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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,72 +1,56 @@ | ||
| import numpy as np | ||
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| from membrane_curvature.lib.mods import derive_surface, mean_curvature, gaussian_curvature | ||
| import numpy as np | ||
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| def __init__(self, universe, select, | ||
| x_bins=100, y_bins=100, | ||
| x_range=None, | ||
| y_range=None, | ||
| pbc=True): | ||
| self.universe = universe | ||
| self.selection = select | ||
| self.pbc = pbc | ||
| self.y_bins = y_bins | ||
| self.x_bins = x_bins | ||
| self.x_range = x_range if x_range else (0, universe.dimensions[0]) | ||
| self.y_range = y_range if y_range else (0, universe.dimensions[1]) | ||
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| # Warns if range doesn't cover entire dimensions of simulation box | ||
| if (x_range < universe.dimensions[0]) or (y_range < universe.dimensions[1]): | ||
| raise ValueError("Minimum range must be ({}, {})").format(self.universe.dimensions[0], | ||
| self.universe.dimenions[1]) | ||
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| def _prepare(self): | ||
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| # Initialize empty np.array(cumulative, count) of results | ||
| cumulative = np.zeros((self.x_bins, self.y_bins)) | ||
| count = np.zeros((self.x_bins, self.y_bins)) | ||
| from membrane_curvature.surface import get_z_surface | ||
| from membrane_curvature.curvature import mean_curvature, gaussian_curvature | ||
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| self.results.surface = (cumulative, count) | ||
| self.results.mean = (cumulative, count) | ||
| self.results.gaussian = (cumulative, count) | ||
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| from MDAnalysis.analysis.base import AnalysisBase | ||
| import MDAnalysis as mda | ||
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| def _output(self, value_per_frame, cumulative_output, counter): | ||
| """ | ||
| Adds np.arrays of mean curvature, gaussian curvature and surface to results. | ||
| """ | ||
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| nans_in_grid = np.isnan(value_per_frame) | ||
| class MembraneCurvature(AnalysisBase): | ||
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| # Calculate cumulative per frame when no nans. | ||
| cumulative_output += np.where(nans_in_grid, 0, value_per_frame) | ||
| def __init__(self, universe, select='all', | ||
| n_x_bins=100, n_y_bins=100, | ||
| x_range=None, | ||
| y_range=None, | ||
| pbc=True, **kwargs): | ||
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| # Count when no nans. | ||
| counter += np.where(nans_in_grid, 0, 1) | ||
| super().__init__(universe.universe.trajectory, **kwargs) | ||
| self.selection = universe.select_atoms(select) | ||
| self.pbc = pbc | ||
| self.n_x_bins = n_x_bins | ||
| self.n_y_bins = n_y_bins | ||
| self.x_range = x_range if x_range else (0, universe.dimensions[0]) | ||
| self.y_range = y_range if y_range else (0, universe.dimensions[1]) | ||
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| # Raise if range doesn't cover entire dimensions of simulation box | ||
| if (self.x_range[1] < universe.dimensions[0]) or (self.y_range[1] < universe.dimensions[1]): | ||
| raise ValueError("Minimum range must be ({}, {})").format(self.universe.dimensions[0], | ||
| self.universe.dimensions[1]) | ||
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| def _single_frame(self): | ||
| def _prepare(self): | ||
| # Initialize empty np.array with results | ||
| self.results.z_surface = np.full((self.n_frames, self.n_x_bins, self.n_y_bins), np.nan) | ||
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| surface_ = derive_surface(n_x_bins=self.n_cells_x, n_y_bins=self.n_cells_y, | ||
| x_range=(0, self.max_width_x), y_range=(0, self.max_width_y)) | ||
| self.results.mean = np.full((self.n_frames, self.n_x_bins, self.n_y_bins), np.nan) | ||
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| mean_ = mean_curvature(surface_) | ||
| gaussian_ = gaussian_curvature(surface_) | ||
| self.results.gaussian = np.full((self.n_frames, self.n_x_bins, self.n_y_bins), np.nan) | ||
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| # Save the results in NumPy array of shape (`n_x_bins`, `n_y_bins`). | ||
| self._output(surface_, self.results.surface[0], self.results.surface[1]) | ||
| self._output(mean_, self.results.mean[0], self.results.mean[1]) | ||
| self._output(gaussian_, self.results.gaussian[0], self.results.gaussian[1]) | ||
| def _single_frame(self): | ||
| # Populate a slice with np.arrays of surface, mean, and gaussian per frame | ||
| self.results.z_surface[self._frame_index] = get_z_surface(self.selection.positions, | ||
| n_x_bins=self.n_x_bins, n_y_bins=self.n_y_bins, | ||
| x_range=self.x_range, y_range=self.y_range) | ||
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| self.results.mean[self._frame_index] = mean_curvature(self.results.z_surface[self._frame_index]) | ||
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| def _conclude(self): | ||
| self.results.gaussian[self._frame_index] = gaussian_curvature(self.results.z_surface[self._frame_index]) | ||
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| # Calculate average of np.array over trajectory normalized over counter | ||
| self.results.average_surface = self.results.surface[0] / self.results.surface[1] | ||
| def _conclude(self): | ||
| # Calculate mean of np.array of surface, mean, gaussian | ||
| self.results.average_z_surface = np.nanmean(self.results.z_surface, axis=0) | ||
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| self.results.average_mean = self.results.mean[0] / self.results.mean[1] | ||
| self.results.average_mean = np.nanmean(self.results.mean, axis=0) | ||
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| self.results.average_gaussian = self.results.surface[0] / self.results.surface[1] | ||
| self.results.average_gaussian = np.nanmean(self.results.gaussian, axis=0) |