Welcome to gripspy!

Introduction

This Python package is intended for analysis of data from the GRIPS long-duration flight of 2016 January 19–30. It can also be used to analyze the pre-flight calibration data that was already being recorded using flight-like packet formats. The code is still very much a work in progress, so not only are many elements still missing, even the parts that have been coded are likely to go through changes in their interfaces and organization.

Table of contents

Installation

Prerequisites

This package depends on:

  • Python 3.5 or 2.7 (untested on other versions)
  • NumPy
  • SciPy
  • matplotlib
  • Cython, and corresponding build environment for compiling C code
  • scikit-image
  • Astropy

However, rather than installing these packages individually, it is highly recommended that one instead use a scientific Python distribution (e.g., Anaconda), which will include useful packages such as Jupyter Notebook (formerly IPython Notebook).

The build environment for C code is a bit trickier to set up on Windows than on the other platforms:

  • Python 3.5:

  • Python 2.7:

    • Install Microsoft Visual C++ Compiler for Python 2.7

    • Patch C:\yourpythoninstall\Lib\distutils\msvc9compiler.py by adding the following highlighted lines at the top of the find_vcvarsall() function:

      def find_vcvarsall(version):
    """Find the vcvarsall.bat file

    At first it tries to find the productdir of VS 2008 in the registry. If
    that fails it falls back to the VS90COMNTOOLS env var.
    """
    vsbase = VS_BASE % version
    vcpath = os.environ['ProgramFiles']
    vcpath = os.path.join(vcpath, 'Common Files', 'Microsoft',
        'Visual C++ for Python', '9.0', 'vcvarsall.bat')
    if os.path.isfile(vcpath): return vcpath
    vcpath = os.path.join(os.environ['LOCALAPPDATA'], 'Programs', 'Common', 'Microsoft', 'Visual C++ for Python', '9.0', 'vcvarsall.bat')
    if os.path.isfile(vcpath): return vcpath
    ...

    • Create a file distutils.cfg in C:\yourpythoninstall\Lib\distutils\ with the following:

      [build]
compiler=msvc

You should now be able to build extensions. If these steps don’t work for you, or you are using a different version of Python, this page or this other page may be helpful.

Installing gripspy

You can download gripspy either as a Git repository or just the code itself. Installing gripspy can be done through the basic ways (e.g., python setup.py), although I prefer using pip so that tracking installations is easier. I recommend installing gripspy in “editable” mode so that it is more convenient for active development, e.g.:

pip install -e .

Quick Start

Simply import gripspy to start.

import gripspy

The classes for handling science data are under gripspy.science. The classes for handling housekeeping data are under gripspy.housekeeping. Here are some examples of what has been implemented so far:

data = gripspy.science.GeData(detector_number, filename)
data = gripspy.science.BGOCounterData(filename)
data = gripspy.science.BGOEventData(filename)
data = gripspy.science.PYSequence(filelist)
data = gripspy.housekeeping.GPSData(filename)

Supported Packets

Only some of the types of GRIPS packets are currently supported.

Support? SystemID TmType Description
0x00 0x02 Flight software data rates
0x00 0x03 Flight software telemetry settings
0x00 0x0E Flight software error
0x00 0x0F Flight software message
0x01 0x02 Flight software watchdog
0x02 0x02 Flight software watchpup
partial 0x03 0x02 To pointing control system computer (PCSC)
partial 0x03 0x03 From pointing control system computer (PCSC)
full 0x05 0x02 GPS
0x07 0x02 Temperatures, through flight computer
0x07 0x03 Thermostats
0x0A 0x02 PDU voltages and currents
0x0A 0x03 Cryostat temperatures
0x0A 0x04 Power statuses
0x0C 0x02 Cryocooler housekeeping
0x0D 0x02 Not-dead-yet information
0x0F 0x02 MPPT housekeeping
0x10 0x1? Ge quicklook spectrum and rates
0x10 0x20 Ge quicklook hitmap strip
0x40 0x02 Aspect housekeeping
0x40 0x03 Aspect settings
0x40 0x04 Temperatures, through aspect computer
0x40 0x10 Aspect science
0x4A 0x20 Aspect pitch-yaw image row
0x4B 0x20 Aspect roll image row
partia 0x8? 0x02 Card cage (Ge) housekeeping
0x8? 0x04 Card cage (Ge) ASIC registers, LV top
0x8? 0x05 Card cage (Ge) ASIC registers, LV bottom
0x8? 0x06 Card cage (Ge) ASIC registers, HV top
0x8? 0x07 Card cage (Ge) ASIC registers, HV bottom
partial 0x8? 0x08 Card cage (Ge) counters
0x8? 0x09 Card cage (Ge) temperatures
full 0x8? 0xF1 Card cage (Ge) event, raw format, LV side
full 0x8? 0xF2 Card cage (Ge) event, raw format, HV side
full 0x8? 0xF3 Card cage (Ge) event, normal format
0x8? 0xFC Card cage (Ge) channel enables
0x9? 0x04 Card cage (Ge) ASIC register differences, LV top
0x9? 0x05 Card cage (Ge) ASIC register differences, LV bottom
0x9? 0x06 Card cage (Ge) ASIC register differences, HV top
0x9? 0x07 Card cage (Ge) ASIC register differences, HV bottom
0xB6 0x02 Shield (BGO) housekeeping
0xB6 0x09 Shield (BGO) temperatures
0xB6 0x13 Shield (BGO) configuration
full 0xB6 0x80 Shield (BGO) events, 8 bytes per event
full 0xB6 0x81 Shield (BGO) counter rates
full 0xB6 0x82 Shield (BGO) events, 5 bytes per event
0xC0 0x02 SMASH housekeeping
0xC0 0x03 SMASH log messages
0xC0 0x10 SMASH science

