repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/stationarity_detrending_adf_kpss.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.api as sm
"""
Explanation: Stationarity and detrending (ADF/KPSS)
Stationarity means that the statistical properties of a time series i.e. mean, variance and covariance do not change over time. Many statistical... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Python-for-Data-Analysis/Pandas/Pandas Exercises/SF Salaries Exercise.ipynb | mit | import pandas as pd
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../../Pierian_Data_Logo.png' /></a>
SF Salaries Exercise
Welcome to a quick exercise for you to practice your pandas skills! We will be using the SF Salaries Dataset from Kaggle! Just follow along and complete the tasks outlined in b... |
cathalmccabe/PYNQ | boards/Pynq-Z2/logictools/notebooks/pattern_generator_and_trace_analyzer.ipynb | bsd-3-clause | from pynq.overlays.logictools import LogicToolsOverlay
logictools_olay = LogicToolsOverlay('logictools.bit')
"""
Explanation: Pattern Generator and Trace Analyzer
This notebook will show how to use the Pattern Generator to generate patterns on I/O pins. The pattern that will be generated is 3-bit up count performed 4... |
dereneaton/ipyrad | newdocs/API-analysis/cookbook-treemix-ipcoal.ipynb | gpl-3.0 | # conda install treemix ipyrad ipcoal -c conda-forge -c bioconda
import ipyrad.analysis as ipa
import toytree
import toyplot
import ipcoal
print('ipyrad', ipa.__version__)
print('toytree', toytree.__version__)
! treemix --version | grep 'TreeMix v. '
"""
Explanation: <h1><span style="color:gray">ipyrad-analysis tool... |
metpy/MetPy | v1.0/_downloads/bb9caa5586d62e19ca46e30c02d29b43/Station_Plot.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from metpy.calc import reduce_point_density
from metpy.cbook import get_test_data
from metpy.io import metar
from metpy.plots import add_metpy_logo, current_weather, sky_cover, StationPlot
"""
Explanation: Station Plot
Make ... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_maxwell_filter.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Mark Wronkiewicz <wronk.mark@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.preprocessing import maxwell_filter
print(__doc__)
data_path = mne.datasets.sample.data_path()
"... |
tkurfurst/deep-learning | reinforcement/Q-learning-cart.ipynb | mit | import gym
import tensorflow as tf
import numpy as np
"""
Explanation: Deep Q-learning
In this notebook, we'll build a neural network that can learn to play games through reinforcement learning. More specifically, we'll use Q-learning to train an agent to play a game called Cart-Pole. In this game, a freely swinging p... |
OceanPARCELS/parcels | parcels/examples/documentation_unstuck_Agrid.ipynb | mit | import numpy as np
import numpy.ma as ma
from netCDF4 import Dataset
import xarray as xr
from scipy import interpolate
from parcels import FieldSet, ParticleSet, JITParticle, ScipyParticle, AdvectionRK4, Variable, Field,GeographicPolar,Geographic
from datetime import timedelta as delta
import matplotlib.pyplot as plt... |
mari-linhares/tensorflow-workshop | code_samples/StructuredDataExample/automobile.ipynb | apache-2.0 | from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
# We're using pandas to read the CSV file. This is easy for small datasets, but for large and complex datasets,
# tensorflow parsing and processing functions are more powerful
import pandas as pd
import numpy a... |
rishuatgithub/MLPy | torch/PYTORCH_NOTEBOOKS/00-Crash-Course-Topics/00-Crash-Course-NumPy/02-NumPy-Operations.ipynb | apache-2.0 | import numpy as np
arr = np.arange(0,10)
