'Coo... |
deepfield/ibis | docs/source/notebooks/tutorial/6-Advanced-Topics-TopK-SelfJoins.ipynb | apache-2.0 | import ibis
import os
hdfs_port = os.environ.get('IBIS_WEBHDFS_PORT', 50070)
hdfs = ibis.hdfs_connect(host='quickstart.cloudera', port=hdfs_port)
con = ibis.impala.connect(host='quickstart.cloudera', database='ibis_testing',
hdfs_client=hdfs)
ibis.options.interactive = True
"""
Explanation: A... |
z01nl1o02/tests | notebooks/PCA.ipynb | gpl-2.0 | import cv2
import sys,os
import numpy as np
sample_size = (64//2,64//2)
smallset_size = 10 #每类下采样，方便调试
flag_debug = True
def load_mnist(num_per_class, dataset_root="C:/dataset/mnist/",resize=sample_size):
data_pairs = []
labeldict = {}
ds_root = os.path.join(dataset_root,'train')
for rdir, pdirs, na... |
Konsang/Reinforcement-Learning | RL-Notebooks/Part1_1.ipynb | mit | import tensorflow as tf
import numpy as np
"""
Explanation: The multi-armed bandit problem
End of explanation
"""
bandits = [0.2, 0, -0.2, -5] # Random order
num_bandits = len(bandits)
def pullBandit(bandit):
# Get a random number
result = np.random.randn(1)
if result > bandit:
# return a positi... |
phoebe-project/phoebe2-docs | 2.2/tutorials/compute.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Compute
Now that we have datasets added to our Bundle, our next step is to run the forward model and compute a synthetic model for each of these datasets.
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line i... |
alasdairtran/mclearn | projects/peerjcs16/uci_datasets.ipynb | bsd-3-clause | import os
import pandas as pd
import numpy as np
from mclearn.tools import fetch_data, download_data
%load_ext autoreload
%autoreload 2
uci_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/'
"""
Explanation: UCI Datasets
End of explanation
"""
url = uci_url + 'ionosphere/ionosphere.data'
dest = 'dat... |
tensorflow/docs | site/en/tutorials/distribute/multi_worker_with_estimator.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... |
alephcero/adsProject | olds/DataAnalysis (para Ilan).ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import os
import sys
import simpledbf
%pylab inline
import matplotlib.pyplot as plt
"""
Explanation: Referencia:
- http://dump.jazzido.com/CNPHV2010-RADIO/
- http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
Variables en el CENSO 2010 (INDEC)
VIVIENDA.I... |
staeiou/reddit_downvote | the_donald/td-analysis.ipynb | mit | import pandas as pd
import seaborn as sns
%matplotlib inline
posts_df = pd.DataFrame.from_csv("reddit_posts_the_donald_201604.csv")
posts_df[0:5]
posts_df['created'] = pd.to_datetime(posts_df.created_utc, unit='s')
posts_df['created_date'] = posts_df.created.dt.date
posts_df['downs'] = posts_df.score - posts_df.up... |
atulsingh0/MachineLearning | HandsOnML/code/05_support_vector_machines.ipynb | gpl-3.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
... |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/Sorting_searching_and_counting_Solutions.ipynb | gpl-3.0 | import numpy as np
np.__version__
author = 'kyubyong. longinglove@nate.com'
"""
Explanation: Soring, searching, and counting
End of explanation
"""
x = np.array([[1,4],[3,1]])
out = np.sort(x, axis=1)
x.sort(axis=1)
assert np.array_equal(out, x)
print out
"""
Explanation: Sorting
Q1. Sort x along the second axis.... |
luctrudeau/DaalaNotebooks | DCT/DCT fundamentals.ipynb | mpl-2.0 | import numpy as np
%pylab inline
v = np.array([4, 2, 4, 2])
def showEnergy(e, title):
x = np.arange(4)
pylab.bar(x, e)
pylab.yticks(range(0,5))
pylab.xticks([1, 2, 3, 4])
pylab.xlim(0, 4);
pylab.ylim(0, 5);
pylab.title(title)
showEnergy(v, "Energy of the input vector")
"""
Explanation: P... |
stijnvanhoey/course_gis_scripting | _solved/00-jupyter_introduction.ipynb | bsd-3-clause | from IPython.display import Image
Image(url='http://python.org/images/python-logo.gif')
"""
Explanation: <p><font size="6"><b>Jupyter notebook INTRODUCTION </b></font></p>
Introduction to GIS scripting
May, 2017
© 2017, Stijn Van Hoey (stijnvanhoey@g... |
samkennerly/Fidelio | fidelio.ipynb | gpl-3.0 | from fidelio_functions import *
"""
Explanation: Fidelio demo notebook
End of explanation
"""
print(ALL_CAPS)
for key, val in sorted(char_to_num(ALL_CAPS).items()):
print(key,val)
"""
Explanation: Choose an alphabet
Before sending any messages, we must agree on a way to represent characters as numbers.
