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aahoo / lab3.ipynb
Created October 18, 2017 19:47
Lab 3
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@aahoo
aahoo / Extracting building envelope elements in IFC.ipynb
Last active January 9, 2017 14:45
Extracting building envelope elements in IFC using IfcOpenShell and BIMserver
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@aahoo
aahoo / A Github Flavored Theme for Ipython or Jupyter Notebook.md
Last active May 20, 2022 09:02
Github Flavored Theme for Ipython/Jupyter Notebook
View A Github Flavored Theme for Ipython or Jupyter Notebook.md

Github Flavored Theme for Ipython/Jupyter Notebook

Put custom.css in .jupyter/custom/ (if you are running recent version of Jupyter).

Note: Tested only on Chrome. Let me know if something is not working.

img1 img1 img1

@aahoo
aahoo / energyplus weather file download python2.py
Last active May 17, 2021 17:35
Automatically download energyplus weather data files (epw and ddy), 2 versions python 2 and 3
View energyplus weather file download python2.py
import json
import re
import urllib2
path_to_save = '' # create a directory and write the name of directory here
data_file = urllib2.urlopen('https://github.com/NREL/EnergyPlus/raw/develop/weather/master.geojson')
data = json.load(data_file)
# or you can download master.geojson and run the below code instead of downloading from the net
# with open('master.geojson') as data_file:
# data = json.load(data_file)
View debug.md
@aahoo
aahoo / code.py
Last active April 20, 2016 06:49
Udacity deep learning course assignment 3 problem 4
View code.py
# download the file @ https://www.dropbox.com/s/urqmc4jgt66hbef/notMNIST.pickle?dl=0
pickle_file = 'notMNIST.pickle'
from time import strftime
from math import sqrt
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
View programming languages relatedness adjacency matrix
{
"directed": false,
"graph": [],
"nodes": [{
"id": "Simulink"
}, {
"id": "IntelliCorp (Software)"
}, {
"id": "Hope (programming language)"
}, {
@aahoo
aahoo / philosophers.json
Last active December 9, 2015 18:18
Networkx sample Adjacency Matrix converted to JSON using json_graph.adjacency_data(G)
View philosophers.json
{
"directed": false,
"graph": [],
"nodes": [{
"id": "Christian Wolff (philosopher)"
}, {
"id": "Martin Heidegger"
}, {
"id": "Lucretius"
}, {
View networkx adjacency export.py
from networkx.readwrite import json_graph
import json
# Return data in adjacency format that is suitable for
# JSON serialization and use in Javascript documents.
# more @https://goo.gl/QoRlbL
data = json_graph.adjacency_data(G)
with codecs.open('matrix.json', 'w', encoding='utf-8') as f:
json.dump(data, f)