def view(self, declevel: int = 0, **kwargs) -> Figure:
"""Produces a 2-D plot with all the masked windows along the bottom for a decimation level
Parameters
----------
declevel : int, optional
The decimation level. Default is 0.
fig : matplotlib.pyplot.figure, optional
A figure object
plotfonts : Dict, optional
A dictionary of plot fonts
"""
from resistics.common.plot import getViewFonts
fig: plt.figure = (plt.figure(kwargs["fig"].number) if "fig" in kwargs
else plt.figure(figsize=(10, 10)))
plotfonts = kwargs[
"plotfonts"] if "plotfonts" in kwargs else getViewFonts()
evalFreq = self.evalFreq[declevel]
numFreq = len(evalFreq)
freqIndex = range(0, numFreq)
levelMasked: Set = set()
numMasked: List[int] = list()
freqLabel: List[str] = list()
for eFreq in evalFreq:
levelMasked.update(self.maskWindows[eFreq])
numMasked.append(len(self.maskWindows[eFreq]))
freqLabel.append("{:.5f}".format(eFreq))
numMaskedTotal = len(levelMasked)
levelMasked = np.array(sorted(list(levelMasked)))
global2localMap = {}
for idx in range(0, levelMasked.size):
global2localMap[levelMasked[idx]] = idx
# make array
data = np.zeros(shape=(levelMasked.size, numFreq), dtype=int)
for idx, eFreq in enumerate(evalFreq):
for gI in self.maskWindows[eFreq]:
lI = global2localMap[gI]
data[lI, idx] = idx + 1
st = fig.suptitle(
"Masked windows for decimation level: {}".format(declevel),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# now plot
ax = plt.subplot(2, 1, 1)
cmap = cm.get_cmap("Paired", numFreq)
cmap.set_under("white")
plt.pcolor(np.transpose(data), cmap=cmap, vmin=1, vmax=numFreq + 1)
ax.set_yticks(np.array(freqIndex) + 0.5)
ax.set_yticklabels(freqLabel)
plt.ylabel("Masked windows for decimation level",
fontsize=plotfonts["axisLabel"])
plt.ylabel("Evaluation Frequency [Hz]",
fontsize=plotfonts["axisLabel"])
plt.title(
"Relative masked windows for decimation level {}".format(declevel),
fontsize=plotfonts["title"],
)
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
# colourbar
cb = plt.colorbar()
cb.ax.set_title("Masked\nwindows")
cb.set_ticks(np.arange(1, numFreq + 1) + 0.5)
cb.set_ticklabels(freqLabel)
# bar graph
ax = plt.subplot(2, 1, 2)
# add the total number masked
freqIndex = range(0, numFreq + 1)
numMasked.append(numMaskedTotal)
freqLabel.append("Total masked for\n decimation level")
plt.bar(freqIndex, numMasked)
ax.set_xticks(freqIndex)
ax.set_xticklabels(freqLabel)
plt.xlabel("Evaluation Frequency [Hz]",
fontsize=plotfonts["axisLabel"])
plt.ylabel("Number of masked windows", fontsize=plotfonts["axisLabel"])
plt.title(
"Number of masked windows per evaluation frequency",
fontsize=plotfonts["title"],
)
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
fig.tight_layout(rect=[0.02, 0.02, 0.98, 0.92])
plt.show()
return fig
def view(self, **kwargs) -> Figure:
"""Plot of the calibration function
Parameters
----------
staticgain : bool, optional
Boolean flag for having static gain on, default is True
degrees : bool, optional
Plot phase in degreesm default is False
fig : matplotlib.pyplot.figure, optional
A figure object
plotFonts : Dict, optional
A dictionary of plot fonts
label : str, optional
Label for the plots
xlim : List, optional
Limits for the x axis
ylim_mag : List, optional
Limits for the magnitude y axis
ylim_phase : List, optional
Limits for the phase y axis
legened : bool
Boolean flag for adding a legend
Returns
-------
plt.figure
Matplotlib figure object
"""
from resistics.common.plot import getViewFonts
if "fig" in kwargs:
fig = plt.figure(kwargs["fig"].number)
else:
fig = plt.figure(figsize=(8, 8))
plotFonts = kwargs[
"plotFonts"] if "plotFonts" in kwargs else getViewFonts()
# static gain
magnitude = self.magnitude
if "staticgain" in kwargs and not kwargs["staticgain"]:
magnitude = magnitude / self.staticGain
# phase
phaseUnit = self.phaseUnit
phase = self.phase
if "degrees" in kwargs and kwargs["degrees"]:
phaseUnit = "degrees"
phase = self.phase * (180 / np.pi)
# plot magnitude
plt.subplot(2, 1, 1)
plt.title("Impulse response magnitude", fontsize=plotFonts["title"])
lab = kwargs["label"] if "label" in kwargs else self.filename
plt.loglog(self.freqs, magnitude, label=lab)