arr
arr + arr
arr * arr
arr - arr
# This will raise a Warning on division by zero, but not an error!
# It just fills the spot with nan
arr/arr
# Also a warning (but not an error) relating to infinity
1/arr
arr**3
"""
Explanation: <a href='http://www.pieriandata.com'><img ... |
awsteiner/o2sclpy | doc/static/examples/interp.ipynb | gpl-3.0 | import o2sclpy
import matplotlib.pyplot as plot
import sys
import math
import numpy
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel
plots=True
if 'pytest' in sys.modules:
plots=False
"""
Explanation: O$_2$scl interpolation example for ... |
AllenDowney/ThinkBayes2 | examples/game_of_ur_soln.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
from thinkbayes2 import Pmf, Cdf, Suite
import thinkplot
"""
Explanation: Think Bayes
This notebook pres... |
phoebe-project/phoebe2-docs | 2.2/tutorials/irrad_method_horvat.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Lambert Scattering (irrad_method='horvat')
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 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
"""... |
kylepjohnson/notebooks | public_talks/2016_10_26_harvard/3.1b Classification, extract features, fewer epithets.ipynb | mit | from cltk.corpus.greek.tlg.parse_tlg_indices import get_epithet_index
import pandas
epithet_frequencies = []
for epithet, _ids in get_epithet_index().items():
epithet_frequencies.append((epithet, len(_ids)))
df = pandas.DataFrame(epithet_frequencies)
df.sort_values(1, ascending=False)
"""
Explanation: Problem of ... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/02_04/Final/.ipynb_checkpoints/Missing Data-checkpoint.ipynb | bsd-3-clause | browser_index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
browser_df = pd.DataFrame({
'http_status': [200,200,404,404,301],
'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
index=browser_index)
browser_df
"""
Explanation: Missing Data
pandas uses np.nan to represent missing data. By defa... |
sarathid/Learning | Deep_learning_ND/tv-script-generation/dlnd_tv_script_generation.ipynb | gpl-3.0 | """
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... |
sastels/Onboarding | 4 - Sorting.ipynb | mit | a = [5, 1, 4, 3]
print sorted(a)
print a
"""
Explanation: Sorting
The easiest way to sort is with the sorted(list) function, which takes a list and returns a new list with those elements in sorted order. The original list is not changed.
End of explanation
"""
strs = ['aa', 'BB', 'zz', 'CC']
print sorted(strs)
print... |
rjdkmr/do_x3dna | docs/notebooks/helical_steps_tutorial.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import dnaMD
%matplotlib inline
"""
Explanation: Analysis of local helical parameters
This tutorial discuss the analyses that can be performed using the dnaMD Python module included in the do_x3dna package. The tutorial is prepared using Jupyter Notebook and this n... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_lcmv_beamformer.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 3
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.datasets import sample
from mne.beamformer import make_lcmv, apply_lcmv
print(__doc__)
data_path = sample.d... |
awagner-mainz/notebooks | gallery/textreuse_mainz_2020/12000-segment-paragraphs.ipynb | mit | import os
import lxml
from lxml import etree
resolved_dir = "./data/processing/10000_resolved"
# we create a dictionary with our editions:
resolved = { os.path.basename(file).split(os.extsep)[0] :
(etree.parse(resolved_dir + "/" + file))
for file in sorted(os.listdir(resolved_dir... |
zczapran/datascienceintensive | data_wrangling_json/sliderule_dsi_json_exercise.ipynb | mit | import pandas as pd
"""
Explanation: JSON examples and exercise
get familiar with packages for dealing with JSON
study examples with JSON strings and files
work on exercise to be completed and submitted
reference: http://pandas.pydata.org/pandas-docs/stable/io.html#io-json-reader
data source: http://jsonstudio.... |
feststelltaste/software-analytics | prototypes/Reading Git logs with Pandas 2.0-checkpoint.ipynb | gpl-3.0 | import git
GIT_LOG_FILE = r'${REPO}/spring-petclinic'
repo = git.Repo(GIT_LOG_FILE)
git_bin = repo.git
git_bin
"""
Explanation: Context
In https://www.feststelltaste.de/reading-a-git-log-file-output-with-pandas/ I show you a way to read in Git log data with Pandas's DataFrame and GitPython.
Looking back, this was re... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/giss-e2-1h/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1h', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: GISS-E2-1H
Topic: Atmoschem
Sub-Topics: Transport... |
mne-tools/mne-tools.github.io | stable/_downloads/aec45e1f20057e833cee12bb6bd292dc/10_evoked_overview.ipynb | bsd-3-clause | import os
import mne
"""
Explanation: The Evoked data structure: evoked/averaged data
This tutorial covers the basics of creating and working with :term:evoked
data. It introduces the :class:~mne.Evoked data structure in detail,
including how to load, query, subselect, export, and plot data from an
:class:~mne.Evoked ... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/ml_fairness_explainability/explainable_ai/solutions/xai_structured_caip.ipynb | apache-2.0 | import os
PROJECT_ID = "" # TODO: your PROJECT_ID here.
