def plotOptionsTransferFunction(**kwargs) -> Dict:
"""Get default plot options for plotting transfer function data
Parameters
----------
figsize : Tuple, optional
Set the figure size
plotfonts : Dict, optional
Font sizes to use for plotting fonts
block : bool, optional
Boolean flag for blocking execution when plot is shown
res_ylim : List[float], optional
y limits for resistivity data
phase_ylim : List[float], optional
y limits for phase data
xlim : List[float], optional
x limits for transfer function data
Returns
-------
out : Dict
Dictionary of default plot options for plotting spectra data
"""
default = plotOptionsStandard()
default["figsize"] = None
default["res_ylim"] = [0.01, 10000]
default["phase_ylim"] = [-20, 90]
default["xlim"] = [0.0001, 10000]
default = parseKeywords(default, kwargs)
return default
def plotOptionsTipper(**kwargs) -> Dict:
"""Get default plot options for plotting transfer function data
Parameters
----------
figsize : Tuple, optional
Set the figure size
plotfonts : Dict, optional
Font sizes to use for plotting fonts
block : bool, optional
Boolean flag for blocking execution when plot is shown
length_ylim : List[float], optional
y limits for length data
angle_ylim : List[float], optional
y limits for angle data
xlim : List[float], optional
x limits for transfer function data
Returns
-------
out : Dict
Dictionary of default plot options for plotting spectra data
"""
default = plotOptionsStandard()
default["figsize"] = (16, 5)
default["length_ylim"] = [0.001, 1000]
default["angle_ylim"] = [-30, 30]
default["xlim"] = [0.0001, 10000]
default = parseKeywords(default, kwargs)
return default
def plotOptionsTime(**kwargs) -> Dict:
"""Get default plot options for plotting time data
Parameters
----------
figsize : Tuple, optional
Set the figure size
plotfonts : Dict, optional
Font sizes to use for plotting fonts
block : bool, optional
Boolean flag for blocking execution when plot is shown
Eylim : List[float], optional
y limits for electric data
Hylim : List[float], optional
y limits for magnetic data
Returns
-------
out : Dict
Dictionary of default plot options for plotting time data
"""
default = plotOptionsStandard()
default["Eylim"] = []
default["Hylim"] = []
default = parseKeywords(default, kwargs)
return default
def getMaskData(projData: ProjectData, site: str, maskName: str,
sampleFreq: Union[float, int], **kwargs) -> MaskData:
"""Get a mask data object
Parameters
----------
projData : projectData
A project instance
site : str
The site for which to get the mask
maskName : str
The name of the mask
sampleFreq : int, float
The sampling frequency for which the mask was created
specdir : str
The spectra directory for which the mask was created
Returns
-------
MaskData
A mask data object with the mask information
"""
options = {}
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options = parseKeywords(options, kwargs)
siteData = projData.getSiteData(site)
maskIO = MaskIO(siteData.getSpecdirMaskPath(options["specdir"]))
maskData = maskIO.read(maskName, sampleFreq)
return maskData
def processProject(projData: ProjectData, **kwargs) -> None:
"""Process a project
Parameters
----------
projData : ProjectData
The project data instance for the project
sites : List[str], optional
List of sites
sampleFreqs : List[float], optional
List of sample frequencies to process
specdir : str, optional
The spectra directories to use
inchans : List[str], optional
Channels to use as the input of the linear system
inputsite : str, optional
Site from which to take the input channels. The default is to use input and output channels from the same site
outchans : List[str], optional
Channels to use as the output of the linear system
remotesite : str, optional
The site to use as the remote site
remotechans : List[str], optional
Channels to use from the remote reference site
crosschannels : List[str], optional
List of channels to use for cross powers
masks : Dict, optional
Masks dictionary for passing mask data. The key should be a site name and the value should either be a string for a single mask or a list of multiple masks.
datetimes : List, optional
List of datetime constraints, each one as a dictionary. For example [{"type": "datetime", "start": 2018-08-08 00:00:00, "end": 2018-08-08 16:00:00, "levels": [0,1]}]. Note that levels is optional.
postpend : str, optional
String to postpend to the transfer function output
"""
options: Dict = dict()
options["sites"]: List[str] = projData.getSites()
options["sampleFreqs"]: List[float] = projData.getSampleFreqs()
options["specdir"]: str = projData.config.configParams["Spectra"][
"specdir"]
options["inchans"]: List[str] = ["Hx", "Hy"]
options["inputsite"]: str = ""
options["outchans"]: List[str] = ["Ex", "Ey"]
options["remotesite"]: str = ""
options["remotechans"]: List[str] = options["inchans"]
options["crosschannels"]: List[str] = []
options["masks"]: Dict = {}
options["datetimes"]: List = []
options["postpend"]: str = ""
options = parseKeywords(options, kwargs)
for site in options["sites"]:
siteData = projData.getSiteData(site)
siteFreqs = siteData.getSampleFreqs()
for sampleFreq in siteFreqs:
# check if not included
if sampleFreq not in options["sampleFreqs"]:
continue
processSite(projData, site, sampleFreq, **options)
def getStatisticDataForSampleFreq(projData: ProjectData,
site: str,
sampleFreq: float,
stat: str,
declevel: int = 0,
**kwargs):
"""Get the statistic data (for a particular decimation level) for all measurements in a site with sampling frequency sampleFreq
Parameters
----------
projData : ProjectData
Project instance
site : str
The site for which to get the statistic data
sampleFreq : float
The sampling frequency
stat : str
The statistic for which to get the measurement
declevel : int, optional
The decimation level to read in. Default is 0.
specdir : str, optional
The spectra directory
Returns
-------
StatisticData : Dict
A statistic data object
"""
options = {}
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options = parseKeywords(options, kwargs)
siteData = projData.getSiteData(site)
if not siteData:
projectError("Unable to find site {} in project".format(site),
quitRun=True)
# load the statistic data
statData: Dict[str, StatisticData] = {}
statIO = StatisticIO()
measurements = siteData.getMeasurements(sampleFreq)
for meas in measurements:
statIO.setDatapath(
os.path.join(siteData.getMeasurementStatPath(meas),
options["specdir"]))
# make sure some data was found
chk = statIO.read(stat, declevel)
if chk:
statData[meas] = statIO.read(stat, declevel)
else:
projectWarning(
"No {} data found for site {} and measurement {}".format(
stat, site, meas))
return statData
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 getSpecReader(
projData: ProjectData, site: str, meas: str, **kwargs
) -> SpectrumReader:
"""Get the spectrum reader for a measurement
Parameters
----------
site : str
Site for which to get the spectra reader
meas : str
The measurement
options : Dict
Options in a dictionary
declevel : int, optional
Decimation level for which to get data
specdir : str, optional
String that specifies spectra directory for the measurement
Returns
-------
SpectrumReader
The spectrum reader object
"""
options = {}
options["declevel"]: int = 0
options["specdir"]: str = projData.config.configParams["Spectra"]["specdir"]
options = parseKeywords(options, kwargs)
siteData = projData.getSiteData(site)
measurements = siteData.getMeasurements()
if meas not in measurements:
projectError("Measurement directory {} not found".format(meas), quitRun=True)
# create the spectrum reader
specReader = SpectrumReader(
os.path.join(siteData.getMeasurementSpecPath(meas), options["specdir"])
)
specReader.printInfo()
# open the spectra file for the current decimation level
check = specReader.openBinaryForReading("spectra", options["declevel"])
if not check:
# probably because this decimation level not calculated
projectError(
"Spectra file does not exist at level {}".format(options["declevel"]),
quitRun=True,
)
return specReader
def getStatisticData(projData: ProjectData,
site: str,
meas: str,
stat: str,
declevel: int = 0,
**kwargs):
"""Get the statistic data for a statistic for a site measurement
Parameters
----------
projData : ProjectData
Project instance
site : str
The site for which to get the statistic data
meas : str
The measurement for which to get the statistic
stat : str
The statistic for which to get the measurement
declevel : int, optional
The decimation level to read in. Default is 0.