Code Reference

Code reference for gripspy

Science

Ge

Module for analyzing data from the germanium detectors

class gripspy.science.ge.GeData(detector, telemetry_file=None, save_file=None, max_triggers=None, reject_glitches=False)[source]

Class for analyzing event data from a germanium detector

Parameters:
  • detector (int) – The detector number (0 to 5, usually)
  • telemetry_file (str (or list)) – The name of the telemetry file to analyze. If None is specified, a save file must be specified.
  • save_file (str (or list)) – The name of a save file from a telemetry file that was previously parsed.
  • max_triggers (int) – If not None, cull any events with more than this number of triggers on either detector side.
  • reject_glitches (bool) – If True, cull any events that have any glitch bit set (even on non-triggered channels)

Notes

event_time is stored in 10-ns steps

a

Shorthand for the adc attribute

append(other, defer_processing=False)[source]

Append the information in another GeData instance

c

Shorthand for the cms attribute

d

Shorthand for the delta_time attribute

e

Shorthand for the event_time attribute

g

Shorthand for the glitch attribute

hitmap

The hitmap of single-trigger events

lightcurve(side, binning, time_step_in_seconds=1.0, energy_coeff=None, max_triggers=1)[source]

Return a detector-side-integrated lightcurve, excluding events that exceed a specified maximum number of triggers on that side. Note that max_triggers == 1 is not quite the same as “single-trigger” events because no selection cut is imposed on the the opposite side.

Parameters:
  • side (0 or 1) – 0 for LV side, 1 for HV side
  • binning (array-like) – The binning to use for the underlying data
  • time_step_in_seconds (float) – The size of the time step in seconds
  • energy_coeff (2x512 ndarray) – If not None, apply these linear coefficients (intercept, slope) to convert to energy in keV
  • max_triggers (int) – Exclude events that have more than this number of triggers on the specified side
plot_conversion_vs_time(asiccha, event_mask=None, time_binning=(250, 501, 1), max_triggers=None, **hist2d_kwargs)[source]

Plot the conversions as a function of time since the preceding conversion. This type of plot is useful for looking for post-conversion baseline effects. This plot only makes sense if no events have been culled from the event list upon parsing (e.g., from having too many triggers or having glitches).

Binning of ADC values is currently automatic around the median of the values.

Parameters:
  • asiccha (tuple or int) – Either (ASIC#, channel#) if a tuple or ASIC# * 64 + channel# if an int
  • event_mask (ndarray) – If not None, bool array where True values are which events to plot. Otherwise, the default is those events where asiccha did not trigger.
  • max_triggers (int) – If not None, only show conversions that do not exceed this number of triggers on the same side as asiccha.
  • time_binning (tuple or array-like) – The binning to use for the time since the preceding conversion in microseconds
plot_depth(binning=array([-595, -585, -575, -565, -555, -545, -535, -525, -515, -505, -495, -485, -475, -465, -455, -445, -435, -425, -415, -405, -395, -385, -375, -365, -355, -345, -335, -325, -315, -305, -295, -285, -275, -265, -255, -245, -235, -225, -215, -205, -195, -185, -175, -165, -155, -145, -135, -125, -115, -105, -95, -85, -75, -65, -55, -45, -35, -25, -15, -5, 5, 15, 25, 35, 45, 55, 65, 75, 85, 95, 105, 115, 125, 135, 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 245, 255, 265, 275, 285, 295, 305, 315, 325, 335, 345, 355, 365, 375, 385, 395, 405, 415, 425, 435, 445, 455, 465, 475, 485, 495, 505, 515, 525, 535, 545, 555, 565, 575, 585, 595]), **hist_kwargs)[source]