if "xlim" in kwargs:
plt.xlim(kwargs["xlim"])
if "ylim_mag" in kwargs:
plt.ylim(kwargs["ylim_mag"])
plt.xlabel("Frequency [Hz]")
plt.ylabel("Magnitude [{}]".format(self.magnitudeUnit))
plt.grid(True)
# legend
if "legend" in kwargs and kwargs["legend"]:
plt.legend(loc=2)
# plot phase
plt.subplot(2, 1, 2)
plt.title("Impulse response phase", fontsize=plotFonts["title"])
lab = kwargs["label"] if "label" in kwargs else self.filename
plt.semilogx(self.freqs, phase, label=lab)
if "xlim" in kwargs:
plt.xlim(kwargs["xlim"])
if "ylim_phase" in kwargs:
plt.ylim(kwargs["ylim_phase"])
plt.xlabel("Frequency [Hz]")
plt.ylabel("Phase [{}]".format(phaseUnit))
plt.grid(True)
# legend
if "legend" in kwargs and kwargs["legend"]:
plt.legend(loc=3)
# show if the figure is not in keywords
if "fig" not in kwargs:
plt.tight_layout(rect=[0, 0.02, 1, 0.96])
plt.show()
return fig
def view(self, **kwargs) -> Figure:
"""Plot spectra data
Parameters
----------
fig : matplotlib.pyplot.figure, optional
A figure object
plotfonts : Dict, optional
A dictionary of plot fonts
powers : List[List[str]], optional
A list of cross powers to plot
xlim : List, optional
Limits for the x axis
color : str, rgba Tuple
The color for the line plot
legend : bool
Boolean flag for adding a legend
Returns
-------
plt.figure
Matplotlib figure object
"""
freqArray = self.freqArray
fig = (plt.figure(kwargs["fig"].number)
if "fig" in kwargs else plt.figure(figsize=(20, 10)))
plotFonts = kwargs[
"plotfonts"] if "plotfonts" in kwargs else getViewFonts()
color = kwargs["color"] if "color" in kwargs else None
# suptitle
st = fig.suptitle("Cross power data", fontsize=plotFonts["suptitle"])
st.set_y(0.98)
# plot data
plotPowers = kwargs["powers"] if "powers" in kwargs else self.powers
for power in plotPowers:
plt.plot(
freqArray,
np.absolute(self.getPower(power[0], power[1])),
color=color,
)
# x axis options
plt.xlabel("Frequency [Hz]", fontsize=plotFonts["axisLabel"])
xlim = kwargs["xlim"] if "xlim" in kwargs else [
freqArray[0], freqArray[-1]
]
plt.xlim(xlim)
# y axis options
plt.ylabel("Cross power amplitude",
fontsize=plotFonts["axisLabel"])
plt.grid()
# set tick sizes
ax = plt.gca()
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotFonts["axisTicks"])
# legend
if "legend" in kwargs and kwargs["legend"]:
plt.legend(loc=4)
# show if the figure is not in keywords
if "fig" not in kwargs:
plt.tight_layout(rect=[0, 0.02, 1, 0.96])
plt.show()
return fig
def view(self, **kwargs) -> Figure:
"""View timeseries data as a line plot
Parameters
----------
sampleStart : int, optional
Sample to start plotting from
sampleStop : int, optional
Sample to plot to
fig : matplotlib.pyplot.figure, optional
A figure object
plotfonts : Dict, optional
A dictionary of plot fonts
chans : List[str]
Channels to plot
label : str, optional
Label for the plots
xlim : List, optional
Limits for the x axis
legened : bool
Boolean flag for adding a legend
Returns
-------
plt.figure
Matplotlib figure object
"""
# the number of samples to plot
sampleStart = 0
sampleStop = 4096
if "sampleStart" in kwargs:
sampleStart = kwargs["sampleStart"]
if "sampleStop" in kwargs:
sampleStop = kwargs["sampleStop"]
if sampleStop >= self.numSamples:
sampleStop = self.numSamples - 1
# get the x axis ready
x = self.getDateArray()
start = x[sampleStart]
stop = x[sampleStop]
# now plot
fig = (plt.figure(kwargs["fig"].number) if "fig" in kwargs else
plt.figure(figsize=(20, 2 * self.numChans)))
plotfonts = kwargs[
"plotfonts"] if "plotfonts" in kwargs else getViewFonts()
# suptitle
st = fig.suptitle(
"Time data from {} to {}, samples {} to {}".format(
start, stop, sampleStart, sampleStop),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# now plot the data
dataChans = kwargs["chans"] if "chans" in kwargs else self.chans
numDataChans = len(dataChans)
for idx, chan in enumerate(dataChans):
ax = plt.subplot(numDataChans, 1, idx + 1)
plt.title("Channel {}".format(chan), fontsize=plotfonts["title"])
# check if channel exists in data and if not, leave empty plot so it's clear
if chan not in self.data:
continue
# label for plot
lab = (kwargs["label"] if "label" in kwargs else