specdir : str, optional
The spectra directory
Returns
-------
StatisticData
A statistic data object
"""
options = {}
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options = parseKeywords(options, kwargs)
siteData = projData.getSiteData(site)
if not siteData:
projectError("Unable to find site {} in project".format(site),
quitRun=True)
# load the statistic data
statIO = StatisticIO()
statIO.setDatapath(
os.path.join(siteData.getMeasurementStatPath(meas),
options["specdir"]))
statData = statIO.read(stat, declevel)
return statData
def olsModel(A, y, **kwargs) -> Tuple:
r"""Ordinary least squares
Solves for :math:`x` where,
.. math::
y = Ax .
Parameters
----------
A : np.ndarray
Predictors, size nobs*nregressors
y : np.ndarray
Observations, size nobs
intercept : bool, optional
True or False for adding an intercept term
Returns
-------
params : np.ndarray
Least squares solution
resids : np.ndarray
Residuals
squareResid : np.ndarray
Square residuals
rank : int
Rank of matrix A
s : np.ndarray
Singular values of A
"""
options = parseKeywords(defaultDictionary(), kwargs, printkw=False)
if options["intercept"]:
# add a constant term for the intercept
A = np.hstack((np.ones(shape=(A.shape[0], 1), dtype="complex"), A))
params, squareResid, rank, s = linalg.lstsq(A, y)
resids = y - np.dot(A, params)
return params, resids, squareResid, rank, s
def plotOptionsStandard(**kwargs) -> Dict:
"""Get a set of standard plot options
Parameters
----------
figsize : Tuple, optional
Set the figure size
plotfonts : Dict, optional
Font sizes to use for plotting fonts
block : bool, optional
Boolean flag for blocking execution when plot is shown
Returns
-------
Dict
Dictionary of standard plot options
"""
default: Dict = {}
default["figsize"] = (20, 12)
default["plotfonts"] = getViewFonts()
default["block"] = True
default = parseKeywords(default, kwargs)
return default
def plotOptionsSpec(**kwargs) -> Dict:
"""Get default plot options for plotting spectra data
Parameters
----------
figsize : Tuple, optional
Set the figure size
plotfonts : Dict, optional
Font sizes to use for plotting fonts
block : bool, optional
Boolean flag for blocking execution when plot is shown
amplim : List[float], optional
Amplitude limits for plotting spectra
Returns
-------
out : Dict
Dictionary of default plot options for plotting spectra data
"""
default = plotOptionsStandard()
default["amplim"] = []
default = parseKeywords(default, kwargs)
return default
def calculateSpectra(projData: ProjectData, **kwargs) -> None:
"""Calculate spectra for the project time data
The philosophy is that spectra are calculated out for all data and later limited using statistics and time constraints
Parameters
----------
projData : ProjectData
A project data object
sites : str, List[str], optional
Either a single site or a list of sites
sampleFreqs : int, float, List[float], optional
The frequencies in Hz for which to calculate the spectra. Either a single frequency or a list of them.
chans : List[str], optional
The channels for which to calculate out the spectra
polreverse : Dict[str, bool]
Keys are channels and values are boolean flags for reversing
scale : Dict[str, float]
Keys are channels and values are floats to multiply the channel data by
calibrate : bool, optional
Flag whether to calibrate the data or not
notch : List[float], optional
List of frequencies to notch
filter : Dict, optional
Filter parameters
specdir : str, optional
The spectra directory to save the spectra data in
"""
# default options
options = {}
options["sites"] = projData.getSites()
options["sampleFreqs"]: List[float] = projData.getSampleFreqs()
options["chans"]: List[str] = []
options["polreverse"]: Union[bool, Dict[str, bool]] = False
options["scale"]: Union[bool, Dict[str, float]] = False
options["calibrate"]: bool = True
options["notch"]: List[float] = []
options["filter"]: Dict = {}
options["specdir"]: str = projData.config.configParams["Spectra"]["specdir"]
options = parseKeywords(options, kwargs)
# prepare calibrator
cal = getCalibrator(projData.calPath, projData.config)
if options["calibrate"]:
cal.printInfo()
datetimeRef = projData.refTime
for site in options["sites"]:
siteData = projData.getSiteData(site)
siteData.printInfo()
# calculate spectra for each frequency
for sampleFreq in options["sampleFreqs"]:
measurements = siteData.getMeasurements(sampleFreq)
projectText(
"Site {} has {:d} measurement(s) at sampling frequency {:.2f}".format(
site, len(measurements), sampleFreq
)
)
if len(measurements) == 0:
continue # no data files at this sample rate
for meas in measurements:
projectText(
"Calculating spectra for site {} and measurement {}".format(
site, meas
)
)
# get measurement start and end times - this is the time of the first and last sample
reader = siteData.getMeasurement(meas)
startTime = siteData.getMeasurementStart(meas)
stopTime = siteData.getMeasurementEnd(meas)
dataChans = (
options["chans"]
if len(options["chans"]) > 0
else reader.getChannels()
)
timeData = reader.getPhysicalData(startTime, stopTime, chans=dataChans)
timeData.addComment(breakComment())
timeData.addComment("Calculating project spectra")
timeData.addComment(projData.config.getConfigComment())
# apply various options
applyPolarisationReversalOptions(options, timeData)
applyScaleOptions(options, timeData)
applyCalibrationOptions(options, cal, timeData, reader)
applyFilterOptions(options, timeData)
applyNotchOptions(options, timeData)
# define decimation and window parameters
decParams = getDecimationParameters(sampleFreq, projData.config)
decParams.printInfo()
numLevels = decParams.numLevels
winParams = getWindowParameters(decParams, projData.config)
dec = Decimator(timeData, decParams)
timeData.addComment(
"Decimating with {} levels and {} frequencies per level".format(
numLevels, decParams.freqPerLevel
)
)
# loop through decimation levels
for iDec in range(0, numLevels):
# get the data for the current level
check = dec.incrementLevel()
if not check:
break # not enough data
timeData = dec.timeData
# create the windower and give it window parameters for current level
sampleFreqDec = dec.sampleFreq
win = Windower(
datetimeRef,
timeData,
winParams.getWindowSize(iDec),
winParams.getOverlap(iDec),
)
if win.numWindows < 2:
break # do no more decimation
# add some comments
timeData.addComment(
"Evaluation frequencies for this level {}".format(
listToString(decParams.getEvalFrequenciesForLevel(iDec))
)
)
timeData.addComment(
"Windowing with window size {} samples and overlap {} samples".format(
winParams.getWindowSize(iDec), winParams.getOverlap(iDec)
)
)
# create the spectrum calculator and statistics calculators
specCalc = SpectrumCalculator(
sampleFreqDec, winParams.getWindowSize(iDec)
)
# get ready a file to save the spectra
specPath = os.path.join(
siteData.getMeasurementSpecPath(meas), options["specdir"]
)
specWrite = SpectrumWriter(specPath, datetimeRef)
specWrite.openBinaryForWriting(
"spectra",
iDec,
sampleFreqDec,
winParams.getWindowSize(iDec),
winParams.getOverlap(iDec),
win.winOffset,
win.numWindows,
dataChans,
)
# loop though windows, calculate spectra and save
for iW in range(0, win.numWindows):
# get the window data
winData = win.getData(iW)
# calculate spectra
specData = specCalc.calcFourierCoeff(winData)
# write out spectra
specWrite.writeBinary(specData)
# close spectra file
specWrite.writeCommentsFile(timeData.getComments())
specWrite.closeFile()
def mmestimateModel(A: np.ndarray, y: np.ndarray, **kwargs):
r"""2 stage M estimate
Solves for :math:`x` where,
.. math::
y = Ax .
