mwavepy.calibration | index /home/alex/docs/python/path/mwavepy/calibration.py

`Contains the Calibration class, and supporting functions`

Modules
`` |  |
mwavepy.mathFunctions numpy | os pylab | mwavepy.touchstone mwavepy.discontinuities.variationalMethods |

Classes
`` |  | __builtin__.object Calibration
class Calibration(__builtin__.object)
`` | `Represents a calibration instance, a generic class to hold sets of measurements, ideals, and calibration results. all calibration algorithms are in calibrationAlgorithms.py, and are referenced by the dictionary in this object called 'calibration_algorihtm_dict'`
| Methods defined here: __init__(self, frequency, type, name=None, is_reciprocal=False, **kwargs) `Calibration initializer takes: frequency: a Frequency object over which the calibration is defined type: string representing what type of calibration is to be performed. supported types at the moment are: 'one port': standard one-port cal. if more than 2 measurement/ideal pairs are given it will calculate the least squares solution. 'one port xds': self-calibration of a unknown-length delay-shorts. **kwargs: key-word arguments passed to teh calibration algorithm. name: name of calibration, just a handy identifing string is_reciprocal: enables the reciprocity assumption on calculated error network` apply_cal(self, input_ntwk) `apply the current calibration to a measurement. takes: input_ntwk: the measurement to apply the calibration to, a Network type. returns: caled: the calibrated measurement, a Network type.` apply_cal_to_all_in_dir(self, dir, contains=None, f_unit='ghz') `convience function to apply calibration to an entire directory of measurements, and return a dictionary of the calibrated results, optionally the user can 'grep' the direction by using the contains switch. takes: dir: directory of measurements (string) contains: will only load measurements who's filename contains this string. f_unit: frequency unit, to use for all networks. see frequency.Frequency.unit for info. returns: ntwkDict: a dictionary of calibrated measurements, the keys are the filenames.` run(self) `runs the calibration algorihtm. this is automatically called the first time any dependent property is referenced (like error_ntwk) , but only the first time. if you change something and want to re-run the calibration use this.` Data descriptors defined here: __dict__ `dictionary for instance variables (if defined)` __weakref__ `list of weak references to the object (if defined)` coefs `coefs: a dictionary holding the calibration coefficients for one port cal's 'directivity':e00 'reflection tracking':e01e10 'source match':e11` error_ntwk `a Network type which represents the error network being calibrated out.` output_from_cal `a dictionary holding all of the output from the calibration algorithm` type `string representing what type of calibration is to be performed. supported types at the moment are: 'one port': standard one-port cal. if more than 2 measurement/ideal pairs are given it will calculate the least squares solution. 'one port xds': self-calibration of a unknown-length delay-shorts. note: algorithms referenced by calibration_algorithm_dict` Data and other attributes defined here: calibration_algorithm_dict = {'one port': <function one_port>, 'one port parameterized': <function parameterized_self_calibration>, 'one port xds': <function xds>}

Functions
`` |  | error_dict_2_network(coefs, frequency=None, is_reciprocal=False, **kwargs) `convert a dictionary holding standard error terms to a Network object. takes: returns:`

Data
`` |  | f_wr1p5 = <mwavepy.frequency.Frequency object> f_wr3 = <mwavepy.frequency.Frequency object> mil = 2.5399999999999997e-05 wb_wr1p5 = <mwavepy.workingBand.WorkingBand object> wg_wr1p5 = <mwavepy.transmissionLine.rectangularWaveguideTE10.RectangularWaveguideTE10 object> wg_wr3 = <mwavepy.transmissionLine.rectangularWaveguideTE10.RectangularWaveguideTE10 object>