"{}: {} to {}".format(chan, start, stop))
# plot data
plt.plot(
x[sampleStart:sampleStop + 1],
self.data[chan][sampleStart:sampleStop + 1],
label=lab,
)
# add time label
if idx == numDataChans - 1:
plt.xlabel("Time", fontsize=plotfonts["axisLabel"])
# set the xlim
xlim = kwargs["xlim"] if "xlim" in kwargs else [start, stop]
plt.xlim(xlim)
# y axis options
if isElectric(chan):
plt.ylabel("mV/km", fontsize=plotfonts["axisLabel"])
else:
plt.ylabel("nT or mV", fontsize=plotfonts["axisLabel"])
plt.grid(True)
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
# legend
if "legend" in kwargs and kwargs["legend"]:
plt.legend(loc=4)
# show if the figure is not in keywords
if "fig" not in kwargs:
plt.tight_layout(rect=[0, 0.02, 1, 0.96])
plt.show()
return fig
def densityplot(self, eFreqI: int, **kwargs) -> Figure:
"""Plots density plots of statistic components for evaluation frequency index
Notes
-----
By default, the density plots plotted are
statistic component 1 vs statistic component 2
statistic component 3 vs statistic component 4
etc
But density plots can be explicity set by using the crossplots keyword. They should be specified as a list of a list of strings
e.g.
crossplots = [[component2, component3], [component1, component4]]
Parameters
----------
eFreqI : int
Evaluation frequency index
maskwindows : List, np.ndarray
Global windows to exclude
crossplots : List[List[str]], optional
The parameters to crossplot
fig : matplotlib.pyplot.figure, optional
A figure object
plotfonts : Dict, optional
A dictionary of plot fonts
label : str, optional
Label for the plots
xlim : List, optional
Limits for the x axis
ylim : List, optional
Limits for the y axis
colortitle : str, optional
Title for the colourbar
legened : bool
Boolean flag for adding a legend
Returns
-------
plt.figure
Matplotlib figure object
"""
# deal with maskwindows, which are global indices
maskWindows = kwargs["maskwindows"] if "maskwindows" in kwargs else []
plotIndices = self.getUnmaskedIndices(maskWindows)
# figure out the crossplots
if "crossplots" in kwargs:
crossplots = kwargs["crossplots"]
else:
crossplots = list(zip(self.winStats[::2], self.winStats[1::2]))
# plot parameters
nrows, ncols = self.getRowsCols(self.maxcols, numStats=len(crossplots))
eFreq = self.evalFreq[eFreqI]
plotfonts = kwargs[
"plotfonts"] if "plotfonts" in kwargs else getViewFonts()
fig = (plt.figure(kwargs["fig"].number) if "fig" in kwargs else
plt.figure(figsize=(4 * ncols, 5 * nrows)))
st = fig.suptitle(
"{} density plots for evaluation frequency: {:.3f} Hz".format(
self.statName, eFreq),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# now plot the data
for idx, cplot in enumerate(crossplots):
ax = plt.subplot(nrows, ncols, idx + 1)
plt.title("{} vs. {}".format(cplot[0], cplot[1]),
fontsize=plotfonts["title"])
label = kwargs["label"] if "label" in kwargs else eFreq
# get plot data
plotI1 = self.winStats.index(cplot[0])
plotData1 = np.squeeze(self.stats[:, eFreqI, plotI1])
plotI2 = self.winStats.index(cplot[1])
plotData2 = np.squeeze(self.stats[:, eFreqI, plotI2])
if plotIndices is not None:
plotData1 = plotData1[plotIndices]
plotData2 = plotData2[plotIndices]
nbins = 200
if "xlim" in kwargs:
plt.xlim(kwargs["xlim"])
rangex = kwargs["xlim"]
else:
minx = np.percentile(plotData1, 2)
maxx = np.percentile(plotData1, 98)
plt.xlim(minx, maxx)
rangex = [minx, maxx]
if "ylim" in kwargs:
plt.ylim(kwargs["ylim"])
rangey = kwargs["ylim"]
else:
miny = np.percentile(plotData2, 2)
maxy = np.percentile(plotData2, 98)
plt.ylim(miny, maxy)
rangey = [miny, maxy]
plt.hist2d(
plotData1,
plotData2,
bins=(nbins, nbins),
range=[rangex, rangey],
cmap=plt.cm.inferno,
)
# axis options
plt.xlabel("Value {}".format(cplot[0]),
fontsize=plotfonts["axisLabel"])
plt.ylabel("Value {}".format(cplot[1]),
fontsize=plotfonts["axisLabel"])
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
plt.grid(True, ls="--")
# legend
if "legend" in kwargs and kwargs["legend"]:
plt.legend(loc=4)
# show if the figure is not in keywords
if "fig" not in kwargs:
fig.tight_layout(rect=[0.02, 0.02, 0.85, 0.92])
plt.show()
return fig
def view(self, **kwargs) -> Figure:
"""Plot spectra data