os.environ["PROJECT_ID"] = PROJECT_ID
BUCKET_NAME = PROJECT_ID # TODO: replace your BUCKET_NAME, if needed
REGION = "us-central1"
os.environ["BUCKET_NAME"] = BUCKET_NAME
os.environ["REGION"] = REGION
"""
Explanation: AI Explanations: Explaining a tabular dat... |
planetlabs/notebooks | jupyter-notebooks/data-api-tutorials/search_and_download_quickstart.ipynb | apache-2.0 | # Stockton, CA bounding box (created via geojson.io)
geojson_geometry = {
"type": "Polygon",
"coordinates": [
[
[-121.59290313720705, 37.93444993515032],
[-121.27017974853516, 37.93444993515032],
[-121.27017974853516, 38.065932950547484],
[-121.59290313720705, 38.065932950547484],
... |
bjshaw/phys202-2015-work | project/NeuralNetworks.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from IPython.html.widgets import interact
from sklearn.datasets import load_digits
digits = load_digits()
print(digits.data.shape)
def show_digit(i):
plt.matshow(digits.images[i]);
interact(show_digit, i=(0,100));
"""
Explanation: Neural Networks
This project w... |
elect000/Journal | value-tracker/report/report.ipynb | bsd-3-clause | import quandl
data = quandl.get('NIKKEI/INDEX')
data[:5]
data_normal = (((data['Close Price']).to_frame())[-10000:-1])['Close Price']
data_normal[-10:-1] # 最新のデータ１０件を表示
"""
Explanation: データの取得方法
ここではQuandl.comからのデータを受け取っています。今回入手した日経平均株価は、
時間、開始値、最高値、最低値、終値のデータを入手していますが、古いデータは終値しかないようですので、終値を用います。
*** TODO いつからデータを入... |
staeiou/github-analytics | github-organizations-intro.ipynb | mit | !pip install pygithub
!pip install geopy
!pip install ipywidgets
from github import Github
#this is my private login credentials, stored in ghlogin.py
import ghlogin
g = Github(login_or_token=ghlogin.gh_user, password=ghlogin.gh_passwd)
"""
Explanation: Querying the GitHub API for repositories and organizations
By... |
SKA-ScienceDataProcessor/crocodile | examples/notebooks/grid-predict.ipynb | apache-2.0 | theta = 0.1
lam = 18000
grid_size = int(theta * lam)
def kernel_oversample(ff, Qpx, s=None, P = 1):
"""
Takes a farfield pattern and creates an oversampled convolution
function.
If the far field size is smaller than N*Qpx, we will pad it. This
essentially means we apply a sinc anti-aliasing kernel... |
xesscorp/skidl | examples/skidl_spice_test/skidl_2_pyspice_check.ipynb | mit | from skidl.pyspice import *
from PySpice.Spice.Netlist import Circuit
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Checking-tool" data-toc-modified-id="Checking-tool-1"><span class="toc-item-num">1 </span>Checking tool</a>... |
DOV-Vlaanderen/pydov | docs/notebooks/search_lithologische_beschrijvingen.ipynb | mit | %matplotlib inline
import os, sys
import inspect
import pydov
"""
Explanation: Example of DOV search methods for lithologische beschrijvingen
Use cases:
Select records in a bbox
Select records in a bbox with selected properties
Select records in a municipality
Get records using info from wfs fields, not available i... |
cmshobe/landlab | notebooks/tutorials/overland_flow/overland_flow_driver.ipynb | mit | from landlab.components.overland_flow import OverlandFlow
from landlab.plot.imshow import imshow_grid
from landlab.plot.colors import water_colormap
from landlab import RasterModelGrid
from landlab.io.esri_ascii import read_esri_ascii
from matplotlib.pyplot import figure
import numpy as np
from time import time
%matplo... |
lneuhaus/pyrpl | docs/example-notebooks/tutorial.ipynb | mit | import pyrpl
print(pyrpl.__file__)
"""
Explanation: Introduction to pyrpl
1) Introduction
The RedPitaya is an affordable FPGA board with fast analog inputs and outputs. This makes it interesting also for quantum optics experiments. The software package PyRPL (Python RedPitaya Lockbox) is an implementation of many devi... |
moble/PostNewtonian | C++/TestBackwardsEvolution.ipynb | mit | v_i = 0.15
m1 = 0.4
m2 = 0.6
chi1_i = [0.1,0.2,0.3]
chi2_i = [0.2,0.3,0.4]
R_frame_i = Quaternions.Quaternion(1,0,0,0)
ForwardInTime = True
v_0 = 0.9*v_i
tA,vA,chi1A,chi2A,R_frameA,PhiA = \
PNEvolution.EvolvePN("TaylorT1", 4.0, v_0, v_i, m1, m2, chi1_i, chi2_i, R_frame_i, ForwardInTime)
plot(tA, vA, label='v_0 = {... |
abulbasar/machine-learning | Scikit - 03 Linear Regression.ipynb | apache-2.0 | df_null_idx = df[df.isnull().sum(axis = 1) > 0].index
df.iloc[df_null_idx]
median_values = df.groupby("State")[["R&D Spend", "Marketing Spend"]].median()
median_values
df["R&D Spend"] = df.apply(lambda row: median_values.loc[row["State"], "R&D Spend"] if np.isnan(row["R&D Spend"]) else row["R&D Spend"], axis = 1 )
d... |
tanmay987/deepLearning | tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.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 scri... |
IBMDecisionOptimization/docplex-examples | examples/mp/jupyter/sparktrans/SparkML_transformers_pipeline.ipynb | apache-2.0 | try:
import numpy as np
except ImportError:
raise RuntimError('This notebook requires numpy')
"""
Explanation: Embedding CPLEX in a ML Spark Pipeline
Spark ML provides a uniform set of high-level APIs that help users create and tune practical machine learning pipelines.
