gaps_online.tof.visual#

Variable plot visualization FIXME - might get moved around

Functions

eventbld_hb_plots([reader, heartbeats])

Plot the relevant quantities from the event builder heartbeats

mtb_rate_plot([datafiles, mtbmonidata, ...])

Show the rate and lost_rate as read from MTB register over time

plot_hg_lg_hits([reader, events, plot_dir, ...])

Plot the HG vs the LG (trigger) hits

plot_ltb_threshold_timeseries(times, ltb_hk)

The LTB thresholds over the given times.

plot_paddle_charge2d([reader, charge_a, ...])

Plot the paddle charge (from the TofHits) in a symmetric 2d histogram

plot_rb_paddles(rb)

Create the TOF projection plots and mark the respective panels corresponding to the ReadoutBoard

timeseries_multiplot(times, variables, labels)

Create a general timeseries plot for multiple variables over the mission elapsed time.

timeseries_plot(times, data[, title, ...])

Create a general timeseries plot for a variable over the mission elapsed time

tof_2dproj([paddle_occupancy, cmap, show_cbar])

Project the whole TOF on the 2d plane in a meaningful way.

tof_projection_xy([paddle_occupancy, cmap, ...])

Show the projection of all paddles which are facing in z-direction These are the whole Umbrella as well as CBE TOP + Bottom.

unroll_cbe_sides([paddle_occupancy, cmap, ...])

Project the sides of the cube on xz and yz as well as add the 'edge' paddles.

unroll_cor([paddle_occupancy, cmap, show_cbar])

Project the cortina on xz and yz as well as add the 'edge' paddles.
gaps_online.tof.visual.timeseries_plot(times, data, title='', xlabel='', ylabel='', savename='')#

Create a general timeseries plot for a variable over the mission elapsed time

# Arguments:

  • times : gcutimes, ideally re-normalized to run start time

  • data : quantity to plot over time

# Keyword Arguments:

title : axis title xlabel : label for the x-axis ylabel : label for the y-axis savevname : save plot with this filename,

if None, don't save

gaps_online.tof.visual.timeseries_multiplot(times, variables, labels, title='', xlabel='', ylabel='', savename='')#

Create a general timeseries plot for multiple variables over the mission elapsed time. This is basically the same as timeseries_plot, however, for multiple variables which will all be plotted in the same axis.

# Arguments:

  • times : gcutimes, ideally re-normalized to run start time

  • variablesquantities to plot over time. This should be a list

    of lists (or arrays)

  • labelsindividual labels for the variables, same ordering

    structure

# Keyword Arguments:

title : axis title xlabel : label for the x-axis ylabel : label for the y-axis savevname : save plot with this filename,

if None, don't save

gaps_online.tof.visual.tof_projection_xy(paddle_occupancy={}, cmap=<matplotlib.colors.LinearSegmentedColormap object>, show_cbar=True)#

Show the projection of all paddles which are facing in z-direction These are the whole Umbrella as well as CBE TOP + Bottom. While this plot can show the occupancy of TOF paddles, it can also be 'hijacked' to just highlight certain paddles.

# Keyword Arguments:

paddle_occupancy : The number of events per paddle cmap : Colormap - can be lambda function

to return color value based on 'occupancy' numbker

show_cbar : Show the colorbar on the figure

gaps_online.tof.visual.unroll_cbe_sides(paddle_occupancy={}, cmap=<matplotlib.colors.LinearSegmentedColormap object>, show_cbar=True)#

Project the sides of the cube on xz and yz as well as add the 'edge' paddles.

While this plot can show the occupancy of TOF paddles, it can also be 'hijacked' to just highlight certain paddles.

# Keyword Arguments:

paddle_occupancy : The number of events per paddle cmap : Colormap - can be lambda function

to return color value based on 'occupancy' numbker

show_cbar : Show the colorbar on the figure

gaps_online.tof.visual.unroll_cor(paddle_occupancy={}, cmap=<matplotlib.colors.LinearSegmentedColormap object>, show_cbar=True)#

Project the cortina on xz and yz as well as add the 'edge' paddles.

While this plot can show the occupancy of TOF paddles, it can also be 'hijacked' to just highlight certain paddles.

# Keyword Arguments:

paddle_occupancy : The number of events per paddle cmap : Colormap - can be lambda function

to return color value based on 'occupancy' numbker

show_cbar : Show the colorbar on the figure

gaps_online.tof.visual.tof_2dproj(paddle_occupancy={}, cmap=<matplotlib.colors.LinearSegmentedColormap object>, show_cbar=True)#

Project the whole TOF on the 2d plane in a meaningful way.

While this plot can show the occupancy of TOF paddles, it can also be 'hijacked' to just highlight certain paddles.

# Keyword Arguments:

paddle_occupancy : The number of events per paddle cmap : Colormap - can be lambda function

to return color value based on 'occupancy' numbker

show_cbar : Show the colorbar on the figure

gaps_online.tof.visual.plot_rb_paddles(rb)#

Create the TOF projection plots and mark the respective panels corresponding to the ReadoutBoard

# Arguments:

rb : go.db.ReadoutBoard

gaps_online.tof.visual.plot_ltb_threshold_timeseries(times, ltb_hk, savename=None)#

The LTB thresholds over the given times.

# Arguments:

  • times : mission elapsed time

  • ltb_hk : a list of LTBMoniData

# Keyword Arguments:

  • savename : filename of the plot to save

gaps_online.tof.visual.plot_paddle_charge2d(reader=None, charge_a=[], charge_b=[], paddle_id=0, charge_bins=array([0., 1.44927536, 2.89855072, 4.34782609, 5.79710145, 7.24637681, 8.69565217, 10.14492754, 11.5942029, 13.04347826, 14.49275362, 15.94202899, 17.39130435, 18.84057971, 20.28985507, 21.73913043, 23.1884058, 24.63768116, 26.08695652, 27.53623188, 28.98550725, 30.43478261, 31.88405797, 33.33333333, 34.7826087, 36.23188406, 37.68115942, 39.13043478, 40.57971014, 42.02898551, 43.47826087, 44.92753623, 46.37681159, 47.82608696, 49.27536232, 50.72463768, 52.17391304, 53.62318841, 55.07246377, 56.52173913, 57.97101449, 59.42028986, 60.86956522, 62.31884058, 63.76811594, 65.2173913, 66.66666667, 68.11594203, 69.56521739, 71.01449275, 72.46376812, 73.91304348, 75.36231884, 76.8115942, 78.26086957, 79.71014493, 81.15942029, 82.60869565, 84.05797101, 85.50724638, 86.95652174, 88.4057971, 89.85507246, 91.30434783, 92.75362319, 94.20289855, 95.65217391, 97.10144928, 98.55072464, 100.]), plot_dir=None)#

Plot the paddle charge (from the TofHits) in a symmetric 2d histogram

gaps_online.tof.visual.mtb_rate_plot(datafiles=None, mtbmonidata=[], use_gcutime=False, mtb_moni_interval=10, plot_dir=None)#

Show the rate and lost_rate as read from MTB register over time

# Arguments

  • datafiles: Can be either str, pathlib.Path or a list of these, or None.

    If not None, walk over them and extract the MTB moni data

gaps_online.tof.visual.plot_hg_lg_hits(reader=None, events=[], plot_dir=None, split_by_threshold=False)#

Plot the HG vs the LG (trigger) hits

gaps_online.tof.visual.eventbld_hb_plots(reader=None, heartbeats=[])#

Plot the relevant quantities from the event builder heartbeats