def ipQuickShow():
folderPath = r'C:\Users\timl\Documents\IP_data_plots\190506_eagle'
folderName = cs.lastName(folderPath)
# Processed result choice.
loadThis = 'zAnyF'
# Loading the data:
pklName = folderName + '_' + loadThis + '.pkl'
filePath = os.path.join(folderPath, pklName)
with open(filePath, 'rb') as f: # Python 3: open(..., 'rb')
a = pickle.load(f)
# File number to be plotted, and its associated index in the class.
fileNum = 14
fileDateStr = '190506'
# Plotting choice.
plotThis = 'zPhase'
# Channel number plotted.
ch = 1
# Harmonic number of the xmitFund plotted.
h = 1
t = 3000 # Initialization.
for fileIdx in range(len(a)):
if (a[fileIdx].fileNum == fileNum
and a[fileIdx].fileDateStr == fileDateStr):
t = fileIdx
qvp.ipQuickVsPkt(a[t], ch, h, plotThis)
def ipQuickSave():
folderPath = r'C:\Users\timl\Documents\IP_data_plots\190506_eagle'
folderName = cs.lastName(folderPath)
# Processed result choice.
loadThis = 'zAnyF'
# Loading the data:
pklName = folderName + '_' + loadThis + '.pkl'
filePath = os.path.join(folderPath, pklName)
with open(filePath, 'rb') as f: # Python 3: open(..., 'rb')
a = pickle.load(f)
crop = False
# Crop information.
if crop:
cs.readCropInfo(a, folderPath, 'pklCropInfo.txt')
# Boolean array telling which files are plotted.
if crop:
filesPlotted = sp.zeros(len(a), dtype=bool)
for t in range(len(a)):
if sp.any(a[t].cropLogic):
filesPlotted[t] = True
else:
filesPlotted = sp.ones(len(a), dtype=bool)
# Harmonic number of the xmitFund plotted.
hList = [1]
# Name of a folder on the save path.
if crop:
highFolder = 'cropQuickVsPkt'
else:
highFolder = 'quickVsPkt'
for t in range(len(a)):
if filesPlotted[t]:
print('Plotting %s_%d %s' %
(a[t].fileDateStr, a[t].fileNum, a[t].descript))
for ch in [0]:
for h in hList:
for plotThis in ['2MagPhys']:
qvp.ipQuickVsPkt(a[t], ch, h, plotThis, crop)
imgFile = ('ch%d_H%d_%d_%s' %
(ch, h, a[t].fileNum, a[t].descript))
imgFolderPath = os.path.join(folderPath, 'plots',
highFolder, plotThis)
imgFilePath = os.path.join(imgFolderPath,
imgFile + '.png')
plt.savefig(imgFilePath)
# Clear the figure before plotting again.
plt.clf()
def ipScatterEverything():
folderPath = r'C:\temp\180827_smallSQUR'
fileStart = cs.lastName(folderPath)
# Processed file choice.
loadThis = 'zOddH'
# Plotting choice.
plotThis = 'zPhase'
# Channel plotted.
ch = 2
# Index of the frequency plotted.
freqIdx = 0
# Colors for plotting main results.
if plotThis == 'zPhase':
color = 'Red'
elif plotThis == 'zMag':
color = 'Green'
elif plotThis == '2MagPhys':
color = 'DarkGoldenRod'
# Loading the data:
fileName = fileStart + '_' + loadThis + '.pkl'
filePath = os.path.join(folderPath, fileName)
with open(filePath, 'rb') as f: # Python 3: open(..., 'rb')
a = pickle.load(f)
# Pick out the desired result data to be plotted as x and y values.
res = cs.emptyClass()
res.yVal = sp.array([])
for t in range(len(a)):
if plotThis == 'zPhase':
res.yVal = sp.hstack((res.yVal, a[t].phaseDiff[ch, :, freqIdx]))
elif plotThis == 'zMag':
res.yVal = sp.hstack((res.yVal, a[t].zMag[ch, :, freqIdx]))
elif plotThis == '2MagPhys':
res.yVal = sp.hstack((res.yVal, 2 * a[t].magPhys[ch, :, freqIdx]))
res.xVal = sp.array(range(len(res.yVal)))
# Initialize plot settings.
ps = cs.emptyClass()
ps.color = color
ps.markerSize = 3
ps.marker = 'o'
ps.titleWrap = 75
ps.xLabel = 'Packets'
if plotThis == 'zPhase':
ps.yLabel = 'Impedance Phase (mrad)'
elif plotThis == 'zMag':
ps.yLabel = 'Impedance Amplitude (m$\Omega$)'
elif plotThis == '2MagPhys':
if ch == 0:
ps.yLabel = 'Twice Complex Mag. (A)'
else:
ps.yLabel = 'Twice Complex Mag. (V)'
# Plot.
# Function plots one thing at a time and can update formatting and labels.
params = {
'legend.fontsize': 'x-large',
'figure.figsize': (9, 6.5),
'axes.labelsize': 'x-large',
'axes.titlesize': 'x-large',
'xtick.labelsize': 'x-large',
'ytick.labelsize': 'x-large'
}
plt.rcParams.update(params)
# Plot the main result.
# Use the string '_nolegend_' to hide the label from the legend display.
plt.scatter(res.xVal, res.yVal, color=ps.color, marker=ps.marker)
titleStr = '%s All Packets' % (fileStart)
if ps.titleWrap < sp.inf:
# Wrap text at a set character length.
titleStr = '\n'.join(wrap(titleStr, ps.titleWrap))
plt.title(titleStr)
plt.xlabel(ps.xLabel)
plt.ylabel(ps.yLabel)
plt.legend()
plt.grid(b=True)
plt.tight_layout()
plt.show()
def ipSurvey():
params = {
'legend.fontsize': 'x-large',
'figure.figsize': (9, 6.5),
'axes.labelsize': 'x-large',
'axes.titlesize': 'x-large',
'xtick.labelsize': 'x-large',
'ytick.labelsize': 'x-large'
}
plt.rcParams.update(params)
# Class object holding plot settings.
ps = cs.emptyClass()
# Whether to save the plotted datasets to txt files.
ps.saveTxt = False
crop = True
ps.folderPath = r'C:\Users\timl\Documents\IP_data_plots\190506_eagle'
folderName = cs.lastName(ps.folderPath)
# Processed result choice.
loadThis = 'zAnyF'
# Read the depth information for each file.
infoPath = os.path.join(ps.folderPath, 'depthInfo.txt')
depthList = []
with open(infoPath, 'r') as f:
for lidx, line in enumerate(f, 1):
# Strip off trailing newline characters.
line = line.rstrip('\n')
