repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
smorton2/think-stats | code/chap04soln.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import nsfg
import first
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/... |
bashtage/statsmodels | examples/notebooks/discrete_choice_overview.ipynb | bsd-3-clause | import numpy as np
import statsmodels.api as sm
"""
Explanation: Discrete Choice Models Overview
End of explanation
"""
spector_data = sm.datasets.spector.load()
spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)
"""
Explanation: Data
Load data from Spector and Mazzeo (1980). Examples follow Gree... |
AkshanshChahal/BTP | Satellite/Data Cleaning.ipynb | mit | cols = list(rice.columns.values)
"""
Explanation: 334 = 10 + 216 + 108
End of explanation
"""
l = rice.shape[0]
b = rice.shape[1]
for row in range(0,l):
vals = np.zeros(18)
bx = False
for col in range(10,b-108,18):
if pd.isnull(rice.iloc[row,col]):
s = cols[col]
#print s
... |
quoniammm/mine-tensorflow-examples | fastAI/deeplearning1/nbs/lesson3.ipynb | mit | from theano.sandbox import cuda
%matplotlib inline
import utils; reload(utils)
from utils import *
from __future__ import division, print_function
#path = "data/dogscats/sample/"
path = "data/dogscats/"
model_path = path + 'models/'
if not os.path.exists(model_path): os.mkdir(model_path)
batch_size=64
"""
Explanati... |
swails/mdtraj | examples/hbonds.ipynb | lgpl-2.1 | t = md.load_pdb('http://www.rcsb.org/pdb/files/2EQQ.pdb')
print(t)
"""
Explanation: Load up some example data. This is a little 28 residue peptide
End of explanation
"""
hbonds = md.baker_hubbard(t, periodic=False)
label = lambda hbond : '%s -- %s' % (t.topology.atom(hbond[0]), t.topology.atom(hbond[2]))
for hbond i... |
Danghor/Algorithms | Python/Chapter-09/Dijkstra.ipynb | gpl-2.0 | %run Set.ipynb
"""
Explanation: Dijkstra's Shortest Path Algorithm
The notebook Set.ipynb implements <em style="color:blue">sets</em> as
<a href="https://en.wikipedia.org/wiki/AVL_tree">AVL trees</a>.
The API provided by Set offers the following API:
- Set() creates an empty set.
- S.isEmpty() checks whether the set ... |
UWashington-Astro300/Astro300-A16 | 03_Units_In_Python.ipynb | mit | import numpy as np
from astropy.table import QTable
from astropy import units as u
from astropy import constants as const
from astropy.units import imperial
imperial.enable()
"""
Explanation: Units in Python
The Astropy package includes a powerful framework that allows users to attach units to scalars and arrays, a... |
landlab/landlab | notebooks/tutorials/network_sediment_transporter/network_sediment_transporter_NHDPlus_HR_network.ipynb | mit | import warnings
warnings.filterwarnings(
"ignore", category=UserWarning, module=".*network_sediment_transporter"
)
import functools
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from tqdm import tqdm
from landlab.components import FlowDirectorSteepest, NetworkSedimentTransporter
from lan... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/text_models/solutions/keras_for_text_classification.ipynb | apache-2.0 | import os
import pandas as pd
from google.cloud import bigquery
%load_ext google.cloud.bigquery
"""
Explanation: Keras for Text Classification
Learning Objectives
1. Learn how to create a text classification datasets using BigQuery
1. Learn how to tokenize and integerize a corpus of text for training in Keras
1. Lea... |
BeatHubmann/17F-U-DLND | embeddings/Skip-Gram_word2vec.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
oasis-open/cti-python-stix2 | docs/guide/custom.ipynb | bsd-3-clause | from stix2 import Identity
Identity(name="John Smith",
identity_class="individual",
x_foo="bar")
"""
Explanation: Custom STIX Content
Custom Properties
Attempting to create a STIX object with properties not defined by the specification will result in an error. Try creating an Identity object with a ... |
rsterbentz/phys202-2015-work | assignments/assignment06/InteractEx05.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.html import widgets
from IPython.display import Image, HTML, SVG, display
"""
Explanation: Interact Exercise 5
Imports
Put the standard imports for Matplotlib, Numpy and the ... |
t--wagner/python_in_the_lab | .ipynb_checkpoints/00_overview-checkpoint.ipynb | gpl-3.0 | import IPython
IPython.__version__
"""
Explanation: <center> <h1>Python in the Lab</h1> </center>
Topics
Python
Control Flow
Data Structures
Modules and Packages
Object-oriented programming
Iterators, Generators
Decorators
Magic Methods
Context Manager
All the other cool stuff
Science
Plotting
Numerical Calcul... |
djgroen/student-resources | programming/python/python-tutorials/abm-tut.ipynb | bsd-3-clause | import random
"""
Explanation: What is agent-based modelling?