In this notebook, we show how to embed C... |
GoogleCloudPlatform/training-data-analyst | self-paced-labs/vertex-ai/vertex-pipelines/tfx/lab_exercise.ipynb | apache-2.0 | GOOGLE_CLOUD_PROJECT_ID = !(gcloud config get-value core/project)
GOOGLE_CLOUD_PROJECT_ID = GOOGLE_CLOUD_PROJECT_ID[0]
GOOGLE_CLOUD_REGION = 'us-central1'
BQ_DATASET_NAME = 'chicago_taxifare_tips'
BQ_TABLE_NAME = 'chicago_taxi_tips_ml'
BQ_LOCATION = 'US'
BQ_URI = f"bq://{GOOGLE_CLOUD_PROJECT_ID}.{BQ_DATASET_NAME}.{BQ... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160621화_18일차_QDALDA QuandraticLinear Discriminant Analysis/1.QDA and LDA.ipynb | mit | N = 100
np.random.seed(0)
X1 = sp.stats.multivariate_normal([ 0, 0], [[0.7, 0],[0, 0.7]]).rvs(100)
X2 = sp.stats.multivariate_normal([ 1, 1], [[0.8, 0.2],[0.2, 0.8]]).rvs(100)
X3 = sp.stats.multivariate_normal([-1, 1], [[0.8, 0.2],[0.2, 0.8]]).rvs(100)
y1 = np.zeros(N)
y2 = np.ones(N)
y3 = 2*np.ones(N)
X = np.vstack([X... |
PyPSA/PyPSA | examples/notebooks/simple-electricity-market-examples.ipynb | mit | import pypsa, numpy as np
# marginal costs in EUR/MWh
marginal_costs = {"Wind": 0, "Hydro": 0, "Coal": 30, "Gas": 60, "Oil": 80}
# power plant capacities (nominal powers in MW) in each country (not necessarily realistic)
power_plant_p_nom = {
"South Africa": {"Coal": 35000, "Wind": 3000, "Gas": 8000, "Oil": 2000}... |
w4zir/ml17s | assignments/.ipynb_checkpoints/assignment02-logistic-regression-and-neural-network-checkpoint.ipynb | mit | import cv2
img = cv2.imread('test.png',0)
resized_image = cv2.resize(img, (28, 28), interpolation = cv2.INTER_AREA)
"""
Explanation: CSAL4243: Introduction to Machine Learning
Muhammad Mudassir Khan (mudasssir.khan@ucp.edu.pk)
Assignment 2:
Digits Recognition using Logistic Regression & Neural Networks
In this assign... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/automl/sdk_automl_video_object_tracking_batch.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex AI SDK for Python: AutoML training video object tracking model for batch prediction
<tab... |
ModestoCabrera/IS360_W7Assignment | Week7_Assignment.ipynb | gpl-2.0 | import urllib2, argparse
from bs4 import BeautifulSoup
import pandas as pd
link = "https://www.globalpolicy.org/component/content/article/109/27519.html"
"""
Explanation: Reading HTML Tables into DataFrame
End of explanation
"""
from week_7_code import *
"""
Explanation: I'd previously coded this in a python fi... |
Applied-Groundwater-Modeling-2nd-Ed/Chapter_5_problems-1 | P5.3_Flopy_Industrial_pond.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 P5.3 Industrial Pond Leakage
In Problem P5.3 from pag... |
MBARIMike/biofloat | notebooks/build_biofloat_cache.ipynb | mit | from biofloat import ArgoData
ad = ArgoData(verbosity=2)
"""
Explanation: Build local cache file from Argo data sources - first in a series of Notebooks
Execute commands to pull data from the Internet into a local HDF cache file so that we can better interact with the data
Import the ArgoData class and instatiate an A... |
marknabil/B31XI-SI-Clustering | 03-clustering.ipynb | gpl-2.0 |
%matplotlib inline
%pprint off
# Matplotlib library
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
# MPLD3 extension
import mpld3
# Numpy library
import numpy as np
# Import the Scipy library for grid... |
prasants/pyds | 04.String_me_along.ipynb | mit | print("Hello World!")
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Strings" data-toc-modified-id="Strings-1"><span class="toc-item-num">1 </span>Strings</a></div><div class="lev2 toc-item"><a href="#Switching-between-Single,-Double-and-Triple-Quotes" data-toc-modified-id="Switc... |
mne-tools/mne-tools.github.io | 0.24/_downloads/09a8b0bb7a57481cdd1f7832f0291ee6/brain.ipynb | bsd-3-clause | # Author: Alex Rockhill <aprockhill@mailbox.org>
#
# License: BSD-3-Clause
"""
Explanation: Plotting with mne.viz.Brain
In this example, we'll show how to use :class:mne.viz.Brain.
