Tuesday, June 30, 2015

Embedding a webserver in Autodesk Revit with the RevitPythonShell

This is a more elaborate example that shows how to embedd a webserver in Autodesk Revit and use it to automate tasks.

How do you access the BIM from outside Revit? With the Revit API it is easy to access the outside world from within Revit. Sometimes you want to write software that needs to read a schedule from a .rvt document - from outside of Revit.

As an example, say you have a shell script that reads in schedule data from a Revit document and saves it to a CSV file.

One way to solve this is to have Revit act as a web server, say, http://localhost:8080. You could then use curl:

curl http://localhost:8080/schedules/my_schedule_name > my_local_file_name.csv

Let us build a RevitPythonShell script that allows you to do just that: Export any schedule in the BIM as a CSV file through a web service. Depending on the URL requested, you could return a screenshot of the current view or ways to open / close documents:

curl http://localhost:8080/screenshot
curl http://localhost:8080/open/Desktop/Project1.rvt

This is a variation on the non-modal dialog issue (see here too!). We want to run a web server in a separate thread, but have handling requests run in the main Revit thread so that we have access to the API. We will be using an external event to solve this.

The web server itself uses the HttpListenerclass, which runs in a separate thread and just waits for new connections. These are then handled by pushing them into a queue and notifying the ExternalEvent that a new event has happened.

This is where the script starts:

def main():
    contexts = ContextQueue()
    eventHandler = RpsEventHandler(contexts)
    externalEvent = ExternalEvent.Create(eventHandler)
    server = RpsServer(externalEvent, contexts)
    serverThread = Thread(ThreadStart(server.serve_forever))

Whoa! What is going on here?

  • a communication channel contexts is created for sending web requests (stashed as HttpListenerContext instances) to the ExternalEvent thread.
  • an IExternalEventHandler implementation called RpsEventHandler that handles producing the output.
  • a web server wrapped in a method serve_forever that listens for web requests with the HttpListener, stores them into the context queue and notifies the external event that there is work to be done.

We’ll look into each component one by one below. Note: The full code can be found here in the rps-sample-scripts GitHub repository.

Let’s start with the ContextQueue:

class ContextQueue(object):
    def __init__(self):
        from System.Collections.Concurrent import ConcurrentQueue
        self.contexts = ConcurrentQueue[HttpListenerContext]()

    def __len__(self):
        return len(self.contexts)

    def append(self, c):

    def pop(self):
        success, context = self.contexts.TryDequeue()
        if success:
            return context
            raise Exception("can't pop an empty ContextQueue!")

This is nothing speciall - just a thin wrapper arround ConcurrentQueue from the .NET library. The RpsServer will append to the context while the RpsEventHandler pops the context.

A more interesting class to look at is probably RpsEventHandler:

class RpsEventHandler(IExternalEventHandler):
    def __init__(self, contexts):
        self.contexts = contexts
        self.handlers = {
            'schedules': get_schedules
            # add other handlers here

    def Execute(self, uiApplication):
        while self.contexts:
            context = self.contexts.pop()
            request = context.Request
            parts = request.RawUrl.split('/')[1:]
            handler = parts[0]  # FIXME: add error checking here!
            args = parts[1:]
                rc, ct, data = self.handlers[handler](args, uiApplication)
                rc = 404
                ct = 'text/plain'
                data = 'unknown error'
            response = context.Response
            response.ContentType = ct
            response.StatusCode = rc
            buffer = Encoding.UTF8.GetBytes(data)
            response.ContentLength64 = buffer.Length
            output = response.OutputStream
            output.Write(buffer, 0, buffer.Length)

    def GetName(self):
        return 'RpsHttpServer'

The Execute method here does the grunt work of working with the .NET libraries and delegating requests to the specific handlers. You can extend this class can by adding new handlers to it. In fact, you don’t even need to extend the class to add handlers - just register them in the handlers dictionary.

Each handler takes a list of path elements and a UIApplication object. The handler runs in the Revit API context. It should return an HTTP error code, a content type and a string containing the response.