Types of agents
When thinking about refugee movements, there are a few basic elements:
- The refugees themselves.
- The locations where the refugees reside
- And possibly the paths (or routes) that interconnect the locations
In its simplest form, this agent... |
mauroalberti/geocouche | pygsf/docs/notebooks/Rasters - geotransform.ipynb | gpl-2.0 | from pygsf.spatial.rasters.geotransform import *
gt1 = GeoTransform(1500, 3000, 10, 10)
gt1
"""
Explanation: Geotransforms
May-June, 2018, Mauro Alberti, alberti.m65@gmail.com
1. Examples
End of explanation
"""
ijPixToxyGeogr(gt1, 0, 0)
xyGeogrToijPix(gt1, 1500, 3000)
ijPixToxyGeogr(gt1, 1, 1)
xyGeogrToijPix(gt... |
rsignell-usgs/notebook | ROMS/.ipynb_checkpoints/sandy_sgrid-checkpoint.ipynb | mit | from netCDF4 import Dataset
url = ('http://geoport.whoi.edu/thredds/dodsC/clay/usgs/users/'
'jcwarner/Projects/Sandy/triple_nest/00_dir_NYB05.ncml')
nc = Dataset(url)
"""
Explanation: pysgrid only works with raw netCDF4 (for now!)
End of explanation
"""
import pysgrid
# The object creation is a lit... |
ES-DOC/esdoc-jupyterhub | notebooks/csiro-bom/cmip6/models/sandbox-1/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-1', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: SANDBOX-1
Topic: Aerosol
Sub-Topics: Transport, Emissi... |
phoebe-project/phoebe2-docs | 2.2/examples/binary_spots.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Binary with Spots
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
"""
%matplotlib inline
im... |
ajul/zerosum | python/examples/pokemon.ipynb | bsd-3-clause | import _initpath
import numpy
import dataset.pokemon
import zerosum.balance
import zerosum.nash
import matplotlib
import matplotlib.pyplot as plt
type_chart = dataset.pokemon.pokemon_6
# Vector of color codes for the types.
colors = [dataset.pokemon.pokemon_type_colors[name] for name in type_chart.row_names]
"""
Exp... |
kuo77122/deep-learning-nd | Lesson15-TFLearn/Sentiment Analysis with TFLearn - Solution.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
TuKo/brainiak | examples/utils/fmrisim_multivariate_example.ipynb | apache-2.0 | %matplotlib notebook
from pathlib import Path
from brainiak.utils import fmrisim
import nibabel
import numpy as np
import matplotlib.pyplot as plt
import scipy.spatial.distance as sp_distance
import sklearn.manifold as manifold
import scipy.stats as stats
import sklearn.model_selection
import sklearn.svm
"""
Explanat... |
alexandrnikitin/workshops | automated-feature-engineering-selection/notebooks/1-featuretools-intro.ipynb | mit | Image(url= "../img/max-order-size.svg", width=600, height=600)
"""
Explanation: Featuretools
a python library/ framework for automated feature engineering
based on "Deep Feature Synthesis" paper/ research
by Featurelabs https://www.featurelabs.com/
Website: https://www.featuretools.com/
Documentation: https://docs.fe... |
uber/ludwig | examples/titanic/model_training_results.ipynb | apache-2.0 | from ludwig.utils.data_utils import load_json
from ludwig.visualize import learning_curves
import pandas as pd
import numpy as np
import os.path
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Custom Analysis of Training Results
Notebook demonstrates two methods for plotting training results. F... |
tensorflow/docs-l10n | site/ja/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa.ipynb | apache-2.0 | # Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
cuttlefishh/emp | methods/figure-data/fig-4/Fig4_data_files.ipynb | bsd-3-clause | # read in exported table for genus
fig4a_genus = pd.read_csv('../../../data/07-entropy-and-covariation/genus-level-distribution.csv', header=0)
# read in exported table for otu
fig4a_otu = pd.read_csv('../../../data/07-entropy-and-covariation/otu-level-distribution-400.csv', header=0)
"""
Explanation: Figure 4 csv ... |
jpn--/larch | larch/doc/example/201_exville_mode_choice.ipynb | gpl-3.0 | import larch, numpy, pandas, os
from larch import P, X
larch.__version__
"""
Explanation: 201: Exampville Mode Choice
Welcome to Exampville, the best simulated town in this here part of the internet!