# Split up comma-delimited information.
(fileDateStr, fileNum, senseFt) = line.split(',')
# Type casting.
fileNum = int(fileNum)
# Estimated water depth on the line.
senseFt = float(senseFt) # (ft)
depth = senseFt / 3.28084 # (m)
# Dump results in a list.
depthList.append([fileDateStr, fileNum, depth])
# Loading the data.
pklName = folderName + '_' + loadThis + '.pkl'
filePath = os.path.join(ps.folderPath, pklName)
with open(filePath, 'rb') as f: # Python 3: open(..., 'rb')
a = pickle.load(f)
# Which files will be plotted.
filesPlotted = cs.readFilesPlotted(a, ps.folderPath)
# Crop information.
if crop:
cs.readCropInfo(a, ps.folderPath, 'pklCropInfo.txt')
# Plotting choice.
ps.plotThis = 'zMag'
# Channel plotted.
ps.ch = 1
# Harmonic number of the xmitFund plotted.
ps.h = 1
# Index of the frequency in the list of odd harmonics.
ps.freqIdx = 4 * ps.h
# Whether the color axis bounds are set manually.
manualColor = True
clipColorData = False
colMin = 1 # /(2*sp.pi*4)
colMax = 5.5 # /(2*sp.pi*4)
# Whether to plot line segments and points along the survey track.
ps.showLines = False
# Whehter to save polygon and line shape files.
ps.saveShape = False
# Rectangle defined by coordinate extrema along longi and lat axes.
ps.ext = mm.coordExtrema(a)
# Offset distance to either side of the survey line color strip extends.
ps.sideRange = 3 # (m)
# Whether to plot in (longi, lat) or a projected reference.
ps.plotWGS84 = False
# The WGS84 latitude-longitude Coordinate Reference System (CRS).
ps.crsWGS84 = {'init': 'epsg:4326'}
# Define an azimuthal equidistant Coordinate Reference System (CRS).
longiCent = (ps.ext.longiMax + ps.ext.longiMin) / 2
latCent = (ps.ext.latMax + ps.ext.latMin) / 2
longiCent = -122.50032907675
latCent = 47.617131187
ccrsAzEq = ccrs.AzimuthalEquidistant(central_longitude=longiCent,
central_latitude=latCent)
# This Cartopy CRS (CCRS) can be converted into a `proj4` string/dict
# compatible with GeoPandas.
ps.crsAzEq = ccrsAzEq.proj4_init
# Initializations.
ps.colMin = sp.inf
ps.colMax = -sp.inf
tList = sp.array(range(len(a)))
tList = tList[filesPlotted]
originalFreqIdx = ps.freqIdx
for t in tList:
ps.freqIdx = originalFreqIdx
if a[t].pktCount > 0:
# Join the longitude and latitude arrays into one matrix with two
# columns. Col 0: longi, Col 1: lat.
# (deg)
a[t].fix = sp.transpose(sp.vstack((a[t].longi, a[t].lat)))
# Associate a water depth with each fix at the ship location.
for idx in range(len(depthList)):
if a[t].fileNum == depthList[idx][1]:
if a[t].fileDateStr == depthList[idx][0]:
depth = depthList[idx][2]
a[t].depth = depth * sp.ones_like(a[t].pkt) # (m)
# Cable lead-length deployed.
a[t].leadin = 65.25 # (m)
# Take an average of the 3rd and 5th harmonics to get a reading
# at 4 Hz when the xmitFund was 1 Hz.
if a[t].xmitFund == 1 and ps.h == 1:
ps.freqIdx = 4 * 4
freqIdx3Hz = 4 * 3
freqIdx5Hz = 4 * 5
a[t].phaseDiff[ps.ch, :, ps.freqIdx] = (
(a[t].phaseDiff[ps.ch, :, freqIdx3Hz] +
a[t].phaseDiff[ps.ch, :, freqIdx5Hz]) / 2)
# Pick which data to map to colors in the plot.
if ps.plotThis == 'zPhase':
a[t].color = a[t].phaseDiff[ps.ch, :, ps.freqIdx]
# Despike phase differences with a threshold spike in mrad.
# a[t].color = despike(a[t].color, 10)
cbarLabel = 'Impedance Phase (mrad)'
elif ps.plotThis == 'zMag':
a[t].color = a[t].zMag[ps.ch, :, ps.freqIdx]
# Despike magnitudes with a threshold spike in mOhm.
# a[t].color = despike(a[t].color, 0.5)
cbarLabel = 'Impedance Magnitude (m$\Omega$)'
elif ps.plotThis == '2MagPhys':
a[t].color = 2 * a[t].magPhys[ps.ch, :, ps.freqIdx]
# a[t].color = despike(a[t].color, 0.01)
if ps.ch == 0:
cbarLabel = 'Twice Complex Mag. (A)'
else:
cbarLabel = 'Twice Complex Mag. (V)'
elif ps.plotThis == 'zTime':
freq = a[t].freq[ps.freqIdx] # (Hz)
a[t].color = (a[t].phaseDiff[ps.ch, :, ps.freqIdx] /
(2 * sp.pi * freq)) # (millisecond)
# Despike phase differences with a threshold spike in us.
# a[t].color = despike(a[t].color, 100)
cbarLabel = 'v-i Time (ms)'
elif ps.plotThis == 'crop':
a[t].color = a[t].cropLogic.astype(float)
# Edit the color data to clip at the manual bounds, if desired.
if clipColorData:
a[t].color[a[t].color < colMin] = colMin
a[t].color[a[t].color > colMax] = colMax
# Keep track of the maximum and minimum color values.
if a[t].xmitFund != 8:
if not crop:
arraMin = sp.amin(a[t].color)
arraMax = sp.amax(a[t].color)
else:
if sp.any(a[t].cropLogic):
arraMin = sp.amin(a[t].color[a[t].cropLogic])
arraMax = sp.amax(a[t].color[a[t].cropLogic])
else:
arraMin = sp.inf
arraMax = -sp.inf
if arraMin < ps.colMin:
ps.colMin = arraMin
if arraMax > ps.colMax:
ps.colMax = arraMax
# Manually set the min and max color values.
if manualColor:
ps.colMin = colMin
ps.colMax = colMax
# Big picture class containing master polygon, color, and line lists for
# all survey lines.
bp = cs.emptyClass()
bp.polyList = []
bp.colorList = []
bp.lineList = []
for t in tList:
if a[t].pktCount > 0:
if not crop or (crop and sum(a[t].cropLogic) > 0):
print('file %s_%d' % (a[t].fileDateStr, a[t].fileNum))
print(a[t].descript)
# Print time the file started.
print(a[t].cpuDTStr[0].t)
plotStrip(bp, a[t], ps, crop)
ps.fig = plt.gcf()
ps.ax = ps.fig.add_subplot(111)
ps.ax.set_aspect('equal')
ps.cmap = 'jet'
ps.lineCol = 'k' # Color of the basic track line shape.
# Geopandas data frame object containing each polygon in the list, along
# with colors.
dfPoly = gpd.GeoDataFrame({'geometry': bp.polyList, 'color': bp.colorList})
dfPoly.crs = ps.crsAzEq
dfLine = gpd.GeoDataFrame({'geometry': bp.lineList})
dfLine.crs = ps.crsAzEq
# Transform back to (longi,lat), if requested.
if ps.plotWGS84:
dfLine = dfLine.to_crs(ps.crsWGS84)
dfPoly = dfPoly.to_crs(ps.crsWGS84)
dfPoly.plot(ax=ps.ax,
column='color',
cmap=ps.cmap,
vmin=ps.colMin,
vmax=ps.colMax)
if ps.showLines:
dfLine.plot(ax=ps.ax, color=ps.lineCol)
# Transform back to (longi,lat).
if ~ps.plotWGS84:
dfLine = dfLine.to_crs(ps.crsWGS84)
dfPoly = dfPoly.to_crs(ps.crsWGS84)
# Keep axis bounds from before the shorelines are plotted.
xlimLeft, xlimRight = plt.xlim()
ylimLeft, ylimRight = plt.ylim()
xlimLeft = -100
xlimRight = 500
ylimLeft = -600
ylimRight = 600
# xlimLeft = -100
# xlimRight = 500
# ylimLeft = -300
# ylimRight = 400
# xlimLeft = 5000
# xlimRight = 9000
# ylimLeft = -3500
# ylimRight = -1500
# xlimLeft = 7800
# xlimRight = 9000
# ylimLeft = -3000
# ylimRight = -2100
if ps.saveShape:
# Save the geodataframes to files for colors and lines.
polyFileName = '%s_%s_Ch%dH%d' % (a[0].fileDateStr, ps.plotThis, ps.ch,
ps.h)
if clipColorData:
polyFileName += '_clip%dand%d' % (colMin, colMax)
lineFileName = '%s_lines' % (a[0].fileDateStr)
shapeFolder = r'C:\temp\181213_dataFrameFileEagle'
polyFilePath = os.path.join(shapeFolder, polyFileName)
lineFilePath = os.path.join(shapeFolder, lineFileName)
dfPoly.to_file(polyFilePath)
dfLine.to_file(lineFilePath)
# Shoreline plotting.
shoreline(ps)
# Reset the axis bounds after shorelines are plotted.
plt.xlim(xlimLeft, xlimRight)
plt.ylim(ylimLeft, ylimRight)
# Display the colormap in use as a sidebar colorbar.
sm = plt.cm.ScalarMappable(cmap=ps.cmap,
norm=plt.Normalize(vmin=ps.colMin,
vmax=ps.colMax))
sm._A = []
# colorbar() requires a scalar mappable, "sm".
if ps.plotThis != 'crop':
# cbaxes = ps.fig.add_axes([0.8, 0.1, 0.03, 0.8])
divider = make_axes_locatable(ps.ax)
cax1 = divider.append_axes("right", size="10%", pad=0.05)
cb = plt.colorbar(sm, cax=cax1)
cb.set_label(cbarLabel)
plt.sca(ps.ax)
# Axes labels.
if not ps.plotWGS84:
plt.xlabel('W-E (m)')
plt.ylabel('S-N (m)')
else:
plt.xlabel('Longitude (deg)')
plt.ylabel('Latitude (deg)')
plt.grid(b=True)
# Plot title. Use notes recorded in one of the files plotted.
tTitle = cs.find(filesPlotted, True)
# titleStr = ('%s Ch %d (%s). Harmonic %d = %.0f Hz. xmitFund = %.0f Hz.'
# % (a[tTitle].fileDateStr, ps.ch, a[tTitle].measStr[ps.ch], ps.h,
# ps.h*a[tTitle].xmitFund, a[tTitle].xmitFund))
titleStr = ('Ch %d (%s). Harmonic %d = %.0f Hz. xmitFund = %.0f Hz.' %
(ps.ch, a[tTitle].measStr[ps.ch], ps.h,
ps.h * a[tTitle].xmitFund, a[tTitle].xmitFund))
# titleStr = ('%s Ch %d (%s). Frequency = %.0f Hz.'
# % (a[tTitle].fileDateStr, ps.ch, a[tTitle].measStr[ps.ch],
# ps.h*a[tTitle].xmitFund))
# titleStr = ('%s_%d Line %s. Ch %d (%s). Frequency = %.0f Hz. '
# 'xmitFund = %.0f Hz.'
# % (a[tTitle].fileDateStr, a[tTitle].fileNum,
# a[tTitle].descript, ps.ch, a[tTitle].measStr[ps.ch],
# a[tTitle].freq[ps.freqIdx], a[tTitle].xmitFund))
if manualColor or clipColorData:
if ps.plotThis == 'zPhase':
titleStr += (' \nColors clipped at %d mrad and %d mrad.' %
(ps.colMin, ps.colMax))
elif ps.plotThis == 'zMag':
titleStr += ((' \nColors clipped at %.1f m$\Omega$ ' +
'and %.1f m$\Omega$.') % (ps.colMin, ps.colMax))
elif ps.plotThis == '2MagPhys':
titleStr += ((' \nColors clipped at %.2f A ' + 'and %.2f A.') %
(ps.colMin, ps.colMax))
elif ps.plotThis == 'zTime':
titleStr += ((' \nColors clipped at %.2f ms ' + 'and %.2f ms.') %
(ps.colMin, ps.colMax))
elif ps.plotThis == 'crop':
titleStr = '%s Orange = Array on Floor (Guess)' % (
a[0].fileDateStr)
# t = 4
# titleStr = ('%s_%d Line %s. Average Speed 1.7 kt.' %
# (a[t].fileDateStr, a[t].fileNum, a[t].descript))
plt.title(titleStr)
def ipPlot():
ps = cs.emptyClass
# Whether to save the plot images.
doSave = False
folderPath = r'C:\temp\180821_artificial'
fileStart = cs.lastName(folderPath)
# Processed result choice.
ps.loadThis = 'zOddH'
# Plotting choice.
ps.plotThis = 'zPhase'
# Channel plotted.
ps.ch = 1
# Number of means to include in each plot. Three indicates target means
# plotted with one baseline sandwiched on either side.
# One indicates files plotted individually.
meanCount = 1
# Whether to omit 60 Hz data from the plots.
ps.omit60Hz = False
# Whether to plot only the first three odd harmonic frequencies.
ps.h135 = False
ps.plotGuidePosts = False
ps.plotCustomPosts = False
# Colors for plotting targets and sandwich baselines.
if ps.plotThis == 'zPhase':
ps.meanCol = ['Blue', 'Green', 'Red']
ps.stdCol = ['DodgerBlue', 'LimeGreen', 'Fuchsia']
elif ps.plotThis == 'zMag':
ps.meanCol = ['Black', 'Blue', 'Green']
ps.stdCol = ['Gray', 'DodgerBlue', 'LimeGreen']
elif ps.plotThis == '2MagPhys':
ps.meanCol = ['Black', 'Blue', 'DarkGoldenRod']
ps.stdCol = ['Gray', 'DodgerBlue', 'Gold']
params = {
'legend.fontsize': 'x-large',
'figure.figsize': (9, 6.5),
'axes.labelsize': 'x-large',
'axes.titlesize': 'x-large',
'xtick.labelsize': 'x-large',
'ytick.labelsize': 'x-large'
}
plt.rcParams.update(params)
# Loading the data:
fileName = fileStart + '_' + ps.loadThis + '.pkl'
filePath = os.path.join(folderPath, fileName)
with open(filePath, 'rb') as f: # Python 3: open(..., 'rb')
a = pickle.load(f)
tarList = []
for t in range(len(a)):
# Identify target files (They aren't baselines).
if a[t].descript != 'baseline' and a[t].descript != 'test':
tarList.append(t)
if meanCount == 3 or meanCount == sp.inf:
tList = [0] # Target files.
elif meanCount == 1:
tList = [0] # range(len(a)) # All files.
for t in tList:
if meanCount == 3:
ps.tar = t # Index of the target file.
plot3Mean(a, ps)
elif meanCount == sp.inf:
ps.tar = t # Index of the target file.