An example of such a handler is get_schedules:

def get_schedules(args, uiApplication):
    '''add code to get a specific schedule by name here'''
    print 'inside get_schedules...'
    from Autodesk.Revit.DB import ViewSchedule
    from Autodesk.Revit.DB import FilteredElementCollector
    from Autodesk.Revit.DB import ViewScheduleExportOptions
    import tempfile, os, urllib

    doc = uiApplication.ActiveUIDocument.Document
    collector = FilteredElementCollector(doc).OfClass(ViewSchedule)
    schedules = {vs.Name: vs for vs in list(collector)}

    if len(args):
        # export a single schedule
        schedule_name = urllib.unquote(args[0])
        if not schedule_name.lower().endswith('.csv'):
            # attach a `.csv` to URL for browsers
            return 302, None, schedule_name + '.csv'
        schedule_name = schedule_name[:-4]
        if not schedule_name in schedules.keys():
            return 404, 'text/plain', 'Schedule not found: %s' % schedule_name
        schedule = schedules[schedule_name]
        fd, fpath = tempfile.mkstemp(suffix='.csv')
        dname, fname = os.path.split(fpath)
        opt = ViewScheduleExportOptions()
        opt.FieldDelimiter = ', '
        schedule.Export(dname, fname, opt)
        with open(fpath, 'r') as csv:
            result = csv.read()
        return 200, 'text/csv', result
        # return a list of valid schedule names
        return 200, 'text/plain', '\n'.join(schedules.keys())

When you write your own handler functions, make sure to implement the function signature: rc, ct, data my_handler_function(args, uiApplication).

In get_schedules, a FilteredElementCollector is used to find all ViewSchedule instances in the currently active document. Using a dict comprehension is a nifty way to quickly make a lookup table for checking the arguments.

The args parameter contains the components of the url after the first part, which is used to select the handler function. So if the requested URL were, say, http://localhost:8080/schedules, then args would be an empty list. In this case, we just return a list of valid schedule names, one per line - see the else at the bottom of the function.

If the URL were, say http://localhost:8080/schedules/My%20Schedule%20Name, then the args list would contain a single element, "My%20Schedule%20Name". The %20 encoding is a standard for URLs and is used to encode a space character. We use urllib to unquote the name.

In order to make the function work nicely with a browser, it is nice to have a .csv ending to it - we redirect to the same URL with a .csv tacked on if it is missing! The code for handling the redirect can be found in the full sample script on GitHub. Notice how the HTTP return code 302 is used as the return value for rc - you can look up all the HTTP return codes online, we will only be using 200 (OK), 302 (Found - used for redirects) and 404 (Not Found).

Next, the script checks to make sure the schedule name is a valid schedule in the document. A 404 return code is used to indicate an error here.

The actual code for returning a schedule makes use of a technique described in Jeremy Tammik’s blog post The Schedule API and Access to Schedule Data. The ViewSchedule.Export method is used to write the schedule to a temporary file in CSV format and then read back into memory before deleting the file on disk. This is a bit of a hack and coming up with a better solution is left as an exercise for the reader…

The final piece in our puzzle is the RpsServer:

class RpsServer(object):
    def __init__(self, externalEvent, contexts, port=8080):
        self.port = port
        self.externalEvent = externalEvent
        self.contexts = contexts

    def serve_forever(self):
            self.running = True
            self.listener = HttpListener()
            prefix = 'http://localhost:%i/' % self.port
                print 'starting listener', prefix
                print 'started listener'
            except HttpListenerException as ex:
                print 'HttpListenerException:', ex
            waiting = False
            while self.running:
                if not waiting:
                    context = self.listener.BeginGetContext(
                waiting = not context.AsyncWaitHandle.WaitOne(100)

    def stop(self):
        print 'stop()'
        self.running = False

    def handleRequest(self, result):
        pass the request to the RevitEventHandler
            listener = result.AsyncState
            if not listener.IsListening:
                context = listener.EndGetContext(result)
                # Catch the exception when the thread has been aborted
            print 'raised external event'

This class implements the serve_forever function that starts an HttpListener on a specified port and uses handleRequest to pass any requests on to the external event for processing inside the Revit API context.

