Using Python from Analytica

Requires the Analytica Developer edition.

Introduction

Short overview
Motivation (i.e., rich ecosystem, many libraries)
Use cases
Advantages of using python from within Analytica vs. plain python.
Downsides: When it is better to stick with Analytica syntax (dependencies, array abstraction, MC, optimization).

Setting up a Python environment

Very brief basics.
- conda create -n MyEnv python=3.11
- Configuring Python Environment or Model Environment sysvars in Analytica.
Link to Setting up a Python environment for detailed info.

Importing Python modules and objects

The Python Startup Code sysvar
Use of "From module Import object"

There are two types of imports in Python: module imports (e.g., import numpy) and selective/wildcard imports (e.g., from numpy import arrays, zeros or from numpy import *). When imports are the last statement in a Python expression, they will be outputted. Module imports will output string representation of the module when evaluated. Selective/wildcard imports will output a list of the alias names (or the original names if the aliases does not exist) of each object.

Evaluation of import numpy as np, os

Evaluation of from numpy import array as arr, zeros

The Python namespace

The py:: namespace prefix

Calling a Python function

py::F(...) calling syntax
What print(...) does

Using Python classes

Using an existing python class
The -> operator to access fields, methods

Using Python code in Definitions

The Python Expression view for definitions
Writing an Analytica UDF with python code in body

Defining your own Python classes and Python functions

Using "def" or "class" in python code.
The Callable
Developing your code outside of Analytica and importing as a module.

Dependencies

The convenience of Analytica's dependencies.
Drawing arrows to indicate dependence where Python doesn't track it.
When dependencies are not tracked

Functions for evaluating Python code

PyEval, PyExec, PyCall

Non-scalar data structures

Distinction between Python data structures and Analytica data structures. How Python objects display in «...»
Why to sometimes keep as python data structure w/o exploding

Converting from Python data to Analytica

Automatic conversions (including list of types)
PyExplode
- Basic usage. Nested usage.
- Include/exclude types
- Collections: np.array, dict, set/map, pandas data frame (documented on a separate library page), etc.
- Images & Figures, PIL.Image, matplotlib.figure.Figure

Converting from Analytica to Python data structures

Automatic conversions (including a list of the types)
PySet, PyDict, PyTuple, PyList, PyArray

Using Analytica locals from Python code

When Analytica locals are visible to python code.
Assignment to an Analytica local from python code.

Accessing Analytica objects from Python code

Analytica module
Get an Analytica object
Evaluate a variable
Analytica.eval(...) -- evaluate an analytica expression
Call an Analytica function (?) -- Don't have something very direct here yet