repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
feststelltaste/software-analytics | demos/20190425_JUGH_Kassel/DataScienceMeetsSoftwareData.ipynb | gpl-3.0 | import pandas as pd
log = pd.read_csv("../dataset/linux_blame_log.csv.gz")
log.head()
"""
Explanation: Mit Datenanalysen Probleme in der Entwicklung aufzeigen
<small>Java User Group Hessen, Kassel, 25.04.2019</small>
<b>Markus Harrer</b>, Software Development Analyst
Twitter: @feststelltaste
Blog: feststelltaste.de
<... |
mne-tools/mne-tools.github.io | 0.19/_downloads/8f7e6dfc30a66795f2d4e4ae5ca6d23e/plot_40_artifact_correction_ica.ipynb | bsd-3-clause | import os
import mne
from mne.preprocessing import (ICA, create_eog_epochs, create_ecg_epochs,
corrmap)
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif... |
hetaodie/hetaodie.github.io | assets/media/uda-ml/supervisedlearning/jc/为慈善机构寻找捐助者/charity_finish/charity/boston_housing/boston_housing.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from sklearn.model_selection import ShuffleSplit
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('hous... |
johnnyliu27/openmc | examples/jupyter/expansion-filters.ipynb | mit | %matplotlib inline
import openmc
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: OpenMC's general tally system accommodates a wide range of tally filters. While most filters are meant to identify regions of phase space that contribute to a tally, there are a special set of functional expansion filt... |
ueapy/enveast_python_course_materials | Day_3/19-Cartopy-Intro.ipynb | mit | import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Brief look at Cartopy
Cartopy is a Python package that provides easy creation of maps with matplotlib.
Cartopy vs Basemap
Cartopy is better integrated with matplotlib and in a more active development state
Proper handling of datelines in cartopy - on... |
mjabri/holoviews | doc/Tutorials/Pandas_Seaborn.ipynb | bsd-3-clause | import itertools
import numpy as np
import pandas as pd
import seaborn as sb
import holoviews as hv
np.random.seed(9221999)
"""
Explanation: In this notebook we'll look at interfacing between the composability and ability to generate complex visualizations that HoloViews provides, the power of pandas library datafra... |
lenovor/lightfm | examples/movielens/learning_schedules.ipynb | apache-2.0 | import numpy as np
import data
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from lightfm import LightFM
train, test = data.get_movielens_data()
train.data = np.ones_like(train.data)
test.data = np.ones_like(test.data)
from sklearn.metrics import roc_auc_score
def preci... |
t-vi/candlegp | notebooks/minibatches.ipynb | apache-2.0 | import sys, os
import numpy
import time
sys.path.append(os.path.join(os.getcwd(),'..'))
import candlegp
from matplotlib import pyplot
import torch
from torch.autograd import Variable
%matplotlib inline
pyplot.style.use('ggplot')
import IPython
M = 50
def func(x):
return torch.sin(x * 3*3.14) + 0.3*torch.cos(x * ... |
mne-tools/mne-tools.github.io | stable/_downloads/fb92190904499e5a95e92ab70177abf7/60_make_fixed_length_epochs.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.preprocessing import compute_proj_ecg
from mne_connectivity import envelope_correlation
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
... |
podondra/bt-spectraldl | notebooks/03-labeled-data.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import collections
import math
import matplotlib.pyplot as plt
import h5py
import csv
LABELS_FILE = 'data/ondrejov-dataset.csv'
with open(LABELS_FILE, newline='') as f:
labels = list(csv.DictReader(f))
"""
Explanation: Labels Addition and Statistics
This notebook adds label... |
strint/tensorflow | tensorflow/examples/tutorials/deepdream/deepdream.ipynb | apache-2.0 | # boilerplate code
from __future__ import print_function
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
import tensorflow as tf
"""
Explanation: DeepDreaming with TensorFlow
Loading and displaying the m... |
w4zir/ml17s | lectures/.ipynb_checkpoints/lec03-gradient-descent-checkpoint.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
# read data in pandas frame
dataframe = pd.read_csv('datasets/house_dataset1.csv')
# assign x and y
X = np.array(dataframe[['Size']])
y = np.array(dataframe[['Price']])
m = y.size # number of tr... |
ilogue/pyrsa | demos/Temporal RSA.ipynb | lgpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import pyrsa
import pickle
from pyrsa.rdm import calc_rdm_movie
"""
Explanation: Temporal RSA
This demo notebook demonstrates how to work with temporal data in the RSA toolbox
So far, it demonstrates how to
(1) import temporal dataset into the pyrsa.data.TemporalData... |
eds-uga/csci1360-fa16 | assignments/A3/A3_Q2.ipynb | mit | import numpy as np
np.random.seed(85473)
list1 = np.random.randint(100, size = 10).tolist()
list2 = np.random.randint(100, size = 10).tolist()
list3 = np.random.randint(100, size = 10).tolist()
### BEGIN SOLUTION
### END SOLUTION
"""
Explanation: Q2
More loops, this time with generators.
