import warnings
import numpy as np
from joblib import Parallel, delayed
from sklearn.base import BaseEstimator, TransformerMixin
from tqdm import tqdm
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def vectorize(movie, pixel_indices: np.ndarray = None, order="C"):
"""
Reshape an array: [time, df, cols] -> [n_pixels, time]
Parameters
----------
movie: np.ndarray
movie of shape [time, df, cols]
pixel_indices: np.ndarray, default None
pixel indices to include in the vectorized output. 1D array of int that represents indices of a fully
vectorized movie
order: str, default "C"
"C" or "F" order
Returns
-------
np.ndarray
vectorized movie, shape [n_pixels, time]
"""
Y = movie.transpose(1, 2, 0).reshape(np.prod(movie.shape[1:]), movie.shape[0], order=order)
if pixel_indices is not None:
return Y[pixel_indices]
return Y
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def unvectorize(Y, shape: tuple[int, int], pixel_indices: np.ndarray = None, order="C"):
"""
Reshape an array: [n_pixels, time] -> [time, df, cols]
or
[n_pixels,] -> [df, cols]
Parameters
----------
Y: np.ndarray
vectorized movie, shape [n_pixels, time]
shape: tuple[int, int]
shape of one frame, [n_rows, n_cols]
pixel_indices: np.ndarray, default None
1D array of indices that map pixel indices in Y to real pixel indices in a "full Y" with all pixels
order: str, default "C"
"C" or "F" order
Returns
-------
np.ndarray
movie of shape [time, df, cols] or a 2D image of shape [df, cols]
"""
if pixel_indices is not None and Y.shape[0] < np.prod(shape):
if Y.ndim == 1:
Y_full_shape = (np.prod(shape),)
else:
Y_full_shape = (np.prod(shape), Y.shape[1])
Y_full = np.zeros(Y_full_shape, dtype=Y.dtype)
Y_full[:] = np.nan
Y_full[pixel_indices] = Y[:]
Y = Y_full
if Y.ndim == 1:
# return 2D image
return Y.reshape(shape, order=order)
# return movie
return Y.reshape(*shape, Y.shape[1], order=order).transpose(-1, 0, 1)
class Vectorizer(TransformerMixin, BaseEstimator):
"""
Vectorize movies
Parameters
----------
order: str, array order
"C" or "F"
"""
def __init__(self, pixel_indices: np.ndarray = None, order: str = "C"):
self.pixel_indices = None
self.order = order
def fit(self, movie, y=None):
"""
Does nothing, exists for API conformity
"""
return self
def transform(self, movie: np.ndarray):
"""
Vectorize the movie. Does nothing if the movie is already vectorized.
Parameters
----------
movie : array-like, shape [time, df, cols]
input movie
Returns
-------
np.ndarray
vectorized movie, shape [n_pixels, time]
"""
if movie.ndim == 2:
return movie
return vectorize(movie, pixel_indices=self.pixel_indices, order=self.order)
def _more_tags(self):
# This is a quick example to show the tags API:\
# https://scikit-learn.org/dev/developers/develop.html#estimator-tags
# Here, our transformer does not do any operation in `fit` and only validate
# the parameters. Thus, it is stateless.
return {"stateless": True}
class UnVectorizer(TransformerMixin, BaseEstimator):
"""
Unvectorize movies
Parameters
----------
shape: tuple, [n_rows, n_cols]
shape of a 2D frame of the movie
order: str, array order
"C" or "F"
"""
def __init__(self, shape: tuple, pixel_indices: np.ndarray = None, order: str = "C"):
if len(shape) != 2:
raise ValueError
self.shape = shape
self.pixel_indices = pixel_indices
self.order = order
def fit(self, movie, y=None):
"""
Does nothing, exists for API conformity
"""
return self
def transform(self, Y: np.ndarray):
"""
Unvectorize the movie. Does nothing if the movie is already unvectorized.
Parameters
----------
Y: array-like, shape [n_pixels, time]
input movie
Returns
-------
np.ndarray
unvectorized movie, shape [time, df, cols]
"""
if Y.ndim == 3:
return Y
return unvectorize(Y, shape=self.shape, pixel_indices=self.pixel_indices, order=self.order)
def _more_tags(self):
# This is a quick example to show the tags API:\
# https://scikit-learn.org/dev/developers/develop.html#estimator-tags
# Here, our transformer does not do any operation in `fit` and only validate
# the parameters. Thus, it is stateless.
return {"stateless": True}
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def calculate_centers(A, dims):
def calculate_center_component(i):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
ixs = np.where(A[:, i].toarray() > 0.07)[0]
if ixs.size == 0:
return np.array([np.nan, np.nan]) # Handle empty slice explicitly
return np.array(np.unravel_index(ixs, dims)).mean(axis=1)[::-1]
print("Computing centers in parallel...")
centers = Parallel(n_jobs=-1)(
delayed(calculate_center_component)(i) for i in tqdm(range(A.shape[1]), desc="Calculating neuron center coordinates")
)
return np.array(centers)