Parameters
----------
fig : matplotlib.pyplot.figure, optional
A figure object
plotfonts : Dict, optional
A dictionary of plot fonts
chans : List[str], optional
A list of channels to plot
label : str, optional
Label for the plots
xlim : List, optional
Limits for the x axis
color : str, rgba Tuple
The color for the line plot
legend : bool
Boolean flag for adding a legend
Returns
-------
plt.figure
Matplotlib figure object
"""
freqArray = self.freqArray
fig = (plt.figure(kwargs["fig"].number) if "fig" in kwargs else
plt.figure(figsize=(20, 2 * self.numChans)))
plotFonts = kwargs[
"plotfonts"] if "plotfonts" in kwargs else getViewFonts()
color = kwargs["color"] if "color" in kwargs else None
# suptitle
st = fig.suptitle(
"Spectra data from {} to {}".format(self.startTime, self.stopTime),
fontsize=plotFonts["suptitle"],
)
st.set_y(0.98)
# now plot the data
dataChans = kwargs["chans"] if "chans" in kwargs else self.chans
numPlotChans = len(dataChans)
for idx, chan in enumerate(dataChans):
ax = plt.subplot(numPlotChans, 1, idx + 1)
plt.title("Channel {}".format(chan), fontsize=plotFonts["title"])
# plot the data
if "label" in kwargs:
plt.plot(
freqArray,
np.absolute(self.data[chan]),
color=color,
label=kwargs["label"],
)
else:
plt.plot(freqArray, np.absolute(self.data[chan]), color=color)
# add frequency label
if idx == numPlotChans - 1:
plt.xlabel("Frequency [Hz]", fontsize=plotFonts["axisLabel"])
# x axis options
xlim = kwargs["xlim"] if "xlim" in kwargs else [
freqArray[0], freqArray[-1]
]
plt.xlim(xlim)
# y axis options
if isElectric(chan):
plt.ylabel("[mV/km]", fontsize=plotFonts["axisLabel"])
else:
plt.ylabel("[nT]", fontsize=plotFonts["axisLabel"])
plt.grid()
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotFonts["axisTicks"])
# legend
if "legend" in kwargs and kwargs["legend"]:
plt.legend(loc=4)
# show if the figure is not in keywords
if "fig" not in kwargs:
plt.tight_layout(rect=[0, 0.02, 1, 0.96])
plt.show()
return fig
def crossplot(self, eFreqI: int, **kwargs) -> Figure:
"""Plots crossplots of statistic components for evaluation frequency index
Notes
-----
By default, the crossplots plotted are
statistic component 1 vs statistic component 2
statistic component 3 vs statistic component 4
etc
But crossplots can be explicity set by using the crossplots keyword. They should be specified as a list of a list of strings
e.g.
crossplots = [[component2, component3], [component1, component4]]
Parameters
----------
eFreqI : int
Evaluation frequency index
maskwindows : List, np.ndarray
Global windows to exclude
crossplots : List[List[str]], optional
The parameters to crossplot
fig : matplotlib.pyplot.figure, optional
A figure object
plotfonts : Dict, optional
A dictionary of plot fonts
label : str, optional
Label for the plots
clim : List, optional
Limits for colourbar axis
xlim : List, optional
Limits for the x axis
ylim : List, optional
Limits for the y axis
colortitle : str, optional
Title for the colourbar
legened : bool
Boolean flag for adding a legend
Returns
-------
plt.figure
Matplotlib figure object
"""
# deal with maskwindows, which are global indices
maskWindows = kwargs["maskwindows"] if "maskwindows" in kwargs else []
plotIndices = self.getUnmaskedIndices(maskWindows)
# figure out the crossplots
if "crossplots" in kwargs:
crossplots = kwargs["crossplots"]
else:
crossplots = list(zip(self.winStats[::2], self.winStats[1::2]))
# plot parameters
nrows, ncols = self.getRowsCols(self.maxcols, numStats=len(crossplots))
eFreq = self.evalFreq[eFreqI]
plotfonts = kwargs[
"plotfonts"] if "plotfonts" in kwargs else getViewFonts()
fig = (plt.figure(kwargs["fig"].number) if "fig" in kwargs else
plt.figure(figsize=(4 * ncols, 5 * nrows)))
st = fig.suptitle(
"{} crossplots for evaluation frequency: {:.3f} Hz".format(
self.statName, eFreq),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# now plot the data
for idx, cplot in enumerate(crossplots):
ax = plt.subplot(nrows, ncols, idx + 1)
plt.title("{} vs. {}".format(cplot[0], cplot[1]),
fontsize=plotfonts["title"])
label = kwargs["label"] if "label" in kwargs else eFreq
# the colourdata
colourbool, colourdata, cmap = self.calcColourData(