A
Print out the correspondi... |
miykael/nipype_tutorial | notebooks/example_normalize.ipynb | bsd-3-clause | %%bash
datalad get -J 4 -d /data/ds000114 /data/ds000114/derivatives/fmriprep/sub-0[2345789]/anat/*h5
"""
Explanation: Example 3: Normalize data to MNI template
This example covers the normalization of data. Some people prefer to normalize the data during the preprocessing, just before smoothing. I prefer to do the 1s... |
MingChen0919/learning-apache-spark | notebooks/02-data-manipulation/2.9-user-defined-sql-function (udf).ipynb | mit | from pyspark.sql.types import *
from pyspark.sql.functions import udf
mtcars = spark.read.csv('../../data/mtcars.csv', inferSchema=True, header=True)
mtcars = mtcars.withColumnRenamed('_c0', 'model')
mtcars.show(5)
"""
Explanation: udf() function and sql types
The pyspark.sql.functions.udf() function is a very import... |
chris1610/pbpython | notebooks/Bullet-Graph-Article.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import FuncFormatter
%matplotlib inline
"""
Explanation: Notebook for Building a bullet chart in python.
Full article posted in http://pbpython.com/bullet-graph.html
End of explanation
"""
sns.palplot(sns.light_palette("green", 5))
sns.pa... |
mercybenzaquen/foundations-homework | foundations_hw/08/Homework8_benzaquen_mass_shooting_data.ipynb | mit | !pip install pandas
!pip install matplotlib
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Mass shootings in America in 2015
Data from http://www.shootingtracker.com/Main_Page
Important!
Their definition of mass shooting is:
FOUR or more shot and/or killed in a single event [... |
jdsanch1/SimRC | 02. Parte 2/15. Clase 15/.ipynb_checkpoints/02Class NB-checkpoint.ipynb | mit | #importar los paquetes que se van a usar
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import datetime
from datetime import datetime
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#algunas opciones para Python
pd.set_option('display.not... |
stevosn/ray_tracing | ray_tracing.ipynb | mit | # setup path to ray_tracing package
import sys
sys.path.append('~/Documents/python/ray_tracing/')
import ray_tracing as rt
from matplotlib import rcParams
rcParams['figure.figsize'] = [8, 4]
import matplotlib.pyplot as plt
plt.ion()
"""
Explanation: Simple ray tracing
End of explanation
"""
osys = rt.OpticalSystem... |
ktaneishi/deepchem | contrib/dragonn/GTC_workshop_tutorial.ipynb | mit | %reload_ext autoreload
%autoreload 2
#from tutorial_utils import *
%matplotlib inline
"""
Explanation: How to train your DragoNN tutorial
Tutorial length: 25-30 minutes with a CPU.
Outline
* How to use this tutorial
* Review of patterns in transcription factor binding sites
* Learning to localize homotypic motif densi... |
tensorflow/docs-l10n | site/ja/probability/examples/Linear_Mixed_Effects_Models.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... |
nnadeau/pybotics | examples/trajectory_generation.ipynb | mit | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def plot_poses(p):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(xs=p[:, 0, -1], ys=p[:, 1, -1], zs=p[:, 2, -1], marker='o')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_xli... |
fzotter/Ambisonic-Jupyter-Notebook | 05-rErVofAmbisonicPanningFunctionsCircle.ipynb | mit | import numpy as np
import scipy as sp
import math
from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook
def inphase_weights(N):
a=np.ones(N+1)
for n in range(1,N+1):
a[n]=(N-n+1)/(1.0*(N+n))*a[n-1]
return a
def maxre_weights(N):
m=np.arange(0,N+1)
a=np.... |
phoebe-project/phoebe2-docs | 2.0/tutorials/atm_passbands.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
"""
Explanation: Atmospheres & Passbands
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplotlib inli... |
kwant-project/kwant-tutorial-2016 | 3.4.graphene_qshe-cheat.ipynb | bsd-2-clause | # We'll have 3D plotting and 2D band structure, so we need a handful of helper functions.