Exampville is a demonstration provided with Larch that walks through some of the
data and tools that a transportation... |
therealAJ/python-sandbox | data-science/learning/ud2/Part 2 Exercise Solutions/Linear Regression/Linear Regression - Project Exercise .ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Linear Regression - Project Exercise
Congratulations! You just got some contract work with an Ecommerce com... |
achave11/bioapi-examples | python_notebooks/pileup.ipynb | apache-2.0 | #Widget()
"""
Explanation: Query for pile-up allignments at region "X"
We can query the API services to obtain reads from a given readgroupset such that we are able to make a pileup for any specified region
NOTE: Under the "Kernel" tab above, do "Restart & Run All" then uncomment the first cell and run it individually... |
dinrker/PredictiveModeling | Session 4 - Features_I_Transformations_DimensionalityReduction.ipynb | mit | from IPython.display import Image
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
%matplotlib inline
"""
Explanation: Feature Engineering
|Session | Session |
|-----------|---------|
|Feature Engineering I | Feature Transformation and Dimension Reduction (PCA)|
|Feature Engineeri... |
simpleblob/ml_algorithms_stepbystep | algo_example_K_nearest_neighbour.ipynb | mit | from sklearn.datasets import make_blobs
df = pd.DataFrame(columns=['X0','X1','Y'])
X, Y = make_blobs(n_samples=1000, n_features=2, centers=3, cluster_std=1.5)
train_test_split = 0.7
train_size = int(X.shape[0]*train_test_split)
test_size = X.shape[0] - train_size
X_train,Y_train,X_test,Y_test = X[0:train_size],Y[0:tr... |
jaehyuk/kaggle_submissions | colab_for_Keras.ipynb | mit | !pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
# 1. Authenticate and create the PyDrive client.
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_... |
buckleylab/Buckley_Lab_SIP_project_protocols | sequence_analysis_walkthrough/QIIME2_Merging_and_Processing.ipynb | mit | import os, re
# Provide the directory where files are located
directory = '/home/roli/FORESTs_BHAVYA/Combined_Libraries/ITS/'
#directory = '/home/roli/FORESTs_BHAVYA/Combined_Libraries/16S/'
# Provide a list of all the FeatureTables you will merge
# Produced by QIIME2 in STEP 7 (i.e. DADA2 Denoising/Merging/FeatureT... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/08_lateral_movement/WIN-190511223310.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: PowerShell Remote Session
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/05/11 |
| modification date | 2020/09/20 |
| playbook related | ['WI... |
kubeflow/kfp-tekton-backend | samples/tutorials/Data passing in python components.ipynb | apache-2.0 | # Put your KFP cluster endpoint URL here if working from GCP notebooks (or local notebooks). ('https://xxxxx.notebooks.googleusercontent.com/')
kfp_endpoint='https://XXXXX.{pipelines|notebooks}.googleusercontent.com/'
# Install Kubeflow Pipelines SDK. Add the --user argument if you get permission errors.
!PIP_DISABLE_... |
synthicity/activitysim | activitysim/examples/example_estimation/notebooks/11_joint_tour_composition.ipynb | agpl-3.0 | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimating Joint Tour Composition
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estimation mode to read household t... |
mayank-johri/LearnSeleniumUsingPython | Section 1 - Core Python/Chapter 09 - Classes & OOPS/OOPs Fundamentals - ABC.ipynb | gpl-3.0 | from abc import ABCMeta, abstractmethod
class Mammal(metaclass=ABCMeta):
## version 2.x ## __metaclass__=ABCMeta
@abstractmethod
def eyes(self, val):
pass
# @abstractmethod
# def hand(self):
# pass
def hair(self):
print("hair")
def neocortex(self):
... |
tensorflow/docs-l10n | site/ja/guide/keras/functional.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... |
ageron/tensorflow | tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb | apache-2.0 | from __future__ import absolute_import, division, print_function
# Import TensorFlow >= 1.10 and enable eager execution
import tensorflow as tf
tf.enable_eager_execution()
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import unicodedata
import re
import numpy as np
import os
i... |
georgetown-analytics/classroom-occupancy | models/GaussianNB_model_KM.ipynb | mit | %matplotlib inline
import os
import json
import time
import pickle
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yellowbrick as yb
import seaborn as sns
sns.set_palette('RdBu', 10)
"""
Explanation: GaussianNB Model
Dataset Information
No. of Features: 12
No. of Instanc... |
MTG/essentia | src/examples/python/tutorial_tonal_hpcpkeyscale.ipynb | agpl-3.0 | import essentia.streaming as ess
import essentia
audio_file = '../../../test/audio/recorded/dubstep.flac'