Plot the depth-information plot, for only single-trigger events

Parameters:binning (array-like) – The binning to use for the time-difference axis in nanoseconds
plot_depth_vs_energy(side, depth_binning=array([-595, -585, -575, -565, -555, -545, -535, -525, -515, -505, -495, -485, -475, -465, -455, -445, -435, -425, -415, -405, -395, -385, -375, -365, -355, -345, -335, -325, -315, -305, -295, -285, -275, -265, -255, -245, -235, -225, -215, -205, -195, -185, -175, -165, -155, -145, -135, -125, -115, -105, -95, -85, -75, -65, -55, -45, -35, -25, -15, -5, 5, 15, 25, 35, 45, 55, 65, 75, 85, 95, 105, 115, 125, 135, 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 245, 255, 265, 275, 285, 295, 305, 315, 325, 335, 345, 355, 365, 375, 385, 395, 405, 415, 425, 435, 445, 455, 465, 475, 485, 495, 505, 515, 525, 535, 545, 555, 565, 575, 585, 595]), energy_binning=array([-128, -120, -112, -104, -96, -88, -80, -72, -64, -56, -48, -40, -32, -24, -16, -8, 0, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 264, 272, 280, 288, 296, 304, 312, 320, 328, 336, 344, 352, 360, 368, 376, 384, 392, 400, 408, 416, 424, 432, 440, 448, 456, 464, 472, 480, 488, 496, 504, 512, 520, 528, 536, 544, 552, 560, 568, 576, 584, 592, 600, 608, 616, 624, 632, 640, 648, 656, 664, 672, 680, 688, 696, 704, 712, 720, 728, 736, 744, 752, 760, 768, 776, 784, 792, 800, 808, 816, 824, 832, 840, 848, 856, 864, 872, 880, 888, 896, 904, 912, 920, 928, 936, 944, 952, 960, 968, 976, 984, 992, 1000, 1008, 1016, 1024, 1032, 1040, 1048, 1056, 1064, 1072, 1080, 1088, 1096, 1104, 1112, 1120, 1128, 1136, 1144, 1152, 1160, 1168, 1176, 1184, 1192, 1200, 1208, 1216, 1224, 1232, 1240, 1248, 1256, 1264, 1272, 1280, 1288, 1296, 1304, 1312, 1320, 1328, 1336, 1344, 1352, 1360, 1368, 1376, 1384, 1392, 1400, 1408, 1416, 1424, 1432, 1440, 1448, 1456, 1464, 1472, 1480, 1488, 1496, 1504, 1512, 1520, 1528, 1536, 1544, 1552, 1560, 1568, 1576, 1584, 1592, 1600, 1608, 1616, 1624, 1632, 1640, 1648, 1656, 1664, 1672, 1680, 1688, 1696, 1704, 1712, 1720, 1728, 1736, 1744, 1752, 1760, 1768, 1776, 1784, 1792, 1800, 1808, 1816, 1824, 1832, 1840, 1848, 1856, 1864, 1872, 1880, 1888, 1896, 1904, 1912, 1920, 1928, 1936, 1944, 1952, 1960, 1968, 1976, 1984, 1992, 2000, 2008, 2016, 2024, 2032, 2040]), energy_coeff=None, min_lv_triggers=1, max_lv_triggers=1, min_hv_triggers=1, max_hv_triggers=1, **hist2d_kwargs)[source]

Plot the depth information versus ‘energy’, integrated over a detector side

Parameters:
  • side (0 or 1) – 0 for LV side, 1 for HV side
  • depth_binning (array-like) – The binning to use for the time-difference axis in nanoseconds
  • energy_binning (array-like) – The binning to use for the ‘energy’ axis in ADC bins or in keV
  • energy_coeff (2x512 ndarray) – If not None, apply these linear coefficients (intercept, slope) to convert to energy in keV
  • min_lv_triggers (int) – Do not include events with fewer than this number of triggers on the LV side
  • max_lv_triggers (int) – Do not include events with more than this number of triggers on the LV side
  • min_hv_triggers (int) – Do not include events with fewer than this number of triggers on the HV side
  • max_hv_triggers (int) – Do not include events with more than this number of triggers on the HV side
plot_hitmap(**imshow_kwargs)[source]

Plot the hitmap of single-trigger events

plot_multiple_trigger_veto(side)[source]

Plot the distribution of multiple-trigger events and veto information

Parameters:side (0 or 1) – 0 for LV side, 1 for HV side
plot_spatial_spectrum(side, binning=array([-128, -120, -112, -104, -96, -88, -80, -72, -64, -56, -48, -40, -32, -24, -16, -8, 0, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 264, 272, 280, 288, 296, 304, 312, 320, 328, 336, 344, 352, 360, 368, 376, 384, 392, 400, 408, 416, 424, 432, 440, 448, 456, 464, 472, 480, 488, 496, 504, 512, 520, 528, 536, 544, 552, 560, 568, 576, 584, 592, 600, 608, 616, 624, 632, 640, 648, 656, 664, 672, 680, 688, 696, 704, 712, 720, 728, 736, 744, 752, 760, 768, 776, 784, 792, 800, 808, 816, 824, 832, 840, 848, 856, 864, 872, 880, 888, 896, 904, 912, 920, 928, 936, 944, 952, 960, 968, 976, 984, 992, 1000, 1008, 1016, 1024, 1032, 1040, 1048, 1056, 1064, 1072, 1080, 1088, 1096, 1104, 1112, 1120, 1128, 1136, 1144, 1152, 1160, 1168, 1176, 1184, 1192, 1200, 1208, 1216, 1224, 1232, 1240, 1248, 1256, 1264, 1272, 1280, 1288, 1296, 1304, 1312, 1320, 1328, 1336, 1344, 1352, 1360, 1368, 1376, 1384, 1392, 1400, 1408, 1416, 1424, 1432, 1440, 1448, 1456, 1464, 1472, 1480, 1488, 1496, 1504, 1512, 1520, 1528, 1536, 1544, 1552, 1560, 1568, 1576, 1584, 1592, 1600, 1608, 1616, 1624, 1632, 1640, 1648, 1656, 1664, 1672, 1680, 1688, 1696, 1704, 1712, 1720, 1728, 1736, 1744, 1752, 1760, 1768, 1776, 1784, 1792, 1800, 1808, 1816, 1824, 1832, 1840, 1848, 1856, 1864, 1872, 1880, 1888, 1896, 1904, 1912, 1920, 1928, 1936, 1944, 1952, 1960, 1968, 1976, 1984, 1992, 2000, 2008, 2016, 2024, 2032, 2040]), energy_coeff=None, use_strip_number=False, **hist2d_kwargs)[source]