%run matplotlib_setup.ipy
from types import SimpleNamespace
from ipywidgets import interact
import matplotlib
from matplotlib import pyplot
from mpl_toolkits import mplot3d
import numpy as np
import kwant
from wraparound impor... |
probml/pyprobml | notebooks/misc/linreg_hierarchical_numpyro.ipynb | mit | %matplotlib inline
!pip install -q numpyro@git+https://github.com/pyro-ppl/numpyro arviz
!pip install arviz
!pip install seaborn
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import arviz as az
import seaborn as sns
import numpyro
from numpyro.infer import MCMC, NUTS, Predictive
import numpyro... |
IS-ENES-Data/submission_forms | test/Templates/CMIP6_submission_form.ipynb | apache-2.0 | from dkrz_forms import form_widgets
form_widgets.show_status('form-submission')
"""
Explanation: DKRZ CMIP6 submission form for ESGF data publication
General Information (to be completed based on official CMIP6 references)
Data to be submitted for ESGF data publication must follow the rules outlined in the CMIP6 Arch... |
NeuroDataDesign/seelviz | Jupyter/Ilastik and Membrane Detection.ipynb | apache-2.0 | ## Titled getspacing.py
from ndreg import *
import matplotlib
import ndio.remote.neurodata as neurodata
import nibabel as nb
inToken = 'Fear197'
inImg = imgDownload(inToken, resolution=5)
print(inImg.GetSpacing())
"""
Explanation: October 19, 2016
Ilastik Membrane Detection
Decision Tree and Random Forest
Decision tr... |
google/starthinker | colabs/smartsheet_report_to_bigquery.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: SmartSheet Report To BigQuery
Move report data into a BigQuery table.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obta... |
rodrigomas/boston_housing | boston_housing.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
#from sklearn.cross_validation import ShuffleSplit
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('ho... |
Lstyle1/Deep_learning_projects | 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... |
aleph314/K2 | Foundations/Data Collection and Analysis/Pandas-Exercise.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
"""
Explanation: Pandas Exercise
When working on real world data tasks, you'll quickly realize that a large portion of your time is spent manipulating raw data into a form that you can actually work with, a process often called data munging or data wrangling. Different programmi... |
riga/order | examples/intro.ipynb | bsd-3-clause | import order as od
import scinum as sn
"""
Explanation: order: An introduction
In this example we get to know the most important classes of order and how they are related to describe your analysis and all external data. We will set up a simple but scalable example analysis that involves most of the API. For more info,... |
feststelltaste/software-analytics | notebooks/demo_pandas_jqassistant.ipynb | gpl-3.0 | import py2neo
import pandas as pd
"""
Explanation: A simple example on how to use jQAssistant with Python Pandas
I'm a huge fan of the software analysis framework jQAssistant (http://www.jqassistant.org). It's a great tool for scanning and validating various software artifacts (get a glimpse at https://buschmais.githu... |
lcdutramartins/UdacityML | titanic_survival_exploration/titanic_survival_exploration.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the dataset
in_file... |
patrickfuller/igraph | examples/ipython.ipynb | mit | import jgraph
jgraph.draw([(1, 2), (2, 3), (3, 4), (4, 1), (4, 5), (5, 2)])
"""
Explanation: jgraph in the IPython notebook
I wrote jgraph to visualize graphs in 3D purely out of curiosity. I couldn't find any 3D force-directed graph libraries when I wrote it, so this happened.
It can be used with the notebook to inte... |
MichaelGrupp/evo | notebooks/metrics.py_API_Documentation.ipynb | gpl-3.0 | from evo.core import metrics
"""
Explanation: metrics.py API & Algorithm Documentation
This notebook documents the API and the theory behind the core metrics.
Setup
End of explanation
"""
from evo.tools import log
log.configure_logging(verbose=True, debug=True, silent=False)
import pprint
import numpy as np
from e... |
mortada/notebooks | blog/fredapi_examples.ipynb | apache-2.0 | from fredapi import Fred
fred = Fred()
"""
Explanation: Import the fredapi module. Note that I have set my api key to the environment variable FRED_API_KEY. You can also pass your key explicitly.