# Initialize algorithms we will use.
loader = ess.MonoLoader(filename=audio_file)
framecutter = ess.FrameCutter(frameSize=4096, hopSize=2048, silentFrames='noise')
windowing = ess.Windowing(type='blackmanharris62... |
rescu/brainstorm | Random_walk_1D.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: <h1>Random motion in 1D</h1>
End of explanation
"""
p = 0.5
q = 1. - p
"""
Explanation: <p>Many processes in physics and chemistry happen randomly or stochastically, whic. This is in contrast to deterministic problems where we ca... |
timothydmorton/usrp-sciprog | day2/numpy-intro.ipynb | mit | # Let's first import the package
import numpy as np
#Tadaaaa now we have all the power of the mighty numpy at our disposal.
#Let's use it responsibly
"""
Explanation: Introduction to numpy
Numpy is a package that contains types and functions for mathematical calculations on arrays. The numpy library is vast and encap... |
google/jax-md | notebooks/neural_networks.ipynb | apache-2.0 | #@title Imports & Utils
!pip install -q git+https://www.github.com/deepmind/haiku
!pip install -q git+https://www.github.com/deepmind/optax
!pip install -q --upgrade git+https://www.github.com/google/jax-md
# Imports
import os
import numpy as onp
import pickle
import jax
from jax import lax
from jax import jit, vma... |
keras-team/keras-io | examples/timeseries/ipynb/timeseries_traffic_forecasting.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import os
import typing
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
"""
Explanation: Traffic forecasting using graph neural networks and LSTM
Author: Arash Khodadadi<br>
Date created: 2021/12/28<br>
Las... |
AllenDowney/ModSimPy | notebooks/chap23.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'
# import functions from the modsim.py module
from modsim import *
"""
Explanation: Modeling and Simulati... |
AntArch/Presentations_Github | 20160202_Nottingham_GIServices_Lecture3_Beck_InteroperabilitySemanticsAndOpenData/.ipynb_checkpoints/20151008_OpenGeo_Reuse_under_licence-checkpoint.ipynb | cc0-1.0 | from IPython.display import YouTubeVideo
YouTubeVideo('F4rFuIb1Ie4')
## PDF output using pandoc
import os
### Export this notebook as markdown
commandLineSyntax = 'ipython nbconvert --to markdown 20151008_OpenGeo_Reuse_under_licence.ipynb'
print (commandLineSyntax)
os.system(commandLineSyntax)
### Export this not... |
jdhp-docs/python-notebooks | python_scipy_optimize_global_optimization_en.ipynb | mit | # Init matplotlib
%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (8, 8)
# Setup PyAI
import sys
sys.path.insert(0, '/Users/jdecock/git/pub/jdhp/pyai')
import numpy as np
import time
import warnings
from scipy import optimize
# Plot functions
from pyai.optimize.utils import plot_conto... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/sandbox-3/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-3', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-3
Topic: Landice
Sub-Topics: Glaciers, Ice.
P... |
NicolasHemidy/udacity-data-nanodegree | P0/Bay_Area_Bike_Share_Analysis.ipynb | apache-2.0 | # import all necessary packages and functions.
import csv
from datetime import datetime
import numpy as np
import pandas as pd
from babs_datacheck import question_3
from babs_visualizations import usage_stats, usage_plot
from IPython.display import display
%matplotlib inline
# file locations
file_in = '201402_trip_da... |
jeffakolb/Data-Science-45min-Intros | comparing-collections/CollectionComparison_PartOne.ipynb | unlicense | import random
import collections
import operator
import time
import numpy as np
import scipy
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
import twitter
%matplotlib inline
import count_min
# some matplotlib color-mapping
cmap = plt.get_cmap('viridis')
c_space = np.linspace(0,99... |
Kaggle/learntools | notebooks/embeddings/raw/4-tsne.ipynb | apache-2.0 | #$HIDE$
%matplotlib inline
import random
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow import keras
#_RM_
input_dir = '../input/movielens_preprocessed'
#_UNCOMMENT_
#input_dir = '../input/movielens-preprocessing'
#_RM_
model_dir = '.'
