Updated mat file conversion: layered, layered vectors...
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fd555e413e
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GPG key ID:
9833D3C5A25BD227
2 changed files with 152 additions and 44 deletions
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@ -9,6 +9,8 @@ from multiprocessing.pool import ThreadPool
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import numpy as np
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import scipy.io as sio
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from .read_mat import ReadSwash
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parser = argparse.ArgumentParser(description="Convert swash output to numpy")
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parser.add_argument("-v", "--verbose", action="count", default=0)
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parser.add_argument("-c", "--config", default="config.ini")
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@ -26,48 +28,16 @@ inp = pathlib.Path(config.get("post", "inp"))
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inp.mkdir(parents=True, exist_ok=True)
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log.info(f"Reading swash output from '{sws_out}'")
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raw_tsec = sio.loadmat(sws_out.joinpath("tsec.mat"))
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i = np.fromiter(
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(k[5:] for k in raw_tsec.keys() if re.compile(r"^Tsec_").match(k)), dtype="U10"
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)
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t = np.fromiter((raw_tsec[f"Tsec_{k}"][0, 0] * 10**3 for k in i), dtype=np.uintc)
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np.save(inp.joinpath("t"), t)
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del raw_tsec
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raw_xp = sio.loadmat(sws_out.joinpath("xp.mat"), variable_names="Xp")
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x = raw_xp["Xp"][0]
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np.save(inp.joinpath("x"), x)
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del raw_xp
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raw_botl = sio.loadmat(sws_out.joinpath("botl.mat"), variable_names="Botlev")
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botl = raw_botl["Botlev"][0]
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np.save(inp.joinpath("botl"), botl)
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del raw_botl, botl
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raw_watl = sio.loadmat(sws_out.joinpath("watl.mat"))
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watl = np.asarray(
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[raw_watl[i0][0] for i0 in np.char.add("Watlev_", i)], dtype=np.single
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)
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np.save(inp.joinpath("watl"), watl)
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del raw_watl, watl
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raw_vel = sio.loadmat(sws_out.joinpath("vel.mat"))
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vel_x = np.asarray([raw_vel[i0][0] for i0 in np.char.add("vel_x_", i)], dtype=np.single)
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np.save(inp.joinpath("vel_x"), vel_x)
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del raw_vel, vel_x
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raw_zk = sio.loadmat(sws_out.joinpath("zk.mat"))
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n_zk = (len(raw_zk.keys()) - 3) // t.size
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zk = np.asarray(
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[
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raw_zk[i0][0]
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for i0 in np.char.add(
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np.char.replace("zki_", "i", np.arange(n_zk).astype("U1"), count=1)[
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None, :
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],
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i[:, None],
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).reshape(-1)
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],
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dtype=np.single,
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).reshape((t.size, n_zk, x.size))
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np.save(inp.joinpath("zk"), zk)
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del raw_zk, zk
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swr = ReadSwash(sws_out, inp)
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swr.save("t")
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swr.save("x")
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swr.save("c", "botl", "Botlev")
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swr.save("s", "watl", "Watlev")
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swr.save("s", "press", "Press")
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swr.save("v", "vel", "vel")
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swr.save("sk", "zk", "zk")
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swr.save("sk", "nhprsk", "Nprs_k")
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swr.save("sk", "pressk", "Pres_k")
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swr.save("sk", "vz", "w")
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swr.save("vk", "velk", "vel_k")
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138
swash/processing/read_mat.py
Normal file
138
swash/processing/read_mat.py
Normal file
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@ -0,0 +1,138 @@
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import logging
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import subprocess
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import tempfile
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import numpy as np
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import scipy.io as sio
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log = logging.getLogger("read_mat")
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class ReadSwash:
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def __init__(self, root, out):
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self._root = root
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self._out = out
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self._n_x = None
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self._n_t = None
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self._i = None
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self._t = None
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self._x = None
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def read_raw(self, var, var_names=None):
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res = sio.loadmat(self._root.joinpath(f"{var}.mat"), variable_names=var_names)
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res.pop("__header__")
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res.pop("__version__")
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res.pop("__globals__")
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return res
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def read_t(self):
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raw_t = self.read_raw("tsec")
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self._i = np.char.lstrip(np.fromiter(raw_t.keys(), dtype="U15"), "Tsec_")
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self._t = np.fromiter(
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(raw_t[k][0, 0] * 10**3 for k in np.char.add("Tsec_", self._i)),
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dtype=np.uintc,
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)
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self._n_t = self._t.size
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return self.t
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def read_x(self):
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self._x = self.read_const("xp", "Xp")
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self._n_x = self._x.size
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return self.x
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def read_scalar(self, var, var_name):
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raw = self.read_raw(var)
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return np.asarray(
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[raw[i0][0] for i0 in np.char.add(f"{var_name}_", self._i)], dtype=np.single
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)
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def read_const(self, var, var_name):
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raw = self.read_raw(var, var_name)
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return raw[var_name][0]
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def read_vector(self, var, var_name):
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raw = self.read_raw(var)
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return (
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np.asarray(
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[raw[i0][0] for i0 in np.char.add(f"{var_name}_x_", self._i)],
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dtype=np.single,
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),
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np.asarray(
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[raw[i0][0] for i0 in np.char.add(f"{var_name}_y_", self._i)],
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dtype=np.single,
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),
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)
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def read_scalar_lay(self, var, var_name):
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raw = self.read_raw(var)
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n = len(raw.keys()) // self._n_t
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ra = (n,) if f"{var_name}0_{self._i[0]}" in raw.keys() else (1, n + 1)
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return np.asarray(
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[
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raw[i0][0]
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for i0 in np.char.add(
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np.char.replace(f"{var_name}[]_", "[]", np.arange(*ra).astype("U1"))[
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None, :
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],
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self._i[:, None],
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).reshape(-1)
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],
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dtype=np.single,
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).reshape((self._n_t, n, self._n_x))
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def read_vector_lay(self, var, var_name):
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raw = self.read_raw(var)
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n = len(raw.keys()) // (self._n_t * 2)
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ra = (n,) if f"{var_name}0_x_{self._i[0]}" in raw.keys() else (1, n + 1)
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return (
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np.asarray(
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[
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raw[i0][0]
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for i0 in np.char.add(
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np.char.replace(
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f"{var_name}[]_x_", "[]", np.arange(*ra).astype("U1")
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)[None, :],
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self._i[:, None],
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).reshape(-1)
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],
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dtype=np.single,
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).reshape((self._n_t, n, self._n_x)),
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np.asarray(
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[
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raw[i0][0]
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for i0 in np.char.add(
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np.char.replace(
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f"{var_name}[]_y_", "[]", np.arange(*ra).astype("U1")
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)[None, :],
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self._i[:, None],
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).reshape(-1)
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],
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dtype=np.single,
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).reshape((self._n_t, n, self._n_x)),
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)
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def save(self, t, var=None, var_name=None):
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fct = {
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"t": self.read_t,
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"x": self.read_x,
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"s": self.read_scalar,
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"c": self.read_const,
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"v": self.read_vector,
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"sk": self.read_scalar_lay,
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"vk": self.read_vector_lay,
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}
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if t in ("x", "t"):
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log.info(f"Converting {t}")
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np.save(self._out.joinpath(t), fct[t]())
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else:
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log.info(f"Converting {var}")
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np.save(self._out.joinpath(var), fct[t](var, var_name))
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@property
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def t(self):
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return self._t
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@property
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def x(self):
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return self._x
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