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79 lines
2.6 KiB
79 lines
2.6 KiB
2 years ago
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import typing
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import matplotlib as mpl
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import numpy as np
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import matplotlib.pyplot as plt
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import sympy as sp
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from utils import eval_func, get_orientation_phase_grid, get_spatial_grid
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sp.init_printing()
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AxOrImg = typing.Union[mpl.axes.Axes, mpl.image.AxesImage]
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# %%
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def plot_spatial(func: sp.Expr, ax: AxOrImg, step_x: float = 0.05, step_y: float = 0.05, size: float = 1,
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title: str = None, show: bool = False,
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patch: typing.Optional[typing.Tuple[float, float, float]] = None
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):
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"""
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Plots a spatial map of the function.
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:param func: function to plot
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:param ax: axes to plot on or the image on axes
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:param step_x: step for the x-coordinate
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:param step_y: step for the y-coordinate
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:param size: size of the grid
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:param title: title of the plot
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:param show: whether to show the plot
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:param patch: optional circle to plot - a tuple (x, y, radius)
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"""
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grid = get_spatial_grid(step_x, step_y, size)
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image: np.ndarray = eval_func(func, x, y, grid)
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if isinstance(ax, mpl.image.AxesImage):
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ax.set_data(image)
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return ax
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img = ax.imshow(image, extent=[-size, size, -size, size], vmin=-size, vmax=size, cmap='gray')
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ax.invert_yaxis()
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if patch is not None:
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ax.add_patch(plt.Circle(patch[:2], radius=patch[2], color='b', fill=False))
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ax.set_title(title)
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if show:
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plt.show()
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return img
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def normalize(img):
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return (img - img.min()) / (img.max() - img.min())
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def plot_tuning_curve(func: typing.Union[sp.Expr, typing.Callable], ax: AxOrImg, step_phase: float = 20,
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step_orientation: float = 15, title: str = None, show: bool = False):
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"""
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Plots a tuning curve of the function.
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:param func: function to plot - sympy or a function of (theta, phi)
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:param ax: axes to plot on or image to update
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:param step_phase: step for the phase (phi) - in degrees
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:param step_orientation: step for the orientation (theta) - in degrees
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:param title: title of the plot
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:param show: whether to show the plot
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"""
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grid = get_orientation_phase_grid(step_phase, step_orientation)
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if isinstance(func, sp.Expr):
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image: np.ndarray = eval_func(func, theta, phi, grid)
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else:
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image = np.array([[func(theta_val, phi_val) for theta_val, phi_val in line] for line in grid])
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image = normalize(image)
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if isinstance(ax, mpl.image.AxesImage):
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ax.set_data(image)
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return ax
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img = ax.imshow(image, extent=[0, 360, 0, 180], cmap='viridis')
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ax.set_title(title)
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if show:
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plt.show()
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return img
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