Plot the subtracted single-trigger spectrum versus channel as a 2D image

Parameters:
  • side (0 or 1) – 0 for LV side, 1 for HV side
  • binning (array-like) – The binning to use for the underlying data
  • energy_coeff (2x512 ndarray) – If not None, apply these linear coefficients (intercept, slope) to convert to energy in keV
  • use_strip_number (bool) – If True, use strip number as the horizontal axis instead of channel number
plot_spectrogram(side, time_step_in_seconds=1.0, binning=array([-128, -120, -112, -104, -96, -88, -80, -72, -64, -56, -48, -40, -32, -24, -16, -8, 0, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 264, 272, 280, 288, 296, 304, 312, 320, 328, 336, 344, 352, 360, 368, 376, 384, 392, 400, 408, 416, 424, 432, 440, 448, 456, 464, 472, 480, 488, 496, 504, 512, 520, 528, 536, 544, 552, 560, 568, 576, 584, 592, 600, 608, 616, 624, 632, 640, 648, 656, 664, 672, 680, 688, 696, 704, 712, 720, 728, 736, 744, 752, 760, 768, 776, 784, 792, 800, 808, 816, 824, 832, 840, 848, 856, 864, 872, 880, 888, 896, 904, 912, 920, 928, 936, 944, 952, 960, 968, 976, 984, 992, 1000, 1008, 1016, 1024, 1032, 1040, 1048, 1056, 1064, 1072, 1080, 1088, 1096, 1104, 1112, 1120, 1128, 1136, 1144, 1152, 1160, 1168, 1176, 1184, 1192, 1200, 1208, 1216, 1224, 1232, 1240, 1248, 1256, 1264, 1272, 1280, 1288, 1296, 1304, 1312, 1320, 1328, 1336, 1344, 1352, 1360, 1368, 1376, 1384, 1392, 1400, 1408, 1416, 1424, 1432, 1440, 1448, 1456, 1464, 1472, 1480, 1488, 1496, 1504, 1512, 1520, 1528, 1536, 1544, 1552, 1560, 1568, 1576, 1584, 1592, 1600, 1608, 1616, 1624, 1632, 1640, 1648, 1656, 1664, 1672, 1680, 1688, 1696, 1704, 1712, 1720, 1728, 1736, 1744, 1752, 1760, 1768, 1776, 1784, 1792, 1800, 1808, 1816, 1824, 1832, 1840, 1848, 1856, 1864, 1872, 1880, 1888, 1896, 1904, 1912, 1920, 1928, 1936, 1944, 1952, 1960, 1968, 1976, 1984, 1992, 2000, 2008, 2016, 2024, 2032, 2040]), energy_coeff=None, max_triggers=1, **hist2d_kwargs)[source]

Plot a detector-side-integrated spectrogram, excluding events that exceed a specified maximum number of triggers on that side. Note that max_triggers == 1 is not quite the same as “single-trigger” events because no selection cut is imposed on the the opposite side.