End of explanation
"""
import os.path as op
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
... |
synthicity/activitysim | activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb | agpl-3.0 | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimating Non-Mandatory Tour Frequency
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estimation mode to read house... |
yugangzhang/CHX_Pipelines | Working_Pipleines/XPCS_Single_2017_V8_debug.ipynb | bsd-3-clause | from chxanalys.chx_packages import *
%matplotlib notebook
plt.rcParams.update({'figure.max_open_warning': 0})
plt.rcParams.update({ 'image.origin': 'lower' })
plt.rcParams.update({ 'image.interpolation': 'none' })
import pickle as cpk
from chxanalys.chx_xpcs_xsvs_jupyter_V1 import *
Javascript( '''
var nb ... |
ComputationalModeling/spring-2017-danielak | past-semesters/fall_2016/day-by-day/day17-analyzing-tweets-with-string-processing/In-Class-Strings-SOLUTION.ipynb | agpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
from string import punctuation
"""
Explanation: Day 17 In-class assignment: Data analysis and Modeling in Social Sciences
Part 3
The first part of this notebook is a copy of a blog post tutorial written by Dr. Neal Caren (University of North Carolina, Chapel Hill). Th... |
ctn-waterloo/best-practices | Confidence Intervals - bootstrap.ipynb | mit | %matplotlib inline
import pylab
import numpy as np
"""
Explanation: Confidence Intervals
Purpose: take data from multiple runs and create aggregate data that is useful for drawing conclusions
End of explanation
"""
rng = np.random.RandomState(seed=0)
data = rng.normal(size=3)
pylab.scatter(np.zeros_like(data), dat... |
mroberge/hydrofunctions | docs/notebooks/Hydrofunctions_Comparing_Stream_Environments.ipynb | mit | import hydrofunctions as hf
%matplotlib inline
"""
Explanation: Comparing Different Stream Environments
This Jupyter Notebook compares four streams in different environments in the U.S.
Using hydrofunctions, we are able to plot the flow duration graphs for all four streams and compare them.
End of explanation
"""
s... |
robertoalotufo/ia898 | master/tutorial_img_ds.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
!ls ../data
f = mpimg.imread('../data/cameraman.tif')
print('Tamanho de f: ', f.shape)
print('Tipo do pixel:', f.dtype)
print('Número total de pixels:', f.size)
print('Pixels:\n', f)
"""
Explanation: Table of Cont... |
ChadFulton/statsmodels | examples/notebooks/statespace_concentrated_scale.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import statsmodels.api as sm
dta = sm.datasets.macrodata.load_pandas().data
dta.index = pd.PeriodIndex(start='1959Q1', end='2009Q3', freq='Q')
"""
Explanation: State space models - concentrating the scale out of the likelihood function
End of explanation
"""
class LocalLevel(s... |
tensorflow/docs-l10n | site/ko/guide/migrate.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... |
deepmind/spurious_normativity | spurious_normativity_figures.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import pickle
import scipy.stats
import seaborn as sns
import tempfile
from google.colab import files
import warnings
warnings.simplefilter('ignore', category=RuntimeWarning)
"""
Explanation: Copyright 2021 DeepMind Technologies Limited.
Licensed under the Apache Lice... |
vbarua/PythonWorkshop | Code/Introduction To Python/1 - Strings, Numbers and Booleans.ipynb | mit | "This is a string!!!"
'This is also a string!!!'
"This string contains single 'quotation' marks!!!"
'This string contains double "quotation" marks!!!'
"""
Explanation: Strings, Numbers and Booleans
Strings
Python has strings, which are written using either single or double quotes.
End of explanation
"""
7
42
""... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session11/Day2/MeasuringCentroidsAndProperMotionSolutions.ipynb | mit | # Load the packages we will use
import numpy as np
import astropy.io.fits as pf
import astropy.coordinates as co
from astropy.wcs import WCS
from matplotlib import pyplot as pl
%matplotlib inline
"""
Explanation: Practice with stellar astrometry
To accompany astrometry lecture from the Rubin Observatory Data Science F... |
gcgruen/homework | foundations-homework/05/homework-05-gruen-nyt_graded.ipynb | mit | #API Key: 0c3ba2a8848c44eea6a3443a17e57448
"""
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 inc... |
valentina-s/GLM_PythonModules | notebooks/MLE_multipleNeuronsWeights.ipynb | bsd-2-clause | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import random
import csv
%matplotlib inline
import os
import sys
sys.path.append(os.path.join(os.getcwd(),'..'))