plot3All(a, ps)
elif meanCount == 1:
plot1Mean(a[t], ps)
imgFile = ('%s_ch%d_%d_%s' %
(a[t].fileDateStr, ps.ch, a[t].fileNum, ps.plotThis))
imgFolder = ps.plotThis + str(meanCount)
if ps.h135:
imgFile += 'H135'
imgFolder += 'H135'
if doSave:
imgPath = os.path.join(folderPath, 'plots', imgFolder,
imgFile + '.png')
plt.savefig(imgPath)
# if t != tList[-1]:
# # Clear the figure before plotting again, unless this is the
# # last plot to be generated.
# plt.clf()
plt.legend()
plt.grid()
plt.tight_layout()
plt.show()
def ipArgand(instruct):
# Whether to save the plot images.
doSave = False
plotThis = 'zPhase'
pklFolder = instruct.pklFolder
fileStart = cs.lastName(pklFolder)
# Processed file choice.
loadThis = 'zAnyF'
# Channels plotted.
ch = 2
# Whether to identify and plot target files.
targetBool = True
# Whether to manually select which file numbers to plot.
manualTargetBool = True
manualTargetArra = instruct.fileNums
# Whether to plot all the files together without erasing at all.
fileTogether = True
# Whether to omit 60 Hz data from the plots.
omit60Hz = True
# Frequencies plotted.
maskChoice = 'oddHUp2'
isYAxStartedFromZero = False
# Whether to subtract baseline phase results from separate files.
subtract1 = False
if subtract1:
# Pick a standard packet range to use.
pktRang = range(17)
# Whether to include the minor note in the legend entries rather than the
# title.
minorLegBool = False
# Whether to include the minor note anywhere at all.
minorBool = True
# Whether to swap the description and minor note text for display purposes.
swapDescriptMinor = False
legOutside = False
loc = 'center right'
stdBool = False
# Whether to plot only one packet's results instead of an average over all
# the packets in the file.
onePkt = False
# File number from which the plot title is taken. inf if it doesn't matter.
titleFileNum = sp.inf
# Let colors be selected automatically if files are plotted together.
if fileTogether:
color = None
stdColor = None
# Loading the data:
fileName = fileStart + '_' + loadThis + '.pkl'
filePath = os.path.join(pklFolder, fileName)
with open(filePath, 'rb') as f: # Python 3: open(..., 'rb')
a = pickle.load(f)
# List of file numbers.
fileNumArra = sp.zeros(len(a))
for t in range(len(a)):
fileNumArra[t] = a[t].fileNum
if swapDescriptMinor:
descript = a[t].descript
a[t].descript = a[t].minor
a[t].minor = descript
if targetBool:
tarList = []
lowFilesA = sp.zeros(len(a), dtype=int)
colorsA = len(a) * ['']
linestylesA = len(a) * ['']
legFilterA = sp.zeros(len(a), dtype=bool)
for t in range(len(a)):
if manualTargetBool:
if any(a[t].fileNum == manualTargetArra):
tarList.append(t)
# Set up the low frequency normalization files for each file.
manualIdx = cs.find(manualTargetArra, a[t].fileNum)
lowFileNum = instruct.lowFiles[manualIdx]
lowFilesA[t] = cs.find(fileNumArra, lowFileNum)
colorsA[t] = instruct.colors[manualIdx]
linestylesA[t] = instruct.linestyles[manualIdx]
legFilterA[t] = instruct.legFilter[manualIdx]
else:
# Identify target files (They aren't baselines or tests).
if a[t].descript != 'baseline' and a[t].descript != 'test':
if t < (len(a) - 1):
tarList.append(t)
# Discover the maximum file number.
maxFile = -sp.inf
for t in range(len(a)):
if a[t].fileNum > maxFile:
maxFile = a[t].fileNum
# Pick out the desired result data to be plotted as x and y values.
res = []
for t in range(len(a)):
# Result class for each possible file to be read.
res.append(cs.emptyClass())
for t in tarList:
if not subtract1 or not any(t == sp.array(tarList)):
# (radian)
phaseDiff = a[t].phaseDiff[ch, ...] / 1000
zMag = a[t].zMag[ch, ...]
else:
# Subtract the baseline phase differences. (radian)
fileOff = 3
phaseDiff = (a[t].phaseDiff[ch, pktRang, :] -
a[t - fileOff].phaseDiff[ch, pktRang, :]) / 1000
# Magnitudes.
zMag = a[t].zMag[ch, pktRang, :]
res[t].xVal = zMag * sp.cos(phaseDiff)
res[t].yVal = zMag * sp.sin(phaseDiff)
# Average over packets.
if not onePkt:
res[t].xVal = sp.mean(res[t].xVal, axis=0)
res[t].yVal = sp.mean(res[t].yVal, axis=0)
else:
meanRang = instruct.pkt + sp.array([-1, 0, 1])
res[t].xVal = res[t].xVal[meanRang, :]
res[t].yVal = res[t].yVal[meanRang, :]
# Average over the chosen packet and those on either side.
res[t].xVal = sp.mean(res[t].xVal, axis=0)
res[t].yVal = sp.mean(res[t].yVal, axis=0)
# Mask out unwanted frequencies.
mask = sp.zeros_like(a[t].freq, dtype=bool)
if maskChoice == 'oddHUp2':
mask[4:len(mask):8] = True
# Number of frequencies included in the plot.
freqCount = 17
mask[(1 + 4 + 8 * (freqCount - 1)):] = False
# Mask out 60 Hz, if requested.
if omit60Hz:
mask[a[t].freq == 60] = False
res[t].xVal = res[t].xVal[mask]
res[t].yVal = res[t].yVal[mask]
# Result X and Y data normalized by the low freq. fundamental real
# component of impedance.
if t == lowFilesA[t]:
maxReal1 = res[lowFilesA[t]].xVal[0]
res[t].xVal /= maxReal1
res[t].yVal /= maxReal1
# If the phase difference baselines were subtracted, divide the
# magnitudes.
if subtract1 and any(t == sp.array(tarList)):
baseMags = sp.sqrt(res[t - fileOff].xVal**2 +
res[t - fileOff].yVal**2)
res[t].xVal /= baseMags
res[t].yVal /= baseMags
# Renormalize.
if t == lowFilesA[t]:
maxReal2 = res[lowFilesA[t]].xVal[0]
res[t].xVal /= maxReal2
res[t].yVal /= maxReal2
# Initialize plot settings.
ps = cs.emptyClass()
# Figure with axes.
ps.color = color
ps.stdColor = stdColor
ps.markerSize = 5
ps.marker = 'o'
ps.linestyle = '-'
ps.markerSize = 4
ps.titleWrap = 83
ps.legOutside = legOutside
ps.omit60Hz = omit60Hz
ps.isYAxStartedFromZero = isYAxStartedFromZero
ps.xLabel = 'REAL'
ps.yLabel = 'IMAG'
ps.stdBool = stdBool
ps.normMag = False
ps.loc = loc
# List of file indices plotted.
if targetBool:
tList = tarList
else:
tList = range(0, len(a))
# List adjacent files with each target file, if there are three per plot.
tarList = tList
# Plot, and save if needed.
for idx in range(len(tList)):
t = tList[idx]
tar = tarList[idx]
ps.ch = ch
ps.color = colorsA[t]
ps.linestyle = linestylesA[t]
# ps.titleStr = ('%s Ch %d (%s). xmitFund = %.0f Hz. %s'
# % (a[t].fileDateStr, ch, a[t].measStr[ch],
# a[t].xmitFund, a[t].major))
ps.titleStr = ('%s Ch %d (%s). xmitFund = %.0f Hz.' %
(a[t].fileDateStr, ch, a[t].measStr[ch], a[t].xmitFund))
# ps.titleStr = ('Phase differences for artificial signals.')
if onePkt:
ps.titleStr += (' Results averaged over three packets centered '
'on each packet listed in the legend.')
if subtract1:
ps.titleStr += (
' Baseline sand phase angles have been subtracted, ' +
'and normalized magnitudes have been divided by ' +
'baseline normalized magnitudes before normalizing ' +
'to a low frequency real value of 1 again.')