Parameters
----------
A : np.ndarray
Predictors, size nobs*nregressors
y : np.ndarray
Observations, size nobs
initial : Dict
Initial solution with parameters, scale and residuals
scale : optional
A scale estimate
intercept : bool, optional
True or False for adding an intercept term
Returns
-------
params : np.ndarray
Values in x
resids : np.ndarray
Residuals = y - Ax
scale : float
Robust measure of variance
weights : np.ndarray
Weights used in robust regression
"""
options = parseKeywords(defaultDictionary(), kwargs, printkw=False)
intercept = options["intercept"]
# this uses an initial mestimate with huber to give a measure of scale
# and then a second with bisquare or hampel weights
if "initial" in kwargs:
if "scale" not in kwargs["initial"]:
kwargs["initial"]["scale"] = sampleMAD0(
kwargs["initial"]["resids"])
params, resids, scale, weights = mestimateModel(
A,
y,
weights="huber",
initial=kwargs["initial"],
intercept=intercept)
# now do another, but with a different weighting function
kwargs["initial"]["scale"] = scale
# kwargs["initial"]["params"] = params # put the new solution in, because simply then doing bisquare, which has zero weights, might mess things up
# kwargs["initial"]["resids"] = resids
params2, resids2, scale2, weights2 = mestimateModel(
A,
y,
weights="bisquare",
initial=kwargs["initial"],
intercept=intercept)
else:
params, resids, scale, weights = mestimateModel(A,
y,
weights="huber",
intercept=intercept)
# now do another, but with a different weighting function
params2, resids2, scale2, weights2 = mestimateModel(
A, y, weights="bisquare", scale=scale, intercept=intercept)
return params2, resids2, scale2, weights2
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 chatterjeeMachlerHadi(X, y, **kwargs):
r"""Regression based on Hadi distances
# Another regression method based on Hadi distances
# implemented from the paper A Re-Weighted Least Squares Method for Robust Regression Estimation
# Billor, Hadi
"""
# basic info
options = parseKeywords(defaultDictionary(), kwargs, printkw=False)
# for the distances, will use absX - do this before adding intercept term
# a column of all ones will cause problems with non full rank covariance matrices
absX = np.absolute(X)
# now calculate p and n
n = absX.shape[0]
p = absX.shape[1]
# we treat the X matrix as a multivariate matrix with n observations and p variables
# first need to find a basic subset free of outliers
correctionFactor = 1 + (1.0 * (p + 1) / (n - p)) + (2.0 / (n - 1 - 3 * p))
chi = stats.chi2(p, 0)
alpha = 0.05
chi2bound = correctionFactor * chi.pdf(alpha / n)
# calculate h, this is the size of the firt basic subset
# note that this is the value h, the index of the hth element is h-1
h = int(1.0 * (n + p + 1) / 2) # here, only want the integer part of this
# need to get the coordinatewise medians - this is the median of the columns
medians = np.median(absX)
# now compute the matrix to help calculate the distance
A = np.zeros(shape=(p, p))
for i in range(0, n):
tmp = absX[i, :] - medians
A += np.outer(tmp, tmp)
A = 1.0 / (n - 1) * A
# now calculate initial distances
dInit = calculateDistCMH(n, absX, medians, A)
# now get the h smallest values of d
sortOrder = np.argsort(dInit)
indices = sortOrder[0:h]
means = np.average(absX[indices, :])
covariance = np.cov(
absX[indices],
rowvar=False) # observations in rows, columns are variables
dH = calculateDistCMH(n, absX, means, covariance)
# rearrange into n observations into order and partition into two initial subsets
# one subset p+1, the n-p-1
sortOrder = np.argsort(dH)
indicesBasic = sortOrder[:p + 1]
# there is a rank issue here, but ignore for now - natural observations will presumably be full rank
means = np.average(absX[indicesBasic, :])
covariance = np.cov(absX[indicesBasic], rowvar=False)
dist = calculateDistCMH(n, absX, means, covariance)
# create the basic subset
r = p + 2
increment = (h - r) / 100
if increment < 1:
increment = 1 # here, limiting to 100 iterations of this
while r <= h:
sortOrder = np.argsort(dist)
indices = sortOrder[:r] # indices start from zero, hence the - 1
means = np.average(absX[indices])
covariance = np.cov(absX[indices], rowvar=False)
dist = calculateDistCMH(n, absX, means, covariance)
if h - r > 0 and h - r < increment:
r = h
else:
r += increment
# now the second part = add more points and exclude outliers to basic set
# all distances above r+1 = outliers
# r = p + 1
# increment = (n - 1 - r)/100
while r < n:
sortOrder = np.argsort(dist)
dist2 = np.power(dist, 2)
if dist2[sortOrder[r]] > chi2bound:
break # then leave, everything else is an outlier - it would be good if this could be saved somehow
# otherwise, continue adding points
sortOrder = np.argsort(dist)
indices = sortOrder[:r]
means = np.average(absX[indices])
covariance = np.cov(absX[indices], rowvar=False)
dist = calculateDistCMH(n, absX, means, covariance)
if n - 1 - r > 0 and n - 1 - r < increment:
r = n - 1
else:
r += increment
# now with the Hadi distances calculated, can proceed to do the robust regression
# normalise and manipulate Hadi distances
dist = dist / np.max(dist)
# for the median, use the basic subset
# indicesBasic = sortOrder[:r]
# distMedian = np.median(dist[indicesBasic]) # I am using on indicesBasic
distMedian = np.median(
dist) # the paper suggests using the median of the complete
tmp = np.maximum(dist, np.ones(shape=(n)) * distMedian)
dist = np.reciprocal(tmp)
dist2 = np.power(dist, 2)
dist = dist2 / np.sum(dist2)
# calculate first set of weights - this is simply dist
weights = dist
# now add the additional constant intercept column if required
if options["intercept"] == True:
# add column of ones for constant term
X = np.hstack((np.ones(shape=(X.shape[0], 1), dtype="complex"), X))
n = X.shape[0]
p = X.shape[1]
# iteratively weighted least squares
iteration = 0
while iteration < options["maxiter"]:
# do the weighted least-squares
Anew, ynew = weightLS(X, y, weights)
paramsNew, squareResidNew, rankNew, sNew = linalg.lstsq(Anew, ynew)
residsNew = y - np.dot(X, paramsNew)
# check residsNew to make sure not all zeros (i.e. will happen in undetermined or equally determined system)
if np.sum(np.absolute(residsNew)) < eps():
# then return everything here
return paramsNew, residsNew, weights
residsAbs = np.absolute(residsNew)
residsSquare = np.power(residsAbs, 2)
residsNew = residsSquare / np.sum(residsSquare)
residsMedian = np.median(residsAbs)
# calculate the new weights
tmpDenom = np.maximum(residsNew,
np.ones(shape=(n), dtype="float") * residsMedian)
tmp = (1 - dist) / tmpDenom
weightsNew = np.power(tmp, 2) / np.sum(np.power(tmp, 2))
# increment iteration
iteration = iteration + 1
weights = weightsNew
params = paramsNew
if iteration > 1:
# check to see whether the change is smaller than the tolerance
changeResids = linalg.norm(residsNew -
resids) / linalg.norm(residsNew)
if changeResids < eps():
# update resids
resids = residsNew
break
# update resids
resids = residsNew
# at the end, return the components
return params, resids, weights
def processSite(projData: ProjectData, site: str,
sampleFreq: Union[int, float], **kwargs):
"""Process a single sampling frequency for a site
The site passed is assumed to be the output site (the output channels will come from this site). If channels from a different site are desired to be used as the input channels, this can be done by specifying the optional inputsite argument.
