Examples for using avaread¶

Below are some examples that demonstrate how to get both data and metadata from different file types using avaread.read_file.

This function only takes a file path as an argument and will return an instance of either avaread.AVSFile or avaread.STRFile depending on the detected file type.

Let's first set things up for a demonstration.

In [1]:

from pathlib import Path
import matplotlib.pyplot as plt

import avaread

normal_files = list(Path("../tests/test_files/").glob("*.raw8"))
sequence_files = list(Path("../tests/test_files/").glob("*.str8"))

from pathlib import Path import matplotlib.pyplot as plt import avaread normal_files = list(Path("../tests/test_files/").glob("*.raw8")) sequence_files = list(Path("../tests/test_files/").glob("*.str8"))

Reading multichannel files (*.raw8,*.rir8,etc)¶

Multichannel files store a spectrum from one or more devices, or channels.

You can easily iterate over the AVSFile container, to access the individual AVSChannels that are contained within.

Each AVSChannel can have a different size sensor, exposure time, etc. and therefore stores this information itself.

This information can be easily retrieved using attribute access (e.g. channel.ID.SerialNumber or channel.exposure).

To access the raw data, dark and reference spectra stored for each channel, you can use the scope, dark and ref attributes, while the signal attribute returns the dark-corrected signal.

In [2]:

for f in normal_files:
    file = avaread.read_file(f)
    plt.figure()
    plt.title(file.name)
    for channel in file:
        plt.plot(channel.wavelength, channel.signal, label=f"{channel.ID.SerialNumber}: {channel.exposure:.1f} ms")
    plt.legend()
    plt.xlabel("wavelength (nm)")
    plt.ylabel("Counts (-)")

for f in normal_files: file = avaread.read_file(f) plt.figure() plt.title(file.name) for channel in file: plt.plot(channel.wavelength, channel.signal, label=f"{channel.ID.SerialNumber}: {channel.exposure:.1f} ms") plt.legend() plt.xlabel("wavelength (nm)") plt.ylabel("Counts (-)")

Reading store-to-RAM files (*.str8,etc.)¶

Store-to-RAM files store a series of spectra acquired by a single channel.

Instead of multiple AVSChannels nested in a AVSFile container, the STRFile object immediatly gives access to all the data.

When you iterate over it, you iterate over the frames stored within.

The metadata, such as device serial number and exposure time is the same for all frames, and therefore accesible on the STFile object itself.

Each frame is simply a numpy array of data, containing the background-corrected spectrum.

If you want the uncorrected data, it can be accessed via the scope attibute, similar to an AVSChannel.

In [3]:

for f in sequence_files:
    file = avaread.read_file(f)
    fig, ax = plt.subplots(2, 1)
    ax[0].set_xlabel("wavelength (nm)")
    ax[1].set_xlabel("delay (ms)")
    fig.suptitle(f"{file.name}: exposure: {file.exposure:.2g} ms")
    for a in ax:
        a.set_ylabel("Counts (-)")

    for frame in file:
        ax[0].plot(file.wavelength, frame)

    ax[1].plot(file.delay, [frame.sum() for frame in file])
    fig.tight_layout()

for f in sequence_files: file = avaread.read_file(f) fig, ax = plt.subplots(2, 1) ax[0].set_xlabel("wavelength (nm)") ax[1].set_xlabel("delay (ms)") fig.suptitle(f"{file.name}: exposure: {file.exposure:.2g} ms") for a in ax: a.set_ylabel("Counts (-)") for frame in file: ax[0].plot(file.wavelength, frame) ax[1].plot(file.delay, [frame.sum() for frame in file]) fig.tight_layout()