#_U... |
jaidevd/inmantec_fdp | notebooks/day3/04_clustering.ipynb | mit | import numpy as np
from sklearn.datasets import load_iris, load_digits
from sklearn.metrics import f1_score
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
iris = load_iris()
X = iris.data
y = iris.target
print(X.shape... |
google/starthinker | colabs/dcm_to_bigquery.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: CM360 Report To BigQuery
Move existing CM report 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 ob... |
Ironlors/SmartIntersection-Ger | Journal/.ipynb_checkpoints/Introduction to Python for Data Science-checkpoint.ipynb | apache-2.0 | list = [1,2,3,4,5]
list
"""
Explanation: Introduction to Python
For Data Science
Autor: Kay Kleinvogel
Dies ist mein Lerndokument für Python.
Die Hauptsächliche Informationsquelle ist das EdX Programm: (https://courses.edx.org/courses/course-v1:Microsoft+DAT208x+3T2017/course/)
Lists
Eine Liste ist eine Sammlung von v... |
jdhp-docs/python-notebooks | python_scipy_io_wave_en.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib
matplotlib.rcParams['figure.figsize'] = (12, 9)
# Import Jupyter's sound player widget
# See: https://ipython.org/ipython-doc/dev/api/generated/IPython.display.html#IPython.display.Audio
from IPython.display import Audio
"""
Explanation: Read and write audio w... |
ES-DOC/esdoc-jupyterhub | notebooks/thu/cmip6/models/ciesm/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'thu', 'ciesm', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: THU
Source ID: CIESM
Topic: Atmoschem
Sub-Topics: Transport, Emissions Concentrat... |
KECB/learn | machine_learning/Machine Learning Notebook.ipynb | mit | from sklearn import tree
features = [[140, 1], [130, 1], [150, 0], [170, 0]]
labels = [0, 0, 1, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(features, labels)
print(clf.predict([[120, 0]]))
"""
Explanation: Machine Learning Recipes with Jsh Gordon Note
Video list
this is a note for watching Machine Learning... |
nilmtk/nilmtk | docs/manual/user_guide/elecmeter_and_metergroup.ipynb | apache-2.0 | %matplotlib inline
from matplotlib import rcParams
import matplotlib.pyplot as plt
import pandas as pd
import nilmtk
from nilmtk import DataSet, MeterGroup
plt.style.use('ggplot')
rcParams['figure.figsize'] = (13, 10)
redd = DataSet('/data/redd.h5')
elec = redd.buildings[1].elec
elec
"""
Explanation: MeterGroup, El... |
napsternxg/ipython-notebooks | Monte Carlo Integration.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from numba import jit # Use it for speed
from scipy import stats
"""
Explanation: Introduction to Monte Carlo Integration
Inspired from the following posts:
http://nbviewer.jupyter.org/github/cs109/content/blob/master/labs/lab7/GibbsSampler.ipyn... |
jhillairet/scikit-rf | doc/source/examples/networktheory/Renormalizing S-parameters.ipynb | bsd-3-clause | import skrf as rf
%matplotlib inline
from pylab import *
rf.stylely()
# this is just for plotting junk
kw = dict(draw_labels=True, marker = 'o', markersize = 10)
"""
Explanation: Renormalizing S-parameters
This example demonstrates how to use skrf to renormalize a Network's s-parameters to new port impedances. Alt... |
kubeflow/code-intelligence | Issue_Triage/notebooks/metrics.ipynb | mit | import altair as alt
import collections
import importlib
import logging
import sys
import os
import datetime
from dateutil import parser as dateutil_parser
import glob
import json
import numpy as np
import pandas as pd
from pandas.io import gbq
# A bit of a hack to set the path correctly
sys.path = [os.path.abspath(os... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/36_Microscopy_Colocalization_Distance_Analysis.ipynb | apache-2.0 | import SimpleITK as sitk
import numpy as np
import pandas as pd
%matplotlib notebook
import gui
%run update_path_to_download_script
from downloaddata import fetch_data as fdata
from IPython.core.display import display, HTML
# Always write output to a separate directory, we don't want to pollute the source director... |
ThunderShiviah/code_guild | interactive-coding-challenges/stacks_queues/stack/stack_solution.ipynb | mit | %%writefile stack.py
class Node(object):
def __init__(self, data):
self.data = data
self.next = None
class Stack(object):
def __init__(self, top=None):
self.top = top
def push(self, data):
node = Node(data)
node.next = self.top
self.top = node
def po... |
nproctor/phys202-2015-work | assignments/assignment03/NumpyEx02.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Numpy Exercise 2
