amgen/sym_model.py

from __future__ import annotations
import typing
from dataclasses import dataclass, field

import matplotlib as mpl
import sympy as sp
import numpy as np

from utils import get_orientation_phase_grid

sp.init_printing()
k, x0, y0, phi, theta, sigma_x, sigma_y, sigma, x, y = sp.symbols(r'k x_0 y_0 \phi \theta \sigma_x \sigma_y \sigma x y')
defaults = {
    k: 6,
    sigma: 0.2,
    phi: sp.pi / 2,
    theta: 0,
    sigma: 1,
    x0: 0, y0: 0
}
sigma_x = sigma_y = sigma
grating_f = sp.cos(k * (x - x0) * sp.cos(theta) + k * (y - y0) * sp.sin(theta) + phi)
receptive_field = 1 / (2 * sp.pi * sigma * sigma) * sp.exp(-(x ** 2 + y ** 2) / (2 * sigma ** 2)) * sp.cos(
    k * x * sp.cos(theta) + k * y * sp.sin(theta) + phi)
receptive_field = receptive_field.subs(theta, 0).subs(phi, 0)
p = sp.cosh(k ** 2 * sigma ** 2 * sp.cos(theta)) * sp.exp(k ** 2 * (1 + sp.cos(theta) ** 2) / 2) * sp.cos(
    phi - k * (x0 * sp.cos(theta) + y0 * sp.sin(theta)))

sigma_split = np.arange(0.1, 1, 0.05)
k_split = np.arange(0.2, 6, 0.2)
xy_split = np.arange(-1, 1, 0.05)


@dataclass
class Cell:
    sigma_val: float = defaults[sigma]
    x0_val: float = defaults[x0]
    y0_val: float = defaults[y0]
    k_val: float = defaults[k]

    @classmethod
    def random(cls, sigma_dist: np.ndarray = np.ones(len(sigma_split)),
               k_val: float = defaults[k],
               xy_dist: np.ndarray = np.ones(len(xy_split))):
        return cls(
            sigma_val=np.random.choice(sigma_split, p=sigma_dist / np.sum(sigma_dist)),
            x0_val=np.random.choice(xy_split, p=xy_dist / np.sum(xy_dist)),
            y0_val=np.random.choice(xy_split, p=xy_dist / np.sum(xy_dist)),
            k_val=k_val
        )

    @property
    def sympy_func(self) -> sp.Expr:
        return receptive_field.subs(sigma, self.sigma_val).subs(x0, self.x0_val).subs(y0, self.y0_val).subs(k,
                                                                                                            self.k_val)

    def get_tuning_function(self) -> typing.Callable[[np.ndarray, np.ndarray], np.ndarray]:
        """
        Get the tuning sympy function as a numpy lambda function of theta and phi.
        :return: a function (theta, phi) -> value
        """
        return sp.lambdify(
            (theta, phi),
            p.subs(sigma, self.sigma_val).subs(x0, self.x0_val).subs(y0, self.y0_val).subs(k, self.k_val),
            'numpy')

    def get_value(self, theta_deg: float, phi_deg: float) -> float:
        return float(self.get_tuning_function()(theta_deg * np.pi / 180, phi_deg * np.pi / 180))

    def get_tuning_plot(self, theta_step_deg: float, phi_step_deg: float) -> np.ndarray:
        grid = get_orientation_phase_grid(theta_step_deg, phi_step_deg)
        return self.get_tuning_function()(grid[:, :, 0], grid[:, :, 1])


@dataclass
class Grating:
    k_val: float = defaults[k]
    phi_val: float = defaults[phi]
    theta_val: float = defaults[theta]

    @property
    def sympy_func(self) -> sp.Expr:
        return grating_f.subs(k, self.k_val).subs(phi, self.phi_val).subs(theta, self.theta_val)


@dataclass
class Population:
    cells: typing.List[Cell] = field(default_factory=list)

    @classmethod
    def random(cls, n: int, sigma_dist: np.ndarray = np.ones(len(sigma_split)),
               k_val: float = defaults[k],
               xy_dist: np.ndarray = np.ones(len(xy_split))):
        return cls(cells=[Cell.random(sigma_dist, k_val, xy_dist) for _ in range(n)])

    def get_response(self, phi_deg: float, theta_deg: float) -> typing.List[float]:
        return [cell.get_value(theta_deg, phi_deg) for cell in self.cells]

    def sample_responses(self, n: int) -> np.ndarray:
        return np.array([
            self.get_response(phi_deg, theta_deg % 180)
            for phi_deg, theta_deg in np.random.uniform(0, 360, (n, 2))
        ])