sys.path.append(os.path.join(os.getcwd(),'..','code'))
sys.path.append(os.path.join(os.getcwd(),'..','data'))
import filters
import li... |
PMEAL/OpenPNM | examples/simulations/steady_state/continuum_heat_transfer.ipynb | mit | %matplotlib inline
import numpy as np
import scipy as sp
import openpnm as op
%config InlineBackend.figure_formats = ['svg']
np.random.seed(10)
ws = op.Workspace()
ws.settings["loglevel"] = 40
np.set_printoptions(precision=5)
"""
Explanation: Fourier Conduction
This examples shows how OpenPNM can be used to simulate t... |
Ruediger-Braun/compana16 | Lektion12-Fehler.ipynb | gpl-3.0 | from sympy import *
init_printing()
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Lektion 12
End of explanation
"""
x = Symbol('x', real=True)
A = Matrix(3,3, [x,x,0,0,x,x,0,0,x])
A
A.exp()
"""
Explanation: Matrixexponentiale
End of explanation
"""
A = Matrix(4,4,[0,1,0,... |
IvarsKarpics/mxcube | bin/mxcube_jupyter_notebook.ipynb | lgpl-3.0 | import os
import sys
cwd = os.getcwd()
print cwd
mxcube_root = cwd[:-4]
print mxcube_root
sys.path.insert(0, mxcube_root)
from HardwareRepository import HardwareRepository
#print "MXCuBE home directory: %s" % cwd
hwr_server = mxcube_root + "/HardwareRepository/configuration/xml-qt"
HardwareRepository.setHardwareRe... |
mattilyra/gensim | docs/notebooks/doc2vec-wikipedia.ipynb | lgpl-2.1 | from gensim.corpora.wikicorpus import WikiCorpus
from gensim.models.doc2vec import Doc2Vec, TaggedDocument
from pprint import pprint
import multiprocessing
"""
Explanation: Doc2Vec to wikipedia articles
We conduct the replication to Document Embedding with Paragraph Vectors (http://arxiv.org/abs/1507.07998).
In this p... |
mne-tools/mne-tools.github.io | 0.18/_downloads/66fec418bceb5ce89704fb8b44930330/plot_3d_to_2d.ipynb | bsd-3-clause | # Authors: Christopher Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
from scipy.io import loadmat
import numpy as np
from mayavi import mlab
from matplotlib import pyplot as plt
from os import path as op
import mne
from mne.viz import ClickableImage # noqa
from mne.viz import plot_alignment, snapshot_... |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-E-corrected-all-ph-out-7d.ipynb | mit | ph_sel_name = "None"
data_id = "7d"
# data_id = "7d"
"""
Explanation: Executed: Mon Mar 27 11:39:17 2017
Duration: 7 seconds.
usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
"""
from fretbursts import *
init... |
matthijsvk/multimodalSR | code/Experiments/Tutorials/EbenOlsen_TheanoLasagne/2 - Lasagne Basics/Digit Recognizer.ipynb | mit | # Uncomment and execute this cell for an example solution
load spoilers/logreg.py
"""
Explanation: Exercises
1. Logistic regression
The simple network we created is similar to a logistic regression model. Verify that the accuracy is close to that of sklearn.linear_model.LogisticRegression.
End of explanation
"""
# U... |
AlbanoCastroSousa/RESSPyLab | examples/Old_RESSPyLab_Parameter_Calibration_Orientation_Notebook.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
import RESSPyLab
"""
Explanation: Import modules
End of explanation
"""
testFileNames=['example_1.csv']
listCleanT... |
dagrha/textual-analysis | textblob_lovecraft.ipynb | mit | from textblob import TextBlob
import pandas as pd
import pylab as plt
import collections
import re
%matplotlib inline
"""
Explanation: Sentiment analysis on
H.P. Lovecraft's The Shunned House
For this, we'll use the TextBlob library (http://textblob.readthedocs.org/en/dev/) and pandas (http://pandas.pydata.org/)
End o... |
AllenDowney/ThinkStats2 | homeworks/homework01.ipynb | gpl-3.0 | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='white')
import utils
from utils import decorate
from thinkstats2 import Pmf, Cdf
"""
Explanation: Homework 1
Load and validate GSS data
Allen Downey
MIT License
End of explanation
"""
def... |
stefan-balke/librosa | examples/LibROSA demo.ipynb | isc | from __future__ import print_function
# We'll need numpy for some mathematical operations
import numpy as np
# matplotlib for displaying the output
import matplotlib.pyplot as plt
import matplotlib.style as ms
ms.use('seaborn-muted')
%matplotlib inline
# and IPython.display for audio output
import IPython.display
... |
gmonce/datascience | src/Mentiras.ipynb | gpl-3.0 | # Datos
# Consideramos los votos a setiembre de diferentes años, para ver cómo van cambiando
votaciones_factum_2014={'votoFA':0.42,'votoPN':0.32,'votoPC':0.15,'votoPI':0.03,'votoIndefinidos':0.04,'votoOtros':0.02}
votaciones_factum_julio_2014={'votoFA':0.42,'votoPN':0.30,'votoPC':0.14,'votoPI':0.03,'votoIndefinidos':0.... |
google/applied-machine-learning-intensive | content/02_data/05_exploratory_data_analysis/colab-part1.ipynb | apache-2.0 | # 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 the L... |
MIT-LCP/mimic-code | mimic-iii/notebooks/aline-aws/aline-awsathena.ipynb | mit | # Install OS dependencies. This only needs to be run once for each new notebook instance.