# Legend text.
if legFilterA[t]:
# With legend.
ps.legStr = 'File %d. Ch %d. %s.' % (a[t].fileNum, ch,
a[t].descript)
if onePkt:
ps.legStr = '(pkt %d) ' % (a[t].pkt[instruct.pkt]) + ps.legStr
else:
# Without legend.
ps.legStr = '_nolegend_'
if minorBool and (ps.legStr != '_nolegend_'):
if not minorLegBool:
if a[t].minor != 'None.':
ps.titleStr += ' %s.' % (a[t].minor)
else:
ps.legStr += ' %s.' % (a[t].minor)
# The plot title is taken from the target file.
ps.titleBool = False
if t == tar:
if (titleFileNum == sp.inf) or (titleFileNum == a[t].fileNum):
ps.titleBool = True
if plotThis == 'argand':
ps.titleStr += ' Normalized Apparent Impedance Argand.'
ps.xVal = res[t].xVal
ps.yVal = res[t].yVal
elif plotThis == 'zMag':
ps.xVal = a[t].freq[mask]
ps.yVal = sp.sqrt(res[t].xVal**2 + res[t].yVal**2)
if subtract1:
ps.titleStr += (
' Normalized magnitudes have been divided by ' +
'baseline normalized magnitudes before normalizing ' +
'to a low frequency real value of 1 again.')
ps.xLabel = 'Frequency (Hz)'
ps.yLabel = 'Impedance Magnitude (Normalized)'
elif plotThis == 'zPhase':
ps.xVal = a[t].freq[mask]
# Milliradian
ps.yVal = 1000 * sp.arctan2(res[t].yVal, res[t].xVal)
ps.xLabel = 'Frequency (Hz)'
ps.yLabel = 'Impedance Phase (mrad)'
pw.basePlot(ps)
if (t == tar):
if ((not fileTogether)
or (fileTogether and idx == len(tList) - 1)):
if doSave:
pass
if not (t == tList[-1]):
plt.clf()
ax = plt.gca()
if plotThis == 'argand':
ax.set_aspect('equal', 'box')
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
if ps.isYAxStartedFromZero:
if ymax > 0:
ax.axis([xmin, xmax, 0, ymax])
else:
ax.axis([0, xmax, ymin, 0])
def ipSurvey():
params = {
'legend.fontsize': 'x-large',
'figure.figsize': (9, 6.5),
'axes.labelsize': 'x-large',
'axes.titlesize': 'x-large',
'xtick.labelsize': 'x-large',
'ytick.labelsize': 'x-large'
}
plt.rcParams.update(params)
# Class object holding plot settings.
ps = cs.emptyClass()
# Whether to save the plotted datasets to txt files.
ps.saveTxt = False
crop = True
ps.folderPath = r'C:\temp\181112_eagle'
folderName = cs.lastName(ps.folderPath)
# Processed result choice.
loadThis = 'zAnyF'
# Read the depth information for each file.
infoPath = os.path.join(ps.folderPath, 'depthInfo.txt')
depthList = []
with open(infoPath, 'r') as f:
for lidx, line in enumerate(f, 1):
# Strip off trailing newline characters.
line = line.rstrip('\n')
# Split up comma-delimited information.
(fileDateStr, fileNum, senseFt) = line.split(',')
# Type casting.
fileNum = int(fileNum)
senseFt = float(senseFt) # (ft)
# Add the depth of the sensor and convert to meter.
depth = (2.5 + senseFt) / 3.28084 # (m)
# Dump results in a list.
depthList.append([fileDateStr, fileNum, depth])
# Loading the data.
pklName = folderName + '_' + loadThis + '.pkl'
filePath = os.path.join(ps.folderPath, pklName)
with open(filePath, 'rb') as f: # Python 3: open(..., 'rb')
a = pickle.load(f)
# Crop information.
if crop:
cs.readCropInfo(a, ps.folderPath)
# Plotting choice.
ps.plotThis = 'zPhase'
# Channel plotted.
ps.ch = 1
# Harmonic number of the xmitFund plotted.
ps.h = 13
# Index of the frequency in the list of odd harmonics.
ps.freqIdx = 4 * ps.h
# Whether the color axis bounds are set manually.
manualColor = False
clipColorData = True
colMin = 185
colMax = 420
# Whether to plot line segments and points along the survey track.
ps.showLines = False
ps.showPts = False
# Rectangle defined by coordinate extrema along longi and lat axes.
ps.ext = mm.coordExtrema(a)
# Offset distance to either side of the survey line color strip extends.
ps.sideRange = 3.5 # (m)
# Whether to plot in (longi, lat) or a projected reference.
ps.plotWGS84 = False
# The WGS84 latitude-longitude Coordinate Reference System (CRS).
ps.crsWGS84 = {'init': 'epsg:4326'}
# Define an azimuthal equidistant Coordinate Reference System (CRS).
longiCent = (ps.ext.longiMax + ps.ext.longiMin) / 2
latCent = (ps.ext.latMax + ps.ext.latMin) / 2
longiCent = -122.50032907675
latCent = 47.617131187
ccrsAzEq = ccrs.AzimuthalEquidistant(central_longitude=longiCent,
central_latitude=latCent)
# This Cartopy CRS (CCRS) can be converted into a `proj4` string/dict
# compatible with GeoPandas.
ps.crsAzEq = ccrsAzEq.proj4_init
# Initializations.
ps.colMin = sp.inf
ps.colMax = -sp.inf
tList = range(len(a))
for t in tList:
if a[t].pktCount > 0:
# Join the longitude and latitude arrays into one matrix with two
# columns. Col 0: longi, Col 1: lat.
# (deg)
a[t].fix = sp.transpose(sp.vstack((a[t].longi, a[t].lat)))
# Associate a water depth with each fix at the ship location.
for idx in range(len(depthList)):
if a[t].fileNum == depthList[idx][1]:
if a[t].fileDateStr == depthList[idx][0]:
depth = depthList[idx][2]
a[t].depth = depth * sp.ones_like(a[t].pkt) # (m)
# Cable lead-length deployed.
a[t].leadin = 57 # (m)
# Pick which data to map to colors in the plot.
if ps.plotThis == 'zPhase':
a[t].color = a[t].phaseDiff[ps.ch, :, ps.freqIdx]
# Despike phase differences with a threshold spike in mrad.
# a[t].color = despike(a[t].color, 10)
cbarLabel = 'Impedance Phase (mrad)'
elif ps.plotThis == 'zMag':
a[t].color = a[t].zMag[ps.ch, :, ps.freqIdx]
# Despike magnitudes with a threshold spike in mOhm.
# a[t].color = despike(a[t].color, 0.5)
cbarLabel = 'Impedance Magnitude (m$\Omega$)'
elif ps.plotThis == '2MagPhys':
a[t].color = 2 * a[t].magPhys[ps.ch, :, ps.freqIdx]
# a[t].color = despike(a[t].color, 0.01)
if ps.ch == 0:
cbarLabel = 'Twice Complex Mag. (A)'
else:
cbarLabel = 'Twice Complex Mag. (V)'
elif ps.plotThis == 'zTime':
freq = a[t].freq[ps.freqIdx] # (Hz)
a[t].color = (a[t].phaseDiff[ps.ch, :, ps.freqIdx] /
(2 * sp.pi * freq)) # (millisecond)
# Despike phase differences with a threshold spike in us.
# a[t].color = despike(a[t].color, 100)
cbarLabel = 'v-i Time (ms)'
# Edit the color data to clip at the manual bounds, if desired.
if clipColorData:
a[t].color[a[t].color < colMin] = colMin
a[t].color[a[t].color > colMax] = colMax
# Keep track of the maximum and minimum color values.
arraMin = sp.amin(a[t].color)
if arraMin < ps.colMin:
ps.colMin = arraMin
arraMax = sp.amax(a[t].color)
if arraMax > ps.colMax:
ps.colMax = arraMax
# Manually set the min and max color values.
if manualColor:
ps.colMin = colMin
ps.colMax = colMax
ps.fig, ps.ax = plt.subplots()
ps.ax.set_aspect('equal')
ps.cmap = 'jet'
ps.lineCol = 'k' # Color of the basic track line shape.