End of explanation
"""
import pandas as pd
pd.options.display.max_colwidth = 60
%matplotlib inline
import matplotlib.pyp... |
suresh/notebooks | Chapter 1 - Python DS Handbook.ipynb | mit | L = list(range(10))
L
type(L)
type(L[0])
L2 = [str(c) for c in L]
type(L2[0])
all(type(e) == int for e in L)
"""
Explanation: Python list is more than a list
End of explanation
"""
L3 = [True, '2', 3.0, 4]
[type(item) for item in L3]
tuple(L3)
"""
Explanation: List can be heterogeneous list
End of explanation... |
lcharleux/numerical_analysis | doc/Interpolation/2D_Interpolation.ipynb | gpl-2.0 | # Setup
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
params = {'font.size' : 14,
'figure.figsize':(15.0, 8.0),
'lines.linewidth': 2.,
'lines.markersize': 15,}
matplotlib.rcParams.update(params)
"""
Explanation: 2D Interpolation (and above)
S... |
d-k-b/udacity-deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
DoWhatILove/turtle | programming/python/notebooks/.ipynb_checkpoints/plot_segmentation_toy-checkpoint.ipynb | mit | print(__doc__)
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
l = ... |
xiaoxiaoyao/MyApp | jupyter_notebook/getKeyWord.ipynb | unlicense | # -*- coding=utf-8 -*-
import jieba.analyse
import jieba
with open('../docs/HLS.TXT', encoding='utf-8') as f:
data = f.read()
"""
Explanation: 如何用Python提取中文关键词？
本文一步步为你演示，如何用Python从中文文本中提取关键词。如果你需要对长文“观其大略”，不妨尝试一下。(单一文本关键词的提取方法)
End of explanation
"""
for keyword, weight in jieba.analyse.extract_tags(data, topK=... |
yy/dviz-course | m04-perception/lab.ipynb | mit | import pandas as pd
import math
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: W3 Lab: Perception
In this lab, we will learn basic usage of pandas library and then perform a small experiment to test the perception of length and area.
End of explanation
"""
from vega_datasets import data
data.li... |
Serulab/Py4Bio | notebooks/Chapter 15 - Sequence Manipulation in Batch.ipynb | mit | !curl https://raw.githubusercontent.com/Serulab/Py4Bio/master/samples/samples.tar.bz2 -o samples.tar.bz2
!mkdir samples
!tar xvfj samples.tar.bz2 -C samples
"""
Explanation: Python for Bioinformatics
This Jupyter notebook is intented to be used alongside the book Python for Bioinformatics
Note: Before opening the fil... |
GoogleCloudPlatform/ai-platform-samples | notebooks/samples/explanations/tf2/ai-explanations-image.ipynb | apache-2.0 | PROJECT_ID = "[your-project-id]" #@param {type:"string"}
if PROJECT_ID == "" or PROJECT_ID is None or PROJECT_ID == "[your-project-id]":
# Get your GCP project id from gcloud
shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null
PROJECT_ID = shell_output[0]
print("Project ID:", ... |
trangel/Insight-Data-Science | analysis-data/.ipynb_checkpoints/Insight medical posts-checkpoint.ipynb | gpl-3.0 |
# Set up paths/ os
import os
import sys
this_path=os.getcwd()
os.chdir("../data")
sys.path.insert(0, this_path)
# Load datasets
import pandas as pd
df = pd.read_csv("MedHelp-posts.csv",index_col=0)
df.head(2)
df_users = pd.read_csv("MedHelp-users.csv",index_col=0)
df_users.head(2)
# 1 classify users as professi... |
LaubachLab/Spikes-and-Fields | Interacting with R.ipynb | gpl-3.0 | import numpy as np, pandas as pd, feather
from scipy.io import loadmat, savemat
"""
Explanation: My data analysis workflow depends on R. I tend to use old Matlab code, run in Octave or via oct2py, or new Python code for data data wrangling. I have moved to matplotlib and seaborn for all graphics. I still depend on R f... |
4dsolutions/Python5 | OverviewNotes_PYTDS.ipynb | mit | import numpy as np
import pandas as pd
squares = np.array(list(64 * " "), dtype = np.str).reshape(8,8)
squares
print('♔♕♖')
squares[0][0] = '♖'
squares[7][0] = '♖'
squares[0][7] = '♖'
squares[7][7] = '♖'
squares
chessboard = pd.DataFrame(squares, index=range(1,9),
columns = ['wR','wKn',... |
uber/pyro | tutorial/source/intro_part_i.ipynb | apache-2.0 | import torch
import pyro
pyro.set_rng_seed(101)
"""
Explanation: An Introduction to Models in Pyro
The basic unit of probabilistic programs is the stochastic function.
This is an arbitrary Python callable that combines two ingredients:
deterministic Python code; and
primitive stochastic functions that call a random... |
pauliacomi/pyGAPS | docs/examples/modelling.ipynb | mit | # import isotherms
%run import.ipynb
# Then the modelling module
import pygaps.modelling as pgm
"""
Explanation: Isotherm model fitting
In this notebook we'll attempt to fit isotherms using the included models.
