# Mental model¶

Greppo’s interface is simple and is designed with the intention of getting out of the users way. We want to make it easy to build a geospatial app quickly.

Greppo focuses on two ideas,

• Use native python code and other standard libraries for analysis.

• Use simple macros to prepare a user code for execution within Greppo.

The first idea ensures as much freedom as possible in terms of compatability with libraries commonly used for data science and geospatial analysis. The second is to keep Greppo in the background and only surface a small interface for the user to work with. Both ideas keep the mental bandwidth of using a new library low and focus on simple and intuitive UX.

With this in mind, the following code snippets breaks down a common user-script to illustrate the two ideas. The entire code as a single file is here and to run the code before looking at the explanation, simply run  .

• Imports

import geopandas as gpd
import numpy as np
import pandas as pd
from greppo import app
• For each Greppo app, we have a base layer that is a required setup from the user.

app.base_layer(
name="CartoDB Light",
visible=True,
url="https://cartodb-basemaps-a.global.ssl.fastly.net/light_all/{z}/{x}/{y}@2x.png",
subdomains=None,
)

• app.number is Greppo primitive for user interaction for a number. Each defines the inputs they want in app, in this case a number, that they will use to manipulate the GIS data. Here, for :code:app.number there is a :code:value arg that defines the initial default value and a :code:name arg that is a unique identifier for that input. Greppo uses the :code:name to provide a reactive feedback when rendering the app.

number_1 = app.number(value=10, name="Number input 1")

• Regular GDF code a Data Scientist is familiar with.

• Here the Greppo user closes the feedback loop and uses the :code:app.number input to perform analysis. This example is trivial but gives you a clear idea of how things work. When Greppo renders this code, the User can interact with the front-end to dynamically pass values and recompute the data.

data_gdf_1["Value"] = pd.Series(
np.ones(len(data_gdf_1["code"])) * number_1, index=data_gdf_1.index
)

• As with the :code:app.base_layer, a Greppo app must define a required :code:app.overlay_layer.

app.overlay_layer(
data_gdf_1,
name="Communes",
description="Communes in Normandy, France",
style={"fillColor": "#F87979"},
visible=True,
)

• Generic Python to perform some action, in this case construct a list of random numbers.

y = []
for i in range(10, 0, -1):
y.append(np.random.randint(0, 100))

• To complete this trivial example, this :code:app.line_chart gives you an idea of the visualization tools a Greppo app has access to. Here, the random number list generated above is plotted as a line chart.

app.line_chart(
name="some-name",
description="some_chart",
x=[i for i in range(10)],
y=y,
)