catalight.analysis.irdata.IRData

class catalight.analysis.irdata.IRData(IR_data_path)

Bases: object

IR camera data and associated processing methods.

IRData compiles and acts on the data collected by a FLIR infrared camera. In the current state, this class loads in a csv file of specific format representing the mean/max temperature of an ROI representing the catalysts. This is a developmental script and may change in future iterations of catalight

__init__(IR_data_path)

Initiate IR camera data class using file location.

On init, this calls import_data() and remove_dropped_frames()

Parameters:: IR_data_path (str) – full path string to filepath. Should be csv file.

Methods

`__init__`(IR_data_path)	Initiate IR camera data class using file location.
`compute_avg_surface_temps`(expts[, ...])	Computes the average surface temperatures during experiments.
`import_data`(IR_data_path)	Import IR cam data as a csv using `read_csv()`.
`plot_averaging`([rezero])	Plots results of temporal averaging of surface temp data.
`plot_raw_data`()	Plots the raw and filtered data.
`remove_dropped_frames`([column_name, ...])	Attemps to remove odd frames that are sometimes captured.
`rezero_time_axis`(t0)	Adds new relative time to surface temp data sets starting a t0.

Attributes

`raw_data`	Uneditted data from csv imported with `import_data()`
`filtered_data`	Filtered data after `remove_dropped_frames()`
`avg_surface_temps`	Avg surface temperatures for each experiment step.
`date_format`	Date format used in the imported csv data.

avg_surface_temps

Avg surface temperatures for each experiment step.

Computed after running the compute_avg_surface_temps() method.

Type:: pandas.DataFrame

compute_avg_surface_temps(expts, measurement_range=20)

Computes the average surface temperatures during experiments.

Imports a list of catalight experiments. Uses the start time of the experiment and the abstime column of the imported IR camera data to determine the time averaged surface temperature of catalyst.

All experiments will have a new attribute “surface_temps” added to it. “surface_temps is a python dict with keys “max” and “min” the value for each key is a list of time averaged temperatures for each step of the experiment.

Parameters:

expts (list[Experiment]) –
List of catalight experiments to perform analysis on.

All experiments will have a new attribute “surface_temps” added to it. “surface_temps is a python dict with keys “max” and “min” the value for each key is a list of time averaged temperatures for each step of the experiment.
measurment_range (int or float) – [minutes] Length of time to average temp on each expt step.

date_format

Date format used in the imported csv data.

Type:: str

filtered_data

Filtered data after remove_dropped_frames()

Type:: pandas.DataFrame

import_data(IR_data_path)

Import IR cam data as a csv using read_csv().

data format should have [“abstime”, “reltime”, max temp, mean temp] The names of columns -1 and -2 are converted to ‘surface temperature - mean’ and ‘surface temperature - max’, respectively

Parameters:: IR_data_path (str) – Full path to IR cam data as a csv file

plot_averaging(rezero=True)

Plots results of temporal averaging of surface temp data.

The point of this function is so that the user can verify whether the IR camera data is being temporally averaged in a satisfactory way. If called with rezero set to True, the user will be able to redefine the start point of the “experimental study” to not plot the full data collection range.

Parameters:: rezero (bool, optional) – If true, ask user to click new time = zero point, by default True

plot_raw_data()

Plots the raw and filtered data.

Plot the raw and filtered data such that the user can verify if the filtering is satisfactory.

Returns:: Figure and axes handles for graphic.
Return type:: ~matplotlib.figure.Firgure, ~matplotlib.axes.Axes

raw_data

Uneditted data from csv imported with import_data()

Type:: pandas.DataFrame

remove_dropped_frames(column_name='surface temperature - mean', window_size=50, threshold=10)

Attemps to remove odd frames that are sometimes captured.

Drop frames that fall “threshold” away from the mean using rolling mean.

Parameters:

column_name (str, optional) – name of the column to use for computing the rolling mean and dropping frames, by default ‘surface temperature - mean’
window_size (int, optional) – number of datapoint to use when computing rolling mean, by default 50
threshold (int, optional) – Degrees K to count as a normal deviation from the rolling mean. A variation larger than this is considered a “dropped” frame that was incorrectly measured by IR camera, by default 10

rezero_time_axis(t0)

Adds new relative time to surface temp data sets starting a t0.

Add the column “graph_time” to raw_data and filtered_data and adds [‘t1 - rel’] and [‘t2 - rel’] columns to avg_surface_temps. These new time values are relative times in which time zero starts at t0.

This time rezeroing can be performed by calling plot_averaging()

Parameters:: t0 (pandas.Timestamp) – Pandas timestamp for the t0 value