for t in tList:
if a[t].pktCount > 0:
if not crop or (crop and sum(a[t].cropLogic) > 0):
print('file %s_%d' % (a[t].fileDateStr, a[t].fileNum))
print(a[t].descript)
plotStrip(a[t], ps, crop)
# Keep axis bounds from before the shorelines are plotted.
xlimLeft, xlimRight = plt.xlim()
ylimLeft, ylimRight = plt.ylim()
xlimLeft = -408.14159194218564
xlimRight = 383.2435609713969
ylimLeft = -375.3266408701022
ylimRight = 325.37655253272123
if ps.saveShape:
# Save the geodataframes to files for colors and lines.
polyFileName = 'ch%d_H%d_%s_%s_%d.txt' % (
ps.ch,
ps.h,
ps.plotThis,
at.fileDateStr,
at.fileNum,
)
# Shoreline plotting.
shoreline(ps)
# Reset the axis bounds after shorelines are plotted.
plt.xlim(xlimLeft, xlimRight)
plt.ylim(ylimLeft, ylimRight)
# Display the colormap in use as a sidebar colorbar.
sm = plt.cm.ScalarMappable(cmap=ps.cmap,
norm=plt.Normalize(vmin=ps.colMin,
vmax=ps.colMax))
sm._A = []
# colorbar() requires a scalar mappable, "sm".
cb = plt.colorbar(sm)
cb.set_label(cbarLabel)
# Axes labels.
if not ps.plotWGS84:
plt.xlabel('W-E (m)')
plt.ylabel('S-N (m)')
else:
plt.xlabel('Longitude (deg)')
plt.ylabel('Latitude (deg)')
plt.grid(b=True)
# Plot title.
titleStr = ('%s Ch %d (%s). Harmonic %d = %.2f Hz. xmitFund = %.2f Hz.' %
(a[0].fileDateStr, ps.ch, a[0].measStr[ps.ch], ps.h,
ps.h * a[0].xmitFund, a[0].xmitFund))
if manualColor or clipColorData:
if ps.plotThis == 'zPhase':
titleStr += (' \nColors clipped at %d mrad and %d mrad.' %
(ps.colMin, ps.colMax))
elif ps.plotThis == 'zMag':
titleStr += ((' \nColors clipped at %.1f m$\Omega$ ' +
'and %.1f m$\Omega$.') % (ps.colMin, ps.colMax))
elif ps.plotThis == '2MagPhys':
titleStr += ((' \nColors clipped at %.2f A ' + 'and %.2f A.') %
(ps.colMin, ps.colMax))
elif ps.plotThis == 'zTime':
titleStr += ((' \nColors clipped at %.2f ms ' + 'and %.2f ms.') %
(ps.colMin, ps.colMax))
plt.title(titleStr)
def ipPlot(inFolder, inFileNums, inPlotThis):
# Whether to save the plot images.
doSave = False
pklFolder = inFolder
fileStart = cs.lastName(pklFolder)
# Processed file choice.
loadThis = 'zAnyF'
# Plotting choice (y-axis).
plotThis = inPlotThis
# Plotting choice (x-axis).
# 'freq': frequency (Hz).
# 'phase': absolute phase (rad).
xChoice = 'freq'
# Frequency index to plot, if not plotting vs frequency.
hChoice = 1
freqIdx1 = 4*hChoice
# Channels plotted.
chList = [6]
# Whether to include the listed channels together in the same plots.
chTogether = False
# Whether to identify and plot target files.
targetBool = True
# Whether to manually select which file numbers to plot.
manualTargetBool = True
manualTargetArra = inFileNums
# Number of files included in each plot.
# 1: just one file.
# 3: 3 files are included, one primary, and each of the two adjacent files.
fileCount = 1
# Whether to subtract an average of results from the adjacent two files.
subtractAdj = False
# Whether to subtract one file's results from the target.
subtract1 = False
# Whether to plot all the files together without erasing at all.
fileTogether = True
# Whether to average over packets.
meanBool = True
if meanBool:
# Whether to plot standard deviation envelopes.
stdBool = True
else:
stdBool = False
# Whether to plot all packets individually on the same plots.
allPktBool = True
if not allPktBool:
# Which packet index to plot alone.
pIso = 0
# Whether to omit 60 Hz data from the plots.
omit60Hz = True
# Frequencies plotted.
maskChoice = 'oddHUp2'
isYAxStartedFromZero = False
# Whether to include the minor note anywhere at all.
minorBool = False
# Whether to include the minor note in the legend entries rather than the
# title.
minorLegBool = False
# Whether to swap the description and minor note text for display purposes.
swapDescriptMinor = False
legOutside = False
loc = 'best'
if plotThis == 'zMag':
# Whether to normalize the impedance magnitudes by the fundamental.
normMag = False
else:
normMag = False
# File number from which the plot title is taken. inf if it doesn't matter.
titleFileNum = sp.inf
# Colors for plotting main results.
if plotThis == 'zPhase':
color = 'Red'
stdColor = 'Fuchsia'
elif plotThis == 'zMag':
color = 'Green'
stdColor = 'LimeGreen'
elif plotThis == '2MagPhys':
color = 'DarkGoldenRod'
stdColor = 'Gold'
elif plotThis == 'inductance':
color = None
stdColor = None
# Let colors be selected automatically if files are plotted together.
if fileTogether:
color = None
stdColor = None
if fileCount == 3:
# Colors for plotting main results.
color3 = ['Black', 'Blue', color]
stdColor3 = ['Gray', 'DodgerBlue', stdColor]
# Colors for plotting results on different channels.
chColor = ['Black', 'SteelBlue', 'LimeGreen', 'Crimson', 'Green',
'DeepPink', 'BlueViolet', 'Orange']
chStdColor = ['Gray', 'SkyBlue', 'YellowGreen', 'Brown', 'ForestGreen',
'HotPink', 'DarkMagenta', 'Coral']
# Loading the data:
fileName = fileStart + '_' + loadThis + '.pkl'
filePath = os.path.join(pklFolder, fileName)
with open(filePath, 'rb') as f: # Python 3: open(..., 'rb')
a = pickle.load(f)
# Pick out the desired result data to be plotted as x and y values.
res = []
for t in range(len(a)):
res.append(cs.emptyClass())
if xChoice == 'freq':
# Frequencies along the x-axis.
res[t].xVal = a[t].freq
elif xChoice == 'phase':
res[t].xVal = abs(abs(a[t].phase) - sp.pi/2)
if plotThis == 'zPhase':
res[t].yVal = a[t].phaseDiff
elif plotThis == 'zMag':
res[t].yVal = a[t].zMag
elif plotThis == '2MagPhys':
res[t].yVal = 2*a[t].magPhys
elif plotThis == 'zTime':
res[t].yVal = sp.zeros_like(a[t].phaseDiff)
# Convert phase differences to time differences.
for idx in range(len(a[t].freq)):
freq = a[t].freq[idx]
if freq != 0:
# (microsecond)
res[t].yVal[:, :, idx] = (10**3 * a[t].phaseDiff[:, :, idx]
/ (2 * sp.pi * freq))
elif plotThis == 'inductance':
# Convert to estimates of the inductance in the circuit.
r2 = 8.1 # Ohm
r3 = sp.inf # initialization
if sp.any(a[t].fileNum == sp.array([2, 5, 9, 10])):
r3 = 1.0 # Ohm
elif sp.any(a[t].fileNum == sp.array([3, 6, 11, 12])):
r3 = 2.0 # Ohm
# Matrix of inductance values.
lMat = sp.zeros_like(a[t].phaseDiff)
for p in range(a[t].pktCount):
for freqIdx in range(len(a[t].freq)):
omega = 2*sp.pi*a[t].freq[freqIdx] # (rad/s)
