def writeTF(self, specdir: str, postpend: str, freq, data: np.ndarray,
variances: np.ndarray, **kwargs):
"""Write the transfer function file
Parameters
----------
specdir : str
The spectra data being used for the transfer function estimate
postpend : str
The optional postpend to the transfer function file
data : np.ndarray
The transfer function estimates
variances : np.ndarray
The transfer function variances
remotesite : str, optional
Optionally, if there is a remote site
remotechans : List[str], optional
Optionally add the remote channels if there is a remote site
"""
# path for writing out to
sampleFreqStr = fileFormatSampleFreq(self.decParams.sampleFreq)
if postpend == "":
filename = "{}_fs{:s}_{}".format(self.outSite, sampleFreqStr,
specdir)
else:
filename = "{}_fs{:s}_{}_{}".format(self.outSite, sampleFreqStr,
specdir, postpend)
datapath = os.path.join(self.outpath, sampleFreqStr)
checkAndMakeDir(datapath)
outfile = os.path.join(datapath, filename)
# now construct the transferFunctionData object
numFreq = len(freq)
dataDict = {}
varDict = {}
for i in range(0, self.outSize):
for j in range(0, self.inSize):
key = "{}{}".format(self.outChannels[i], self.inChannels[j])
dataArray = np.empty(shape=(numFreq), dtype="complex")
varArray = np.empty(shape=(len(freq)), dtype="float")
for ifreq in range(0, numFreq):
dataArray[ifreq] = data[ifreq][i, j]
varArray[ifreq] = variances[ifreq][i, j]
dataDict[key] = dataArray
varDict[key] = varArray
tfData = TransferFunctionData(freq, dataDict, varDict)
# now make the writer and write out
tfWriter = TransferFunctionWriter(outfile, tfData)
tfWriter.setHeaders(
sampleFreq=self.decParams.sampleFreq,
insite=self.inSite,
inchans=self.inChannels,
outsite=self.outSite,
outchans=self.outChannels,
)
if "remotesite" in kwargs:
tfWriter.addHeader("remotesite", kwargs["remotesite"])
if "remotechans" in kwargs:
tfWriter.addHeader("remotechans", kwargs["remotechans"])
tfWriter.write()
def getFileNames(self, maskName: str,
sampleFreq: float) -> Tuple[str, str]:
"""Get the name of a mask file
This method is here to give consistent file names for mask files.
Parameters
----------
maskName : str
The name of the mask
sampleFreq : float
The sampling frequency of the data
Returns
-------
infoFile : str
The name of the mask infoFile
winFile : str
The name of the mask winFile
"""
checkAndMakeDir(self.datapath)
sampleFreqStr = fileFormatSampleFreq(sampleFreq)
name: str = maskName + "_{}".format(sampleFreqStr)
infoFile: str = os.path.join(self.datapath, name + ".info")
winFile: str = os.path.join(
self.datapath, name) # no need for extension here, numpy adds one
return infoFile, winFile
def getTransferFunctionData(projData: ProjectData, site: str,
sampleFreq: float,
**kwargs) -> TransferFunctionData:
"""Get transfer function data
Parameters
----------
projData : projecData
The project data
site : str
Site to get the transfer functiond data for
sampleFreq : int, float
The sampling frequency for which to get the transfer function data
specdir : str, optional
The spectra directories used
postpend : str, optional
The postpend on the transfer function files
"""
options: Dict = dict()
options["specdir"]: str = projData.config.configParams["Spectra"][
"specdir"]
options["postpend"]: str = ""
options = parseKeywords(options, kwargs)
# deal with the postpend
if options["postpend"] != "":
postpend = "_{}".format(options["postpend"])
else:
postpend = options["postpend"]
siteData = projData.getSiteData(site)
sampleFreqStr = fileFormatSampleFreq(sampleFreq)
path = os.path.join(
siteData.transFuncPath,
"{:s}".format(sampleFreqStr),
"{}_fs{:s}_{}{}".format(site, sampleFreqStr, options["specdir"],
postpend),
)
# check path
if not checkFilepath(path):
projectWarning("No transfer function file with name {}".format(path))
return False
projectText(
"Reading transfer function for site {}, sample frequency {}, file {}".
