Added RMS error plotting to measured results. Theoretical is still a WiP.
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54f1c18e07
5 changed files with 235 additions and 42 deletions
161
parsePy.py
161
parsePy.py
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@ -15,7 +15,7 @@ args_parser.add_argument('--polar','-p', action='store_true',
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help='do polar plotting (wide bandwidth)')
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args_parser.add_argument('--headless','-q', action='store_true',
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help='Remain neadless even if we aren\'t saving files.')
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args_parser.add_argument('-n', type=int, default=3,
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args_parser.add_argument('-n', type=int, default=4,
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help='plot testing number')
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args = args_parser.parse_args()
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@ -36,6 +36,7 @@ import skrf as rf
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from scipy.io import loadmat
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from collections import namedtuple
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import LPRDefaultPlotting
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from tankComputers import *
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import re
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import json
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################################################################################
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@ -70,8 +71,10 @@ class MeasurementConfig(namedtuple('config', ['r','c','inv','bias'])):
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@property
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def fn_str(self):
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return "C%02d_R%1d_I%1d_B%0.4f" % (self.c, self.r, self.inv, self.bias)
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Measurement = namedtuple('measurement', ['cfg','gain','phase','f','s21', 'slope'])
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Measurement = namedtuple('measurement', ['cfg', 'pwr','gain','phase','f','s21', 'slope'])
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plottingBandwidthMax = 2.01
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plottingBandwidthFreq = 28+np.array([-1,1])*0.5*plottingBandwidthMax
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slopeBandwidthMax = 1
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slopeBandwidthFreq = 28+np.array([-1,1])*0.5*slopeBandwidthMax
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@ -90,7 +93,7 @@ BDE_list.append(BDE(
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np.array([ 4.06488853e-03, -5.11527396e-01, 2.53053550e+01]),
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np.array([-1.62202706e-03, 6.94343608e-01, -1.80381551e+02]),
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-60,
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'S02bB_C+02dB_M0'
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'S02bB_C+00dB_M0'
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))
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# 2018-05-16
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BDE_list.append(BDE(
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@ -98,7 +101,7 @@ BDE_list.append(BDE(
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np.array([ 4.08875413e-03, -5.13017311e-01, 2.54047949e+01]),
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np.array([-1.29541398e-03, 6.74431785e-01, -1.80127388e+02]),
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-60,
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'S02bB_C+02dB_M0'
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'S02bB_C+00dB_M0'
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))
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# 2018-05-21
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#PolyGain=np.array( [ 4.08875413e-03, -5.13017311e-01, 2.54047949e+01])
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@ -108,7 +111,7 @@ BDE_list.append(BDE(
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np.array([ 4.08875413e-03, -5.13017311e-01, 2.54047949e+01]),
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np.array([-1.29541398e-03, 6.74431785e-01, -1.80127388e+02]),
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-60,
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'S02bB_C+02dB_M0'
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'S02bB_C+00dB_M0'
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))
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# 2018-05-25
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#PolyGain=np.array( [ 4.06488853e-03, -5.11527396e-01, 2.53053550e+01])
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@ -118,7 +121,7 @@ BDE_list.append(BDE(
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np.array([ 4.06488853e-03, -5.11527396e-01, 2.53053550e+01]),
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np.array([-1.62202706e-03, 6.94343608e-01, -1.80381551e+02]),
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-70,
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'S02bB_C+06dB_M0'
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'S02bB_C+00dB_M0'
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))
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source_directory='fromMat/%s_mat/' % SRC_DATA_NAME
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@ -131,6 +134,29 @@ for BDEx in BDE_list:
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FamStr=BDEx.mstr
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break
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with open(SRC_DATA_SUMMARY, 'r') as h_sumDat:
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sumDat = json.load(h_sumDat)
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def fetchSumDat_pwr(cfg):
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global sumDat
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mR = np.array(sumDat['r']) == cfg.r
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mC = np.array(sumDat['c']) == cfg.c
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mI = np.array(sumDat['inv']) == cfg.inv
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mB = np.abs(np.array(sumDat['bias_dp_set'])-cfg.bias) < 0.0005
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ind = np.squeeze(np.where(np.all((mR,mC,mI,mB),0)))
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if ind.size == 0:
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print("ERROR EVERYTHING IS BROKEN! AND i'M TIRED")
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return -1
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else:
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return sumDat['ivdd'][ind]*sumDat['vdd'][ind]
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def sumTuple_avgMinMax(data_list):
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existing_data = []
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for datum in data_list:
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existing_data.extend([np.mean(datum), np.min(datum), np.max(datum)])
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return tuple(existing_data)
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combined_rms=np.array([])
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for filename in os.listdir(source_directory):
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filename=source_directory+filename
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group_filename_string = filename.split('/')[-1][:-4]
