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.

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.

Brix is now on pip. Just do:

pip install cs-brix

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 take in data and produce a result. Depending on the type of indicator you are building, the result can be a number, a heatmap, an annotation, or a complex simulation of agents moving around the screen. If you are building a very complex module, your indicator might return all of the above. 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 = 'My numeric indicator'
        self.indicator_type = 'numeric'
        self.viz_type = 'radar'

        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.
                All data loading actions should go here.
                '''
                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

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.

To access geogrid_data you will need to instantiate a brix.Handler object that will handle all communication with the table. Go ahead, take a look at how this object looks like by creating a brix.Handler and linking it to a table:

from brix import Handler
H = Handler('dungeonmaster',quietly=False)
H.get_geogrid_data()

By default, each brix.Handler is set to work quietly in the background. If you wish to get feedback on what your Handler is doing, you can set quietly=False when you create your Handler. This is useful for debugging.

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.

The function brix.Handler.get_geogrid_data() accepts to optional keyword arguments include_geometries and with_properties. These arguments correspond to brix.Indicator.requires_geometry and brix.Indicator.requires_geogrid_props parameters defined in the Indicator setup function. For example, if requires_geogrid_props=True in the setup, and the Indicator is linked to the table, the Handler will know to return geogrid_data with with_properties=True.

Go ahead and see how geogrid_data would look like if you set requires_geometry=True:

H.get_geogrid_data(include_geometries=True)

Please note that geogrid_data behaves very much like a list of dictionaries, 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 = H.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.

Try seeing your geogrid_data as a pandas.DataFrame:

geogrid_data = H.get_geogrid_data()
geogrid_data.as_df()

Additionally, you can remove non-interactive cells from geogrid_data by using brix.GEOGRIDDATA.remove_noninteractive() and get the table bounds by using brix.GEOGRIDDATA.bounds().

The following example gets a grid, remove all non interactive cells and transforms it to a dataframe:

from brix import Handler
H = Handler('dungeonmaster')
geogrid_data = H.get_geogrid_data()
geogrid_data = geogrid_data.remove_noninteractive()
geogrid_data.as_df()

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 recap, an indicator is build by defining at least a brix.Indicator.setup() function that takes care of configuring the indicator and a brix.Indicator.return_indicator() that will calculate the value of the indicator for a given geogrid_data.

Here’s an example of simple brix.Indicator.setup() and brix.Indicator.return_indicator() functions for a numeric indicator:

def setup(self):
        self.name = 'My numeric indicator'
        self.indicator_type = 'numeric'
        self.viz_type = 'radar'

def return_indicator(self,geogrid_data):
        return 1

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

H = Handler('dungeonmaster')
geogrid_data = H.get_geogrid_data()
I.return_indicator(geogrid_data)

Brix distinguish between four different types of indicators defined using the attribute brix.Indicator.indicator_type defined in brix.Indicator.setup(): numeric, heatmap, textual, and hybrid.

indicator_type='numeric' is the default and refers to a simple numeric indicator (e.g. average, density, diversity, etc.). When defining a numeric indicator, 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 attribute, also defined in the brix.Indicator.setup(). 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. For a numeric indicator, the brix.Indicator.return_indicator() function can simply return a number or a list of numbers, all of which will be added to the same front end visualization (e.g. all bar charts, all radar numbers). If you want to have more fine control of where each indicator is displayed, we recommend building your brix.Indicator.return_indicator() function such that it returns a dictionary with the following structure:

[
        {'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'}
]

Note that if you define viz_type in the return dictionary of return_indicator, it will overwrite any default property defined in brix.Indicator.setup().

indicator_type='heatmap' refers to a heatmap indicator that will be displayed not in a chart but projected directly on the table (e.g. density, traffic congestion, etc.). For a heatmap indicator, the brix.Indicator.return_indicator() function should return a geojson of points with attributes, or a geopandas.GeoDataFrame also with points and attributes. This type of indicator is a bit more complicated to build and will often require knowledge of spatial analytics. See the examples if you are interested.

indicator_type='textual' refers to an indicator that is displayed as a text annotation in one of the cells. This can be used to highlight something important about that cell to the front end user. For a textual indicator, the brix.Indicator.return_indicator() function should return a list of dictionaries, each with two keys, id that identified the cell to annotate, and info with a string that will be projected over that cell in the front end. Here’s an example of a list that annotated cell 450 with yes! and cell 40 with no!:

[{
        "id": 450,
        "info": "yes!"
},{
        "id": 40,
        "info": "no!"
}]

Finally, indicator_type='hybrid' is used when building a very complex module that returns information to be displayed in multiple different formats. Think of a complex energy usage simulation that will display the total energy consumed as bar in the bar chart, that will show the energy used by each cell projected on the table as a heatmap, and that might annotate some cells when they do not have enough energy available to them. For a hybrid indicators, you have two ways of organization your code. You can define your own brix.Indicator.return_indicator() function, or you can define specific functions for each of the available types of indicators: brix.Indicator.return_indicator_numeric(), brix.Indicator.return_indicator_heatmap(), and brix.Indicator.return_indicator_textual(). If you do not define a brix.Indicator.return_indicator() function, brix will run first the heatmap, then the numeric indicator, and finally the textual indicator. If you chose to have tighter control of the order in which the simulation runs, you can also define your own brix.Indicator.return_indicator() by calling these three functions. This function should return a dictionary with three keys: heatmap, numeric, and textual. Not all three keys have to be present. See the example below:

def return_indicator(self, geogrid_data):
out = {}
out['heatmap'] = self.return_indicator_heatmap(geogrid_data)
out['numeric'] = self.return_indicator_numeric(geogrid_data)
out['textual'] = self.return_indicator_textual(geogrid_data)
return out

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.

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.

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

To automatically test your module, this library provides the brix.User class that simulates the behavior of a user interacting with your grid. This user runs in its own new thread to free up your main thread so that you can keep wokring on your indicator.

The following example consists of a brix.Handler that contains a diversity brix.Indicator that reponds to the updates of the brix.User:

from brix import Handler
from brix.examples import Diversity
from brix.test_tools import User
table_name = 'dungeonmaster'
U = User(table_name)
H = Handler(table_name,quietly=False)
div = Diversity()
H.add_indicator(div)
U.start_user()
H.listen()