Parameters:
  • side (0 or 1) – 0 for LV side, 1 for HV side
  • time_step_in_seconds (float) – The size of the time step in seconds
  • binning (array-like) – The binning to use for the underlying data
  • energy_coeff (2x512 ndarray) – If not None, apply these linear coefficients (intercept, slope) to convert to energy in keV
  • max_triggers (int) – Exclude events that have more than this number of triggers on the specified side
plot_spectrum(asiccha, binning=array([ 0, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 264, 272, 280, 288, 296, 304, 312, 320, 328, 336, 344, 352, 360, 368, 376, 384, 392, 400, 408, 416, 424, 432, 440, 448, 456, 464, 472, 480, 488, 496, 504, 512, 520, 528, 536, 544, 552, 560, 568, 576, 584, 592, 600, 608, 616, 624, 632, 640, 648, 656, 664, 672, 680, 688, 696, 704, 712, 720, 728, 736, 744, 752, 760, 768, 776, 784, 792, 800, 808, 816, 824, 832, 840, 848, 856, 864, 872, 880, 888, 896, 904, 912, 920, 928, 936, 944, 952, 960, 968, 976, 984, 992, 1000, 1008, 1016, 1024, 1032, 1040, 1048, 1056, 1064, 1072, 1080, 1088, 1096, 1104, 1112, 1120, 1128, 1136, 1144, 1152, 1160, 1168, 1176, 1184, 1192, 1200, 1208, 1216, 1224, 1232, 1240, 1248, 1256, 1264, 1272, 1280, 1288, 1296, 1304, 1312, 1320, 1328, 1336, 1344, 1352, 1360, 1368, 1376, 1384, 1392, 1400, 1408, 1416, 1424, 1432, 1440, 1448, 1456, 1464, 1472, 1480, 1488, 1496, 1504, 1512, 1520, 1528, 1536, 1544, 1552, 1560, 1568, 1576, 1584, 1592, 1600, 1608, 1616, 1624, 1632, 1640, 1648, 1656, 1664, 1672, 1680, 1688, 1696, 1704, 1712, 1720, 1728, 1736, 1744, 1752, 1760, 1768, 1776, 1784, 1792, 1800, 1808, 1816, 1824, 1832, 1840, 1848, 1856, 1864, 1872, 1880, 1888, 1896, 1904, 1912, 1920, 1928, 1936, 1944, 1952, 1960, 1968, 1976, 1984, 1992, 2000, 2008, 2016, 2024, 2032, 2040, 2048, 2056, 2064, 2072, 2080, 2088, 2096, 2104, 2112, 2120, 2128, 2136, 2144, 2152, 2160, 2168, 2176, 2184, 2192, 2200, 2208, 2216, 2224, 2232, 2240, 2248, 2256, 2264, 2272, 2280, 2288, 2296, 2304, 2312, 2320, 2328, 2336, 2344, 2352, 2360, 2368, 2376, 2384, 2392, 2400, 2408, 2416, 2424, 2432, 2440, 2448, 2456, 2464, 2472, 2480, 2488, 2496, 2504, 2512, 2520, 2528, 2536, 2544, 2552, 2560, 2568, 2576, 2584, 2592, 2600, 2608, 2616, 2624, 2632, 2640, 2648, 2656, 2664, 2672, 2680, 2688, 2696, 2704, 2712, 2720, 2728, 2736, 2744, 2752, 2760, 2768, 2776, 2784, 2792, 2800, 2808, 2816, 2824, 2832, 2840, 2848, 2856, 2864, 2872, 2880, 2888, 2896, 2904, 2912, 2920, 2928, 2936, 2944, 2952, 2960, 2968, 2976, 2984, 2992, 3000, 3008, 3016, 3024, 3032, 3040, 3048, 3056, 3064, 3072, 3080, 3088, 3096, 3104, 3112, 3120, 3128, 3136, 3144, 3152, 3160, 3168, 3176, 3184, 3192, 3200, 3208, 3216, 3224, 3232, 3240, 3248, 3256, 3264, 3272, 3280, 3288, 3296, 3304, 3312, 3320, 3328, 3336, 3344, 3352, 3360, 3368, 3376, 3384, 3392, 3400, 3408, 3416, 3424, 3432, 3440, 3448, 3456, 3464, 3472, 3480, 3488, 3496, 3504, 3512, 3520, 3528, 3536, 3544, 3552, 3560, 3568, 3576]))[source]

Plot the raw spectrum for a specified channel

Parameters:
  • asiccha (tuple or int) – Either (ASIC#, channel#) if a tuple or ASIC# * 64 + channel# if an int
  • binning (array-like) – The binning to use for the underlying data
plot_subtracted_spectrum(asiccha, binning=array([-128, -120, -112, -104, -96, -88, -80, -72, -64, -56, -48, -40, -32, -24, -16, -8, 0, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 264, 272, 280, 288, 296, 304, 312, 320, 328, 336, 344, 352, 360, 368, 376, 384, 392, 400, 408, 416, 424, 432, 440, 448, 456, 464, 472, 480, 488, 496, 504, 512, 520, 528, 536, 544, 552, 560, 568, 576, 584, 592, 600, 608, 616, 624, 632, 640, 648, 656, 664, 672, 680, 688, 696, 704, 712, 720, 728, 736, 744, 752, 760, 768, 776, 784, 792, 800, 808, 816, 824, 832, 840, 848, 856, 864, 872, 880, 888, 896, 904, 912, 920, 928, 936, 944, 952, 960, 968, 976, 984, 992, 1000, 1008, 1016, 1024, 1032, 1040, 1048, 1056, 1064, 1072, 1080, 1088, 1096, 1104, 1112, 1120, 1128, 1136, 1144, 1152, 1160, 1168, 1176, 1184, 1192, 1200, 1208, 1216, 1224, 1232, 1240, 1248, 1256, 1264, 1272, 1280, 1288, 1296, 1304, 1312, 1320, 1328, 1336, 1344, 1352, 1360, 1368, 1376, 1384, 1392, 1400, 1408, 1416, 1424, 1432, 1440, 1448, 1456, 1464, 1472, 1480, 1488, 1496, 1504, 1512, 1520, 1528, 1536, 1544, 1552, 1560, 1568, 1576, 1584, 1592, 1600, 1608, 1616, 1624, 1632, 1640, 1648, 1656, 1664, 1672, 1680, 1688, 1696, 1704, 1712, 1720, 1728, 1736, 1744, 1752, 1760, 1768, 1776, 1784, 1792, 1800, 1808, 1816, 1824, 1832, 1840, 1848, 1856, 1864, 1872, 1880, 1888, 1896, 1904, 1912, 1920, 1928, 1936, 1944, 1952, 1960, 1968, 1976, 1984, 1992, 2000, 2008, 2016, 2024, 2032, 2040]))[source]