First, make sure the data is imported by running the import notebook.
End of explanation
"""
isotherm = n... |
TheMitchWorksPro/DataTech_Playground | PY_Basics/TMWP_PY_CrazyList_Indexing_and_Related_Experiments.ipynb | mit | stupidList = [[1,2,3],[4,5,6]]
print(stupidList)
stupidList[0][1]
"""
Explanation: <div align="right">Python 2.7</div>
Indexing and Related Experiments in Python 2.7
Though this content is in Python 2.7, most if not all of it should work the same in Python 3.x.
TOC
Indexing Experiments - Explores different complex s... |
dcavar/python-tutorial-for-ipython | notebooks/Parsing Natural Language in Python.ipynb | apache-2.0 | import sys
"""
Explanation: Parsing Natural Language in Python
(C) 2018 by Damir Cavar
License: Creative Commons Attribution-ShareAlike 4.0 International License (CA BY-SA 4.0)
This is a tutorial related to the discussion of parsing with Probabilistic Context Free Grammars (PCFG) in the class Advanced Natural Language... |
googleinterns/bizview-semi-supervised-learning | Mixmatch/streetview_dataset/parse_data_to_tfrecord_main.ipynb | apache-2.0 | from parse_data_to_tfrecord_lib import read_tfrecord, write_tfrecord_from_images, filter_image_with_confidence_threshold, batch_read_write_tfrecords
import numpy as np
import tensorflow as tf
import os # used for directory operations
from shutil import copyfile
tf.enable_eager_execution()
# Global constants
INPUT_RE... |
IBMDecisionOptimization/docplex-examples | examples/mp/jupyter/logical_cts.ipynb | apache-2.0 | import sys
try:
import docplex.mp
except:
raise Exception('Please install docplex. See https://pypi.org/project/docplex/')
"""
Explanation: Use logical constraints with decision optimization
This tutorial includes everything you need to set up decision optimization engines, build a mathematical programming mod... |
bobflagg/deepER | deeper/part1-NER.ipynb | apache-2.0 | import sys, os
from numpy import *
from matplotlib.pyplot import *
%matplotlib inline
matplotlib.rcParams['savefig.dpi'] = 100
%load_ext autoreload
%autoreload 2
"""
Explanation: CS 224D Assignment #2
Part [1]: Deep Networks: NER Window Model
For this first part of the assignment, you'll build your first "deep" netwo... |
blua/deep-learning | tv-script-generation/olds_ipnbs/old_dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
ga7g08/ga7g08.github.io | _notebooks/2015-07-06-More-Parallelising-emcee-using-IPython-parallel.ipynb | mit | %matplotlib inline
from __future__ import print_function
import emcee
import triangle
import numpy as np
import scipy.optimize as op
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
# Reproducible results!
np.random.seed(123)
# Choose the "true" parameters.
m_true = -0.9594
b_true = 4.294
... |
SRI-CSL/libpoly | examples/cad/SMT 2017 (CAD).ipynb | lgpl-3.0 | # Get all the reductums (including the polynomial itself), but not the constants
def get_reductums(f, x):
R = []
while f.var() == x: R.append(f); f = f.reductum()
return R
"""
Explanation: Get the reductums of a polynomial:
- f is a polynomial
- x the top variable
End of explanation
"""
# Add polynomials to pr... |
foxan/dataquest | Data Analysis with Pandas - Intermediate/Challenge - Summarizing Data.ipynb | apache-2.0 | # %sh
# # download source file
# wget https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/all-ages.csv
# wget https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/recent-grads.csv
# ls -l
import pandas as pd
all_ages = pd.read_csv("all-ages.csv")
print all_ages.columns... |
arank/mxnet | example/recommenders/demo1-MF2-fancy.ipynb | apache-2.0 | import mxnet as mx
from movielens_data import get_data_iter, max_id
from matrix_fact import train
# If MXNet is not compiled with GPU support (e.g. on OSX), set to [mx.cpu(0)]
# Can be changed to [mx.gpu(0), mx.gpu(1), ..., mx.gpu(N-1)] if there are N GPUs
ctx = [mx.gpu(0)]
train_test_data = get_data_iter(batch_size=... |
zhmcclient/python-zhmcclient | docs/notebooks/02_connections.ipynb | apache-2.0 | import zhmcclient
"""
Explanation: Tutorial 2: Connecting to an HMC
In order to use the zhmcclient package in a Jupyter notebook, it must be installed in the Python environment that was used to start Jupyter. Trying to import it shows whether it is installed:
End of explanation
"""
zhmc = '9.152.150.65'
session = z... |
mne-tools/mne-tools.github.io | 0.21/_downloads/7bbeb6a728b7d16c6e61cd487ba9e517/plot_morph_volume_stc.ipynb | bsd-3-clause | # Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import nibabel as nib