!pip install PyAthena
from pyathena import connect
from pyathena.util import as_pandas
from __future__ import print_function
# Import libraries
import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as ... |
y2ee201/Deep-Learning-Nanodegree | 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... |
OpenWeavers/openanalysis | doc/OpenAnalysis/04 - String Matching.ipynb | gpl-3.0 | x = 'this is some random text used for illustrative purposes'
x
'this' in x
'not' in x
x.index('is')
x.index('not')
"""
Explanation: String Matching Analysis
Consider a string of finite length $m$ Let it be $T$. Finding whether a string $P$ of length $n$ exsists in $T$ is known as String Matching, Following is so... |
UWashington-Astro300/Astro300-W17 | 08_Images_In_Python.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import linalg
plt.style.use('ggplot')
plt.rc('axes', grid=False) # turn off the background grid for images
"""
Explanation: Multidimentional data - Matrices and Images
End of explanation
"""
my_matrix = np.array([[1,2],[1,1]])
pri... |
google/starthinker | colabs/cm360_report_replicate.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: CM360 Report Replicate
Replicate a report across multiple networks and advertisers.
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... |
Neuroglycerin/neukrill-net-work | notebooks/model_modifications/Adding MLP Results.ipynb | mit | import pylearn2.utils
import pylearn2.config
import theano
import neukrill_net.dense_dataset
import neukrill_net.utils
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import holoviews as hl
%load_ext holoviews.ipython
import sklearn.metrics
cd ..
settings = neukrill_net.utils.Settings("settings.... |
tpin3694/tpin3694.github.io | python/data_structure_basics.ipynb | mit | # Create a list of countries, then print the results
allies = ['USA','UK','France','New Zealand',
'Australia','Canada','Poland']; allies
# Print the length of the list
len(allies)
# Add an item to the list, then print the results
allies.append('China'); allies
# Sort list, then print the results
allies.sor... |
Diyago/Machine-Learning-scripts | DEEP LEARNING/Pytorch from scratch/word2vec-embeddings/Negative_Sampling.ipynb | apache-2.0 | # read in the extracted text file
with open('data/text8') as f:
text = f.read()
# print out the first 100 characters
print(text[:100])
"""
Explanation: Skip-gram Word2Vec
In this notebook, I'll lead you through using PyTorch to implement the Word2Vec algorithm using the skip-gram architecture. By implementi... |
rjleveque/binder_experiments | clawpack_tests/pyclaw1.ipynb | bsd-2-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from clawpack import pyclaw
from clawpack import riemann
"""
Explanation: A quick introduction to PyClaw
PyClaw is a solver for hyperbolic PDEs, based on Clawpack. You can read more about PyClaw in this paper (free version here.
In this notebook,... |
WomensCodingCircle/CodingCirclePython | Lesson14_NumpyAndMatplotlib/numpy.ipynb | mit | # by convention, we typically import numpy as the alias np
import numpy as np
"""
Explanation: Adapted from Scientific Python: Part 1 (lessons/thw-numpy/numpy.ipynb)
Introducing NumPy
NumPy is a Python package implementing efficient collections of specific types of data (generally numerical), similar to the standard a... |
UserAd/data_science | Twitter bots/Botnet search.ipynb | mit | seeds = ['volya_belousova', 'egor4rgurev', 'kirillfrolovdw', 'ilyazhuchhj']
auth = tweepy.OAuthHandler(OAUTH_KEY, OAUTH_SECRET)
auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)
graph = Graph(user=NEO4J_USER, password=NEO4J_SECRET)
... |
fweik/espresso | doc/tutorials/visualization/visualization.ipynb | gpl-3.0 | from matplotlib import pyplot
import espressomd
import numpy
espressomd.assert_features("LENNARD_JONES")
# system parameters (10000 particles)
box_l = 10.7437
density = 0.7
# interaction parameters (repulsive Lennard-Jones)
lj_eps = 1.0
lj_sig = 1.0
lj_cut = 1.12246
lj_cap = 20
# integration parameters
system = esp... |
mahieke/maschinelles_lernen | a3/Aufgabe_3.1.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
from numpy import linalg as LA
import scipy as sp
import urllib2
from urllib2 import urlopen, URLError, HTTPError
import zipfile
import tarfile
import sys
import os
from skimage import data, io, filter
from PIL import Image
"""
Explanat... |
rringham/deep-learning-notebooks | udacity/1_notmnist.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
%matplotlib inline
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimag... |
ikegami-yukino/madoka-python | Benchmark.ipynb | bsd-3-clause | import collections
import subprocess
import itertools
import os
import time
import madoka
import numpy as np
import redis
ALPHANUM = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'
NUM_ALPHANUM_COMBINATION = 238328
zipf_array = np.random.zipf(1.5, NUM_ALPHANUM_COMBINATION)
def python_memory_usage()... |
stevetjoa/stanford-mir | why_mir.ipynb | mit | ipd.display( ipd.YouTubeVideo("grL4JMs0hDc", start=75) )
"""
Explanation: ← Back to Index
What is Music Information Retrieval?