Imports
End of explanation
"""
def np_fact(n):
if n == 0:
return 1
else:
#This puts the numbers 1 to n in steps of one in an array
numbers = np.arange(1.0... |
wanderer2/pymc3 | docs/source/notebooks/gaussian-mixture-model-advi.ipynb | apache-2.0 | %matplotlib inline
import theano
theano.config.floatX = 'float64'
import pymc3 as pm
from pymc3 import Normal, Metropolis, sample, MvNormal, Dirichlet, \
DensityDist, find_MAP, NUTS, Slice
import theano.tensor as tt
from theano.tensor.nlinalg import det
import numpy as np
import matplotlib.pyplot as plt
import se... |
ericmjl/be-stats-iap2016 | Inferential Statistics.ipynb | mit | null_flips = binom.rvs(n=20, p=0.5, size=10000)
plt.hist(null_flips)
plt.axvline(16)
alpha = 5 / 100
null_flips = binom.rvs(n=20, p=0.5, size=10000)
plt.hist(null_flips)
plt.axvline(16)
sum(null_flips >=16) / 10000
"""
Explanation: Administrative Stuff
Connect to the Jupyter server that I have created on Amazon EC2... |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/31_dcgan_svhn/notebooks/DCGAN.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
KshitijT/fundamentals_of_interferometry | 1_Radio_Science/1_10_limits_of_single_dishes.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
1. Radio Science using Interferometric Arrays
Previous: 1.9 A brief introduction to interferometry
Next: 1.11 Modern Interferometric... |
llclave/Springboard-Mini-Projects | data_wrangling_json/.ipynb_checkpoints/sliderule_dsi_json_exercise-checkpoint.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-docs.github.io/pandas-docs-travis/io.html#json
data source: http://jsonstudio.com/re... |
matthewljones/computingincontext | Simple document term matrix example.ipynb | gpl-2.0 | from sklearn.metrics.pairwise import cosine_similarity
similarity=cosine_similarity(document_term_matrix)
pd.DataFrame(similarity)
"""
Explanation: Similarity among documents
End of explanation
"""
similarity=cosine_similarity(document_term_matrix.T)
pd.DataFrame(similarity, index=vocab, columns=vocab)
"""
Expla... |
quantopian/research_public | notebooks/data/eventvestor.share_repurchases/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.eventvestor import share_repurchases
# or if you want to import the free dataset, use:
# from quantopian.interactive.data.eventvestor import share_repurchases_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/nicam16-9d-l78/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'nicam16-9d-l78', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MIROC
Source ID: NICAM16-9D-L78
Sub-Topics: Radiative Forcings.
Propert... |
setiQuest/ML4SETI | tutorials/Step_2_reading_SETI_code_challenge_data.ipynb | apache-2.0 | #The ibmseti package contains some useful tools to faciliate reading the data.
#The `ibmseti` package version 1.0.5 works on Python 2.7.
# !pip install --user ibmseti
#A development version runs on Python 3.5.
# !pip install --user ibmseti==2.0.0.dev5
# If running on DSX, YOU WILL NEED TO RESTART YOUR SPARK K... |
ogoann/StatisticalMethods | examples/SDSScatalog/CorrFunc.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
import numpy as np
import SDSS
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import copy
# We want to select galaxies, and then are only interested in their positions on the sky.
data = pd.read_csv("downloads/SDSSobjects.csv",usecols=['ra','dec','u','g',\
... |
mbeyeler/opencv-machine-learning | notebooks/03.04-Applying-Lasso-and-Ridge-Regression.ipynb | mit | import numpy as np
import cv2
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
from sklearn import linear_model
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
plt.rcParams.update({'font.size': 16})
"""
Explanation: <!--BOOK_INFORMATION-->
<a hre... |
hanezu/cs231n-assignment | assignment2/FullyConnectedNets.ipynb | mit | # As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver import Solver
%matplotlib inline
... |
enbanuel/phys202-2015-work | assignments/assignment06/InteractEx05.ipynb | mit | # YOUR CODE HERE
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.html import widgets
from IPython.display import SVG, display
"""
Explanation: Interact Exercise 5
Imports
Put the standard imports for Matplotlib, Numpy and ... |
MarioPerezEsteso/Python-Machine-Learning | 20newsgroup/20newsgroup.ipynb | apache-2.0 | %pylab inline
from sklearn import datasets
"""
Explanation: 20 NEWS GROUPS
Antes de nada, hay que importar los paquetes necesarios.
