```{python}
#| code-fold: true
def view_fiducial_scans(
scan_id_start,
maxima = np.full(10, np.nan),
photodiode = "WAXS Beamstop",
):
"""
View fiducial scans and optionally the calculated peak maxima.
Args:
scan_id_start: int
Scan ID for the first fiducial scan in the scan series.
maxima: list of 10 float values
Optional list of peak maxima found by Bluesky that can be overlaid onto the fiducial scan plots.
If no list is provided, default is a list of nan, which does not show up on the plot.
Returns:
Plots of each fiducial scan.
Raises:
Examples:
"""
x_lookup = [
"solid_sample_y",
"solid_sample_x",
"RSoXS Sample Up-Down",
"RSoXS Sample Outboard-Inboard",
]
y_lookup = [
"DM7 photodiode",
#"WAXS Beamstop",
#"SAXS Beamstop",
]
## TODO: come up with more robust way to handle this
y_lookup = [photodiode]
subplot_title_labels = np.array([" \n y2 ", " \n x2 at -90° ", " \n x2 at 0° ", " \n x2 at 90° ", " \n x2 at 180° ",
" \n y1 ", " \n x1 at -90° ", " \n x1 at 0° ", " \n x1 at 90° ", " \n x1 at 180° "])
## Make figure
number_rows, number_columns = 2, 5
fig, axs = plt.subplots(number_rows, number_columns, figsize=(number_columns*3.25, number_rows*3.25), edgecolor=(0, 0, 0, 0), linewidth=3); #figsize=(3.25, 3.25) for figure
#fig.suptitle((""), color=(0, 0, 0, 1), fontname="Calibri", size=24)
## Enxure axs always stays a 2D array
if number_rows == 1 and number_columns == 1: axs = np.array([[axs]])
if number_rows == 1 and number_columns > 1: axs = axs.reshape(1, number_columns)
if number_rows > 1 and number_columns == 1: axs = axs.reshape(number_rows, 1)
## Fiducial scan series has 10 scans
scan_ids = np.arange(scan_id_start, (scan_id_start + 10), 1)
for index_scan_id, scan_id in enumerate(scan_ids):
## Load scan. If the scan does not exist yet, stop the loop.
try: scan_raw = catalog[int(scan_id)]
except: break
## Gather x and y data
data_variable_names = list(scan_raw["primary"]["data"].read().data_vars.keys())
x_axis_name, y_axis_name = "", ""
for data_variable_name in data_variable_names:
if data_variable_name in x_lookup:
x_axis_name = data_variable_name
if data_variable_name in y_lookup:
y_axis_name = data_variable_name
## Plot
ax = axs.flatten()[index_scan_id]
ax.set_title(("Scan ID = " + str(scan_id) + subplot_title_labels[index_scan_id]), color=(0, 0, 0, 1), fontname="Calibri", size=12)
ax.plot(scan_raw["primary"]["data"][x_axis_name].read(), scan_raw["primary"]["data"][y_axis_name].read(), label="", marker=".", markersize=0, color=(0, 0, 0, 1), linestyle="solid")
ax.axvline(maxima[index_scan_id], color=(0, 0.7, 0, 1), linestyle="dashed")
ax.set_xlabel(x_axis_name, color=(0, 0, 0, 1), size=12)
ax.set_ylabel(y_axis_name, color=(0, 0, 0, 1), size=12)
## Plot Formatting
for index_row in np.arange(0, number_rows, 1):
for index_column in np.arange(0, number_columns, 1):
ax = axs[index_row, index_column]
## Axes scaling and ranges
ax.set_xscale("linear")
ax.set_yscale("linear")
## Border formatting
for Border in np.array(["top", "bottom", "left", "right"]):
ax.spines[Border].set_linewidth(2) ## axes/border linewidths
ax.spines[Border].set_color((0, 0, 0, 1)) ## axes/border colors
for Axis in np.array(["x", "y"]): ax.tick_params(axis=Axis,colors=(0, 0, 0, 1), width=2)
plt.tight_layout() ## Ensures that subplots don't overlap
plt.show()
view_fiducial_scans(
scan_id_start = 111007,
maxima = [3.218824999999981, -2.8042000000000087, 0.7505399999999938, 1.2411399999999944, -2.321355000000011, -187.27430500000003, -2.0051250000000067, 0.10652499999999065, 0.7922649999999933, -1.3923850000000115], ## From automated fiducial scan
)
```