While you listen to these excerpts, name as many of its musical characteristics as you can. Can you name the genre? tempo? instruments? mood? time signature? key signature? chord progress... |
sympy/scipy-2017-codegen-tutorial | notebooks/cython-examples.ipynb | bsd-3-clause | import numpy as np
x = np.random.randn(10000)
"""
Explanation: Writing Cython
In this notebook, we'll take a look at how to implement a simple function using Cython. The operation we'll implement is the first-order diff, which takes in an array of length $n$:
$$\mathbf{x} = \begin{bmatrix} x_1 \ x_2 \ \vdots \ x_n\end... |
CNS-OIST/STEPS_Example | user_manual/source/API_2/Interface_Tutorial_4_Complexes.ipynb | gpl-2.0 | import steps.interface
from steps.model import *
mdl = Model()
with mdl:
A0, A1, A2 = SubUnitState.Create()
ASU = SubUnit.Create([A0, A1, A2])
CA = Complex.Create([ASU, ASU, ASU, ASU], statesAsSpecies=True)
"""
Explanation: Multi-state complexes
<div class="admonition note">
**Topics**: Comple... |
Kaggle/learntools | notebooks/data_cleaning/raw/tut1.ipynb | apache-2.0 | # modules we'll use
import pandas as pd
import numpy as np
# read in all our data
nfl_data = pd.read_csv("../input/nflplaybyplay2009to2016/NFL Play by Play 2009-2017 (v4).csv")
# set seed for reproducibility
np.random.seed(0)
"""
Explanation: Welcome to the Data Cleaning course on Kaggle Learn!
Data cleaning is a k... |
folivetti/PIPYTHON | Aula08Recursividade.ipynb | mit | def imprime(i):
print (i)
def imprimeLista(l):
for e in l:
imprime (e)
imprimeLista([1, 3, 5, 7])
"""
Explanation: Introdução à Programação em Python
Recursão
Em um programa é muito comum chamarmos uma função dentro de uma outra função.
End of explanation
"""
def fatorial(n):
fat = 1
while... |
moble/spherical_functions | Notes/conventions.ipynb | mit | import csv
import sympy
from sympy import sin, cos
from sympy.parsing.mathematica import mathematica
from sympy.physics.quantum.spin import Rotation
from sympy.abc import _clash
import numpy as np
import quaternion
import spherical_functions as sf
"""
Explanation: NOTE: I've run this notebook with the correction to s... |
conferency/find-my-reviewers | tutorials/Preprocessing_and_Training_LDA.ipynb | mit | # Loading metadata from trainning database
con = sqlite3.connect("F:/FMR/data.sqlite")
db_documents = pd.read_sql_query("SELECT * from documents", con)
db_authors = pd.read_sql_query("SELECT * from authors", con)
data = db_documents # just a handy alias
data.head()
"""
Explanation: Preparing Data
In this step, we are ... |
phoebe-project/phoebe2-docs | development/tutorials/dpdt.ipynb | gpl-3.0 | #!pip install "phoebe>=2.4,<2.5"
"""
Explanation: Period Change (dpdt)
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 session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy as np
impo... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: INPE
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection... |
GoogleCloudPlatform/ai-platform-samples | notebooks/templates/ai_platform_notebooks_template_hybrid.ipynb | apache-2.0 | %pip install -U missing_or_updating_package --user
"""
Explanation: <table align="left">
<td>
<a href="https://colab.research.google.com/github/GoogleCloudPlatform/ai-platform-samples/blob/main/notebooks/templates/ai_platform_notebooks_template_hybrid.ipynb"">
<img src="https://cloud.google.com/ml-engine/i... |
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