# Solving the quadratic for L.
# (rad)
phaseDiff = a[t].phaseDiff[chList[0], p, freqIdx]/1000
tangent = sp.tan(phaseDiff)
aQ = omega**2*tangent
b = -omega*r2
c = r3*(r3 + r2)*tangent
# (H)
lMat[chList[0], p, freqIdx] = (-b - sp.sqrt(b**2 - 4*aQ*c)
)/(2*aQ)
# Convert to microHenry.
lMat *= 1e6 # (uH)
res[t].yVal = lMat
if xChoice == 'freq':
# Mask out unwanted frequencies.
mask = sp.zeros_like(a[t].freq, dtype=bool)
if maskChoice == 'oddH':
# Save odd harmonics of the transmit fundamental.
mask[4:len(mask):8] = True
elif maskChoice == 'fund':
mask[4] = True
elif maskChoice == 'blockSine':
mask[4:len(mask):8] = True
mask[12:len(mask):24] = False
elif maskChoice == 'oddHUp2':
mask[4:len(mask):8] = True
# Number of frequencies included in the plot.
freqCount = 17
mask[(1+4+8*(freqCount-1)):] = False
elif maskChoice == 'oddUp2':
mask[1:len(mask):2] = True
mask[10:] = False
elif maskChoice == 'nonzero':
mask[1:150] = True
elif maskChoice == 'custom':
mask[sp.array([4, 12, 20, 27, 35, 43, 51, 59, 66, 74, 82, 90, 98, 106,
113, 121, 129, 137, 145])] = True
# Mask out 60 Hz, if requested.
if omit60Hz:
mask[a[t].freq == 60] = False
res[t].xVal = res[t].xVal[mask]
res[t].yVal = res[t].yVal[..., mask]
if targetBool:
tarList = []
for t in range(len(a)):
if manualTargetBool:
if any(a[t].fileNum == manualTargetArra):
tarList.append(t)
else:
# Identify target files (They aren't baselines or tests).
if a[t].descript != 'baseline' and a[t].descript != 'test':
if t < (len(a) - 1):
tarList.append(t)
# List of file numbers.
fileNumList = []
for t in range(len(a)):
fileNumList.append(a[t].fileNum)
if swapDescriptMinor:
descript = a[t].descript
a[t].descript = a[t].minor
a[t].minor = descript
# Initialize plot settings.
ps = cs.emptyClass()
# Figure with axes.
ps.color = color
ps.stdColor = stdColor
ps.stdBool = stdBool
ps.markerSize = 3
ps.marker = 'o'
if xChoice == 'freq':
ps.linestyle = 'solid'
else:
ps.linestyle = 'none'
ps.markerSize = 4
ps.titleWrap = 75
ps.legOutside = legOutside
ps.omit60Hz = omit60Hz
ps.isYAxStartedFromZero = isYAxStartedFromZero
ps.normMag = normMag
ps.loc = loc
if xChoice == 'freq':
ps.xLabel = 'Frequency (Hz)'
elif xChoice == 'phase':
ps.xLabel = 'abs(abs(Phase) - pi/2) (rad)'
if plotThis == 'zPhase':
ps.yLabel = 'Impedance Phase (mrad)'
if subtractAdj or subtract1:
ps.yLabel = 'Phase Displacement from Baseline (mrad)'
elif plotThis == 'zMag':
if not normMag:
ps.yLabel = 'Impedance Magnitude (m$\Omega$)'
else:
ps.yLabel = 'Impedance Magnitude (Normalized)'
elif plotThis == '2MagPhys':
if chList[0] == 0:
ps.yLabel = 'Twice Complex Mag. (A)'
else:
ps.yLabel = 'Twice Complex Mag. (V)'
elif plotThis == 'zTime':
ps.yLabel = 'Voltage Lead Time ($\mu$s)'
elif plotThis == 'inductance':
ps.yLabel = 'Inductance L ($\mu$H)'
# List of file indices plotted.
if targetBool:
tList = tarList
else:
tList = range(0, len(a))
# List adjacent files with each target file, if there are three per plot.
if fileCount == 3:
newTList = []
tarList = []
for t in tList:
newTList.extend(t + sp.array([-1, 1, 0]))
tarList.extend(3*[t])
tList = newTList
del newTList
else:
tarList = tList
# Plot, and save if needed.
for idx in range(len(tList)):
imgFolder = plotThis + str(fileCount)
t = tList[idx]
tar = tarList[idx]
if False:
if a[t].fileNum <= 10 + 2:
ps.color = 'C0'
elif a[t].fileNum <= 22 + 2:
ps.color = 'C1'
elif a[t].fileNum <= 34 + 2:
ps.color = 'C2'
for ch in chList:
ps.ch = ch
if chTogether:
ps.color = chColor[ch]
ps.stdColor = chStdColor[ch]
ps.xVal = res[t].xVal
if fileCount == 3:
colorIdx = cs.find((t - tar) == [-1, 1, 0], True)
ps.color = color3[colorIdx]
ps.stdColor = stdColor3[colorIdx]
# ps.titleStr = ('%s Ch %d (%s). xmitFund = %.2f Hz. %s'
# % (a[t].fileDateStr, ch, a[t].measStr[ch],
# a[t].xmitFund, a[t].major))
ps.titleStr = ('%s Ch %d (%s). %s'
% (a[t].fileDateStr, ch, a[t].measStr[ch],
a[t].major))
if xChoice == 'phase':
ps.titleStr = ('Plotted freq. = %d Hz. '
% (a[t].freq[freqIdx1]) + ps.titleStr)
# Linestyle choice.
if a[t].minor == 'Sand.':
ps.linestyle = '--'
else:
ps.linestyle = 'solid'
# Legend text.
if a[t].xmitFund == 1 and sp.mod(a[t].fileNum, 12) != 0:
# With legend.
ps.legStr = '%s' % (a[t].descript[:-6])
else:
# Without legend.
ps.legStr = '_nolegend_'
# ps.legStr = ('%d. %s' % (a[t].fileNum, a[t].descript))
if chTogether:
ps.titleStr = ('%s_%d %s. xmitFund = %.1f Hz. %s'
% (a[t].fileDateStr, a[t].fileNum,
a[t].descript, a[t].xmitFund,
a[t].major))
ps.legStr = 'Ch %d (%s)' % (ch, a[t].measStr[ch])
if minorBool and (ps.legStr != '_nolegend_'):
if not minorLegBool:
ps.titleStr = '%s ' % (a[t].minor) + ps.titleStr
else:
ps.legStr = '%s ' % (a[t].minor) + ps.legStr
# if not sp.any(a[t].fileNum ==
# (32 + sp.array([1, 3, 5, 7, 9, 11, 13, 15]))):
# ps.legStr = '_nolegend_'
# The plot title is taken from the target file.
ps.titleBool = False
if t == tar:
if (titleFileNum == sp.inf) or (titleFileNum == a[t].fileNum):
ps.titleBool = True
imgFile = ('%s_ch%d_%d_%s'
% (a[t].fileDateStr, ch, a[t].fileNum, plotThis))
if meanBool:
# Result Y data.
ps.yVal = res[t].yVal[ch, ...]