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@ -147,24 +173,31 @@ for filename in os.listdir(source_directory):
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s2p_file = rf.Network(SRC_DATA_LOC + (FILE_PAT % pt.fn_str) )
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freq = np.squeeze(s2p_file.f*1e-9)
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inds_keep = np.where(np.all((freq >= plottingBandwidthFreq[0],
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freq <= plottingBandwidthFreq[1]),0))
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sdat_raw = np.squeeze(s2p_file.s21.s)
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freq = freq[inds_keep]
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sdat_raw = sdat_raw[inds_keep]
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buffer_gain = np.polyval(PolyGain,freq)
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buffer_phase = np.polyval(PolyPhase,freq)
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buffer_phase = buffer_phase - np.mean(buffer_phase) + \
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PhaseFixedRotationFactor*np.pi/180
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buffer_sdat = np.power(10,buffer_gain/20)*np.exp(1j*buffer_phase)
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sdat = np.squeeze(s2p_file.s21.s)/buffer_sdat
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sdat = sdat_raw/buffer_sdat
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slope_valid_inds = np.where(np.all((freq >= slopeBandwidthFreq[0],
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freq <= slopeBandwidthFreq[1]),0))
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sub_angles = np.unwrap(np.angle(sdat[slope_valid_inds]))*180/np.pi
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sub_freq = freq[slope_valid_inds]-np.mean(freq[slope_valid_inds])
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slope = np.polyfit(sub_freq,sub_angles-np.mean(sub_angles),1)[0]
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index = np.squeeze(np.argwhere(freq==28))
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collectedData.append(Measurement(pt,
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dB20(sdat[index]),
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ang_deg(sdat[index]),
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freq, sdat, slope))
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index_f0 = np.squeeze(np.argwhere(freq==28))
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collectedData.append(Measurement(cfg=pt, pwr=fetchSumDat_pwr(pt),
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gain=dB20(sdat[index_f0]),
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phase=ang_deg(sdat[index_f0]),
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f=freq, s21=sdat, slope=slope))
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# Find the indicies close to 0 and 180 as my reference curves
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phis = np.array([s.phase for s in collectedData])
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@ -172,44 +205,105 @@ for filename in os.listdir(source_directory):
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slope_list = np.array([s.slope for s in collectedData])
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slope_avg = np.mean(slope_list[best_slopes])
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h=pp.figure()
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ref_index = np.argmin(np.abs(phis))
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unwrapped_ref_phase = 180/np.pi*np.unwrap(ang(collectedData[ref_index].s21))
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if args.polar:
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h=pp.figure()
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ax=h.add_subplot(1,1,1, projection='polar')
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else:
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h2=pp.figure()
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h=pp.figure(figsize=(3.4*figScaleSize, 4.5*figScaleSize))
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h2=pp.figure(figsize=(3.4*figScaleSize, 2.8*figScaleSize))
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ax=h.subplots(2,1)
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ax = np.append(ax, h2.subplots(1,1))
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print("---------------------||------------------------------")
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print(" _C R I _Bias_ || Gain Phase ")
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print("---------------------||------------------------------")
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ax = np.append(ax, ax[1].twinx())
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ax = np.append(ax, ax[2].twinx())
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summary_msg = \
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"/---------------------\/----------------------------------------\\\n"\
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"| _C R I _Bias_ || Gain Phase Power |\n"\
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"|---------------------||----------------------------------------|\n"
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all_sdat = np.column_stack([imeas.s21 for imeas in collectedData])
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ang_rms = delta_rms(np.angle(all_sdat), 2*np.pi/16)*180/np.pi
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for imeas in collectedData:
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if args.polar:
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#ax.plot(ang(imeas.s21)-buffer_phase, dB20(imeas.s21)-buffer_gain)
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ax.plot(ang(imeas.s21), dB20(imeas.s21))
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else:
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#ax[0].plot(imeas.f, dB20(imeas.s21)-buffer_gain)
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ax[0].plot(imeas.f, dB20(imeas.s21))
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#unwrapped_phase = 180/np.pi*np.unwrap(ang(imeas.s21)-buffer_phase)
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#ax[1].plot(imeas.f, unwrapped_phase)
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unwrapped_phase = 180/np.pi*np.unwrap(ang(imeas.s21))
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ax[1].plot(imeas.f, unwrapped_phase)
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slope_relative = (imeas.f-28)*slope_avg
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ax[2].plot(imeas.f, unwrapped_phase-slope_relative)
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print(" %2d %d %d %.4f || %+7.1f dB %+9.2f deg" % \
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#ax[1].plot(imeas.f, unwrapped_phase)
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relative_phase_curve = unwrapped_phase-unwrapped_ref_phase
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if np.any(relative_phase_curve < 0):
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relative_phase_curve += 360
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#relative_phase_curve -= 180-22.5/2
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ax[1].plot(imeas.f, relative_phase_curve)
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#slope_relative = (imeas.f-28)*slope_avg
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#ax[2].plot(imeas.f, unwrapped_phase-slope_relative)
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ax[2].plot(imeas.f, relative_phase_curve)
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pwr_overage = int(2*(imeas.pwr*1e3 - 10))
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pwr_string = (int(pwr_overage/2)*"=") + (np.mod(pwr_overage,2)*">")
