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h5.py 7.74 KiB
import h5py
import numpy as np
from lmfit.models import Gaussian2dModel, GaussianModel
META_MATRIX = ("UB",)
META_CELL = ("cell",)
META_STR = ("name",)
def read_h5meta(filepath):
"""Open and parse content of a h5meta file.
Args:
filepath (str): File path of a h5meta file.
Returns:
dict: A dictionary with section names and their content.
"""
with open(filepath) as file:
content = parse_h5meta(file)
return content
def parse_h5meta(file):
content = dict()
section = None
for line in file:
line = line.strip()
if line.startswith("#begin "):
section = line[len("#begin ") :]
if section in ("detector parameters", "crystal"):
content[section] = {}
else:
content[section] = []
elif line.startswith("#end"):
section = None
elif section:
if section in ("detector parameters", "crystal"):
if "=" in line:
variable, value = line.split("=", 1)
variable = variable.strip()
value = value.strip()
if variable in META_STR:
pass
elif variable in META_CELL:
value = np.array(value.split(",")[:6], dtype=float)
elif variable in META_MATRIX:
value = np.array(value.split(",")[:9], dtype=float).reshape(3, 3)
else: # default is a single float number
value = float(value)
content[section][variable] = value
else:
content[section].append(line)
return content
def read_detector_data(filepath, cami_meta=None):
"""Read detector data and angles from an h5 file.
Args:
filepath (str): File path of an h5 file.
Returns:
ndarray: A 3D array of data, omega, gamma, nu.
"""
with h5py.File(filepath, "r") as h5f:
counts = h5f["/entry1/area_detector2/data"][:].astype(float)
n, cols, rows = counts.shape
if "/entry1/experiment_identifier" in h5f: # old format
# reshape images (counts) to a correct shape (2006 issue)
counts = counts.reshape(n, rows, cols)
else:
counts = counts.swapaxes(1, 2)
scan = {"counts": counts, "counts_err": np.sqrt(np.maximum(counts, 1))}
scan["original_filename"] = filepath
scan["export"] = True
if "/entry1/zebra_mode" in h5f:
scan["zebra_mode"] = h5f["/entry1/zebra_mode"][0].decode()
else:
scan["zebra_mode"] = "nb"
# overwrite zebra_mode from cami
if cami_meta is not None:
if "zebra_mode" in cami_meta:
scan["zebra_mode"] = cami_meta["zebra_mode"][0]
if "/entry1/control/Monitor" in h5f:
scan["monitor"] = h5f["/entry1/control/Monitor"][0]
else: # old path
scan["monitor"] = h5f["/entry1/control/data"][0]
scan["idx"] = 1
if "/entry1/sample/rotation_angle" in h5f:
scan["omega"] = h5f["/entry1/sample/rotation_angle"][:]
else:
scan["omega"] = h5f["/entry1/area_detector2/rotation_angle"][:]
if len(scan["omega"]) == 1:
scan["omega"] = np.ones(n) * scan["omega"]
scan["gamma"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]
scan["twotheta"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]
if len(scan["gamma"]) == 1:
scan["gamma"] = np.ones(n) * scan["gamma"]
scan["twotheta"] = np.ones(n) * scan["twotheta"]
scan["nu"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][0]
scan["ddist"] = h5f["/entry1/ZEBRA/area_detector2/distance"][0]
scan["wave"] = h5f["/entry1/ZEBRA/monochromator/wavelength"][0]
if scan["zebra_mode"] == "nb":
scan["chi"] = np.array([180])
scan["phi"] = np.array([0])
elif scan["zebra_mode"] == "bi":
scan["chi"] = h5f["/entry1/sample/chi"][:]
scan["phi"] = h5f["/entry1/sample/phi"][:]
if len(scan["chi"]) == 1:
scan["chi"] = np.ones(n) * scan["chi"]
if len(scan["phi"]) == 1:
scan["phi"] = np.ones(n) * scan["phi"]
if h5f["/entry1/sample/UB"].size == 0:
scan["ub"] = np.eye(3) * 0.177
else:
scan["ub"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
scan["name"] = h5f["/entry1/sample/name"][0].decode()
scan["cell"] = h5f["/entry1/sample/cell"][:]
if n == 1:
# a default motor for a single frame file
scan["scan_motor"] = "omega"
else:
for var in ("omega", "gamma", "chi", "phi"): # TODO: also nu?
if abs(scan[var][0] - scan[var][-1]) > 0.1:
scan["scan_motor"] = var
break
else:
raise ValueError("No angles that vary")
scan["scan_motors"] = [scan["scan_motor"]]
# optional parameters
if "/entry1/sample/magnetic_field" in h5f:
scan["mf"] = h5f["/entry1/sample/magnetic_field"][:]
if "mf" in scan:
# TODO: NaNs are not JSON compliant, so replace them with None
# this is not a great solution, but makes it safe to use the array in bokeh
scan["mf"] = np.where(np.isnan(scan["mf"]), None, scan["mf"])
if "/entry1/sample/temperature" in h5f:
scan["temp"] = h5f["/entry1/sample/temperature"][:]
elif "/entry1/sample/Ts/value" in h5f:
scan["temp"] = h5f["/entry1/sample/Ts/value"][:]
if "temp" in scan:
# TODO: NaNs are not JSON compliant, so replace them with None
# this is not a great solution, but makes it safe to use the array in bokeh
scan["temp"] = np.where(np.isnan(scan["temp"]), None, scan["temp"])
# overwrite metadata from .cami
if cami_meta is not None:
if "crystal" in cami_meta:
cami_meta_crystal = cami_meta["crystal"]
if "name" in cami_meta_crystal:
scan["name"] = cami_meta_crystal["name"]
if "UB" in cami_meta_crystal:
scan["ub"] = cami_meta_crystal["UB"]
if "cell" in cami_meta_crystal:
scan["cell"] = cami_meta_crystal["cell"]
if "lambda" in cami_meta_crystal:
scan["wave"] = cami_meta_crystal["lambda"]
if "detector parameters" in cami_meta:
cami_meta_detparam = cami_meta["detector parameters"]
if "dist2" in cami_meta_detparam:
scan["ddist"] = cami_meta_detparam["dist2"]
return scan
def fit_event(scan, fr_from, fr_to, y_from, y_to, x_from, x_to):
data_roi = scan["counts"][fr_from:fr_to, y_from:y_to, x_from:x_to]
model = GaussianModel()
fr = np.arange(fr_from, fr_to)
counts_per_fr = np.sum(data_roi, axis=(1, 2))
params = model.guess(counts_per_fr, fr)
result = model.fit(counts_per_fr, x=fr, params=params)
frC = result.params["center"].value
intensity = result.params["height"].value
counts_std = counts_per_fr.std()
counts_mean = counts_per_fr.mean()
snr = 0 if counts_std == 0 else counts_mean / counts_std
model = Gaussian2dModel()
xs, ys = np.meshgrid(np.arange(x_from, x_to), np.arange(y_from, y_to))
xs = xs.flatten()
ys = ys.flatten()
counts = np.sum(data_roi, axis=0).flatten()
params = model.guess(counts, xs, ys)
result = model.fit(counts, x=xs, y=ys, params=params)
xC = result.params["centerx"].value
yC = result.params["centery"].value
scan["fit"] = {"frame": frC, "x_pos": xC, "y_pos": yC, "intensity": intensity, "snr": snr}