""" Manipulating the Paroscientific pressure logs. Log format: [:,0] (1) Time (sec, since 1 January 1970) [:,1] (2) T_p (usec) [:,3] (3) T_t (usec) [:,4] (4) T_p zero (usec), [:,5] (5) T_t zero (usec) [:,6] (6) P (mbar) [:,7] (7) T (degC) 27 May 2008, Lev """ # Changelog # 5 Jan 2008, Lev # Add 'loadMCT', 'loadLowP' # # 8 Jan 2009, Lev # use ascii2numpy.loadascii # # 11 Jul 2010, Lev # add calibration support # high P cell gauge is recallibrated automatically upon loading using pressure zero-offset relevant in may-june 2010 # # Jan 2012, Lev # add a Zeolite log import os import os.path import time import datetime import numpy import matplotlib.dates import flib import timelog import ascii2numpy class ParoLog(timelog.TimeLog): """ Represents a ULT Paroscientific log over a certain time. Two logs can be concatenated with a '&' sign 'self.device' is the gauge name, two logs must have the same gauge names in order to concatenate them. log format: [:,0] (1) Time (sec, since 1 January 1970) [:,1] (2) T_p (usec) [:,2] (3) T_t (usec) [:,3] (4) T_p zero (usec) [:,4] (5) T_t zero (usec) [:,5] (6) P (mbar) [:,6] (7) T (degC) """ @staticmethod def fieldcount(): "return number of columns in the self.data"; return 7 def P(self): "pressure [mbar]"; return self.data[:,5] def T(self): "temperature [degC]"; return self.data[:,6] def T_p(self): "pressure period raw reading [usec]"; return self.data[:,1] def T_t(self): "temperature period raw reading [usec]"; return self.data[:,2] def T_p0(self): "zero-offset pressure period raw reading [usec]"; return self.data[:,3] def T_t0(self): "zero-offset temperature period raw reading [usec]"; return self.data[:,4] def __repr__(self): return "Paroscientific Log %s, gauge='%s'" % (self.datespan(), self.device) gaugeNames = { '_Paro_high_P_sample.txt': 'highP', '_Paro_MCT.txt': 'MCT', '_Paro_2D_sample.txt': 'lowP'} pressureFactors = { '_Paro_high_P_sample.txt': 1.0, '_Paro_MCT.txt': 1.0e3, '_Paro_2D_sample.txt': 1.0} @staticmethod def load(filename): """ Load a Paroscientific log from a given file A gauge is infered from the filename suffix. The following gauges are known: filename suffix gauge name pressure units "_Paro_high_P_sample.txt" "highP" mbar "_Paro_MCT.txt" "MCT" bar "_Paro_2D_sample.txt" "lowP" mbar """ basename = os.path.basename(filename) suffix = basename[8:] if not ParoLog.gaugeNames.has_key(suffix): return None # data = flib.loadascii(filename, usecols=range(ParoLog.fieldcount())) data = ascii2numpy.loadascii(filename, cols=ParoLog.fieldcount()) # timestamps are stored as seconds since 00:00 1 Jan 1904. # Convert to seconds since 00:00 1 Jan 1970, C and UNIX time format. data[:,0] = flib.labview2tm(data[:,0]) # MCT pressure is stored in bar, others in mbar # do a conversion into mbar data[:,5] *= ParoLog.pressureFactors[suffix] return ParoLog(data, ParoLog.gaugeNames[suffix]) def only_Tt_repeats(self): """ return a subset of the log with only those entries, where T_t is the same as in