Olaflow processing for article
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137
olaflow/processing/flow_velocity.py
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137
olaflow/processing/flow_velocity.py
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import argparse
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import gzip
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from itertools import starmap
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import logging
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from multiprocessing import pool
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import pathlib
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import pickle
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import sys
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from cycler import cycler
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import matplotlib.pyplot as plt
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from matplotlib.ticker import MultipleLocator
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import numpy as np
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from scipy import interpolate
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from .olaflow import OFModel
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parser = argparse.ArgumentParser(description="Post-process olaflow results")
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parser.add_argument("-v", "--verbose", action="count", default=0)
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parser.add_argument(
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"-o",
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"--output",
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type=pathlib.Path,
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help="Output directory for pickled data",
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required=True,
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)
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parser.add_argument(
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"-f",
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"--func",
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type=str,
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help="Post-process function to compare",
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default="graphUniform",
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choices=("graphUniform", "graphUniform2"),
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)
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args = parser.parse_args()
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logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
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log = logging.getLogger("ola_post")
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log.info("Plotting comparison of model output")
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def get_pickle(out):
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with (
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path.open("rb")
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if (path := out.joinpath("pickle")).exists()
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else gzip.open(path.with_suffix(".gz"), "rb")
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) as f:
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return pickle.load(f)
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model = get_pickle(args.output)
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figsize = 15 / 2.54, 6 / 2.54
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fig, ax_ = plt.subplots(
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2,
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figsize=figsize,
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dpi=200,
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constrained_layout=True,
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)
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_ax = ax_[0]
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v = np.nanmax(np.abs(np.where(
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model.post_fields[args.func]["alpha.water"] > 0.5,
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#np.linalg.norm(model.post_fields[args.func]["U"], axis=2),
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model.post_fields[args.func]["U"][..., 0],
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np.nan,
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)))
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v150 = np.nanmax(np.abs(np.where(
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(model.post_fields[args.func]["alpha.water"] > 0.5) & (model.t[:, None] > 170) & (model.t[:, None] < 200),
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#np.linalg.norm(model.post_fields[args.func]["U"], axis=2),
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model.post_fields[args.func]["U"][..., 0],
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np.nan,
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)))
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_data = model.post_fields[args.func]["U"][..., 0].T
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#_c = _ax.contourf(
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# model.t,
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# model.post_fields[args.func]["x_U"],
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# _data,
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# cmap="PiYG",
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# #levels=[-15, -10, -5, -2, -1, 0, 1, 2, 5, 10, 15],
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# vmin=-np.nanmax(np.abs(_data)),
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# vmax=np.nanmax(np.abs(_data)),
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# extend="both",
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#)
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_c = _ax.imshow(
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_data[::-1],
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cmap="PiYG",
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alpha=np.clip(model.post_fields[args.func]["alpha.water"], 0, 1).T[::-1],
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extent=(
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model.t.min(),
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model.t.max(),
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model.post_fields[args.func]["x_U"].min(),
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model.post_fields[args.func]["x_U"].max(),
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),
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vmin=-v150,
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vmax=v150,
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aspect="auto",
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)
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_ax.set(xlim=(0, 400))
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_ax.set(facecolor="k")
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_ax.xaxis.set_minor_locator(MultipleLocator(10))
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_ax.yaxis.set_minor_locator(MultipleLocator(1))
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_ax.set(ylabel="z (m)")
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_ax.axes.set_xticklabels([])
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fig.colorbar(_c, label=f"U (m/s)", ax=_ax)
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log.info(f"Vitesse max: {v}m/s")
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log.info(f"Vitesse max [170,200]: {v150}m/s")
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log.info(f"Écart: {abs(np.nanmax(_data)-17.7)/17.7:%}")
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x = model.post_fields[args.func]["x_U"]
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i0 = np.argmin(np.abs(x - 5))
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_data = model.post_fields[args.func]["U"][..., i0, 0]
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_alpha = model.post_fields[args.func]["alpha.water"][..., i0]
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ax = ax_[1]
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ax.fill_between(model.t, np.where(_alpha > 0.5, _data, 0), lw=1, color="#898989", edgecolor="k")
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#ax.autoscale(True, "x", True)
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ax.set(xlim=(0, 400))
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ax.set(xlabel="t (s)", ylabel="U (m/s)")
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ax.grid(c="k", alpha=.2)
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ax.xaxis.set_minor_locator(MultipleLocator(10))
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ax.yaxis.set_minor_locator(MultipleLocator(2))
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fig.savefig(
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args.output.joinpath(
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f"U_{args.func}.pdf"
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)
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)
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fig.savefig(
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args.output.joinpath(
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f"U_{args.func}.jpg"
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)
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)
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79
olaflow/processing/snap.py
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79
olaflow/processing/snap.py
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import argparse
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import gzip
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import logging
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import multiprocessing as mp
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import pathlib
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import pickle
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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from matplotlib.gridspec import GridSpec
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from matplotlib.ticker import MultipleLocator
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import numpy as np
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from scipy import interpolate
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from .olaflow import OFModel
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parser = argparse.ArgumentParser(description="Post-process olaflow results")
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parser.add_argument("-v", "--verbose", action="count", default=0)
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parser.add_argument(
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"-o",
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"--output",
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type=pathlib.Path,
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help="Output directory for pickled data",
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required=True,
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)
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parser.add_argument(
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"-m",
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"--max",
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help="Only compute maximum rather than animation",
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action="store_true",
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)
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parser.add_argument(
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"-i",
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"--initial",
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help="Only compute initial domain",
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action="store_true",
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)
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args = parser.parse_args()
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logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
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log = logging.getLogger("ola_post")
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log.info("Animating olaFlow output")
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out = args.output
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out.mkdir(parents=True, exist_ok=True)
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with (
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path.open("rb")
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if (path := out.joinpath("pickle")).exists()
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else gzip.open(path.with_suffix(".gz"), "rb")
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) as f:
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model = pickle.load(f)
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x0, idx0 = np.unique(model.x.astype(np.half), return_inverse=True)
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z0, idz0 = np.unique(model.z.astype(np.half), return_inverse=True)
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ix0 = np.argsort(x0)
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iz0 = np.argsort(z0)[::-1]
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X, Z = np.meshgrid(x0, z0)
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P = np.full((model.t.size, *X.shape), np.nan)
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P[:, iz0[idz0], ix0[idx0]] = model.fields["porosity"]
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AW = np.full((model.t.size, *X.shape), np.nan)
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AW[:, iz0[idz0], ix0[idx0]] = model.fields["alpha.water"]
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#U = np.full((model.t.size, *X.shape), np.nan)
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#U[:, iz0[idz0], ix0[idx0]] = np.linalg.norm(model.fields["U"], axis=1)
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i0 = np.argmin(np.abs(model.t[:, None] - np.asarray((102, 118, 144.5, 176.5))[None, :]), axis=0)
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fig, ax_ = plt.subplots(
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2, 2, figsize=(15 / 2.54, 4 / 2.54), dpi=200, constrained_layout=True
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)
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for ax, i in zip(ax_.flatten(), i0):
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ax.imshow(AW[i], cmap="Blues", vmin=0, vmax=1)
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fig.savefig(out.joinpath("snap.pdf"))
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