Plot the common-mode-subtracted spectrum for a specified channel

Parameters:
  • asiccha (tuple or int) – Either (ASIC#, channel#) if a tuple or ASIC# * 64 + channel# if an int
  • binning (array-like) – The binning to use for the underlying data
s

Shorthand for the single_triggers attribute

s_hv

Shorthand for the single_triggers_lv attribute

s_lv

Shorthand for the single_triggers_lv attribute

save(save_file=None, use_current_directory=False)[source]

Save the parsed data for future reloading. The data is stored in gzip-compressed binary pickle format.

Parameters:
  • save_file (str) – The name of the save file to create. If none is provided, the default is the name of the telemetry file with the extension ”.ge?.pgz” appended if a single telemetry file is the source.
  • use_current_directory (bool) – If True, remove any directory specification from save_file

Notes

Save files may not be compatible between Python 2 and 3

t

Shorthand for the trigger attribute

v

Shorthand for the veto attribute

vh

Shorthand for the HV guard-ring vetoes

vl

Shorthand for the LV guard-ring vetoes

vm

Shorthand for the multiple-trigger vetoes

vs

Shorthand for the shield vetoes

BGO

Module for analyzing data from the BGO shields

class gripspy.science.bgo.BGOEventData(telemetry_file=None, save_file=None)[source]

Class for analyzing event data from the BGO shields

Parameters:
  • telemetry_file (str (or list)) – The name of the telemetry file to analyze. If None is specified, a save file must be specified.
  • save_file (str (or list)) – The name of a save file from a telemetry file that was previously parsed.

Notes

event_time is stored in 10-ns steps

append(other)[source]

Append the information in another BGOEventData instance

c

Shorthand for the channel attribute

e

Shorthand for the event_time attribute

l

Shorthand for the level attribute

l0

Indices for the events of crossing threshold level 0

l1

Indices for the events of crossing threshold level 1

l2

Indices for the events of crossing threshold level 2

l3

Indices for the events of crossing threshold level 3

save(save_file=None, use_current_directory=False)[source]

Save the parsed data for future reloading. The data is stored in gzip-compressed binary pickle format.

Parameters:
  • save_file (str) – The name of the save file to create. If none is provided, the default is the name of the telemetry file with the extension ”.bgoe.pgz” appended if a single telemetry file is the source.
  • use_current_directory (bool) – If True, remove any directory specification from save_file

Notes

Save files may not be compatible between Python 2 and 3

class gripspy.science.bgo.BGOCounterData(telemetry_file=None, save_file=None)[source]

Class for analyzing event data from the BGO shields

Parameters:
  • telemetry_file (str (or list)) – The name of the telemetry file to analyze. If None is specified, a save file must be specified.
  • save_file (str (or list)) – The name of a save file from a telemetry file that was previously parsed.
append(other)[source]

Append the information in another BGOCounterData instance

c

Shorthand for the channel_count attribute

l

Shorthand for the channel_livetime attribute

save(save_file=None, use_current_directory=False)[source]

Save the parsed data for future reloading. The data is stored in gzip-compressed binary pickle format.

Parameters:
  • save_file (str) – The name of the save file to create. If none is provided, the default is the name of the telemetry file with the extension ”.bgoc.pgz” appended if a single telemetry file is the source.
  • use_current_directory (bool) – If True, remove any directory specification from save_file

Notes

Save files may not be compatible between Python 2 and 3

t

Shorthand for the counter_time attribute

tl

Shorthand for the total_livetime attribute

v

Shorthand for the veto_count attribute

Aspect

Module for processing aspect data

class gripspy.science.aspect.PYFrame(filename, uid=158434)[source]

Class for a pitch-yaw image

Parameters:
  • filename (str) – The name of the FITS file of the camera frame
  • uid (int) – Defaults to the UID of the pitch-yaw camera, but can be set to a different camera’s UID or to None
plot_image(**imshow_kwargs)[source]

Plots the pitch-yaw image, including Sun/fiducial detections.

class gripspy.science.aspect.PYSequence(list_of_files)[source]

Class for a sequence of pitch-yaw images

Parameters:list_of_files (list of str) – List of FITS files with pitch-yaw images
dataframe

Obtain a pandas DataFrame

plot_centers(**dataframe_plot_kwargs)[source]

Plot the center of the brightest Sun across the entire image sequence

class gripspy.science.aspect.RFrame(filename, uid=142974)[source]

Class for a roll image

Parameters:
  • filename (str) – The name of the FITS file of roll image
  • uid (int) – Defaults to the UID of the roll camera, but can be set to a different camera’s UID or to None
static atmosphere_asym(x, midpoint, scale, amp1, amp2, dc)[source]