the previous line. This is a nasty way to remove entries, when temperature was measured, rather than pressure. However if temperature is stable, no detection is made. """ Tt = self.T_t() Tt_before = numpy.concatenate([Tt[0:1], Tt[:-1]]) return self.subset(Tt == Tt_before) def recalibrate(self, cal, P0): """ change pressure and temperature in place using the parocientific periods, a given calibration (a string name or ParoLog.Calibration object). The zero pressure offset [mbar] is given by P0 that is either a number of a function P0(time, T), where 'time' is in sec. since Unix epoch and 'T' is the gauge temperature. T_p0, T_t0 are set to NaN. """ if isinstance(cal, basestring): cal = ParoLog.Calibration.load_cal(cal) T, P = cal.convert(self.T_t(), self.T_p()) if not numpy.isscalar(P0): P0 = P0(self.t(), T) self.data[:,5] = P - P0 self.data[:,6] = T self.data[:,3:4] = numpy.nan def remove_spikes(self, spike_factor=10, min_dP=2): """ Return a subset of the log with pressure spikes removed. A spike is defined as a point which is different from its two adjucent points by more than 'spike_factor' times the difference between them and than 'min_dP' at the same time. """ P0 = self.P()[0:-3] P1 = self.P()[1:-2] P2 = self.P()[2:-1] ii = ~((numpy.abs(P1 - P0) > spike_factor*numpy.abs(P2-P0)) & (numpy.abs(P1-P0) > min_dP)) ii = numpy.concatenate([[False], ii, [False]]) return self.subset(ii) @staticmethod def loadHighP(run, start=None, end=None, calname="Serial No 86110 ULT high P sample", P0=lambda t, T: 2.841e-07 * (t - 1249534540.0) + 61.7): """ Return high pressure paroscientific log for a given run, withing [start, end] period if boundaries are specified. """ p = ParoLog.loaddir(flib.smbpath('//phpc338/Run%d/Paroscientific/highP-cell-paro' % run), start=start, end=end).period(start, end) if calname is not None: p.recalibrate(calname, P0) return p @staticmethod def loadMCT(run, start=None, end=None): """ Return MCT paroscientific log for a given run, withing [start, end] period if boundaries are specified. """ return ParoLog.loaddir(flib.smbpath('//phpc338/Run%d/Paroscientific/MCT_paro' % run), start=start, end=end).period(start, end) @staticmethod def loadLowP(run, start=None, end=None): """ Return low pressure paroscientific log for a given run, withing [start, end] period if boundaries are specified. """ return ParoLog.loaddir(flib.smbpath('//phpc338/Run%d/Paroscientific/low-field-NMR-paro' % run), start=start, end=end).period(start, end) @staticmethod def loadZeolite(run, start=None, end=None): """ Return zeolite pressure (temporarily hooked up) paroscientific log for a given run, withing [start, end] period if boundaries are specified. """ return ParoLog.loaddir(flib.smbpath('//phpc338/Run%d/Paroscientific/Zeo_paro' % run), start=start, end=end).period(start, end) # 1 psi = 68.94757290 mbar @staticmethod def psi2mbar(): "psi to mbar conversion factor"; return 68.94757290 class Calibration(object): "paroscientific calibrations" def __init__(self, name, maxPpsi, U0, Y1, Y2, Y3, C1, C2, C3, D1, D2, T1, T2, T3, T4, T5): self.name = name self.maxP = maxPpsi * ParoLog.psi2mbar() self.U0 = U0 self.Y1 = Y1 self.Y2 = Y2 self.Y3 = Y3 self.C1 = C1 self.C2 = C2 self.C3 = C3 self.D1 = D1 self.D2 = D2 self.T1 = T1 self.T2 = T2 self.T3 = T3 self.T4 = T4 self.T5 = T5 # Paroscientific gauge calibration # [0] gauge name # [1] max pressure [psi] # [2] U0 [usec] # [3] Y1 [degC/usec] # [4] Y2 [degC/usec^2] # [5] Y3 [degC/usec^3] # [6] C1 [psi] # [7] C2 [psi/usec] # [8] C3 [psi/usec^2] # [9] D1 # [10] D2 [1/usec] # [11] T1 [usec] # [12] T2 [usec/usec] # [13] T3 [usec/usec^2] # [14] T4 [usec/usec^3] # [15] T5 [usec/usec^4] calibrations = [ ("Serial No 30517 mK fridge MCT", 9.0000000000E+2, 5.8395150000E+0, -3.9547040000E+3, -8.8176770000E+3, 5.4031580000E+4, 5.0796180000E+3, -5.7832810000E+2, -1.5006390000E+4, -8.1900000000E-4, -3.0477000000E-1, 2.4811420000E+1, 1.8423900000E-1, 1.5281670000E+1, 0.0000000000E+0, 0.0000000000E+0), ("Serial No 32503 top of mK fridge", 1.5000000000E+1, 5.8739060000E+0, -3.8804540000E+3, -1.0734880000E+4, -1.1622760000E+4, 7.0019470000E+1, 7.6188790000E+0, -7.6868540000E+1, 3.6081000000E-2, 0.0000000000E+0, 2.6608790000E+1, 6.8987300000E-1, 1.9650340000E+1, 0.0000000000E+0, 0.0000000000E+0), ("Serial No 88855 MgO GHS", 1.5000000000E+1, 5.8551540000E+0, -3.9120860000E+3, -1.0848450000E+4, 0.0000000000E+0, 9.6851260000E+1, 3.9928410000E+0, -1.6011610000E+2, 3.3908000000E-2, 0.0000000000E+0, 2.7756950000E+1, 8.5295200000E-1, 2.0019850000E+1, 3.1028800000E+1, 0.0000000000E+0), ("Serial No 95720 zeolithe GHS", 1.5000000000E+1, 5.8182870000E+0, -3.8916130000E+3, -1.0415240000E+4, 0.0000000000E+0, 9.9321590000E+1, 4.0915350000E+0, -1.2839530000E+2, 2.9694000000E-2, 0.0000000000E+0, 2.7580840000E+1, 8.2430000000E-1, 2.0003550000E+1, 3.3516130000E+1, 0.0000000000E+0), ("INCORRECT Serial No 30517 mK fridge MCT", 9.0000000000E+2, 5.8395150000E+0, -3.9547040000E+3, -8.8176770000E+3, 5.4031580000E+4, 5.0795730000E+3, -5.7832810000E+2, -1.5006390000E+4, -8.1910000000E-4, -3.0477000000E-1, 2.4811420000E+1, 1.8423860000E-1, 1.5281670000E+1, 0.0000000000E+0, 0.0000000000E+0), ("INCORRECT Serial No 32503 top of mK fridge", 1.5000000000E+1, 5.8739060000E+0, -3.8804540000E+3, -1.0734880000E+4, -1.1622760000E+4, 7.0012929000E+1, 7.6188790000E+0, -7.6868540000E+1, 3.6080600000E-2, 0.0000000000E+0, 2.6608790000E+1, 6.8987290000E-1, 1.9650340000E+1, 0.0000000000E+0, 0.0000000000E+0), ("Serial No 60476 Brian MCT", 1.0000000000E+3, 5.8533280000E+0, -3.9105350000E+3, -1.0546670000E+4, 0.0000000000E+0, -4.0968460000E+3, 3.9656550000E+2, 1.1620370000E+4, 6.2669000000E-2, 0.0000000000E+0, 2.8353410000E+1, 1.8164560000E+0, 4.3669830000E+1, 1.0937250000E+2, 0.0000000000E+0), ("Serial No 86148 Brian sample GHS", 1.0000000000E+3, 5.8594470000E+0, -3.9255150000E+3, -1.0631740000E+4, 0.0000000000E+0, -6.1544250000E+3, 4.1745260000E+2, 1.8197590000E+4, 7.0164000000E-2, 