.. todo:: Give a few different examples here
Parameters
----------
projData : ProjectData
The project data instance for the project
site : str
Site to process
sampleFreq : float, int
Sample frequency to process
specdir : str, optional
The spectra directories to use
inchans : List[str], optional
Channels to use as the input of the linear system
inputsite : str, optional
Site from which to take the input channels. The default is to use input and output channels from the same site
outchans : List[str], optional
Channels to use as the output of the linear system
remotesite : str, optional
The site to use as the remote site
remotechans : List[str], optional
Channels to use from the remote reference site
crosschannels : List[str], optional
List of channels to use for cross powers
masks : Dict, optional
Masks dictionary for passing mask data. The key should be a site name and the value should either be a string for a single mask or a list of multiple masks.
datetimes : List, optional
List of datetime constraints, each one as a dictionary. For example [{"type": "datetime", "start": 2018-08-08 00:00:00, "end": 2018-08-08 16:00:00, "levels": [0,1]}]. Note that levels is optional.
postpend : str, optional
String to postpend to the transfer function output
"""
options = {}
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options["inchans"] = ["Hx", "Hy"]
options["inputsite"] = ""
options["outchans"] = ["Ex", "Ey"]
options["remotesite"] = ""
options["remotechans"] = options["inchans"]
options["crosschannels"] = []
options["masks"] = {}
options["datetimes"] = []
options["postpend"] = ""
options = parseKeywords(options, kwargs)
if options["inputsite"] == "":
options["inputsite"] = site
projectText("Processing site {}, sampling frequency {}".format(
site, sampleFreq))
siteData = projData.getSiteData(site)
# define decimation parameters
decParams = getDecimationParameters(sampleFreq, projData.config)
decParams.printInfo()
winParams = getWindowParameters(decParams, projData.config)
# window selector
winSelector = getWindowSelector(projData, decParams, winParams)
# if two sites are duplicated (e.g. input site and output site), winSelector only uses distinct sites. Hence using site and inputSite is no problem even if they are the same
processSites = []
if options["remotesite"]:
processSites = [site, options["inputsite"], options["remotesite"]]
winSelector.setSites(processSites)
else:
# if no remote site, then single site processing
processSites = [site, options["inputsite"]]
winSelector.setSites(processSites)
# add window masks
if len(list(options["masks"].keys())) > 0:
for maskSite in options["masks"]:
if maskSite not in processSites:
# there is a site in the masks dictionary which is of no interest
continue
if isinstance(options["masks"][maskSite], str):
# a single mask
winSelector.addWindowMask(maskSite, options["masks"][maskSite])
continue
if all(
isinstance(item, str)
for item in options["masks"][maskSite]):
# list of masks for the site
for mask in options["masks"][maskSite]:
winSelector.addWindowMask(maskSite, mask)
# add datetime constraints
for dC in options["datetimes"]:
levels = None
if "levels" in dC:
levels = dC["levels"]
if dC["type"] == "datetime":
winSelector.addDatetimeConstraint(dC["start"], dC["stop"], levels)
if dC["type"] == "time":
winSelector.addTimeConstraint(dC["start"], dC["stop"], levels)
if dC["type"] == "date":
winSelector.addDateConstraint(dC["date"], levels)
# calculate the shared windows and print info
winSelector.calcSharedWindows()
winSelector.printInfo()
winSelector.printDatetimeConstraints()
winSelector.printWindowMasks()
winSelector.printSharedWindows()
winSelector.printWindowsForFrequency()
# now have the windows, pass the winSelector to processors
outPath = siteData.transFuncPath
if options["remotesite"]:
projectText(
"Remote reference processing with sites: in = {}, out = {}, reference = {}"
.format(options["inputsite"], site, options["remotesite"]))
processor = getRemoteReferenceProcessor(winSelector, outPath,
projData.config)
processor.setRemote(options["remotesite"], options["remotechans"])
else:
projectText(
"Single site processing with sites: in = {}, out = {}".format(
options["inputsite"], site))
processor = getSingleSiteProcessor(winSelector, outPath,
projData.config)
# add the input and output site
processor.setInput(options["inputsite"], options["inchans"])
processor.setOutput(site, options["outchans"])
if len(options["crosschannels"]) > 0:
processor.crossChannels = options["crosschannels"]
processor.postpend = options["postpend"]
processor.printInfo()
processor.process()
def viewTipper(projData: ProjectData, **kwargs) -> None:
"""View transfer function data
Parameters
----------
projData : projecData
The project data
sites : List[str], optional
List of sites to plot transfer functions for
sampleFreqs : List[float], optional
List of samples frequencies for which to plot transfer functions
specdir : str, optional
The spectra directories used
postpend : str, optional
The postpend on the transfer function files
cols : bool, optional
Boolean flag, True to arrange tipper plot as 1 row with 3 columns
show : bool, optional
Show the spectra plot
save : bool, optional
Save the plot to the images directory
plotoptions : Dict
A dictionary of plot options. For example, set the resistivity y limits using res_ylim, set the phase y limits using phase_ylim and set the xlimits using xlim
"""
options = {}
options["sites"] = projData.getSites()
options["sampleFreqs"] = projData.getSampleFreqs()
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options["postpend"] = ""
options["cols"] = True
options["save"] = False
options["show"] = True
options["plotoptions"] = plotOptionsTipper()
options = parseKeywords(options, kwargs)
# loop over sites
for site in options["sites"]:
siteData = projData.getSiteData(site)
sampleFreqs = set(siteData.getSampleFreqs())
# find the intersection with the options["freqs"]
sampleFreqs = sampleFreqs.intersection(options["sampleFreqs"])
sampleFreqs = sorted(list(sampleFreqs))
# if prepend is a string, then make it a list
if isinstance(options["postpend"], str):
options["postpend"] = [options["postpend"]]
plotfonts = options["plotoptions"]["plotfonts"]
# now loop over the postpend options
for pp in options["postpend"]:
# add an underscore if not empty
postpend = "_{}".format(pp) if pp != "" else pp
fig = plt.figure(figsize=options["plotoptions"]["figsize"])
mks = ["o", "*", "d", "^", "h"]
lstyles = ["solid", "dashed", "dashdot", "dotted"]
# loop over sampling frequencies
includedFreqs = []
for idx, sampleFreq in enumerate(sampleFreqs):
tfData = getTransferFunctionData(projData,
site,
sampleFreq,
specdir=options["specdir"],
postpend=pp)
if not tfData:
continue
includedFreqs.append(sampleFreq)
projectText(
"Plotting tipper for site {}, sample frequency {}".format(
site, sampleFreq))
mk = mks[idx % len(mks)]
ls = lstyles[idx % len(lstyles)]
tfData.viewTipper(
fig=fig,
rows=options["cols"],
mk=mk,
ls=ls,
label="{}".format(sampleFreq),
xlim=options["plotoptions"]["xlim"],
length_ylim=options["plotoptions"]["length_ylim"],
angle_ylim=options["plotoptions"]["angle_ylim"],
plotfonts=options["plotoptions"]["plotfonts"],
)