End of explanation
"""
def loadDataset(directory):
dataset = datasets.load_files(directory)
print "Loaded %d documents" % len(dataset.data)
print "Loaded %d categories"... |
sgkang/DamGeophysics | notebook/Kalman Filters_LIM-Waterlevel-EKF.ipynb | mit | from SimPEG import *
%pylab inline
# Import a Kalman filter and other useful libraries
from pykalman import KalmanFilter
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import poly1d
"""
Explanation: Kalman Filters
By Evgenia "Jenny" Nitishinskaya, Dr. Aidan O'Mahony, and Delaney Gran... |
ClementPhil/deep-learning | intro-to-tflearn/TFLearn_Sentiment_Analysis_Solution.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
piraces/Trabajo_Python_Odoo | Trabajo_Python_Odoo.ipynb | mit | client = erppeek.Client(server=SERVER)
for database in client.db.list():
print('Base de datos: %r' % (database,))
"""
Explanation: La documentación necesaria para poder superar este ejercicio se encuentra en la documentación de ERPpeek
Tarea 1 - Conexión
Demuestra que sabes conectarte a una instancia de Odoo y li... |
google/TensorNetwork | colabs/Tensor_Networks_in_Neural_Networks.ipynb | apache-2.0 | !pip install tensornetwork
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
# Import tensornetwork
import tensornetwork as tn
# Set the backend to tesorflow
# (default is numpy)
tn.set_default_backend("tensorflow")
"""
Explanation: TensorNetworks in Neural Networks.
Here, we have a small toy... |
pymir3/pymir3 | doc/spectrogram_demo.ipynb | mit | import mir3.modules.tool.wav2spectrogram as spec
converter = spec.Wav2Spectrogram()
s = converter.convert(open("examples/157447__nengisuls__solo-loops-2.wav"), window_length=1024,
dft_length=1024, window_step=512, spectrum_type='magnitude', save_metadata=True)
#s = converter.convert(open("example... |
Islast/BrainNetworksInPython | tutorials/introductory_tutorial.ipynb | mit | import matplotlib.pylab as plt
%matplotlib inline
import networkx as nx
import numpy as np
import seaborn as sns
sns.set(context="notebook", font_scale=1.5, style="white")
import scona as scn
import scona.datasets as datasets
from scona.scripts.visualisation_commands import view_corr_mat
"""
Explanati... |
eshlykov/mipt-day-after-day | optimizaion/eshlykov-met-opt-lab-1.ipynb | unlicense | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
EPS = 1e-6
MAXITER = 100000
"""
Explanation: Практикум 1 по курсу "Методы оптимизации"
Автор: Евгений Шлыков
Группа: 596
Семинарист: Алексей Глибичук
End of explanation
"""
def find_entering_leaving(st, phase, method='blend'):
"""
Находит... |
dipanjank/ml | data_analysis/balance_scale_classification.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
%pylab inline
pylab.style.use('ggplot')
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/balance-scale/balance-scale.data'
balance_df = pd.read_csv(url, header=None)
balance_df.columns = ['class_name', 'left_weight', 'left_distance', 'right_weight', 'right_dist... |
rvernagus/data-science-notebooks | Data Science From Scratch/10 - Working With Data.ipynb | mit | def bucketize(point, bucket_size):
"""floor the point to the next lower multiple of bucket size"""
return bucket_size * math.floor(point / bucket_size)
def make_histogram(points, bucket_size):
return Counter(bucketize(point, bucket_size) for point in points)
def plot_histogram(points, bucket_size, title='... |
nbokulich/short-read-tax-assignment | ipynb/cross-validated/taxonomy-assignment.ipynb | bsd-3-clause | from os import system
from os.path import join, expandvars
from joblib import Parallel, delayed
from glob import glob
from tax_credit.framework_functions import (recall_novel_taxa_dirs,
parameter_sweep,
move_results_to_repository)
... |
ericmjl/graph-fingerprint | notebooks/20160526-inspect_weights_and_biases.ipynb | mit | import pickle as pkl
from pprint import pprint
def open_wb(path):
with open(path, 'rb') as f:
wb = pkl.load(f)
return wb
wb = open_wb('../experiments/wbs/fp_linear-cf.score_sum-5000_iters-10_wb.pkl')
pprint(wb)
"""
Explanation: 26 May 2016
I trained a simple fp_linear network (FingerprintLayer ... |
NeuPhysics/aNN | ipynb/vacuum-Copy2.ipynb | mit | # This line configures matplotlib to show figures embedded in the notebook,
