X-ray Emission Spectroscopy Outlier Removal and Concentration Correction

This example demonstrates processing X-ray emission spectroscopy (XES) data, including concentration correction and outlier detection/removal with visualization.

import logging
import sys

import matplotlib.pyplot as plt

from daxs.measurements import Xes
from daxs.sources import Hdf5Source
from daxs.utils import resources

# Create a logger to track the processing steps.
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
# The default level for the *daxs* logger is set to INFO.
# Increase to DEBUG to get more detailed output.
logging.getLogger("daxs").setLevel(logging.INFO)

# Define data mappings for XES data.
data_mappings = {
    "x": ".1/measurement/xes_en_texs",
    "signal": [
        ".1/measurement/g02",
        ".1/measurement/g03",
        ".1/measurement/g04",
        ".1/measurement/g05",
        ".1/measurement/g06",
        ".1/measurement/g07",
        ".1/measurement/g09",
        ".1/measurement/g10",
        ".1/measurement/g11",
        ".1/measurement/g12",
        ".1/measurement/g13",
        ".1/measurement/g14",
    ],
    "monitor": ".1/measurement/I0t",
}

# Load data and create measurement object.
hdf5_filename = resources.getfile("P_XES.h5")
source = Hdf5Source(hdf5_filename, 134, data_mappings=data_mappings)
measurement = Xes(source)

# Apply concentration correction.
measurement.concentration_correction(135)

# Find outliers.
measurement.find_outliers(threshold=9)
for scan in measurement.scans:
    fig, ax = plt.subplots()
    scan.plot(ax)
plt.tight_layout()
plt.show()

# Remove outliers and plot before/after.
fig, ax = plt.subplots()
ax.plot(measurement.x, measurement.signal, label="Before")
measurement.remove_outliers()
ax.plot(measurement.x, measurement.signal, label="After")
ax.set_xlabel("Energy (keV)")
ax.set_ylabel("Intensity (a.u.)")
ax.legend(fancybox=False)
ax.set_title("Outlier Removal")

plt.tight_layout()
plt.show()

Detailed Explanations

We start by importing modules and setting up logging for detailed output during processing.

import logging
import sys

import matplotlib.pyplot as plt

from daxs.measurements import Xes
from daxs.sources import Hdf5Source
from daxs.utils import resources

logging.basicConfig(level=logging.INFO, stream=sys.stdout)
logging.getLogger("daxs").setLevel(logging.INFO)

Next, define data mappings for the HDF5 file, specifying x-axis (emission energy), multiple signal channels, and monitor.

data_mappings = {
    "x": ".1/measurement/xes_en_texs",
    "signal": [
        ".1/measurement/g02",
        ".1/measurement/g03",
        ".1/measurement/g04",
        ".1/measurement/g05",
        ".1/measurement/g06",
        ".1/measurement/g07",
        ".1/measurement/g09",
        ".1/measurement/g10",
        ".1/measurement/g11",
        ".1/measurement/g12",
        ".1/measurement/g13",
        ".1/measurement/g14",
    ],
    "monitor": ".1/measurement/I0t",
}

Load the data from scan 134 and create an Xes measurement object.

hdf5_filename = resources.getfile("P_XES.h5")
source = Hdf5Source(hdf5_filename, 134, data_mappings=data_mappings)
measurement = Xes(source)

Apply concentration correction using information from scan 135.

measurement.concentration_correction(135)

Now that the data is loaded and concentration corrected, detect outliers with a threshold of 9. Plot all scans in a single figure to visualize the detected outliers.

measurement.find_outliers(threshold=9)
for scan in measurement.scans:
    fig, ax = plt.subplots()
    scan.plot(ax)
plt.tight_layout()
plt.show()

Finally, the outliers are removed and the signal before and after removal is plotted for comparison.

fig, ax = plt.subplots()
ax.plot(measurement.x, measurement.signal, label="Before")
measurement.remove_outliers()
ax.plot(measurement.x, measurement.signal, label="After")
ax.set_xlabel("Energy (keV)")
ax.set_ylabel("Intensity (a.u.)")
ax.legend(fancybox=False)
ax.set_title("Outlier Removal")
plt.tight_layout()
plt.show()