0.0000000000E+0, 3.0072870000E+1, 1.4647360000E+0, 4.7107460000E+1, 1.2907140000E+2, 0.0000000000E+0), ("Serial No 101614 Brian new cell gauge", 3.0000000000E+3, 5.7568480000E+0, -3.9955540000E+3, -1.0572700000E+4, 0.0000000000E+0, -1.4437740000E+4, -6.0170570000E+2, 4.3191330000E+4, 4.3944000000E-2, 0.0000000000E+0, 3.0140120000E+1, 4.6720200000E-1, 5.7038810000E+1, 1.6799080000E+2, 0.0000000000E+0), ("Serial No 32697 ULT MCT", 9.0000000000E+2, 5.8171920000E+0, -3.9679810000E+3, -8.7458360000E+3, -1.8107050000E+5, 4.8331220000E+3, -6.3796780000E+2, -1.7114720000E+4, 1.5279900000E-2, -4.0725400000E-1, 2.5139870000E+1, 2.2480620000E-1, 1.8004980000E+1, 0.0000000000E+0, 0.0000000000E+0), ("Serial No 36252 ULT low P sample", 1.5000000000E+1, 5.8224570000E+0, -3.9448440000E+3, -9.7067960000E+3, 0.0000000000E+0, 8.4236210000E+1, 1.0715790000E+1, -6.3733010000E+1, 4.0823800000E-2, 0.0000000000E+0, 2.4989430000E+1, 4.5040060000E-1, 1.5651930000E+1, 0.0000000000E+0, 0.0000000000E+0), ("Serial No 86110 ULT high P sample", 1.0000000000E+3, 5.8613240000E+0, -3.9618540000E+3, -1.0309860000E+4, 0.0000000000E+0, -6.2133590000E+3, 1.5064370000E+1, 1.6577590000E+4, 7.1523000000E-2, 0.0000000000E+0, 3.0318840000E+1, 5.5249300000E-1, 4.2582560000E+1, 1.0886880000E+2, 0.0000000000E+0) ] @staticmethod def load_cal(name): "load a calibration with a given name" for c in ParoLog.Calibration.calibrations: if c[0] == name: return ParoLog.Calibration(*c) raise ValueError('invalid gauge name') @staticmethod def list_cals(): "list known calibrations by name"; return [c[0] for c in ParoLog.Calibration.calibrations] # # Paroscientific's CD-model: # # Temp (in degC) = Y1*U + Y2*U^2 + Y3*U^3 # # where: # U = Temp. period (in usec) - U0 # ------------- # P (in psi) = C*x*(1-D*x). with x=1-T0^2/tau^2, tau=pressure period (in usec) # # where: # C = C1 + C2*U + C3*U^2 # D = D1 + D2*U # T0 = T1 + T2*U + T3*U^2 + T4*U^3 +T5*U^4 # def convert(self, tau_T, tau_P): """ convert temperature and pressure periods [usec] into temperature [degC] and pressure [mbar], returned as a tuple. No zero offset correction is made """ scalar = numpy.isscalar(tau_T) if scalar: tau_T = numpy.array([tau_T]) tau_P = numpy.array([tau_P]) else: tau_T = numpy.asarray(tau_T) tau_P = numpy.asarray(tau_P) if tau_T.shape != tau_P.shape: raise ValueError('different size tau_T and tau_P arrays are given') # Temp (in degC) = Y1*U + Y2*U^2 + Y3*U^3 # # where: # U = Temp. period (in usec) - U0 U = tau_T - self.U0 T = numpy.polyval([self.Y3, self.Y2, self.Y1, 0.0], U) # P (in psi) = C*x*(1-D*x). with x=1-T0^2/tau^2, tau=pressure period (in usec) # # where: # C = C1 + C2*U + C3*U^2 # D = D1 + D2*U # T0 = T1 + T2*U + T3*U^2 + T4*U^3 +T5*U^4 C = numpy.polyval([self.C3, self.C2, self.C1], U) D = numpy.polyval([self.D2, self.D1], U) T0 = numpy.polyval([self.T5, self.T4, self.T3, self.T2, self.T1], U) x = 1 - T0**2 / tau_P**2 P = C*x*(1-D*x) * ParoLog.psi2mbar() if scalar: T = T[0] P = P[0] return T, P