mirror of
https://github.com/Findus23/halo_comparison.git
synced 2024-09-19 16:03:50 +02:00
69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
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from pathlib import Path
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from typing import List
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import h5py
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.colors import LogNorm
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from scipy.interpolate import griddata
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from utils import create_figure
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def create_2d_slice(input_file: Path, center: List[float], property: str, axis="Z", thickness=3):
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axis_names = ["X", "Y", "Z"]
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cut_axis = axis_names.index(axis)
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with h5py.File(input_file) as f:
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pt0 = f["PartType0"]
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coords = pt0["Coordinates"]
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data = pt0[property]
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print((center[cut_axis] - thickness < coords[::, cut_axis]).shape)
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# in_slice = (center[cut_axis] - thickness < coords[::, cut_axis]) & (
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# coords[::, cut_axis] < center[cut_axis] + thickness)
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# print("got slice")
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# coords_in_slice = coords[in_slice]
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# data_in_slice = data[in_slice]
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print("stats")
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other_axis = {
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"X": ("Y", "Z"),
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"Y": ("X", "Z"),
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"Z": ("X", "Y")
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}
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x_axis_label, y_axis_label = other_axis[axis]
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x_axis = axis_names.index(x_axis_label)
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y_axis = axis_names.index(y_axis_label)
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xrange = np.linspace(coords[::, x_axis].min(), coords[::, x_axis].max(), 1000)
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yrange = np.linspace(coords[::, y_axis].min(), coords[::, y_axis].max(), 1000)
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gx, gy, gz = np.meshgrid(xrange, yrange, center[cut_axis])
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print("interpolating")
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grid = griddata(
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coords,
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data,
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(gx, gy, gz),
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method="linear"
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)[::, ::, 0]
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print(grid.shape)
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# stats, x_edge, y_edge, _ = binned_statistic_2d(
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# coords_in_slice[::, x_axis],
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# coords_in_slice[::, y_axis],
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# data_in_slice,
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# bins=500,
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# statistic="mean"
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# )
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fig, ax = create_figure()
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# stats = np.nan_to_num(stats)
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print("plotting")
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img = ax.imshow(
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grid.T,
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norm=LogNorm(),
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interpolation='nearest',
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extent=[xrange[0], xrange[-1], yrange[0], yrange[-1]]
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)
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ax.set_title(input_file.parent.stem)
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ax.set_xlabel(x_axis_label)
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ax.set_ylabel(y_axis_label)
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fig.colorbar(img, label=property)
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fig.tight_layout()
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plt.show()
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exit()
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