import mne
from mne.datasets import sample, fetch_fsaverage
from mne.minimum_norm import apply_inverse, read_inverse_operator
from nilearn.plotting import plot_glass_brain
print(__doc__)
"""
Explanation: M... |
xmnlab/notebooks | udacity/deep-learn/1_notmnist.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from IPython.display import display, Image
from scipy import ndimage
from sklearn.linear_model import LogisticRegression
from six.moves.urllib.request import urlretrieve
from six.moves import cPickle as pickle
... |
wesleybeckner/salty | scripts/vae/wes_vae_one.ipynb | mit | properties = ['density', 'cpt', 'viscosity', 'thermal_conductivity',
'melting_point']
for i in range(len(properties)):
props = properties[:i+1]
devmodel = salty.aggregate_data(props, merge='Union')
devmodel.Data['smiles_string'] = devmodel.Data['smiles-cation'] + "." + devmodel.Data['smiles-an... |
jorgemauricio/INIFAP_Course | ejercicios/Pandas/5_Merge, Join, and Concat.ipynb | mit | # Librerias
import pandas as pd
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=[0, 1, 2, 3])
df2 = pd.DataFrame({'A': ... |
scientific-visualization-2016/ClassMaterials | Week-03/03-netcdf.ipynb | cc0-1.0 | from netCDF4 import Dataset
import numpy as np
import numpy.ma as ma
filename = "tos_O1_2001-2002.nc"
ds = Dataset(filename, mode="r")
"""
Explanation: A Short Introduction to netCDF
What is netCDF?
"NetCDF is an abstraction that supports a view of data as a collection of self-describing, portable objects that can b... |
ual/hedonic-models | 08_linear_regression.ipynb | bsd-3-clause | # imports
import pandas as pd
import matplotlib.pyplot as plt
# this allows plots to appear directly in the notebook
%matplotlib inline
"""
Explanation: Introduction to Linear Regression
Adapted from Chapter 3 of An Introduction to Statistical Learning
||continuous|categorical|
|---|---|---|
|supervised|regression|cl... |
5agado/data-science-learning | deep learning/GAN/DCGAN.ipynb | apache-2.0 | import sys
import yaml
import tensorflow as tf
import numpy as np
import pandas as pd
import functools
from pathlib import Path
from datetime import datetime
from tqdm import tqdm_notebook as tqdm
# Plotting
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import animation
plt.rcParams['animation.ffmp... |
tomekkorbak/lstm-for-aspect-based-sentiment-analysis | presentation/Presentation.ipynb | gpl-3.0 | import json
from itertools import chain
from pprint import pprint
from time import time
import os
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from gensim.models import Word2Vec
from gensim.corpora.dictionary import Dictionary
os.environ['THEANO_... |
jorgemauricio/INIFAP_Course | ejercicios/Machine_Learning/MachineLearning.ipynb | mit | # librerias
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
# leer el csv
data = pd.read_csv('../../data/db_join_wrf_tpro_10_25_tmid_10_20.csv')
# estructura del dataFrame
data.head()
# columnas del dataframe
data.columns
# información del dataFrame
data.info()
# utilizar s... |
sarvex/PythonMachineLearning | Chapter 2/Linear models.ipynb | isc | from sklearn.datasets import make_regression
from sklearn.cross_validation import train_test_split
X, y, true_coefficient = make_regression(n_samples=80, n_features=30, n_informative=10, noise=100, coef=True, random_state=5)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=5)
print(X_train.shape)... |
whitead/numerical_stats | unit_6/hw_2017/problem_set_2.ipynb | gpl-3.0 | #The points awarded this cell corresopnd to partial credit and/or documentation
### BEGIN SOLUTION
def power(x, p=2):
'''Computes x^p
Args:
x: input number
p: input power, defaults to 2
returns: x^p as a floating point
'''
return x**p
### END SOLUTION
'''Check if your... |
biothings/biothings_explorer | jupyter notebooks/COVID_demo.ipynb | apache-2.0 | !pip install git+https://github.com/biothings/biothings_explorer#egg=biothings_explorer
"""
Explanation: Introduction
This notebook demonstrates basic usage of BioThings Explorer, an engine for autonomously querying a distributed knowledge graph. BioThings Explorer can answer two classes of queries -- "EXPLAIN" and "P... |
elastic/examples | Machine Learning/Class Assigment Objectives/classification-class-assignment-objective.ipynb | apache-2.0 | # Some general notebook setting
host = 'http://localhost:9200'
