Difference between revisions of "Python data and runfile modules"
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This provides a way to load SMALL frame data files into python. SMALL means less than 50000 frames. | This provides a way to load SMALL frame data files into python. SMALL means less than 50000 frames. | ||
+ | |||
+ | >>> from mce_data import * | ||
+ | >>> f = SmallMCEFile('/data/cryo/current_data/data002') | ||
+ | >>> d = f.Read() | ||
+ | 18600000 items requested but only 37200 read | ||
+ | >>> d.data | ||
+ | array([[0, 0, 0, ..., 0, 0, 0], | ||
+ | [0, 0, 0, ..., 0, 0, 0], | ||
+ | [0, 0, 0, ..., 0, 0, 0], | ||
+ | ..., | ||
+ | [0, 0, 0, ..., 0, 0, 0], | ||
+ | [0, 0, 0, ..., 0, 0, 0], | ||
+ | [0, 0, 0, ..., 0, 0, 0]], dtype=int32) | ||
+ | |||
+ | The data member of d is a 2d array, [n_detectors, n_frames]: | ||
+ | >>> d.data.shape | ||
+ | (328, 100) | ||
+ | |||
+ | The list of rows and columns associated with the 328 detectors is available through col_list and row_list: | ||
+ | >>> d.col_list | ||
+ | [8, 9, 10, 11, 12, 13, 14, 15, 8, 9, 10, 11, 12, 13, 14, 15, | ||
+ | 8, 9, 10, 11, 12, 13, 14, 15, 8, 9, 10, 11, 12, 13, 14, 15, | ||
+ | ... | ||
+ | 8, 9, 10, 11, 12, 13, 14, 15] | ||
+ | |||
+ | The data from the first frame header is available in the header member: | ||
+ | >>> d.header | ||
+ | {'status': 2052, 'data_rate': 47, 'userfield': 0, 'num_rows_reported': 41, | ||
+ | 'runfile_id': 1231969044, 'row_len': 64, 'header_version': 6, 'rc_present': | ||
+ | [False, True, False, False], 'address0_ctr': 23142826, 'num_rows': 41, | ||
+ | 'ramp_value': 0, 'frame_counter': 0, 'sync_box_num': 0, 'ramp_addr': 0} | ||
+ | |||
+ | If you want to extract raw data, with no conversion based on data mode, force the data mode to 0 in the Read call: | ||
+ | >>> d = f.Read(force_data_mode=0) | ||
== mce_runfile.py == | == mce_runfile.py == |
Revision as of 16:06, 14 January 2009
Python. Way of the future.
The python modules mce_data.py and mce_runfile.py provide roughly the same functionality that mas_data.pro and mas_runfile.pro provide for IDL.
mce_data.py
This provides a way to load SMALL frame data files into python. SMALL means less than 50000 frames.
>>> from mce_data import * >>> f = SmallMCEFile('/data/cryo/current_data/data002') >>> d = f.Read() 18600000 items requested but only 37200 read >>> d.data array([[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]], dtype=int32)
The data member of d is a 2d array, [n_detectors, n_frames]:
>>> d.data.shape (328, 100)
The list of rows and columns associated with the 328 detectors is available through col_list and row_list:
>>> d.col_list [8, 9, 10, 11, 12, 13, 14, 15, 8, 9, 10, 11, 12, 13, 14, 15, 8, 9, 10, 11, 12, 13, 14, 15, 8, 9, 10, 11, 12, 13, 14, 15, ... 8, 9, 10, 11, 12, 13, 14, 15]
The data from the first frame header is available in the header member:
>>> d.header {'status': 2052, 'data_rate': 47, 'userfield': 0, 'num_rows_reported': 41, 'runfile_id': 1231969044, 'row_len': 64, 'header_version': 6, 'rc_present': [False, True, False, False], 'address0_ctr': 23142826, 'num_rows': 41, 'ramp_value': 0, 'frame_counter': 0, 'sync_box_num': 0, 'ramp_addr': 0}
If you want to extract raw data, with no conversion based on data mode, force the data mode to 0 in the Read call:
>>> d = f.Read(force_data_mode=0)
mce_runfile.py
Load a runfile:
>>> from mce_runfile import * >>> runfile_name = '/data/cryo/current_data/1220531790_dat.run' >>> rf = MCERunfile(runfile_name)
Recall that the structure of runfiles is such that a line of data has an address defined by its 'block' and 'key' (where the key is the tag + specifiers...). The contents of rf include a dictionary of dictionaries of all the block / key pairs. For example:
>>> print rf.data['HEADER']['RB rc1 data_mode'] 00000010 >>> print rf.data['SQUID']['SQ_tuning_dir'] 1220510497
However, the member function "Item" allows us to repackage the runfile data by specifying a data type ('string', 'int', 'float') and whether or not we expect an array or a single value. For example:
>>> print rf.Item('HEADER', 'RB rc1 data_mode') ['00000010'] >>> print rf.Item('HEADER', 'RB rc1 data_mode', type='int') [10] >>> print rf.Item('HEADER', 'RB rc1 data_mode', type='int', array=False) 10 >>> print rf.Item('HEADER', 'RB rc1 data_mode', type='float') [10.0]
Some runfile entries are really 2d arrays, entered row by row. For example, in the 'IV' block there are per-column entries for responsivity:
<IV> ... <Responsivity(W/DACfb)_C0> 1.92290e-16 1.92119e-16 0.00000 1.93100e-16 1.92978e-16 1.89769e-16 1.91119e-16 ... <Responsivity(W/DACfb)_C1> 0.00000 1.82838e-16 0.00000 1.84197e-16 1.84822e-16 1.84447e-16 1.83693e-16 ... <Responsivity(W/DACfb)_C2> 1.89962e-16 1.88649e-16 0.00000 1.85339e-16 1.84462e-16 1.82965e-16 1.84045e-16 ... ... </IV>
These can be extracted column by column:
>>> print rf.Item('IV', 'Responsivity(W/DACfb)_C24', type='float') [1.46858e-16, 1.4528800000000001e-16, 0.0, 1.4240899999999999e-16, 1.4162699999999999e-16, 1.4100399999999999e-16, 1.4088899999999999e-16, 1.39608e-16, 1.3787599999999999e-16, 1.37429e-16, 1.3614499999999999e-16, 1.34423e-16, 1.3543599999999999e-16, 1.3587200000000001e-16, 1.3711399999999999e-16, 0.0, 0.0, 1.39424e-16, 1.40202e-16, 1.4450300000000001e-16, 1.47099e-16, 1.4857100000000001e-16, 1.49762e-16, 1.5139199999999999e-16, 1.5468600000000001e-16, 1.5743600000000001e-16, 1.5934699999999999e-16, 1.62431e-16, 1.6465e-16, 1.65256e-16, 1.6683999999999999e-16, 1.6823900000000001e-16, 0.0]
But they can all be extracted at once if you pass a printf-style format string to the member function Item2d:
>>> a = rf.Item2d('IV', 'Responsivity(W/DACfb)_C%i', type='float') >>> print len(a) 32 >>> print len(a[0]) 33 >>> print a[2][1] 1.88649e-16
(i.e. a[2][1] is the responsivity for column 2, row 1.)