JamesPHoughton

from zipfile import ZipFile

def unpack_zip(zipfile='', path_from_local=''):
    filepath = path_from_local+zipfile
    extract_path = filepath.strip('.zip')+'/'
    parent_archive = ZipFile(filepath)
    parent_archive.extractall(extract_path)
    namelist = parent_archive.namelist()
    parent_archive.close()
    for name in namelist:

JamesPHoughton

class MinModel(object):
    ##########  boilerplate stuff from the existing pysd #########
    def __init__(self):
        self._stocknames = [name[:-5] for name in dir(self) if name[-5:] == '_init']
        self._stocknames.sort() #inplace
        self._dfuncs = [getattr(self, 'd%s_dt'%name) for name in self._stocknames]
        self.state = dict(zip(self._stocknames, [None]*len(self._stocknames)))
        self.reset_state()
        self.functions = functions.Functions(self)

JamesPHoughton

""" Scraping with xpath
Here's a basic demo on how I scraped a page using xpath to get specific pieces of info.
While I've only accessed a few single pieces of info on the page, xpath can also 
get you a list of all the elements that meet a specific path, and will return them as
a list or iterable.

A tool you might like for debugging your xpath expressions is: xpath-helper
https://chrome.google.com/webstore/detail/xpath-helper/hgimnogjllphhhkhlmebbmlgjoejdpjl
"""

JamesPHoughton

def make_python_identifier(string, namespace=None, reserved_words=None,
                           convert='drop', handle='force'):
    """
    Takes an arbitrary string and creates a valid Python identifier.
    If the python identifier created is already in the namespace,
    or if the identifier is a reserved word in the reserved_words
    list, or is a python default reserved word,
    adds _1, or if _1 is in the namespace, _2, etc.

    Parameters

JamesPHoughton

Known Vensim Bugs

1. Passing zero delay time into a Delay fails silently

JamesPHoughton

g = nx.complete_graph([
    "playground equipment", "evanescent champagne", "curved spacetime", 
    "magic flute", "market returns", "spotty memory",
    "languid feeling", "include numpy as np", "acidic chemical", 
    "downton abbey", "tumble weeds", "precede the cause"])
node_locs = nx.circular_layout(g)
theta = {k: np.arctan2(v[1], v[0]) * 180/np.pi for k, v in node_locs.items() }

JamesPHoughton