cs-brix 0.0.7

Brix is a python library for CityScope modules which handles communication with City I/O.

Brix is a python library for CityScope modules which handles communication with City I/O.

Full documentation can be found here.

Please note that brix is still being developed, which is why some features might change fairly quickly. You can follow the updates by keeping track of brix development in the github repo.

Introduction

What is this library for? If you have never heard of a CityScope before, you might want to stop reading and learn about them here. CityScope is an awesome way to interact, explore, and co-create urban interventions in a way that can be accessed by multiple people with different background. If you know what they are, please keep reading.

What is a CityScope table? a ‘table’ is our way of describing a CityScope project. Why table then? Since historically, most CityScope instances were composed of a mesh between a physical table-top 3D model of a city, augmented with projections, software, and other interface hardware. So a table => project.

What is an indicator? An indicator is the result of running a module for CityScope. Indicators work by listening for updated from the CityScope table they are linked to, calculating some values by using a model, some function of the data, or a simulation, and then post the result of the calculations to CityIO to be displayed in the table.

What are the types of indicators you can build? Indicators can be anything that could be displayed on a CityScope table, including the supporting screens associated to it. For the purpose of this library, we distinguish three types of indicator: numeric, heatmap, simulation.

• Numeric: Numeric indicators are just a number or set of numbers. They are usually displayed in a chart (bar chart, radar chart, etc) next to the table. The most common numeric indicator are the numbers that go in the radar plot, which display information about density, diversity, and proximity.

• Heatmap: These indicators are geodata. They are made up of geometries (points, lines, or polygons) and properties associated to them. These indicators are displayed as layers directly on the CityScope table.

• Simulation: These type of indicators are also displayed on the table but they are the result of an agent based simulation and are therefore displayed as a dynamic layer. They change over time like a short movie. These are not yet supported by this library.

Installation

Brix is now on pip. Just do:

pip install cs-brix

Tutorial

Basics of building a CityScope indicator

Let’s get to it. First, what table are you building for? If you don’t have a specific table, that is totally okay and you can create one here. Note: by the time you read this, CityScope might pose some limitations on new projects (tables). Please follow instructions in the link above. For this tutorial, we crated one called dungeonmaster.

After creating a table, open the frond end given by the tool and edit the table at least once. Change some blocks, and push those changes to CityIO.

An indicator will basically take in data, and produce a result. Each new indicator is built as an subclass of the brix.Indicator class provided in this library. Make sure you define three functions: brix.Indicator.setup(), brix.Indicator.load_module(), and brix.Indicator.return_indicator(). Here’s a barebones example of an indicator:

from brix import Indicator
class MyIndicator(Indicator):
        '''
        Write a description for your indicator here.
        '''
        def setup(self):
                '''
                Think of this as your __init__.
                Here you will define the properties of your indicator.
                Although there are no required properties, be nice and give your indicator a name.
                '''
                self.name = 'Alfonso'

        def load_module(self):
                '''
                This function is not strictly necessary, but we recommend that you define it if you want to load something from memory. It will make your code more readable.
                '''
                pass

        def return_indicator(self, geogrid_data):
                '''
                This is the main course of your indicator.
                This function takes in `geogrid_data` and returns the value of your indicator.
                The library is flexible enough to handle indicators that return a number or a dictionary.
                '''
                return 1

Let’s talk data (input)

What is geogrid_data? Every time we create a CityScope table, we define a regularly spaced grid which is overlaid on the city district we’re modelling. These grid cells are the basic unit of analysis for the CityScope modules. Every grid cell has properties such as the Type which represents the land use and Height which represents the number of floors. These data are dynamic and are updated each time a user interacts with the CityScope table, experimenting with the spatial organisation of land uses and infrastructure. These dynamic data are stored the variable geogrid_data. This is a list of ojects: one for each grid cell in the CityScope table. The contents of each object really depends on the specific table you are building for and on the properties assigned to your indicator. There are two options that will control what geogrid_data contains which are: brix.Indicator.requires_geometry and brix.Indicator.requires_geogrid_props. These two properties are set to False by default, but you can change them inside the brix.Indicator.setup() function depending on the needs of your indicator.

Go ahead, take a look at how this object looks like by instantiating your class and linking it to a table:

I = MyIndicator()
I.link_table('dungeonmaster')
I.get_geogrid_data()

Bear in mind that the endpoint GEOGRIDDATA is created only after your first edit to the table. If you just created your table, you need to go to the front end and edit the table at least once for GEOGRIDDATA to show up.

Please note that the brix.Indicator.link_table() should only be used when developing the indicator. For deployment, we’ll use the brix.Handler class that is more efficient. You can also skip the brix.Indicator.link_table() step by defining the Indicator.table_name='dungeonmaster' property in your setup function. You will also notice that as you change the brix.Indicator.requires_geometry and brix.Indicator.requires_geogrid_props parameters in setup, the output of brix.Indicator.get_geogrid_data() will change.