# IMPORTANT: create a file credentials.json with credentials for your Elasticsearch instance!
with open('credentials.json') as f:
data = json.load(f)
username = data['username']
password = data['password']
es = Elasticsearch(host, http_auth=(u... |
sangheestyle/ml2015project | howto/model08_refactoring_functions.ipynb | mit | import gzip
import pickle
from os import path
from collections import defaultdict
from numpy import sign
"""
Load buzz data as a dictionary.
You can give parameter for data so that you will get what you need only.
"""
def load_buzz(root='../data', data=['train', 'test', 'questions'], format='pklz'):
buzz_data = {... |
astroNN/astroNN | notebooks/1_notmnist.ipynb | mit | # Third-party packages
import h5py
import matplotlib.pyplot as pl
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
# this package
from astronn.data import fetch_notMNIST
"""
Explanation: Deep Learning
Assignment 1
The objective of this assignment is to learn about simple data ... |
RagsX137/TF_Tutorial | My+own+KNN+Classifier.ipynb | apache-2.0 | from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
# Iris.data contains the features or independent variables.
y = iris.target
# Iris.target contains the labels or the dependent variables.
"""
Explanation: Tutorial : Creating a Simple Nearest Neighbor Classifier from scratch
This is based on the K... |
jgacostag/Taller | TallerETVL_Módulo1 - JgAG.ipynb | mit | import pandas as pd
x=pd.DataFrame() #Mejor hasta ahora
for m in range(1995,2018):
if m < 2016:
o='.xlsx'
else:
o='.xls'
if m < 2000:
sK=3
else:
sK=2
n='Precio_Bolsa_Nacional_($kwh)_' + str(m) + o
y=pd.read_excel(n, skiprows=sK, parse_cols=24)
x= x.app... |
feststelltaste/software-analytics | demos/20180731_Munich/Wertloser Code.ipynb | gpl-3.0 | import pandas as pd
coverage = pd.read_csv("../dataset/jacoco_production_coverage_spring_petclinic.csv")
coverage.head()
"""
Explanation: Demo
Strategic Redesign für das Projekt „Spring Petclinic“
Auslastungsdaten vom Produktivbetrieb
Datenquelle: Gemessen wurde der Anwendungsbetrieb der Software über einen Zeitraum ... |
darkomen/TFG | medidas/13082015/.ipynb_checkpoints/Análisis de datos Ensayo 1-checkpoint.ipynb | cc0-1.0 | #Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#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__))
#Abrimos el fichero csv con los datos... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/hadgem3-gc31-hh/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'hadgem3-gc31-hh', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NERC
Source ID: HADGEM3-GC31-HH
Topic: Atmos
Sub-Topics: Dynamical Core, Radia... |
UCBerkeleySETI/breakthrough | PKS/voyager.ipynb | gpl-3.0 | %matplotlib inline
import blimpy as bl
import pylab as plt
import numpy as np
plt.rcParams['font.size'] = 12
"""
Explanation: Voyager 2
Example data taken on 2018-10-22 during MARS receiver testing, using the Breakthrough Listen backend.
Data recorded over full bandwidth of MARS receiver, here we have extracted a sm... |
raschuetz/foundations-homework | 05/NYT-API.ipynb | mit | import requests
"""
Explanation: All API's: http://developer.nytimes.com/
Article search API: http://developer.nytimes.com/article_search_v2.json
Best-seller API: http://developer.nytimes.com/books_api.json#/Documentation
Test/build queries: http://developer.nytimes.com/
Tip: Remember to include your API key in all re... |
Erhil/PythonNpCourse | materials/week 1/IPython_intro.ipynb | mit | print(math.sqrt(4))
import math
"""
Explanation: Jupyter Notebook -- это удобно!
Код организван отдельными болками. Блоки кода можно выполнять в произвольном порядке. Сочетает в себе достоинства полноценных скриптов и интерактивной оболочки. Порядок выполнения блоков указан слева от ячейки.