Exponential atmospheric model with asymmetry in normalization

static atmosphere_sym(x, midpoint, scale, amp, dc)[source]

Exponential atmospheric model with symmetry (i.e., hyperbolic cosine)

plot_image(**imshow_kwargs)[source]

Plots the roll image, including annotations.

class gripspy.science.aspect.RSequence(list_of_files)[source]

Class for a sequence of roll images

Parameters:list_of_files (list of str) – List of FITS files with roll images
dataframe

Obtain a pandas DataFrame

plot_midpoint(**dataframe_plot_kwargs)[source]

Plot the midpoints

Housekeeping

Card cages

Module for analyzing card-cage information. The information is currently split between CardCageControl and CardCageInfo because of the two types of packets. This awkward implementation is likely to change in the future.

class gripspy.housekeeping.cc.CardCageControl(telemetry_file=None, save_file=None)[source]

Class for analyzing card-cage information from the housekeeping packet

Parameters:
  • telemetry_file (str (or list)) – The name of the telemetry file to analyze. If None is specified, a save file must be specified.
  • save_file (str) – The name of a save file from a telemetry file that was previously parsed.

Notes

This implementation extracts only a subset of the information, and the API is subject to change.

append(other)[source]

Append the information in another CardCageControl instance

attributes_all = ['systime', 'count_watchpup_reset', 'daq_running', 'last_cmdtype', 'mode_abort_ramp', 'mode_coincidence', 'mode_sample_last_edge', 'mode_veto_guard_ring_hv_hard', 'mode_veto_guard_ring_lv_hard', 'mode_veto_mult_trig_hard', 'mode_veto_shield_hard', 'mult_trig_threshold', 'window_coincidence', 'window_full_ramp', 'window_mult_trig_collect', 'window_mult_trig_veto', 'window_reset_adc', 'window_sample_hv', 'window_sample_lv', 'window_start_adc', 'window_trigger_wait', 'period_counters', 'period_housekeeping', 'time_since_last_reset']
attributes_convert = ['period_counters', 'period_housekeeping', 'time_since_last_reset']
attributes_simple = ['systime', 'count_watchpup_reset', 'daq_running', 'last_cmdtype', 'mode_abort_ramp', 'mode_coincidence', 'mode_sample_last_edge', 'mode_veto_guard_ring_hv_hard', 'mode_veto_guard_ring_lv_hard', 'mode_veto_mult_trig_hard', 'mode_veto_shield_hard', 'mult_trig_threshold', 'window_coincidence', 'window_full_ramp', 'window_mult_trig_collect', 'window_mult_trig_veto', 'window_reset_adc', 'window_sample_hv', 'window_sample_lv', 'window_start_adc', 'window_trigger_wait']
save(save_file=None, use_current_directory=False)[source]

Save the parsed data for future reloading. The data is stored in gzip-compressed binary pickle format.

Parameters:
  • save_file (str) – The name of the save file to create. If none is provided, the default is the name of the telemetry file with the extension ”.cccontrol.pgz” appended if a single telemetry file is the source.
  • use_current_directory (bool) – If True, remove any directory specification from save_file

Notes

Save files may not be compatible between Python 2 and 3

class gripspy.housekeeping.cc.CardCageInfo(telemetry_file=None, save_file=None)[source]

Class for analyzing card-cage information from the counters packet

Parameters:
  • telemetry_file (str (or list)) – The name of the telemetry file to analyze. If None is specified, a save file must be specified.
  • save_file (str) – The name of a save file from a telemetry file that was previously parsed.

Notes

This implementation extracts all of the information, but the API is subject to change.

append(other)[source]

Append the information in another CardCageInfo instance

attributes_all = ['systime', 'elapsed_time', 'busy_time', 'busy_count', 'veto_mult_trig_hard', 'veto_mult_trig_soft', 'busy_time_interface', 'busy_count_interface', 'busy_time_system', 'busy_count_system', 'guard_ring_lv_time', 'guard_ring_lv_count', 'guard_ring_hv_time', 'guard_ring_hv_count', 'shield_time', 'shield_count', 'reboot_count', 'event_count', 'veto_shield_soft', 'veto_guard_ring_lv_soft', 'veto_guard_ring_hv_soft', 'veto_shield_hard', 'veto_guard_ring_lv_hard', 'veto_guard_ring_hv_hard', 'dropped_event_count', 'busy_fraction']
attributes_simple = ['systime', 'elapsed_time', 'busy_time', 'busy_count', 'veto_mult_trig_hard', 'veto_mult_trig_soft', 'busy_time_interface', 'busy_count_interface', 'busy_time_system', 'busy_count_system', 'guard_ring_lv_time', 'guard_ring_lv_count', 'guard_ring_hv_time', 'guard_ring_hv_count', 'shield_time', 'shield_count', 'reboot_count', 'event_count', 'veto_shield_soft', 'veto_guard_ring_lv_soft', 'veto_guard_ring_hv_soft', 'veto_shield_hard', 'veto_guard_ring_lv_hard', 'veto_guard_ring_hv_hard', 'dropped_event_count']
save(save_file=None, use_current_directory=False)[source]

Save the parsed data for future reloading. The data is stored in gzip-compressed binary pickle format.