# instead of opening a new window for each figure. More about that later.
# If you are using an old version of IPython, try using '%pylab inline' instead.
%matplotlib inline
%load_ext snakeviz
import numpy as np
from scipy.optimize import mi... |
tbarrongh/cosc-learning-labs | src/notebook/01_device_connect.ipynb | apache-2.0 | %run ../learning_lab/01_inventory_dismount_atomic.py
from basics.odl_http import http_history_clear
http_history_clear()
"""
Explanation: COSC Learning Lab
01_device_connect.py
Related Scripts:
* 03_management_interface.py
Table of Contents
Table of Contents
Preamble
Documentation
Implementation
Execution
HTTP
Pream... |
xxPeterxx/RelaunchedFunds | Version 1.0.ipynb | gpl-2.0 | import pandas as pd
from datetime import timedelta
# ****************** Program Settings ******************
Folder = "" # Location of program scripts
Data = "temp/" # Location to which temporary files are generated
DataSource = "data/" # Location of the original data files (ASCII)
Gap_Days = 60 # To b... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/25. Introduction to Testing.ipynb | mit | def divide(a, b):
""""a, b are ints or floats. Returns a/b"""
return a / b
"""
Explanation: Introduction to Testing
Testing is an easy thing to understand but there is also an art to it as well; writing good tests often requires you to try to figure out what input(s) are most likely to break your program.
In ... |
HrantDavtyan/Data_Scraping | Week 2/Intro_2.ipynb | apache-2.0 | print("Imagine all the people living life in peace... John Lennon")
"""
Explanation: Introductino to Python (part II)
This notebook provides introduction to python and includes material covered during the lecture.
Printing
End of explanation
"""
print("Imagine all the people \nliving life in peace... \nJohn Lennon")... |
tlake/bikeshare | bikeshare.ipynb | mit | from pandas import Series, DataFrame
import pandas as pd
import numpy as np
weather = pd.read_table('data/daily_weather.tsv')
weather
type(weather)
weather.groupby('season_desc')['temp'].mean()
weather.loc[weather['season_code'] == 1, 'season_desc'] = 'winter'
weather
weather.loc[weather['season_code'] == 2, '... |
hashiprobr/redes-sociais | encontro06/simulacao.ipynb | gpl-3.0 | import sys
sys.path.append('..')
import socnet as sn
"""
Explanation: Encontro 06: Simulação de Negociações
Importando a biblioteca:
End of explanation
"""
sn.graph_width = 360
sn.graph_height = 360
sn.node_size = 25
def load_graph(path):
g = sn.load_graph(path, has_pos=True)
for n, m in g.edges():
... |
ImAlexisSaez/deep-learning-specialization-coursera | course_1/week_3/assignment_1/planar_data_classification_with_one_hidden_layer_v1.ipynb | mit | # Package imports
import numpy as np
import matplotlib.pyplot as plt
from testCases import *
import sklearn
import sklearn.datasets
import sklearn.linear_model
from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets
%matplotlib inline
np.random.seed(1) # set a seed so that t... |
meduz/ipython_magics | tikzmagic_test.ipynb | mit | %tikz \draw (0,0) rectangle (1,1);
%%tikz --scale 2 --size 300,300 -f jpg
\draw (0,0) rectangle (1,1);
\filldraw (0.5,0.5) circle (.1);
%%tikz --scale 2 --size 300,300 -f svg
\draw (0,0) rectangle (1,1);
\filldraw (0.5,0.5) circle (.1);
"""
Explanation: a MWE
End of explanation
"""
%%tikz -s 400,400 -sc 1.2 -f png... |
Kaggle/learntools | notebooks/feature_engineering_new/raw/ex4.ipynb | apache-2.0 | # Setup feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.feature_engineering_new.ex4 import *
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.model_selection import cross_val_score
from... |
fierval/retina | Notebooks/Publish/Custom Filter Banks with OpenCV.ipynb | mit | # Auxillary stuff
%matplotlib inline
from matplotlib import pyplot as plt
from matplotlib import cm
import numpy as np
import cv2
import pandas as pd
from math import exp, pi, sqrt
from numbapro import vectorize
def show_images(images,titles=None, scale=1.3):
"""Display a list of images"""
n_ims = len(images)... |
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