self.globalIndices, cplot[0], eFreqI, kwargs)
# get plot data
plotI1 = self.winStats.index(cplot[0])
plotData1 = np.squeeze(self.stats[:, eFreqI, plotI1])
plotI2 = self.winStats.index(cplot[1])
plotData2 = np.squeeze(self.stats[:, eFreqI, plotI2])
if plotIndices is not None:
plotData1 = plotData1[plotIndices]
plotData2 = plotData2[plotIndices]
colourdata = colourdata[plotIndices]
# scatter plot
scat = plt.scatter(
plotData1,
plotData2,
c=colourdata,
edgecolors="none",
marker="o",
s=12,
cmap=cmap,
label=label,
)
clim = (kwargs["clim"] if "clim" in kwargs else
[colourdata.min(), colourdata.max()])
scat.set_clim(clim)
# x axis options
plt.xlabel("Value {}".format(cplot[0]),
fontsize=plotfonts["axisLabel"])
if "xlim" in kwargs:
plt.xlim(kwargs["xlim"])
# y axis options
plt.ylabel("Value {}".format(cplot[1]),
fontsize=plotfonts["axisLabel"])
if "ylim" in kwargs:
plt.ylim(kwargs["ylim"])
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
plt.grid(True, ls="--")
# legend
if "legend" in kwargs and kwargs["legend"]:
plt.legend(loc=4)
# show if the figure is not in keywords
if "fig" not in kwargs:
fig.tight_layout(rect=[0.02, 0.02, 0.85, 0.92])
cax = fig.add_axes([0.88, 0.10, 0.03, 0.80])
if not colourbool:
self.addColourbarDates(scat, cax, "Time", plotfonts)
else:
colourtitle = (kwargs["colortitle"]
if "colortitle" in kwargs else "Value")
self.addColourbar(scat, cax, colourtitle, plotfonts)
plt.show()
return fig
def histogram(self, eFreqI: int, **kwargs) -> Figure:
"""Plot statistics for evaluation frequency index
Plots a histogram of each statistic with bins on the xaxis and count on the yaxis. Ideal for exploring the distribution of statistic values over the windows.
Parameters
----------
eFreqI : int
Evaluation frequency index
maskwindows : List, np.ndarray
Global windows to exclude
numbins : int
The number of bins for the histogram data binning
fig : matplotlib.pyplot.figure, optional
A figure object
plotfonts : Dict, optional
A dictionary of plot fonts
label : str, optional
Label for the plots
xlim : List, optional
Limits for the x axis
legened : bool
Boolean flag for adding a legend
Returns
-------
plt.figure
Matplotlib figure object
"""
# deal with maskwindows, which are global indices
maskWindows = kwargs["maskwindows"] if "maskwindows" in kwargs else []
plotIndices = self.getUnmaskedIndices(maskWindows)
eFreq = self.evalFreq[eFreqI]
# plot options
numbins = kwargs["numbins"] if "numbins" in kwargs else 40
nrows, ncols = self.getRowsCols(self.maxcols)
plotfonts = kwargs[
"plotfonts"] if "plotfonts" in kwargs else getViewFonts()
fig = (plt.figure(kwargs["fig"].number) if "fig" in kwargs else
plt.figure(figsize=(4 * ncols, 4 * nrows)))
st = fig.suptitle(
"{} data for evaluation frequency: {}".format(
self.statName, eFreq),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# plot the data
for idx, val in enumerate(self.winStats):
ax = plt.subplot(nrows, ncols, idx + 1)
plt.title("Value {}".format(val), fontsize=plotfonts["title"])
label = kwargs["label"] if "label" in kwargs else eFreq
# data
plotData = np.squeeze(self.stats[:, eFreqI, idx])
if plotIndices is not None:
plotData = plotData[plotIndices]
# remove infinities and nans
plotData = plotData[np.isfinite(plotData)]
# x axis options
xlim = (kwargs["xlim"] if "xlim" in kwargs else
[np.min(plotData), np.max(plotData)])
plt.xlim(xlim)
plt.xlabel("Value", fontsize=plotfonts["axisLabel"])
# now plot with xlim in mind
plt.hist(plotData,
numbins,
range=xlim,
facecolor="red",
alpha=0.75)
# y axis options
plt.ylabel("Count", fontsize=plotfonts["axisLabel"])
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
plt.grid(True, ls="--")
# legend
if "legend" in kwargs and kwargs["legend"]:
plt.legend(loc=4)
# show if the figure is not in keywords
if "fig" not in kwargs:
fig.tight_layout(rect=[0.02, 0.02, 0.98, 0.92])
plt.show()
return fig
def view(self, eFreqI: int, **kwargs) -> Figure:
"""Plot statistics for evaluation frequency index
Plots a simple scatter of each statistic with datetime on the xaxis (datetime of the window start dates). Number of subplots is equal to numStaStatPerWindow.
