psp-liquids-daq-parser 0.0.7

Used to parse, convert, pickle, and pre-process TDMS files from BCLS or other NI daq boxes.

Purdue Space Program: Liquids BCLS DAQ utils

Used to parse, convert, pickle, and pre-process TDMS files from BCLS or other NI daq boxes.

Written in typed Python for easier linting (pref: ruff, default config)

Installation

To install, simply run pip install psp-liquids-daq-parser

Use

There are two functions and two classes that come with this package:

Function: parseTDMS:

start_time_unix_ms defines the start time of the TDMS file in milliseconds since the UNIX epoch. This is a required argument, and offsets the TDMS time data with the given start time converted to seconds.

If file_path_custom isn't specified, the file picker dialog comes up to select a tdms file. Then, we check to see if there's an equivalent pickle file in the same directory as the chosen tdms file.

If there's a pickle file, we parse that. Otherwise, we parse the TDMS file and save the resulting object to a pickle file for later.

Function: extendDatasets

Basically makes all the datasets of all the channel the same length. Uses the numpy "edge" method for the time dataset. Uses constant values for channel data (o for analog data, 0.5 for binary data)

For example, if you had: { "channel1": [0, 1, 2], "channel2": [23, 234, 235, 12, 456] } , this function would return: { "channel1": [0, 1, 2, 0, 0], "channel2": [23, 234, 235, 12, 456] }

Class: DigitalChannelData

Stores a dataset of digital channel data. Has the following properties:

• data: NDArray[float64]
• properties: OrderedDict
• name: str
• channelType: str
• description: str

Is set/constructed by calling the class with the following required arguments: DigitalChannelData(rawData: NDArray[float64], properties: OrderedDict, name: str, description: str, channel_type: str)

Class: AnalogChannelData

Stores a dataset of analog channel data. Has the following properties:

• rawData: NDArray[float64]
• data: NDArray[float64] = (rawData * slope) + zeroing_correction + offset
• properties: OrderedDict
• name: str
• slope: float
• offset: float
• zeroing_target: float
• zeroing_correction: float
• description: str
• units: str
• channelType: str
• constant_cjc: float
• tc_type: str
• min_v: float
• max_v: float

Is set/constructed by calling the class with the following required arguments: AnalogChannelData(rawData: NDArray[float64], properties: OrderedDict, name: str, slope: float, offset: float, zeroing_target: float, zeroing_correction: float, description: str, units: str, channelType: str, constant_cjc: float, tc_type: str, min_v: float, max_v: float

Matplotlib Example

Assuming you've downloaded CMS's coldflow 2 data into a folder called "cf2" and pip installed this package:

from psp_liquids_daq_parser import parseTDMS, extendDatasets
from matplotlib import pyplot as plt

channel_datasets = parseTDMS(5, file_path_custom="./cf2/DataLog_2024-0406-1828-28_CMS_Data_Wiring_5.tdms")

channel_datasets.update(parseTDMS(6, file_path_custom="./cf2/DataLog_2024-0406-1828-28_CMS_Data_Wiring_6.tdms"))

(available_channels, data) = extendDatasets(channel_datasets)

binary_multiplier: float = 1

PT_FU_04 = data["pt-fu-04"]
PT_HE_01 = data["pt-he-01"]
PT_OX_04 = data["pt-ox-04"]
PT_N2_01 = data["pt-n2-01"]
PT_FU_02 = data["pt-fu-02"]
PT_OX_02 = data["pt-ox-02"]
TC_OX_04 = data["tc-ox-04"]
TC_FU_04 = data["tc-fu-04"]
TC_OX_02 = data["tc-ox-02"]
TC_FU_02 = data["tc-fu-02"]
RTD_FU = data["rtd-fu"]
RTD_OX = data["rtd-ox"]
PT_FU_202 = data["pt-fu-202"]
PT_HE_201 = data["pt-he-201"]
PT_OX_202 = data["pt-ox-202"]
PT_TEST_AI_20 = data["pt-test-ai-20"]
PI_HE_01 = data["pi-he-01"] * binary_multiplier
PI_FU_02 = data["pi-fu-02"] * binary_multiplier
PI_OX_02 = data["pi-ox-02"] * binary_multiplier
PI_FU_03 = data["pi-fu-03"] * binary_multiplier
PI_OX_03 = data["pi-ox-03"] * binary_multiplier
REED_BP_01 = data["reed-bp-01"] * binary_multiplier
PI_FU_201 = data["pi-fu-201"] * binary_multiplier
PI_OX_201 = data["pi-ox-201"] * binary_multiplier
REED_MAROTTA_1 = data["reed-marotta-1"] * binary_multiplier
REED_MAROTTA_2 = data["reed-marotta-2"] * binary_multiplier
REED_N2_02 = data["reed-n2-02"] * binary_multiplier
REED_MAROTTA_3 = data["reed-marotta-3"] * binary_multiplier
TC_OX_201 = data["tc-ox-201"]
TC_FU_201 = data["tc-fu-201"]
time: list[float] = data["time"]