# check if any files found
if len(includedFreqs) == 0:
continue
# sup title
sub = "Site {} tipper: {}".format(site,
options["specdir"] + postpend)
sub = "{}\nfs = {}".format(
sub, arrayToString(includedFreqs, decimals=3))
st = fig.suptitle(sub, fontsize=plotfonts["suptitle"])
st.set_y(0.99)
fig.tight_layout()
fig.subplots_adjust(top=0.85)
if options["save"]:
impath = projData.imagePath
filename = "tipper_{}_{}{}".format(site, options["specdir"],
postpend)
savename = savePlot(impath, filename, fig)
projectText("Image saved to file {}".format(savename))
if not options["show"]:
plt.close("all")
else:
plt.show(block=options["plotoptions"]["block"])
def calculateMask(projData: ProjectData, maskData: MaskData, **kwargs):
"""Calculate masks sites
Parameters
----------
projData : projectData
A project instance
maskData : MaskData
A mask data instance
sites : List[str], optional
A list of sites to calculate masks for
specdir : str, optional
The spectra directory for which to calculate statistics
"""
options = {}
options["sites"] = projData.getSites()
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options = parseKeywords(options, kwargs)
# create a maskCalculator object
maskCalc = MaskCalculator(projData, maskData, specdir=options["specdir"])
maskIO = MaskIO()
sampleFreq = maskData.sampleFreq
# loop over sites
for site in options["sites"]:
# see if there is a sample freq
siteData = projData.getSiteData(site)
siteSampleFreqs = siteData.getSampleFreqs()
if sampleFreq not in siteSampleFreqs:
continue
# decimation and window parameters
decParams = getDecimationParameters(sampleFreq, projData.config)
decParams.printInfo()
winParams = getWindowParameters(decParams, projData.config)
# clear previous windows from maskCalc
maskCalc.clearMaskWindows()
# calculate masked windows
maskCalc.applyConstraints(site)
maskCalc.maskData.printInfo()
# write maskIO file
maskIO.datapath = os.path.join(
siteData.getSpecdirMaskPath(options["specdir"]))
maskIO.write(maskCalc.maskData)
# test with the window selector
winSelector = WindowSelector(projData,
sampleFreq,
decParams,
winParams,
specdir=options["specdir"])
winSelector.setSites([site])
winSelector.addWindowMask(site, maskData.maskName)
winSelector.calcSharedWindows()
winSelector.printInfo()
winSelector.printDatetimeConstraints()
winSelector.printWindowMasks()
winSelector.printSharedWindows()
winSelector.printWindowsForFrequency()
def chatterjeeMachler(A: np.ndarray, y: np.ndarray, **kwargs) -> Tuple:
r"""Robust bounded influence solver
Solves for :math:`x` where,
.. math::
y = Ax .
Being a bounded influence operator, should be robust against both outliers in dependent and independent variables.
Parameters
----------
A : np.ndarray
Predictors, size nobs*nregressors
y : np.ndarray
Observations, size nobs
intercept : bool, optional
True or False for adding an intercept term
Returns
-------
params : np.ndarray
Values in x
resids : np.ndarray
Residuals = y - Ax
weights : np.ndarray
Weights used in robust regression
"""
options = parseKeywords(defaultDictionary(), kwargs, printkw=False)
# generalPrint("S-Estimate", "Using weight function = {}".format(weightFnc))
if options["intercept"] == True:
# add column of ones for constant term
A = np.hstack((np.ones(shape=(A.shape[0], 1), dtype="complex"), A))
# now calculate p and n
n = A.shape[0]
p = A.shape[1]
pnRatio = 1.0 * p / n
# calculate the projection matrix
q, r = linalg.qr(A)
Pdiag = np.empty(shape=(n), dtype="float")
for i in range(0, n):
Pdiag[i] = np.absolute(np.sum(q[i, :] * np.conjugate(q[i, :]))).real
del q, r
# and save an array for later
Pdiag = Pdiag / np.max(Pdiag)
weightsNom = np.power(1.0 - Pdiag, 2)
# weights for the first iteration
tmp = np.ones(shape=(n), dtype="float") * pnRatio
tmp = np.maximum(Pdiag, tmp)
weights = np.reciprocal(tmp)
# iteratively weighted least squares
iteration = 0
while iteration < options["maxiter"]:
# do the weighted least-squares
Anew, ynew = weightLS(A, y, weights)
paramsNew, squareResidNew, rankNew, sNew = linalg.lstsq(Anew, ynew)
residsNew = y - np.dot(A, paramsNew)
# check residsNew to make sure not all zeros (i.e. will happen in undetermined or equally determined system)
if np.sum(np.absolute(residsNew)) < eps():
# return everything here
return paramsNew, residsNew, weights
residsAbs = np.absolute(residsNew)
residsMedian = np.median(residsAbs)
# now compute the new weights
weightsDenom = np.maximum(
residsAbs,
np.ones(shape=(n), dtype="float") * residsMedian)
weightsNew = weightsNom / weightsDenom
# increment iteration
iteration = iteration + 1
weights = weightsNew
params = paramsNew
if iteration > 1:
# check to see whether the change is smaller than the tolerance
changeResids = linalg.norm(residsNew -
resids) / linalg.norm(residsNew)
if changeResids < eps():
# update resids
resids = residsNew
break
# update resids
resids = residsNew
return params, resids, weights
def preProcess(projData: ProjectData, **kwargs) -> None:
"""Pre-process project time data
Preprocess the time data using filters, notch filters, resampling or interpolation. A new measurement folder is created under the site. The name of the new measurement folder is:
prepend_[name of input measurement]_postpend. By default, prepend is "proc" and postpend is empty.
Processed time series data can be saved in a new site by using the outputsite option.
Parameters
----------
projData : ProjectData
A project data object
sites : str, List[str], optional
Either a single site or a list of sites
sampleFreqs : int, float, List[float], optional
The frequencies to preprocess
start : str, optional
Start date of data to preprocess in format "%Y-%m-%d %H:%M:%S"
stop : str, optional
Stop date of data to process in format "%Y-%m-%d %H:%M:%S"
outputsite : str, optional
A site to output the preprocessed time data to. If this site does not exist, it will be created
polreverse : Dict[str, bool]
Keys are channels and values are boolean flags for reversing
scale : Dict[str, float]
Keys are channels and values are floats to multiply the channel data by
calibrate : bool, optional
Boolean flag for calibrating the data. Default is false and setting to True will calibrate where files can be found.
normalise : bool, optional
Boolean flag for normalising the data. Default is False and setting to True will normalise each channel independently.
filter : Dict, optional
Filtering options in a dictionary
notch : List[float], optional
List of frequencies to notch in spectra given as a list of floats
resamp : Dict, optional
Resampling parameters in a dictionary with entries in the format: {sampleRateFrom: sampleRateTo}. All measurement directories of sampleRateFrom will be resampled to sampleRateTo
interp : bool, optional
Boolean flag for interpolating the data on to the second, so that sampling is coincident with seconds. This is not always the case. For example, SPAM data is not necessarily sampled on the second, whereas ATS data is. This function is useful when combining data of multiple formats. Interpolation does not change the sampling rate. Default is False.
prepend : str, optional
String to prepend to the output folder. Default is "proc".
postpend : str, optional
String to postpend to the output folder. Default is empty.