# ps.legStr += ' (Avg. of %d packets)' % (a[t].pktCount)
if stdBool:
ps.titleStr += ' Shaded patches are means +/- 1 STD.'
if not subtractAdj and not subtract1:
pw.plot1Mean(ps)
elif subtract1:
ps.yValA = res[t-3].yVal[ch, ...]
pw.plot1MeanSubtract1(ps)
elif subtractAdj:
ps.yValA = res[t-1].yVal[ch, ...]
ps.yValB = res[t+1].yVal[ch, ...]
pw.plot1MeanSubtractAdj(ps)
else:
if allPktBool:
pList = sp.array(range(a[t].pktCount))
else:
pList = sp.array([pIso])
if xChoice != 'freq':
pList = sp.array([0])
for idx in range(len(pList)):
p = pList[idx]
if idx != 0:
# Only the first packet in the list appears in the
# legend.
ps.legStr = '_nolegend_'
else:
if allPktBool:
ps.legStr += '\n (%d packets)' % (a[t].pktCount)
else:
ps.legStr += ' (pkt %d)' % (a[t].pkt[p])
if xChoice != 'freq':
# When x is not frequency.
ps.xVal = res[t].xVal[ch, :, freqIdx1]
ps.yVal = res[t].yVal[ch, :, freqIdx1]
else:
ps.yVal = res[t].yVal[ch, p, :]
pw.basePlot(ps)
if (t == tar):
if (not chTogether) or (chTogether and ch == chList[-1]):
if ((not fileTogether) or
(fileTogether and idx == len(tList) - 1)):
if doSave:
cs.saveImg(pklFolder, imgFolder, imgFile)
if not ((t == tList[-1]) and (ch == chList[-1])):
plt.clf()
ax = plt.gca()
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
# plt.xscale('log')
if ps.isYAxStartedFromZero:
if ymax > 0:
ax.axis([xmin, xmax, 0, ymax])
else:
ax.axis([xmin, xmax, ymin, 0])
def pklCrop():
folderPath = r'C:\temp\181112_eagle'
folderName = cs.lastName(folderPath)
# Processed result choice.
loadThis = 'zAnyF'
# Loading the data:
pklName = folderName + '_' + loadThis + '.pkl'
filePath = os.path.join(folderPath, pklName)
with open(filePath, 'rb') as f: # Python 3: open(..., 'rb')
a = pickle.load(f)
# Read the crop information.
infoPath = os.path.join(folderPath, 'pklCropInfo.txt')
with open(infoPath, 'r') as f:
for lidx, line in enumerate(f, 1):
# Strip off trailing newline characters.
line = line.rstrip('\n')
# Split up comma-delimited information.
(fileDateStr, fileNum, startPkt, endPkt) = line.split(',')
# Type casting.
fileNum = int(fileNum)
startPkt = int(startPkt)
# Identify the corresponding file in the structure.
t = -1 # Initialization.
for fileIdx in range(len(a)):
if (a[fileIdx].fileNum == fileNum
and a[fileIdx].fileDateStr == fileDateStr):
t = fileIdx
if endPkt == 'inf':
endPkt = a[t].pktCount - 1
else:
endPkt = int(endPkt)
pktRang = range(startPkt, endPkt + 1)
# Crop the data.
a[t].pktCount = len(pktRang)
# 0-indexed by packet number.
a[t].pkt = a[t].pkt[pktRang]
a[t].cpuDTStr = a[t].cpuDTStr[startPkt:(endPkt + 1)]
a[t].cpuDT = a[t].cpuDT[startPkt:(endPkt + 1)]
a[t].gpsDTStr = a[t].gpsDTStr[startPkt:(endPkt + 1)]
a[t].gpsDT = a[t].gpsDT[startPkt:(endPkt + 1)]
a[t].lat = a[t].lat[pktRang]
a[t].longi = a[t].longi[pktRang]
# 0-indexed by channel number.
# 0-indexed by packet number.
a[t].clipHi = a[t].clipHi[:, pktRang]
a[t].clipLo = a[t].clipLo[:, pktRang]
a[t].meanPct = a[t].meanPct[:, pktRang]
a[t].meanUpPct = a[t].meanUpPct[:, pktRang]
a[t].meanDnPct = a[t].meanDnPct[:, pktRang]
a[t].meanPhys = a[t].meanPhys[:, pktRang]
a[t].meanUpPhys = a[t].meanUpPhys[:, pktRang]
a[t].meanDnPhys = a[t].meanDnPhys[:, pktRang]
a[t].countUp = a[t].countUp[:, pktRang]
a[t].countDn = a[t].countDn[:, pktRang]
# 0-indexed by frequency index.
a[t].freq = a[t].freq
# 0-indexed by channel number.
# 0-indexed by packet number.
# 0-indexed by frequency index.
a[t].phaseDiff = a[t].phaseDiff[:, pktRang, :]
a[t].magPhys = a[t].magPhys[:, pktRang, :]
a[t].zMag = a[t].zMag[:, pktRang, :]
# Save the cropped data.
pklName = folderName + '_' + loadThis + '_crop.pkl'
pklPath = os.path.join(folderPath, pklName)
# Saving the list object:
with open(pklPath, 'wb') as f: # Python 3: open(..., 'wb')
pickle.dump(a, f)
def ipPlotRaw():
ps = cs.emptyClass
folderPath = r'C:\Users\timl\Documents\IP_data_plots\190506_eagle'
folderName = cs.lastName(folderPath)
doSave = False
# Processed result choice.
loadThis = 'raw'
# Loading the data:
pklName = folderName + '_' + loadThis + '.pkl'
filePath = os.path.join(folderPath, pklName)
with open(filePath, 'rb') as f: # Python 3: open(..., 'rb')
a = pickle.load(f)
# File number to be plotted.
for fileNum in sp.arange(5, 6):
t = 3000 # Initialization.
for fileIdx in range(len(a)):
if a[fileIdx].fileNum == fileNum:
t = fileIdx
at = a[t]
# Channel numbers plotted.
chList = sp.hstack((sp.arange(1, 4, dtype=int), sp.zeros(1,
dtype=int)))
params = {
'legend.fontsize': 'x-large',
'figure.figsize': (9, 6.5),
'axes.labelsize': 'x-large',
'axes.titlesize': 'x-large',
'xtick.labelsize': 'x-large',
'ytick.labelsize': 'x-large'
}
plt.rcParams.update(params)
for ch in chList:
ps.ch = ch
plot1Raw(at, ps)
titleStr = ('%s_%d Pkt %d. xmitFund = %.0f Hz.' %
(at.fileDateStr, at.fileNum, at.pkt, at.xmitFund))
if at.minor != 'None.':
titleStr += ' %s.' % (at.minor)
# Wrap text at a set character length.
titleStr = '\n'.join(wrap(titleStr, 75))
plt.title(titleStr)
plt.xlabel('Sample Number (int)')
plt.ylabel('Raw Voltage (% of range)')
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
ax = plt.gca()
ax.axis([xmin, xmax, -110, 200])
plt.legend(loc='upper left')
plt.grid()
plt.tight_layout()
plt.show()
if doSave:
imgFile = ('%s_%d_%s' % (at.fileDateStr, at.fileNum, 'raw'))
cs.saveImg(folderPath, 'raw', imgFile)
plt.clf()