fig, host = plt.subplots()
ax1 = host.twinx()
host.plot(time, PT_FU_202)
ax1.plot(time, REED_MAROTTA_1)
host.set_xlabel("time (s)")
host.set_ylabel("pressure (psi)")
ax1.set_ylabel("binary")

fig, host = plt.subplots()
ax1 = host.twinx()
host.plot(time, PT_OX_202)
ax1.plot(time, REED_MAROTTA_2)
plt.show()

Plotly Example

Assuming you've downloaded CMS's coldflow 2 data into a folder called "cf2" and pip installed both this package and dash:

from psp_liquids_daq_parser import extendDatasets, parseTDMS

from dash import Dash, html, dcc, callback, Output, Input
import plotly.express as px
import pandas as pd

# Use the tdms file's functions to get multiple tdms files, then combine them
channel_data = parseTDMS(
    5,
    file_path_custom="./cf2/DataLog_2024-0406-1828-28_CMS_Data_Wiring_5.tdms",  # the "file_path_custom" arg is optional
)
channel_data.update(
    parseTDMS(
        6,
        file_path_custom="./cf2/DataLog_2024-0406-1828-28_CMS_Data_Wiring_6.tdms",
    )
)
# after combining, make all the datasets the same length by extending the datasets if necessary
available_channels, df_list_constant = extendDatasets(channel_data)


app = Dash(__name__)

app.layout = html.Div(
    [
        html.H1(
            children="Coldflow 2 Data",
            style={"textAlign": "center", "fontFamily": "sans-serif"},
        ),
        html.I("scale PI/binary data by: "),
        dcc.Input(
            id="input_{}".format("number"),
            type="number",
            placeholder="input type {}".format("number"),
            debounce=True,
            value=5000,
        ),
        dcc.Graph(id="graph-content", style={"width": "95vw", "height": "85vh"}),
    ]
)


# This is called whenver input is submitted (usually by the user clicking out of the input box), and re-draws the UI
@callback(Output("graph-content", "figure"), Input("input_number", "value"))
def update_graph(value):
    binary_multiplier: float = float(value)
    print(available_channels)
    df_list = {}
    df_list.update(df_list_constant)

    for channel in available_channels:
        if "reed-" in channel or "pi-" in channel:
            df_list.update(
                {
                    channel: df_list[channel] * binary_multiplier,
                }
            )
    df = pd.DataFrame.from_dict(df_list)
    fig = px.line(df, x="time", y=df.columns[0:-1])
    return fig


if __name__ == "__main__":
    app.run(debug=True, host="0.0.0.0", port="80")

Updating this package

After making the necessary code changes, simply push to github and create a new release with the next logical version number.

[!IMPORTANT]
The version number must be of the format: #.#.# and cannot contain any letters. This is the version number that is used to upload to PyPi (PyPi package link)

Detailed Steps

To create a new release, first ensure you've committed/pushed/merged your code onto the main branch of the repo

1. Open the create a new release page
2. Near the top of the form, click on "Choose a tag"
3. Type your new version number and then click Create new tag: #.#.# on publish
4. Click Generate release notes
5. Near the bottom fo the form, ensure the box labeled Set as the latest release is checked/enabled
6. Click Publish Release

After that, a GitHub Action will kick off to build and publish the new package version. Check on it's status here and click into each job/task to view logs.

Originally written by Rajan Phadnis for PSP-Liquids (CMS) (contact)