"""
options: Dict = {}
options["sites"]: List = projData.getSites()
options["sampleFreqs"]: List[float] = projData.getSampleFreqs()
options["start"]: Union[bool, str] = False
options["stop"]: Union[bool, str] = False
options["outputsite"]: str = ""
options["polreverse"]: Union[bool, Dict[str, bool]] = False
options["scale"]: Union[bool, Dict[str, float]] = False
options["calibrate"]: bool = False
options["normalise"]: bool = False
options["filter"]: Dict = {}
options["notch"]: List[float] = []
options["resamp"]: Dict = {}
options["interp"]: bool = False
options["prepend"]: str = "proc"
options["postpend"]: str = ""
options = parseKeywords(options, kwargs)
# print info
text: List = ["Processing with options"]
for op, val in options.items():
text.append("\t{} = {}".format(op, val))
projectBlock(text)
if isinstance(options["sites"], str):
options["sites"] = [options["sites"]]
# outputting to another site
if options["outputsite"] != "":
projectText(
"Preprocessed data will be saved to output site {}".format(
options["outputsite"]
)
)
# create the site
projData.createSite(options["outputsite"])
projData.refresh()
outputSitePath = projData.getSiteData(options["outputsite"]).timePath
# output naming
outPre = options["prepend"] + "_" if options["prepend"] != "" else ""
outPost = "_" + options["postpend"] if options["postpend"] != "" else ""
if outPre == "" and outPost == "" and options["outputsite"] == "":
outPre = "proc_"
# create a data calibrator writer instance
cal = Calibrator(projData.calPath)
if options["calibrate"]:
cal.printInfo()
writer = DataWriterInternal()
# format dates
if options["start"]:
options["start"] = datetime.strptime(options["start"], "%Y-%m-%d %H:%M:%S")
if options["stop"]:
options["stop"] = datetime.strptime(options["stop"], "%Y-%m-%d %H:%M:%S")
for site in options["sites"]:
siteData = projData.getSiteData(site)
siteData.printInfo()
# loop over frequencies
for sampleFreq in options["sampleFreqs"]:
measurements = siteData.getMeasurements(sampleFreq)
if len(measurements) == 0:
# no data files at this sample rate
continue
# otherwise, process
for meas in measurements:
# get the reader
projectText("Processing site {}, measurement {}".format(site, meas))
reader = siteData.getMeasurement(meas)
startTime = reader.getStartDatetime()
stopTime = reader.getStopDatetime()
if (options["start"] or options["stop"]) and not checkDateOptions(
options, startTime, stopTime
):
continue
# if the data contributes, copy in the data if relevant
if options["start"]:
startTime = options["start"]
if options["stop"]:
stopTime = options["stop"]
# calculate the samples
sampleStart, sampleEnd = reader.time2sample(startTime, stopTime)
# now get the data
timeData = reader.getPhysicalSamples(
startSample=sampleStart, endSample=sampleEnd
)
timeData.printInfo()
headers = reader.getHeaders()
chanHeaders, chanMap = reader.getChanHeaders()
# apply options
applyPolarisationReversalOptions(options, timeData)
applyScaleOptions(options, timeData)
applyCalibrationOptions(options, cal, timeData, reader)
applyFilterOptions(options, timeData)
applyNotchOptions(options, timeData)
applyInterpolationOptions(options, timeData)
applyResampleOptions(options, timeData)
applyNormaliseOptions(options, timeData)
# output dataset path
if options["outputsite"] != "":
timePath = outputSitePath
else:
timePath = siteData.timePath
outPath = os.path.join(timePath, "{}{}{}".format(outPre, meas, outPost))
# write time data - need to manually change some headers (hence the keywords)
writer = DataWriterInternal()
writer.setOutPath(outPath)
writer.writeData(
headers,
chanHeaders,
timeData,
start_time=timeData.startTime.strftime("%H:%M:%S.%f"),
start_date=timeData.startTime.strftime("%Y-%m-%d"),
stop_time=timeData.stopTime.strftime("%H:%M:%S.%f"),
stop_date=timeData.stopTime.strftime("%Y-%m-%d"),
numSamples=timeData.numSamples,
sample_freq=timeData.sampleFreq,
physical=True,
)
writer.printInfo()
def viewTime(
projData: ProjectData, startDate: str, endDate: str, **kwargs
) -> Union[plt.figure, None]:
"""View timeseries in the project
Parameters
----------
startDate : str
The start of the data range to plot
endDate : str
The end of the date range to plot
sites : List[str], optional
List of sites
sampleFreqs : List[float], optional
List of sample frequencies to plot
chans : List[str], optional
List of channels to plot
polreverse : Dict[str, bool]
Keys are channels and values are boolean flags for reversing
calibrate : bool, optional
Boolean flag to calibrate data
normalise : bool, optional
Boolean flag to normalise the data. Default is False and setting to True will normalise each channel independently.
notch : List[float], optional
List of frequencies to notch out
filter : Dict, optional
Filter parameters
show : bool, optional
Boolean flag to show the plot
save : bool, optional
Boolean flag to save the plot to images folder
plotoptions : Dict
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.