format(site, sampleFreq, path))
tfReader = TransferFunctionReader(path)
tfReader.printInfo()
return tfReader.tfData
def viewStatisticCrossplot(projData: ProjectData, site: str,
sampleFreq: Union[int, float], stat: str,
crossplots: List[List[str]],
**kwargs) -> Union[plt.figure, None]:
"""View statistic data for a single sampling frequency of a site
Parameters
----------
projData : ProjectData
A project instance
site : str
The site for which to plot statistics
stat : str
The statistic to plot
sampleFreq : float
The sampling frequency for which to plot statistics
crossplots : List[List[str]]
The statistic element pairs to crossplot
declevel : int
The decimation level to plot
eFreqI : int
The evaluation frequency index
specdir : str
The spectra directory
maskname : str
Mask name
xlim : List, optional
Limits for the x axis
ylim : List, optional
Limits for the y axis
maxcols : int
The maximum number of columns in the plots
show : bool, optional
Show the spectra plot
save : bool, optional
Save the plot to the images directory
plotoptions : Dict, optional
Dictionary of plot options
Returns
-------
matplotlib.pyplot.figure or None
A matplotlib figure unless the plot is not shown and is saved, in which case None and the figure is closed.
"""
options = {}
options["declevel"] = 0
options["eFreqI"] = 0
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options["maskname"] = ""
options["xlim"] = []
options["ylim"] = []
options["maxcols"] = 2
options["show"] = True
options["save"] = False
options["plotoptions"] = plotOptionsSpec()
options = parseKeywords(options, kwargs)
projectText(
"Plotting crossplot for statistic {}, site {} and sampling frequency {}"
.format(stat, site, sampleFreq))
statData = getStatisticDataForSampleFreq(
projData,
site,
sampleFreq,
stat,
declevel=options["declevel"],
specdir=options["specdir"],
)
statMeas = list(statData.keys())
# get the evaluation frequency
eFreq = statData[statMeas[0]].evalFreq[options["eFreqI"]]
# get the mask data
maskWindows = []
if options["maskname"] != "":
maskData = getMaskData(projData, site, options["maskname"], sampleFreq)
maskWindows = maskData.getMaskWindowsFreq(options["declevel"],
options["eFreqI"])
# plot information
nrows, ncols = getPlotRowsAndCols(options["maxcols"], len(crossplots))
plotfonts = options["plotoptions"]["plotfonts"]
fig = plt.figure(figsize=options["plotoptions"]["figsize"])
# suptitle
st = fig.suptitle(
"{} crossplots for {}, sampling frequency {} Hz, decimation level {} and evaluation frequency {} Hz"
.format(stat, site, sampleFreq, options["declevel"], 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("Crossplot {}".format(cplot), fontsize=plotfonts["title"])
for meas in statMeas:
stats = statData[meas].getStats(maskwindows=maskWindows)
plotI1 = statData[meas].winStats.index(cplot[0])
plotData1 = np.squeeze(stats[:, options["eFreqI"], plotI1])
plotI2 = statData[meas].winStats.index(cplot[1])
plotData2 = np.squeeze(stats[:, options["eFreqI"], plotI2])
scat = plt.scatter(plotData1,
plotData2,
edgecolors="none",
marker="o",
s=12,
label=meas)
# x axis options
if len(options["xlim"]) > 0:
plt.xlim(options["xlim"])
if len(options["ylim"]) > 0:
plt.ylim(options["ylim"])
plt.xlabel(cplot[0], fontsize=plotfonts["axisLabel"])
plt.ylabel(cplot[1], fontsize=plotfonts["axisLabel"])
plt.grid(True)
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
plt.legend(loc=2, markerscale=4, fontsize=plotfonts["legend"])
# plot format, show and save
fig.tight_layout(rect=[0.02, 0.02, 0.98, 0.92])
if options["save"]:
impath = projData.imagePath
sampleFreqStr = fileFormatSampleFreq(sampleFreq)
filename = "statCrossplot_{:s}_{:s}_{:s}_dec{:d}_efreq{:d}_{:s}".format(
stat,
site,
sampleFreqStr,
options["declevel"],
options["eFreqI"],
options["specdir"],
)
if options["maskname"] != "":
filename = "{}_{}".format(filename, options["maskname"])
savename = savePlot(impath, filename, fig)
projectText("Image saved to file {}".format(savename))
if options["show"]:
plt.show(block=options["plotoptions"]["block"])
if not options["show"] and options["save"]:
plt.close(fig)
return None
return fig
def viewStatisticHistogram(projData: ProjectData, site: str, sampleFreq: float,
stat: str, **kwargs) -> Union[plt.figure, None]:
"""View statistic histograms for a single sampling frequency of a site
Parameters
----------
projData : ProjectData
A project instance
site : str
The site for which to plot statistics
stat : str