Check the rpshttpserver.py example on GitHub.

Monday, June 1, 2015

Using esoreader to parse EnergyPlus eso files

A short while ago I posted a short tutorial on the esoreader module. This post is an update, showing off the new pandas interface that makes life so much easier when exploring EnergyPlus output files.

The building simulation engine EnergyPlus stores its main output in a file with the ending ‘.eso’. This format makes it easy to log variable values during simulation, but is hard to use for post-processing. EnergyPlus offers a sqlite version of this data, but using it requires understanding the eso file format itself. EnergyPlus also can output a csv file, but that is limited in the number of columns.

The esoreader module makes it very easy to explore the output of EnergyPlus, say, in an IPython notebook interactive environment.

I wrote this module as part of my work at the chair for Architecture and Building Systems (A/S) at the Institute of Technology in Architecture, ETH Z├╝rich, Switzerland.

In [1]: import esoreader

In [2]: eso = esoreader.read_from_path(r"C:\...\experiment01.eso")

In [3]: eso.find_variable('heating')
[('TimeStep', None, 'Heating:EnergyTransfer'),
  'Zone Ideal Loads Zone Total Heating Energy')]

In [4]: df = eso.to_frame('heating energy')

In [5]: df[:10]
0                            8596050.719384
1                            8672511.667988
2                            8737544.119096
3                            8799182.506582
4                            8862116.803218
5                            8928593.537248
6                            5296266.226576
7                                  0.000000
8                                  0.000002
9                                  0.000000

In [6]: df.plot()
Out[6]: <matplotlib.axes._subplots.AxesSubplot at 0x7854090>

In [7]: %matplotlib tk

In [8]: df.plot()
Out[8]: <matplotlib.axes._subplots.AxesSubplot at 0x7b66670>

Zone Ideal Loads Zone Total Heating Energy

Notice in the above example how the variable is matched by substring - you don’t have to specify the whole variable name. Each matching variable will show up in the resulting DataFrame with the key used as the column name - in this case ‘DEFAULT_ZONEZONEHVAC:IDEALLOADSAIRSYSTEM’.

Also, as this is an IPython session, I used the magic variable incantation %matplotlib tk to switch on the GUI loop that allowes plotting. You can choose another backend if you like, but I am pretty sure that tk should be available with your Python distribution.

An example with multiple columns:

In [1]: eso.find_variable('net thermal radiation heat gain energy')
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy'),
  'Surface Outside Face Net Thermal Radiation Heat Gain Energy')]

In [2]: df = eso.to_frame('net thermal radiation heat gain energy')

In [3]: df.plot()
Out[3]: <matplotlib.axes._subplots.AxesSubplot at 0xbd11150>

Net Thermal Radiation Heat Gain Energy

The key parameter to to_frame

You can use the key parameter to select a single column:

In [1]: df = eso.to_frame('net thermal radiation', key='DPVROOF:1157058.3')

In [2]: df[:10]
0    -8985934.016604
1    -8453530.628023
2    -7611418.498363
3    -6936246.291753
4    -6206109.857522
5    -5879653.262523
6    -5676601.453020
7    -5606988.050900
8    -5844912.195173
9    -4712551.701917

The index parameter to to_frame

You can use the index parameter to specify an index for the DataFrame. Since this is time-series data, a common pattern could be:

In [1]: hours_in_year = pd.date_range('2013-01-01', '2013-12-31 T23:00', freq='H')

In [2]: df = eso.to_frame('heating energy', index=hours_in_year)

In [3]: df[:10]
2013-01-01 00:00:00                            8596050.719384
2013-01-01 01:00:00                            8672511.667988
2013-01-01 02:00:00                            8737544.119096
2013-01-01 03:00:00                            8799182.506582
2013-01-01 04:00:00                            8862116.803218
2013-01-01 05:00:00                            8928593.537248
2013-01-01 06:00:00                            5296266.226576
2013-01-01 07:00:00                                  0.000000
2013-01-01 08:00:00                                  0.000002
2013-01-01 09:00:00                                  0.000000