"""
# default options
options = {}
options["sites"]: List[str] = projData.sites
options["sampleFreqs"]: Union[List[float], List[str]] = projData.getSampleFreqs()
options["chans"]: List[str] = ["Ex", "Ey", "Hx", "Hy", "Hz"]
options["polreverse"]: Union[bool, Dict[str, bool]] = False
options["calibrate"]: bool = False
options["normalise"]: bool = False
options["filter"]: Dict = {}
options["notch"]: List[float] = []
options["show"]: bool = True
options["save"]: bool = False
options["plotoptions"]: Dict = plotOptionsTime()
options = parseKeywords(options, kwargs)
# prepare calibrator
cal = getCalibrator(projData.calPath, projData.config)
if options["calibrate"]:
cal.printInfo()
# format startDate and endDate
start = datetime.strptime("{}.000".format(startDate), "%Y-%m-%d %H:%M:%S.%f")
stop = datetime.strptime("{}.000".format(endDate), "%Y-%m-%d %H:%M:%S.%f")
# collect relevant data - dictionary to store timeData
timeDataAll = {}
for site in options["sites"]:
siteData = projData.getSiteData(site)
siteData.printInfo()
measurements = siteData.getMeasurements()
timeDataAll[site] = {}
# loop over measurements and save data for each one
for meas in measurements:
sampleFreq = siteData.getMeasurementSampleFreq(meas)
if sampleFreq not in options["sampleFreqs"]:
continue
# check if data contributes to user defined time period
siteStart = siteData.getMeasurementStart(meas)
siteStop = siteData.getMeasurementEnd(meas)
if siteStop < start or siteStart > stop:
continue
reader = siteData.getMeasurement(meas)
# get the samples of the datetimes
sampleStart, sampleStop = reader.time2sample(start, stop)
# as the samples returned from time2sample are rounded use sample2time to get the appropriate start and end times for those samples
readStart, readStop = reader.sample2time(sampleStart, sampleStop)
# get the data for any available channels meaning even those sites with missing channels can be plotted
timeData = reader.getPhysicalData(readStart, readStop)
projectText(
"Plotting measurement {} of site {} between {} and {}".format(
meas, site, readStart, readStop
)
)
# apply various options
applyPolarisationReversalOptions(options, timeData)
applyCalibrationOptions(options, cal, timeData, reader)
applyFilterOptions(options, timeData)
applyNotchOptions(options, timeData)
applyNormaliseOptions(options, timeData)
timeDataAll[site][meas] = timeData
# plot all the data
plotfonts = options["plotoptions"]["plotfonts"]
fig = plt.figure(figsize=options["plotoptions"]["figsize"])
for site in timeDataAll:
for meas in timeDataAll[site]:
timeData = timeDataAll[site][meas]
timeData.view(
sampleStop=timeDataAll[site][meas].numSamples - 1,
fig=fig,
chans=options["chans"],
label="{} - {}".format(site, meas),
xlim=[start, stop],
plotfonts=plotfonts,
)
# add the suptitle
st = fig.suptitle(
"Time data from {} to {}".format(
start.strftime("%Y-%m-%d %H-%M-%S"), stop.strftime("%Y-%m-%d %H-%M-%S")
),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# do the axis labels
numChans = len(options["chans"])
for idx, chan in enumerate(options["chans"]):
plt.subplot(numChans, 1, idx + 1)
# do the yaxis
if isElectric(chan):
plt.ylabel("mV/km", fontsize=plotfonts["axisLabel"])
if len(options["plotoptions"]["Eylim"]) > 0:
plt.ylim(options["plotoptions"]["Eylim"])
else:
if options["calibrate"]:
plt.ylabel("nT", fontsize=plotfonts["axisLabel"])
else:
plt.ylabel("mV", fontsize=plotfonts["axisLabel"])
if len(options["plotoptions"]["Hylim"]) > 0:
plt.ylim(options["plotoptions"]["Hylim"])
plt.legend(loc=1, fontsize=plotfonts["legend"])
# plot format
fig.tight_layout(rect=[0, 0.02, 1, 0.96])
fig.subplots_adjust(top=0.92)
# plot show and save
if options["save"]:
impath = projData.imagePath
filename = "timeData_{}_{}".format(
start.strftime("%Y-%m-%d_%H-%M-%S_"), stop.strftime("%Y-%m-%d_%H-%M-%S")
)
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 mestimateModel(A: np.ndarray, y: np.ndarray, **kwargs) -> Tuple:
r"""Mestimate robust least squares
Solves for :math:`x` where,
.. math::
y = Ax .
Good method for dependent outliers (in :math:`y`). Not robust against independent outliers (leverage points)
Parameters
----------
A : np.ndarray
Predictors, size nobs*nregressors
y : np.ndarray
Observations, size nobs
initial :
scale : optional
A scale estimate
intercept : bool, optional
True or False for adding an intercept term
Returns
-------
params : np.ndarray
Values in x
resids : np.ndarray
Residuals = y - Ax
scale : float
Robust measure of variance
weights : np.ndarray
Weights used in robust regression
"""
options = parseKeywords(defaultDictionary(), kwargs, printkw=False)
# calculate the leverage
n = A.shape[0]
p = A.shape[1]
pnRatio = 1.0 * p / n
# calculate the projection matrix
q, r = linalg.qr(A)
Pdiag = np.empty(shape=(n), dtype="float")
for i in range(0, n):
Pdiag[i] = np.absolute(np.sum(q[i, :] * np.conjugate(q[i, :]))).real
del q, r
Pdiag = Pdiag / np.max(Pdiag)
leverageScale = sampleMAD0(Pdiag)
leverageWeights = getRobustLocationWeights(
Pdiag / leverageScale, "huber"
) # this should nowhere be equal to zero because of the previous line
if options["intercept"] == True:
# add column of ones for constant term
A = np.hstack((np.ones(shape=(A.shape[0], 1), dtype="complex"), A))
# see whether to do an initial OLS model or whether one is provided
if options["initial"]:
params, resids, scale = initialFromDict(options["initial"])
else:
params, resids, squareResid, rank, s = olsModel(A, y)
scale = sampleMAD0(resids)
# if an initial model was not provided but an initial scale was, replace the one here
if options["scale"]:
scale = options["scale"]
# standardised residuals and weights
weights = (getRobustLocationWeights(resids / scale, options["weights"]) *
leverageWeights)
# iteratively weighted least squares
iteration = 0
while iteration < options["maxiter"]:
# do the weighted least-squares
Anew, ynew = weightLS(A, y, weights)
paramsNew, squareResidNew, rankNew, sNew = linalg.lstsq(Anew, ynew)
residsNew = y - np.dot(A, paramsNew)
# check residsNew to make sure not all zeros (i.e. will happen in undetermined or equally determined system)
if np.sum(np.absolute(residsNew)) < eps():
# then return everything here
return paramsNew, residsNew, scale, weights
scale = sampleMAD0(residsNew)
# standardise and calculate weights
weightsNew = (
getRobustLocationWeights(residsNew / scale, options["weights"]) *
leverageWeights)
# increment iteration and save weightsNew
iteration = iteration + 1
weights = weightsNew
params = paramsNew
# check to see whether the change is smaller than the tolerance
# use the R method of checking change in residuals (can check change in params)
changeResids = linalg.norm(residsNew - resids) / linalg.norm(residsNew)
if changeResids < eps():
# update residuals
resids = residsNew
break
# update residuals
resids = residsNew
return params, resids, scale, weights
def viewSpectraStack(
projData: ProjectData, site: str, meas: str, **kwargs
) -> Union[plt.figure, None]:
"""View spectra stacks for a measurement
Parameters
----------
projData : projecData
The project data
site : str
The site to view
meas: str
The measurement of the site to view
chans : List[str], optional
Channels to plot
declevel : int, optional
Decimation level to plot
numstacks : int, optional
The number of windows to stack
coherences : List[List[str]], optional
A list of coherences to add, specified as [["Ex", "Hy"], ["Ey", "Hx"]]
specdir : str, optional
String that specifies spectra directory for the measurement
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["chans"] = []