Parameters:
  • save_file (str) – The name of the save file to create. If none is provided, the default is the name of the telemetry file with the extension ”.ccinfo.pgz” appended if a single telemetry file is the source.
  • use_current_directory (bool) – If True, remove any directory specification from save_file

Notes

Save files may not be compatible between Python 2 and 3

GPS

Module for analyzing GPS and pressure data from the SIP

class gripspy.housekeeping.gps.GPSData(telemetry_file=None, save_file=None)[source]

Class for analyzing GPS and pressure data from the SIP

Parameters:
  • telemetry_file (str) – The name of the telemetry file to analyze. If None is specified, a save file must be specified.
  • save_file (str) – The name of a save file from a telemetry file that was previously parsed.

Notes

Trusts the “user” GPS information Averages the SIP1 and SIP2 pressure information

save(save_file=None)[source]

Save the parsed data for future reloading. The data is stored in gzip-compressed binary pickle format.

Parameters:save_file (str) – The name of the save file to create. If none is provided, the default is the name of the telemetry file with the extension ”.gps.pgz” appended.

Notes

Save files may not be compatible between Python 2 and 3

Pointing

Module for analyzing pointing data

class gripspy.housekeeping.pointing.PointingData(telemetry_file=None, save_file=None)[source]

Class for analyzing pointing data

Parameters:
  • telemetry_file (str) – The name of the telemetry file to analyze. If None is specified, a save file must be specified.
  • save_file (str) – The name of a save file from a telemetry file that was previously parsed.

Notes

This implementation is still incomplete!

save(save_file=None)[source]

Save the parsed data for future reloading. The data is stored in gzip-compressed binary pickle format.

Parameters:save_file (str) – The name of the save file to create. If none is provided, the default is the name of the telemetry file with the extension ”.pointing.pgz” appended.

Notes

Save files may not be compatible between Python 2 and 3

Telemetry

Subpackage for parsing telemetry files

gripspy.telemetry.packet_generator(f, verbose=False, raise_exceptions=False)[source]

Generator that yields valid packets from a telemetry-file object. Packets that have invalid checksums are skipped past.

Parameters:
  • f (file object) – e.g., an already open file object
  • verbose (boolean) – If True, output the percentage of the file as a progress indicator
  • raise_exceptions (boolean) – If True, raise exceptions (e.g., if an invalid checksum is encountered)
gripspy.telemetry.parser_generator(f, filter_systemid=None, filter_tmtype=None, verbose=False)[source]

Generator that yields parsed packet contents from a telemetry-file object. Packets that have invalid checksums are skipped past.

Parameters:
  • f (file object) – e.g., an already open file object
  • filter_systemid (int) – If specified, only yield packets that have a matching SystemId
  • filter_tmtype (int) – If specified, only yield packets that have a match TmType
  • verbose (boolean) – If True, output the percentage of the file as a progress indicator
gripspy.telemetry.inspect(filename)[source]

Report top-level information about the contents of one or more telemetry files

Parameters:filename (str or list of str) – One or more telemetry files
gripspy.telemetry.parser(packet, filter_systemid=None, filter_tmtype=None)[source]

Parse a telemetry packet for its contents

Parameters:
  • packet (bytearray-like) – A full telemetry packet including the 16-byte header
  • filter_systemid (int) – If specified, only parse the packet if the SystemID matches
  • filter_tmtype (int) – If specified, only parse the packet if the TmType matches
Returns:

out – The contents of the telemetry packet
Return type:

dict
gripspy.telemetry.print_parsers()[source]

Print the SystemID and TmType pairs that have a parsing function defined. The parsing function may not be fully implemented.

Utilities

gripspy.util.checksum

Adapted from the C implmentation written by Lammert Bies

gripspy.util.checksum.crc16(__Pyx_memviewslice data) → unsigned short

Compute the CRC16 checksum of a bytearray or equivalent. Because of Cython limitations, the input must be writeable (e.g., not a string)

gripspy.util.coincidence
gripspy.util.coincidence.find_nearest_in

For two sorted arrays a and b, returns a list of the indices of the nearest element in b for each element in a. The two arrays must be of the same numeric type.

gripspy.util.time

Module for time conversion

gripspy.util.time.oeb2utc(systime_array, seconds_of_gps_delay=6)[source]

Converts 6-byte system time (AKA gondola time) to UTC during the flight. Note that the detectors report a higher-precision “event” time that must first be divided by 10 to convert to system time.

Parameters:
  • systime_array (ndarray) – Array of system times (normally int64)
  • seconds_of_gps_delay (int) – Lag in seconds between GPS measurement and GPS recording (default: 6 seconds)
Returns:

utc_array – Array of UTC times datetime64
Return type:

ndarray

Notes

This conversion function works as intended for only system times during the flight. The flight computer was restarted on 2016 Jan 25, which leads to a discontinuity in system times, and this discontinuity is taken into account by the function.

Indices and tables