The statistic to plot
sampleFreq : float
The sampling frequency for which to plot statistics
declevel : int
The decimation level to plot
eFreqI : int
The evaluation frequency index
specdir : str
The spectra directory
maskname : str
Mask name
numbins : int
The number of bins for the histogram data binning
xlim : List, optional
Limits for the x axis
maxcols : int
The maximum number of columns in the plots
show : bool, optional
Show the spectra plot
save : bool, optional
Save the plot to the images directory
plotoptions : Dict, optional
Dictionary of plot options
Returns
-------
matplotlib.pyplot.figure or None
A matplotlib figure unless the plot is not shown and is saved, in which case None.
"""
options = {}
options["declevel"] = 0
options["eFreqI"] = 0
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options["maskname"] = ""
options["numbins"] = 40
options["xlim"] = []
options["maxcols"] = 4
options["show"] = True
options["save"] = False
options["plotoptions"] = plotOptionsSpec()
options = parseKeywords(options, kwargs)
projectText(
"Plotting histogram for statistic {}, site {} and sampling frequency {}"
.format(stat, site, sampleFreq))
statData = getStatisticDataForSampleFreq(
projData,
site,
sampleFreq,
stat,
declevel=options["declevel"],
specdir=options["specdir"],
)
statMeas = list(statData.keys())
# get the statistic components
statComponents = statData[statMeas[0]].winStats
# get the evaluation frequency
eFreq = statData[statMeas[0]].evalFreq[options["eFreqI"]]
# get the mask data
maskWindows = []
if options["maskname"] != "":
maskData = getMaskData(projData, site, options["maskname"], sampleFreq)
maskWindows = maskData.getMaskWindowsFreq(options["declevel"],
options["eFreqI"])
# plot information
nrows, ncols = getPlotRowsAndCols(options["maxcols"], len(statComponents))
numbins = options["numbins"]
plotfonts = options["plotoptions"]["plotfonts"]
fig = plt.figure(figsize=options["plotoptions"]["figsize"])
# suptitle
st = fig.suptitle(
"{} histogram for {}, sampling frequency {} Hz, decimation level {} and evaluation frequency {} Hz"
.format(stat, site, sampleFreq, options["declevel"], eFreq),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# now plot the data
for idx, val in enumerate(statComponents):
ax = plt.subplot(nrows, ncols, idx + 1)
plt.title("Histogram {}".format(val), fontsize=plotfonts["title"])
plotData = np.empty(shape=(0))
for meas in statMeas:
stats = statData[meas].getStats(maskwindows=maskWindows)
plotData = np.concatenate(
(plotData, np.squeeze(stats[:, options["eFreqI"], idx])))
# remove infinities and nans
plotData = plotData[np.isfinite(plotData)]
# x axis options
xlim = (options["xlim"] if len(options["xlim"]) > 0 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)
plt.grid()
# 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"])
# plot format, show and save
fig.tight_layout(rect=[0.02, 0.02, 0.98, 0.92])
if options["save"]:
impath = projData.imagePath
sampleFreqStr = fileFormatSampleFreq(sampleFreq)
filename = "statHist_{:s}_{:s}_{:s}_dec{:d}_efreq{:d}_{:s}".format(
stat,
site,
sampleFreqStr,
options["declevel"],
options["eFreqI"],
options["specdir"],
)
if options["maskname"] != "":
filename = "{}_{}".format(filename, options["maskname"])
savename = savePlot(impath, filename, fig)
projectText("Image saved to file {}".format(savename))
if options["show"]:
plt.show(block=options["plotoptions"]["block"])
if not options["show"] and options["save"]:
plt.close(fig)
return None
return fig
def viewStatistic(projData: ProjectData, site: str, sampleFreq: Union[int,
float],
stat: str, **kwargs) -> Union[plt.figure, None]:
"""View statistic data for a single sampling frequency of a site
Parameters
----------
projData : ProjectData
A project instance
site : str
The site for which to plot statistics
stat : str
The statistic to plot
sampleFreq : float
The sampling frequency for which to plot statistics
declevel : int
The decimation level to plot
eFreqI : int
The evaluation frequency index
specdir : str
The spectra directory
maskname : str
Mask name
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
show : bool, optional
Show the spectra plot
save : bool, optional
Save the plot to the images directory
plotoptions : Dict, optional
Dictionary of plot options
Returns
-------
matplotlib.pyplot.figure or None
A matplotlib figure unless the plot is not shown and is saved, in which case None and the figure is closed.