If you are testing and are curious how geogrid_data would look like if you set requires_geometry=True, you can pass the argument to get_geogrid_data:

I.get_geogrid_data(include_geometries=True)

Please note that geogrid_data behaves very much like a list, but it is not a list. It belongs to the class brix.GEOGRIDDATA, which is an extension of a list to include additional functions and properties related to the table. For example, you can get the meta-properties of the table (such as type definitions, location, etc.) by using brix.GEOGRIDDATA.get_geogrid_props(). This is useful if, for example, you are interested in counting the total number of block types, including those that are not currently on the table. Run the following example to see how geogrid_props looks like:

geogrid_data = I.get_geogrid_data()
geogrid_data.get_geogrid_props()

Depending on the needs of your indicator, you can generate different views of this object. For example, you can use brix.GEOGRIDDATA.as_df() to return the pandas.DataFrame version of your object. Similarly, you can use brix.GEOGRIDDATA.as_graph() to return the networkx.Graph representation of GEOGRIDDATA. The graph representation is the network connecting every cell to its 4 closest neighbors.

Build and test your indicator (output)

This library ensures that you can focus on what you do best: writing a kick ass brix.Indicator.return_indicator() function that will make everyone’s urban planning life better.

To test your function while debugging it, you can use the object returned by brix.Indicator.get_geogrid_data():

geogrid_data = I.get_geogrid_data()
I.return_indicator(geogrid_data)

The property brix.Indicator.indicator_type will toggle between a Heatmap indicator or a numeric indicator (numeric for numeric and heatmap for heatmap).

For numeric indicators, there are multiple ways in which the front end can display them (e.g. bar chart, radar plot, etc.). This is controlled by the brix.Indicator.viz_type property of the class. The default value is set to self.viz_type=radar which means that unless it is specified otherwise, all numeric indicators will be added to the radar plot. When building an indicator that returns a single number you can just change the value of this parameter in the brix.Indicator.setup(). When building an indicator that returns multiple numbers it will just assume every number should be displayed in the same front end visualization. If you want to have more fine control of where each indicator is displayed, we recommend building your return_indicator function such that it returns a dictionary with the following structure:

{
        'name': 'Social Wellbeing',
        'value': random.random(),
        'viz_type': 'bar'
}

Note that if you define viz_type in the return dictionary of return_indicator, it will overwrite any default property defined in setup. Remember that your return_indicator function can also return a list of indicators. In the following example of a return value for the return_indicator function, the indicator returns two numbers that should be displayed in the radar plot, and one to be displayed as a bar chart.

[
        {'name': 'Social Wellbeing', 'value': 0.3, 'viz_type': 'radar'},
        {'name': 'Environmental Impact', 'value': 0.1, 'viz_type': 'radar'},
        {'name': 'Mobility Impact', 'value': 0.5, 'viz_type': 'bar'}
]

Deploy your indicator

Finally, once you have build a series of indicators, the right way to deploy them is to use the brix.Handler class. A brix.Handler object should be the go-to connection to the table and will handle all possible exceptions. The two most important methods are brix.Handler.add_indicators() which takes a list of brix.Indicator objects and connects them to the table, and brix.Handler.listen() that is a method that runs continuously waiting for updates in the CityScope table. This method can also creates its own thread, to free up the main thread in case the user needs to connect to other tables (by setting new_thread=True). The example below assumes you have already defined indicators named Density, Diversity and Proximity in a file named myindicators.py.

from brix import Handler
from myindicators import Density, Diversity, Proximity

dens = Density()
divs = Diversity()
prox = Proximity()

H = Handler('dungeonmaster', quietly=False)
H.add_indicators([
        dens,
        divs,
        prox
])
H.listen()

To see the indicators in the handler you can use H.list_indicators() to list the indicator names, and use H.return_indicator(<indicator_name>) to see the value of the indicator. Finally, the function H.update_package() will return the data that will be posted on CityIO.

Additional tools

This module also contains a set of other useful functions that integrate with brix.Handler and brix.Indicator.

The functions brix.get_OSM_geometries() and brix.get_OSM_nodes() help you get data from Open Street Maps for your table.

Auto-updates of GEOGRIDDATA

Brix also has the capability of automatically updating GEOGRIDDATA. For simple one-time updates, follow the documentation of brix.Handler.update_geogrid_data(). To use this feeature, you first need to define a function that takes a brix.GEOGRIDDATA as an input. When used with brix.Handler.update_geogrid_data(), this function can take any number of keyword arguments. The following example raises the height of all cells by 3 units:

def add_height(geogrid_data, levels=1):
        for cell in geogrid_data:
                cell['height'] += levels
        return geogrid_data

H = Handler('dungeonmaster', quietly=False)
H.update_geogrid_data(add_height,levels=3)

Brix also supports GEOGRIDDATA updates everytime there is a registered user interaction in the front end. To add a function to the update schedule, use brix.Handler.add_geogrid_data_update_function(). This has the limitation that your update funcion cannot take in any arguments other. If this limitation proves too restrictive, please submit an issue and we’ll consider pushing an update.

The following example updates the whole grid to Light Industrial use everytime there’s a user interaction:

def update_g(geogrid_data):
        for cell in geogrid_data:
                cell['name'] = 'Light Industrial'
        return geogrid_data

H = Handler(table_name,quietly=False)
H.add_geogrid_data_update_function(update_g)
H.listen()

The updates triggered by brix.Handler.listen() follow the following order:

1. get GEOGRIDDATA

2. run all GEOGRIDDATA updates using the result of 1 as input

3. get the new GEOGRIDDATA

4. update all indicators using the GEOGRIDDATA object resulting from 3