End of explanation
"""
i... |
Bismarrck/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... |
dacoex/pvlib-python | docs/tutorials/solarposition.ipynb | bsd-3-clause | import datetime
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
# seaborn makes your plots look better
try:
import seaborn as sns
sns.set(rc={"figure.figsize": (12, 6)})
... |
brockk/clintrials | tutorials/matchpoint/DTPs.ipynb | gpl-3.0 | import numpy as np
from scipy.stats import norm
from clintrials.dosefinding.efftox import EffTox, LpNormCurve, efftox_dtp_detail
from clintrials.dosefinding.efficacytoxicity import dose_transition_pathways, print_dtps
real_doses = [7.5, 15, 30, 45]
trial_size = 30
cohort_size = 3
first_dose = 3
prior_tox_probs = (0.0... |
dsacademybr/PythonFundamentos | Cap04/Notebooks/DSA-Python-Cap04-04-Datetime.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 4</font>
Download: http://github.com/dsacademybr
End of explanation
"""
import ... |
INM-6/Python-Module-of-the-Week | session08-pandas/Pandas PYMOTW.ipynb | mit | import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: PANDAS ARE AWESOME
<img src="https://i.imgur.com/vxKOi.gif" width="1200" height="1200">
<a href="https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PandasPythonForDataScience.pdf">Down... |
sdonapar/data_analysis_python | pandas_overview.ipynb | mit | person_height_ft = pd.Series([5.5,5.2,5.8,6.1,4.8],name='height',
index = ['person_a','person_b','person_c','person_d','person_e'],dtype=np.float64)
person_height_ft
person_height_ft.values
person_height_ft.index
"""
Explanation: Pandas has two important data strucures Series and DataFrame
Series
Se... |
esa-as/2016-ml-contest | MandMs/Facies_classification-M&Ms_plurality_voting_classifier.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
import pandas as pd
from pandas import set_option
set_option("display.max_rows", 10)
pd.options.mode.chained_assignment = None
from ... |
aje/POT | docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb | mit | # Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import numpy as np
import matplotlib.pylab as pl
import ot
import ot.plot
"""
Explanation: 2D Optimal transport for different metrics
2D OT on empirical distributio with different gound metric.
Stole the figure idea from Fig. 1 and 2 in
https://... |
GeosoftInc/gxpy | examples/jupyter_notebooks/Tutorials/Tilt-Depth.ipynb | bsd-2-clause | import geosoft.gxpy.gx as gx
import geosoft.gxpy.utility as gxu
import geosoft.gxpy.grid as gxgrd
import geosoft.gxpy.grid_utility as gxgrdu
import geosoft.gxpy.map as gxmap
import geosoft.gxpy.view as gxview
import geosoft.gxpy.group as gxgrp
import numpy as np
from IPython.display import Image
gxc = gx.GXpy()
gxu.c... |
empirical-org/WikipediaSentences | notebooks/BERT-4 Experiments Multilabel.ipynb | agpl-3.0 | from multilabel import EATINGMEAT_BECAUSE_MAP, EATINGMEAT_BUT_MAP, JUNKFOOD_BECAUSE_MAP, JUNKFOOD_BUT_MAP
label_map = EATINGMEAT_BECAUSE_MAP
import torch
from pytorch_transformers.tokenization_bert import BertTokenizer
from pytorch_transformers.modeling_bert import BertForSequenceClassification
BERT_MODEL = 'bert-l... |
google/iree | samples/dynamic_shapes/dynamic_shapes.ipynb | apache-2.0 | #@title Licensed under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
"""
Explanation: Copyright 2021 The IREE Authors
End of explanation
"""
#@title General setup
import os
import tempfile
ARTIFACT... |
thewtex/ieee-nss-mic-scipy-2014 | 2_IPython.ipynb | apache-2.0 | print("Hello world!")
"""
Explanation: IPython Notebook
The IPython Notebook is a web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document.
<img src="images/ipython_logo.png" width="400">
This is one of the 100 recipes of the ... |
william-gray/data-science-python | ML-clustering/Related Article Clustering/Wikipedia_Related_Article_Clustering.ipynb | mit | import os
from urllib import urlretrieve
import graphlab
URL = 'https://d396qusza40orc.cloudfront.net/phoenixassets/people_wiki.csv'
def get_data(filename='people_wiki.csv', url=URL, force_download=False):
"""Download and cache the fremont data
Parameters
----------
filename: string (optiona... |
texib/deeplearning_homework | muki-batch.ipynb | mit | img_count = 0
def showimg(img):
muki_pr = np.zeros((500,500,3))
l =img.tolist()
count = 0
for x in range(500):
for y in range(500):
muki_pr[y][x] = l[count]
count += 1
plt.imshow(muki_pr)
def saveimg(fname,img):
muki_pr = np.zeros((500,500,3))
l =img.tolist()
... |
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