Parameters
----------
eFreqI : int
Evaluation frequency index
maskwindows : List, np.ndarray
Global windows to exclude
fig : matplotlib.pyplot.figure, optional
A figure object
plotfonts : Dict, optional
A dictionary of plot fonts
label : str, optional
Label for the plots
clim : List, optional
Limits for colourbar axis
xlim : List, optional
Limits for the x axis
ylim : List, optional
Limits for the y axis
colortitle : str, optional
Title for the colourbar
legened : bool
Boolean flag for adding a legend
Returns
-------
plt.figure
Matplotlib figure object
"""
# get windows to plot and global dates
maskWindows = kwargs["maskwindows"] if "maskwindows" in kwargs else []
plotIndices = self.getUnmaskedIndices(maskWindows)
globalDates = self.getGlobalDates()
eFreq = self.evalFreq[eFreqI]
# plot params
nrows, ncols = self.getRowsCols(self.maxcols)
plotfonts = kwargs[
"plotfonts"] if "plotfonts" in kwargs else getViewFonts()
fig: plt.figure = (plt.figure(kwargs["fig"].number) if "fig" in kwargs
else plt.figure(figsize=(4 * ncols, 5 * nrows)))
st = fig.suptitle(
"{} data for evaluation frequency: {}".format(
self.statName, eFreq),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# plot the data
for idx, val in enumerate(self.winStats):
ax = plt.subplot(nrows, ncols, idx + 1)
plt.title("Value {}".format(val), fontsize=plotfonts["title"])
label = kwargs["label"] if "label" in kwargs else eFreq
# limit the data by plotIndices if not False
plotData = np.squeeze(self.stats[:, eFreqI, idx])
plotDates = globalDates
if plotIndices is not None:
plotData = plotData[plotIndices]
plotDates = plotDates[plotIndices]
# the colourdata
colourbool, colourdata, cmap = self.calcColourData(
plotData, val, eFreqI, kwargs)
# scatter plot
if not colourbool:
scat = plt.scatter(
plotDates,
plotData,
edgecolors="none",
marker="o",
s=12,
label=label,
)
else:
scat = plt.scatter(
plotDates,
plotData,
c=colourdata,
edgecolors="none",
marker="o",
s=12,
cmap=cmap,
label=label,
)
clim = (kwargs["clim"] if
("clim" in kwargs and len(kwargs["clim"]) > 0) else
[colourdata.min(), colourdata.max()])
scat.set_clim(clim)
# x axis options
plt.xlabel("Time", fontsize=plotfonts["axisLabel"])
xlim = (kwargs["xlim"] if
("xlim" in kwargs and len(kwargs["xlim"]) > 0) else
[globalDates[0], globalDates[-1]])
plt.xlim(xlim)
ax.format_xdata = DateFormatter("%H-%M-%S")
fig.autofmt_xdate()
# y axis options
if "ylim" in kwargs and len(kwargs["ylim"]) > 0:
plt.ylim(kwargs["ylim"])
plt.ylabel("Value {}".format(val), fontsize=plotfonts["axisLabel"])
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
plt.grid(True, ls="--")
# legend
if "legend" in kwargs and kwargs["legend"]:
plt.legend(loc=4)
# show if the figure is not in keywords
if "fig" not in kwargs:
if colourbool:
fig.tight_layout(rect=[0.02, 0.02, 0.85, 0.92])
cax = fig.add_axes([0.88, 0.10, 0.03, 0.80])
colourtitle = (kwargs["colortitle"]
if "colortitle" in kwargs else "Value")
self.addColourbar(scat, cax, colourtitle, plotfonts)
else:
fig.tight_layout(rect=[0.02, 0.02, 0.98, 0.92])
plt.show()
return fig
def viewTipper(self, **kwargs) -> Figure:
"""Plots tipper data where available
For length data, both axes are log scale (period and length). For angle data, period is in log scale and phase is linear scale.
Units, x axis is seconds, length is dimensionless and angle is degrees.
Parameters
----------
fig : matplotlib.pyplot.figure, optional
A figure object
cols : bool, optional
There are three tipper plots: tipper length, tipper real angle, tipper imaginary angle. These can either be plotted in rows or columns. Set to True to plot in with one row and three columns.