Fidelio... |
mne-tools/mne-tools.github.io | dev/_downloads/6d98b103d247000f4433763dd76607c0/25_background_filtering.ipynb | bsd-3-clause | import numpy as np
from numpy.fft import fft, fftfreq
from scipy import signal
import matplotlib.pyplot as plt
from mne.time_frequency.tfr import morlet
from mne.viz import plot_filter, plot_ideal_filter
import mne
sfreq = 1000.
f_p = 40.
# limits for plotting
flim = (1., sfreq / 2.) # frequencies
dlim = (-0.2, 0.2... |
Diyago/Machine-Learning-scripts | general studies/learning python/learning pandas/stocks analysis.ipynb | apache-2.0 | brent['Изм. %'] = brent['Изм. %'].str.replace(',', '.')
brent['Изм. %'] = brent['Изм. %'].astype(float)
print ('Brent сумма %: ' , brent['Изм. %'].sum())
rub['Изм. %'] = rub['Изм. %'].str.replace(',', '.')
rub['Изм. %'] = rub['Изм. %'].astype(float)
print ('USD/RUB сумма %: ' , rub['Изм. %'].sum())
"""
Explanation:... |
Boialex/MIPT-ML | hw1/2.prac.ipynb | gpl-3.0 | digits = datasets.load_digits()
digits
digits_frame = DataFrame(digits.data)
digits_frame['target'] = digits.target
digits_frame.head()
"""
Explanation: Digits dataset
End of explanation
"""
breast_cancer = datasets.load_breast_cancer()
breast_cancer
print breast_cancer.feature_names
breast_cancer_frame = DataF... |
KasperPRasmussen/bokeh | examples/howto/charts/bar.ipynb | bsd-3-clause | df['neg_mpg'] = 0 - df['mpg']
"""
Explanation: Calculate some negative values to show handling of them
End of explanation
"""
defaults.width = 550
defaults.height = 400
"""
Explanation: Override some default values to avoid requiring input on each chart
End of explanation
"""
bar_plot = Bar(df, label='cyl', title... |
tensorflow/docs-l10n | site/en-snapshot/probability/examples/Variational_Inference_and_Joint_Distributions.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... |
UDST/activitysim | activitysim/examples/example_estimation/notebooks/21_stop_frequency.ipynb | bsd-3-clause | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimating Stop Frequency
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estimation mode to read household travel su... |
rohithmohan/aesop | docs/barnase_barstar_alascan.ipynb | gpl-3.0 | from aesop import Alascan, plotScan_interactive, plotNetwork_interactive
path_apbs = 'path\to\executable\apbs'
path_coulomb = 'path\to\executable\coulomb'
path_pdb2pqr = 'path\to\executable\pdb2pqr'
jobname = 'alascan'
pdbfile = 'barnase_barstar.pdb'
selstr = ['chain A', 'chain B']
alascan = Alascan(pdb=pdbfile,... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/write_low_level_code.ipynb | apache-2.0 | import numpy as np
from matplotlib import pyplot as plt
import tensorflow as tf
print(tf.__version__)
"""
Explanation: Writing Low-Level TensorFlow Code
Learning Objectives
Practice defining and performing basic operations on constant Tensors
Use Tensorflow's automatic differentiation capability
Learn how to train a... |
TomTranter/OpenPNM | examples/topology/Using Queries - Level 1.ipynb | mit | import openpnm as op
import numpy as np
np.random.seed(10)
%matplotlib inline
ws = op.Workspace()
ws.settings["loglevel"] = 40
pn = op.network.Cubic(shape=[4, 4, 1])
"""
Explanation: Using Neighborhood Queries
The OpenPNM GenericNetwork objects (e.g. Cubic, Voronoi, etc) have methods that let you query the connected ... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/pandas/03.01-Introducing-Pandas-Objects.ipynb | mit | import numpy as np
import pandas as pd
"""
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.
The text is released under th... |
jbusecke/xarrayutils | docs/utils.ipynb | mit | import xarray as xr
import numpy as np
%matplotlib inline
path = 'http://apdrc.soest.hawaii.edu:80/dods/public_data/Argo_Products/monthly_mean/monthly_mixed_layer'
ds = xr.open_dataset(path, use_cftime=True)
ds
ds.load()
"""
Explanation: Utilities for large scale climate data analysis
The xarrayutils.utils module c... |
aravindhv10/CPP_Wrappers | AntiQCD4/ML4JETS/ML4JetsCompare.ipynb | gpl-2.0 | !pip install lightgbm
!pip install shap
"""
Explanation: Install important packages for boosted decision trees
End of explanation
"""
%tensorflow_version 1.x
import lzma
from google.colab import drive
import numpy as np
import tensorflow as tf
import keras
from keras import backend as K
from keras.layers import Inpu... |
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