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summary_msg += "| %2d %d %d %.4f || "\
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" %+7.1f dB %+9.2f deg %4.1f mW |%s\n" % \
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(imeas.cfg.c, imeas.cfg.r, imeas.cfg.inv, imeas.cfg.bias, \
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imeas.gain, imeas.phase))
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print("---------------------||------------------------------")
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imeas.gain, imeas.phase, imeas.pwr*1e3, pwr_string)
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summary_msg += \
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"\_____________________/\________________________________________/\n"
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pwr_list=np.array([imeas.pwr*1e3 for imeas in collectedData])
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gain_list=np.array([imeas.gain for imeas in collectedData])
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summary_msg += \
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"/ \\\n" \
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"|===> Power: % 7.1f mW (% 7.1f mW - % 7.1f mW) |\n" \
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"|===> Gain: %+7.1f dB (%+7.1f dB - %+7.1f dB) | \n" \
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"|===> RMS: %6.1f deg (%6.1f deg - %6.1f deg) | \n" \
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"\_______________________________________________________________/" % \
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(sumTuple_avgMinMax([pwr_list, gain_list, ang_rms]))
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if args.polar:
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ax.set_ylim(LPRDefaultPlotting.POLAR_YLIM_CONST)
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ax.set_ylim(LPRDefaultPlotting.POLAR_YLIM_CONST_MEAS)
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if args.polar:
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ax.set_title('Measured Performance')
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else:
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# Usually this also has crappy lower ylimits, so we fix that here.
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# get ALL THE LOWER bounds
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np.min([np.min(line.get_ydata()) for line in ax[2].get_lines()])
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ax[0].set_title('Measured Performance')
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ax[0].set_ylabel('Gain (dB)');
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ax[1].set_ylabel('Phase (deg)');
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ax[2].set_ylabel('Phase (deg)');
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ax[0].set_ylabel('Gain (dB)')
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ax[1].set_ylabel('Relative Phase (deg)')
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ax[2].set_ylabel('Relative Phase (deg)')
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ax[2].set_title('Relative Phase')
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for i in range(3,5):
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aT=ax[i]
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aR=ax[i-2]
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# make the ticks, and the y-axis line up in a tidy manner
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# Recall that the ylimits should be 0-360 basically.
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aT.set_ylabel('RMS Error (deg)')
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aT.plot(imeas.f, ang_rms)
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# The goal is to take the usual step size of 50,
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# and then equate that with a 1-degree step in RMS Error
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# and to then adjust the y-limit of the twin-axis to align
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# the grid markers
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if False:
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yRscl=np.diff(aR.get_yticks()[-2:])
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yTscl=np.diff(aT.get_yticks()[-2:])
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# Now find the ratio of the ylimits margin verses their
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# extreme tick marks.
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yRmrks = aR.get_yticks()[[0,-1]]
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yTmrks = aT.get_yticks()[[0,-1]]
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tickTotal = max(len(aT.get_yticks()), len(aR.get_yticks()))
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yRover = (aR.get_ylim()-yRmrks)/yRscl
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yTover = (aT.get_ylim()-yTmrks)/yTscl
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yRTover = np.stack((yRover,yTover))
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yXover = np.array([np.min(yRTover[:,0]), np.max(yRTover[:,1])])
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aR.set_ylim(yRscl*yXover + yRmrks)
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aT.set_ylim(yTscl*yXover + yTmrks)
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aT.set_ylim(aR.get_ylim()/np.array(50)+3)
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aT.grid()
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aT.get_lines()[0].set_linewidth(2.0)
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aT.get_lines()[0].set_linestyle('-.')
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aT.get_lines()[0].set_color('black')
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for aT in ax:
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aT.set_xlabel('Frequency (GHz)')
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aT.grid()
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@ -225,6 +319,11 @@ for filename in os.listdir(source_directory):
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if not args.polar:
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h2.tight_layout()
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if args.save:
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with open('%s/Summary-%s-%s.txt' % (figdir, FamStr,
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group_filename_string), 'w') as summary_file:
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summary_file.write(summary_msg)
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summary_file.write("\n")
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summary_file.close()
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if args.polar:
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h.savefig('%s/PolarGain-%s-%s.%s' % (figdir, FamStr,
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group_filename_string, fig_ext))
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@ -233,6 +332,8 @@ for filename in os.listdir(source_directory):
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group_filename_string, fig_ext))
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h2.savefig('%s/RelStdPlots-%s-%s.%s' % (figdir, FamStr,
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group_filename_string, fig_ext))
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else:
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print(summary_msg)
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if HEADLESS:
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if not args.polar:
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pp.close()
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