"""
options = {}
options["declevel"] = 0
options["eFreqI"] = 0
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options["maskname"] = ""
options["clim"] = []
options["xlim"] = []
options["ylim"] = []
options["colortitle"] = ""
options["show"] = True
options["save"] = False
options["plotoptions"] = plotOptionsSpec()
options = parseKeywords(options, kwargs)
projectText(
"Plotting statistic {} for site {} and sampling frequency {}".format(
stat, site, sampleFreq))
statData = getStatisticDataForSampleFreq(
projData,
site,
sampleFreq,
stat,
declevel=options["declevel"],
specdir=options["specdir"],
)
# get the statistics
statMeas = list(statData.keys())
# get the evaluation frequency
eFreq = statData[statMeas[0]].evalFreq[options["eFreqI"]]
# get the mask data
maskWindows = []
if options["maskname"] != "":
maskData = getMaskData(projData, site, options["maskname"], sampleFreq)
maskWindows = maskData.getMaskWindowsFreq(options["declevel"],
options["eFreqI"])
# setup the figure
plotfonts = options["plotoptions"]["plotfonts"]
fig = plt.figure(figsize=options["plotoptions"]["figsize"])
# get the date limits
siteData = projData.getSiteData(site)
if len(options["xlim"]) == 0:
start = siteData.getMeasurementStart(statMeas[0])
end = siteData.getMeasurementEnd(statMeas[0])
for meas in statMeas:
start = min(start, siteData.getMeasurementStart(meas))
end = max(end, siteData.getMeasurementEnd(meas))
options["xlim"] = [start, end]
# do the plots
for meas in statMeas:
statData[meas].view(
options["eFreqI"],
fig=fig,
xlim=options["xlim"],
ylim=options["ylim"],
clim=options["clim"],
label=meas,
plotfonts=options["plotoptions"]["plotfonts"],
maskwindows=maskWindows,
)
# add a legened
plt.legend(markerscale=4, fontsize=plotfonts["legend"])
# do the title after all the plots
st = fig.suptitle(
"{} values for {}, sampling frequency = {:.2f} Hz, decimation level = {} and evaluation frequency {} Hz"
.format(stat, site, sampleFreq, options["declevel"], eFreq),
fontsize=plotfonts["suptitle"],
)
# plot format, show and save
fig.tight_layout(rect=[0.02, 0.02, 0.98, 0.92])
if options["save"]:
impath = projData.imagePath
sampleFreqStr = fileFormatSampleFreq(sampleFreq)
filename = "stat_{:s}_{:s}_{:s}_dec{:d}_efreq{:d}_{:s}".format(
stat,
site,
sampleFreqStr,
options["declevel"],
options["eFreqI"],
options["specdir"],
)
if options["maskname"] != "":
filename = "{}_{}".format(filename, options["maskname"])
savename = savePlot(impath, filename, fig)
projectText("Image saved to file {}".format(savename))
if options["show"]:
plt.show(block=options["plotoptions"]["block"])
if not options["show"] and options["save"]:
plt.close(fig)
return None
return fig