colours : str, optional
Colour of plot
mk : str, optional
Plot marker type
ls : str, optional
Line style
plotfonts : Dict, optional
A dictionary of plot fonts
label : str, optional
Label for the plots
xlim : List, optional
Limits for the x axis
length_ylim : List, optional
Limits for the length y axis
angle_ylim : List, optional
Limits for the angle y axis
"""
if "HzHx" not in self.polarisations or "HzHy" not in self.polarisations:
return False
# rows or columns
cols = kwargs["cols"] if "cols" in kwargs else True
if cols:
nrows = 1
ncols = 3
else:
nrows = 3
ncols = 1
# plot options
mk = kwargs["mk"] if "mk" in kwargs else "o"
ls = kwargs["ls"] if "ls" in kwargs else "none"
plotfonts = kwargs["plotfonts"] if "plotfonts" in kwargs else getViewFonts()
# limits
xlim = kwargs["xlim"] if "xlim" in kwargs else [1e-3, 1e4]
length_ylim = kwargs["length_ylim"] if "length_ylim" in kwargs else [1e-2, 1e3]
angle_ylim = kwargs["angle_ylim"] if "angle_ylim" in kwargs else [-30, 30]
fig = (
plt.figure(kwargs["fig"].number)
if "fig" in kwargs
else plt.figure(figsize=(16, 7))
)
st = fig.suptitle("Tipper")
st.set_y(0.98)
# plot
tipperLength, tipperAngleRe, tipperAngleIm = self.getTipper()
# lengthError, angleError = self.getTipperErrors()
label = kwargs["label"] if "label" in kwargs else "tipper"
# plot resistivity
ax1 = plt.subplot(nrows, ncols, 1)
plt.title("Tipper Length")
ax1.errorbar(
self.period,
tipperLength,
yerr=tipperLength / 50,
ls=ls,
marker=mk,
markerfacecolor="white",
markersize=9,
color="red",
ecolor="red",
mew=1.1,
elinewidth=1.0,
capsize=4,
barsabove=False,
label=label,
)
ax1.set_xscale("log")
ax1.set_yscale("log")
# axis options
plt.xlabel("Period [s]", fontsize=plotfonts["axisLabel"])
plt.ylabel("Length [dimensionless]", fontsize=plotfonts["axisLabel"])
plt.xlim(xlim)
plt.ylim(length_ylim)
# set tick sizes and legend
for lab in ax1.get_xticklabels() + ax1.get_yticklabels():
lab.set_fontsize(plotfonts["axisTicks"])
leg = plt.legend(loc="upper right", fontsize=plotfonts["legend"])
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_facecolor("w")
plt.grid(True, ls="--")
# plot real tipper angles
ax2 = plt.subplot(nrows, ncols, 2)
plt.title("Tipper Angle Real")
ax2.errorbar(
self.period,
tipperAngleRe,
yerr=tipperAngleRe / 50,
ls="none",
marker=mk,
markerfacecolor="white",
markersize=9,
color="red",
ecolor="red",
mew=1.1,
elinewidth=1.0,
capsize=4,
barsabove=False,
label=label,
)
ax2.set_xscale("log")
# axis options
plt.xlabel("Period [s]", fontsize=plotfonts["axisLabel"])
plt.ylabel("Angle [degrees]", fontsize=plotfonts["axisLabel"])
plt.xlim(xlim)
plt.ylim(angle_ylim)
# set tick sizes
for lab in ax2.get_xticklabels() + ax2.get_yticklabels():
lab.set_fontsize(plotfonts["axisTicks"])
leg = plt.legend(loc="upper right", fontsize=plotfonts["legend"])
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_facecolor("w")
plt.grid(True, ls="--")
# plot imaginary tipper angles
ax3 = plt.subplot(nrows, ncols, 3)
plt.title("Tipper Angle Imaginary")
ax3.errorbar(
self.period,
tipperAngleIm,
yerr=tipperAngleIm / 50,
ls="none",
marker=mk,
markerfacecolor="white",
markersize=9,
color="red",
ecolor="red",
mew=1.1,
elinewidth=1.0,
capsize=4,
barsabove=False,
label=label,
)
ax3.set_xscale("log")
# axis options
plt.xlabel("Period [s]", fontsize=plotfonts["axisLabel"])
plt.ylabel("Angle [degrees]", fontsize=plotfonts["axisLabel"])
plt.xlim(xlim)
plt.ylim(angle_ylim)
# set tick sizes
for lab in ax3.get_xticklabels() + ax3.get_yticklabels():
lab.set_fontsize(plotfonts["axisTicks"])
leg = plt.legend(loc="upper right", fontsize=plotfonts["legend"])
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_facecolor("w")
plt.grid(True, ls="--")
# show if the figure is not in keywords
if "fig" not in kwargs:
# layout options
plt.tight_layout()
fig.subplots_adjust(top=0.90)
plt.show()
return fig
def viewImpedance(self, **kwargs) -> Figure:
"""Plots the transfer function data
For resistivity data, both axes are log scale (period and resistivity). For phase data, period is in log scale and phase is linear scale.
Units, x axis is seconds, resistivity is Ohm m and phase is degrees.
Parameters
----------
polarisations : List[str], optional
Polarisations to plot
fig : matplotlib.pyplot.figure, optional
A figure object
oneplot : bool, optional
Boolean flag for plotting all polarisations on one plot rather than separate plots
colours : Dict[str, str], optional
Colours dictionary for plotting impedance components
mk : str, optional
Plot marker type
ls : str, optional
Line style
plotfonts : Dict, optional
A dictionary of plot fonts
label : str, optional
Label for the plots
xlim : List, optional
Limits for the x axis
res_ylim : List, optional
Limits for the resistivity y axis
phase_ylim : List, optional
Limits for the phase y axis
Returns
-------
plt.figure
Matplotlib figure object
"""
polarisations = (
kwargs["polarisations"] if "polarisations" in kwargs else self.polarisations
)
# limits
xlim = kwargs["xlim"] if "xlim" in kwargs else [1e-3, 1e4]
res_ylim = kwargs["res_ylim"] if "res_ylim" in kwargs else [1e-2, 1e3]
phase_ylim = kwargs["phase_ylim"] if "phase_ylim" in kwargs else [-30, 120]
# markers
colours = (
kwargs["colours"] if "colours" in kwargs else transferFunctionColours()
)
mk = kwargs["mk"] if "mk" in kwargs else "o"
ls = kwargs["ls"] if "ls" in kwargs else "none"
plotfonts = kwargs["plotfonts"] if "plotfonts" in kwargs else getViewFonts()
# calculate number of rows and columns
oneplot = False
if "oneplot" in kwargs and kwargs["oneplot"]:
oneplot = True
nrows = 2
ncols = 1 if oneplot else len(polarisations)
# a multiplier to make sure all the components end up on the right plot
plotNumMult = 0 if ncols > 1 else 1
# plot
if "fig" in kwargs:
fig = plt.figure(kwargs["fig"].number)
else:
figsize = getTransferFunctionFigSize(oneplot, len(polarisations))
fig = plt.figure(figsize=figsize)
st = fig.suptitle(
"Impedance tensor apparent resistivity and phase",
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
for idx, pol in enumerate(polarisations):
res, phase = self.getResAndPhase(pol)
resError, phaseError = self.getResAndPhaseErrors(pol)
label = kwargs["label"] + " - {}".format(pol) if "label" in kwargs else pol
# plot resistivity
ax1 = plt.subplot(nrows, ncols, idx + 1 - plotNumMult * idx)
# the title
if not oneplot:
plt.title("Polarisation {}".format(pol), fontsize=plotfonts["title"])
else:
plt.title(
"Polarisations {}".format(listToString(polarisations)),
fontsize=plotfonts["title"],
)
# plot the data
ax1.errorbar(
self.period,
res,
yerr=resError,
ls=ls,
marker=mk,
markersize=7,
markerfacecolor="white",
markeredgecolor=colours[pol],
mew=1.1,
color=colours[pol],
ecolor=colours[pol],
elinewidth=1.0,
capsize=4,
barsabove=False,
label=label,
)
ax1.set_xscale("log")
ax1.set_yscale("log")
ax1.set_aspect("equal", adjustable="box")
# axis options
plt.ylabel("Apparent Res. [Ohm m]", fontsize=plotfonts["axisLabel"])
plt.xlim(xlim)
plt.ylim(res_ylim)
# set tick sizes
for lab in ax1.get_xticklabels() + ax1.get_yticklabels():
lab.set_fontsize(plotfonts["axisTicks"])
# plot phase
ax2 = plt.subplot(nrows, ncols, ncols + idx + 1 - plotNumMult * idx)
# plot the data
ax2.errorbar(
self.period,
phase,
yerr=phaseError,
ls="none",
marker=mk,
markersize=7,
markerfacecolor="white",
markeredgecolor=colours[pol],
mew=1.1,
color=colours[pol],
ecolor=colours[pol],
elinewidth=1.0,
capsize=4,
barsabove=False,
label=label,
)
ax2.set_xscale("log")
# axis options
plt.xlabel("Period [s]", fontsize=plotfonts["axisLabel"])
plt.ylabel("Phase [degrees]", fontsize=plotfonts["axisLabel"])
plt.xlim(xlim)
plt.ylim(phase_ylim)
# set tick sizes
for lab in ax2.get_xticklabels() + ax2.get_yticklabels():
lab.set_fontsize(plotfonts["axisTicks"])
# add the legend
for idx, pol in enumerate(polarisations):
ax1 = plt.subplot(nrows, ncols, idx + 1 - plotNumMult * idx)
leg = plt.legend(loc="lower left", fontsize=plotfonts["legend"])
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_facecolor("w")
plt.grid(True, ls="--")
ax2 = plt.subplot(nrows, ncols, ncols + idx + 1 - plotNumMult * idx)
leg = plt.legend(loc="lower left", fontsize=plotfonts["legend"])
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_facecolor("w")
plt.grid(True, ls="--")
# show if the figure is not in keywords
if "fig" not in kwargs:
# layout options
plt.tight_layout()
fig.subplots_adjust(top=0.92)
plt.show()
return fig