options["declevel"] = 0
options["numstacks"] = 10
options["coherences"] = []
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options["show"] = True
options["save"] = False
options["plotoptions"] = plotOptionsSpec()
options = parseKeywords(options, kwargs)
projectText(
"Plotting spectra stack for measurement {} and site {}".format(meas, site)
)
specReader = getSpecReader(projData, site, meas, **options)
# channels
dataChans = specReader.getChannels()
if len(options["chans"]) > 0:
dataChans = options["chans"]
numChans = len(dataChans)
# get windows
numWindows = specReader.getNumWindows()
sampleFreqDec = specReader.getSampleFreq()
f = specReader.getFrequencyArray()
# calculate num of windows to stack in each set
stackSize = int(np.floor(1.0 * numWindows / options["numstacks"]))
# calculate number of rows - in case interested in coherences too
nrows = (
2
if len(options["coherences"]) == 0
else 2 + np.ceil(1.0 * len(options["coherences"]) / numChans)
)
# setup the figure
plotfonts = options["plotoptions"]["plotfonts"]
cmap = colorbarMultiline()
fig = plt.figure(figsize=options["plotoptions"]["figsize"])
st = fig.suptitle(
"Spectra stack, fs = {:.6f} [Hz], decimation level = {:2d}, windows in each set = {:d}".format(
sampleFreqDec, options["declevel"], stackSize
),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# do the stacking
for iP in range(0, options["numstacks"]):
stackStart = iP * stackSize
stackStop = min(stackStart + stackSize, numWindows)
color = cmap(iP/options["numstacks"])
# dictionaries to hold data for this section
stackedData = {}
ampData = {}
phaseData = {}
powerData = {}
# assign initial zeros
for c in dataChans:
stackedData[c] = np.zeros(shape=(specReader.getDataSize()), dtype="complex")
ampData[c] = np.zeros(shape=(specReader.getDataSize()), dtype="complex")
phaseData[c] = np.zeros(shape=(specReader.getDataSize()), dtype="complex")
for c2 in dataChans:
powerData[c + c2] = np.zeros(
shape=(specReader.getDataSize()), dtype="complex"
)
# now stack the data and create nice plots
for iW in range(stackStart, stackStop):
winData = specReader.readBinaryWindowLocal(iW)
for c in dataChans:
stackedData[c] += winData.data[c]
ampData[c] += np.absolute(winData.data[c])
phaseData[c] += np.angle(winData.data[c]) * (180.0 / np.pi)
# get coherency data
for c2 in dataChans:
powerData[c + c2] += winData.data[c] * np.conjugate(
winData.data[c2]
)
if iW == stackStart:
startTime = winData.startTime
if iW == stackStop - 1:
stopTime = winData.stopTime
# scale powers and stacks
ampLim = options["plotoptions"]["amplim"]
for idx, c in enumerate(dataChans):
stackedData[c] = stackedData[c] / (stackStop - stackStart)
ampData[c] = ampData[c] / (stackStop - stackStart)
phaseData[c] = phaseData[c] / (stackStop - stackStart)
for c2 in dataChans:
# normalisation
powerData[c + c2] = 2 * powerData[c + c2] / (stackStop - stackStart)
# normalisation
powerData[c + c2][[0, -1]] = powerData[c + c2][[0, -1]] / 2
# plot
ax1 = plt.subplot(nrows, numChans, idx + 1)
plt.title("Amplitude {}".format(c), fontsize=plotfonts["title"])
h = ax1.semilogy(
f,
ampData[c],
color=color,
label="{} to {}".format(
startTime.strftime("%m-%d %H:%M:%S"),
stopTime.strftime("%m-%d %H:%M:%S"),
),
)
if len(ampLim) > 2:
ax1.set_ylim(ampLim)
else:
ax1.set_ylim(0.01, 1000)
ax1.set_xlim(0, sampleFreqDec / 2.0)
if isMagnetic(c):
ax1.set_ylabel("Amplitude [nT]", fontsize=plotfonts["axisLabel"])
else:
ax1.set_ylabel("Amplitude [mV/km]", fontsize=plotfonts["axisLabel"])
ax1.set_xlabel("Frequency [Hz]", fontsize=plotfonts["axisLabel"])
plt.grid(True)
# set tick sizes
for label in ax1.get_xticklabels() + ax1.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
# plot phase
ax2 = plt.subplot(nrows, numChans, numChans + idx + 1)
plt.title("Phase {}".format(c), fontsize=plotfonts["title"])
ax2.plot(
f,
phaseData[c],
color=color,
label="{} to {}".format(
startTime.strftime("%m-%d %H:%M:%S"),
stopTime.strftime("%m-%d %H:%M:%S"),
),
)
ax2.set_ylim(-180, 180)
ax2.set_xlim(0, sampleFreqDec / 2.0)
ax2.set_ylabel("Phase [degrees]", fontsize=plotfonts["axisLabel"])
ax2.set_xlabel("Frequency [Hz]", fontsize=plotfonts["axisLabel"])
plt.grid(True)
# set tick sizes
for label in ax2.get_xticklabels() + ax2.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
# plot coherences
for idx, coh in enumerate(options["coherences"]):
c = coh[0]
c2 = coh[1]
cohNom = np.power(np.absolute(powerData[c + c2]), 2)
cohDenom = powerData[c + c] * powerData[c2 + c2]
coherence = cohNom / cohDenom
ax = plt.subplot(nrows, numChans, 2 * numChans + idx + 1)
plt.title("Coherence {} - {}".format(c, c2), fontsize=plotfonts["title"])
ax.plot(
f,
coherence,
color=color,
label="{} to {}".format(
startTime.strftime("%m-%d %H:%M:%S"),
stopTime.strftime("%m-%d %H:%M:%S"),
),
)
ax.set_ylim(0, 1.1)
ax.set_xlim(0, sampleFreqDec / 2)
ax.set_ylabel("Coherence", fontsize=plotfonts["axisLabel"])
ax.set_xlabel("Frequency [Hz]", fontsize=plotfonts["axisLabel"])
plt.grid(True)
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
# fig legend and layout
ax = plt.gca()
h, l = ax.get_legend_handles_labels()
fig.tight_layout(rect=[0.01, 0.01, 0.98, 0.81])
# legend
legax = plt.axes(position=[0.01, 0.82, 0.98, 0.12], in_layout=False)
plt.tick_params(left=False, labelleft=False, bottom=False, labelbottom="False")
plt.box(False)
legax.legend(h, l, ncol=4, loc="upper center", fontsize=plotfonts["legend"])
# plot show and save
if options["save"]:
impath = projData.imagePath
filename = "spectraStack_{}_{}_dec{}_{}".format(
site, meas, options["declevel"], options["specdir"]
)
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
def calculateStatistics(projData: ProjectData, **kwargs):
"""Calculate statistics for sites
Parameters
----------
projData : ProjectData
A project data instance
sites : List[str], optional
A list of sites to calculate statistics for
sampleFreqs : List[float], optional
List of sampling frequencies for which to calculate statistics
chans : List[str], optional
List of data channels to use
specdir : str, optional
The spectra directory for which to calculate statistics
stats : List[str], optional
The statistics to calculate out. Acceptable values are: "absvalEqn" "coherence", "psd", "poldir", "transFunc", "resPhase", "partialcoh". Configuration file values are used by default.
"""
options = {}
options["sites"] = projData.getSites()
options["sampleFreqs"] = projData.getSampleFreqs()
options["chans"] = []
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options["stats"] = projData.config.configParams["Statistics"]["stats"]
options = parseKeywords(options, kwargs)
projectText("Calculating stats: {} for sites: {}".format(
listToString(options["stats"]), listToString(options["sites"])))
# create the statistic calculator and IO object
statCalculator = StatisticCalculator()
statIO = StatisticIO()
# loop through sites and calculate statistics
for site in options["sites"]:
siteData = projData.getSiteData(site)
measurements = siteData.getMeasurements()
for meas in measurements:
sampleFreq = siteData.getMeasurementSampleFreq(meas)
if sampleFreq not in options["sampleFreqs"]:
# don't need to calculate statistics for this sampling frequency
continue
projectText("Calculating stats for site {}, measurement {}".format(
site, meas))
# decimation parameters
decParams = getDecimationParameters(sampleFreq, projData.config)
decParams.printInfo()
numLevels = decParams.numLevels
# create the spectrum reader
specReader = SpectrumReader(
os.path.join(siteData.getMeasurementSpecPath(meas),
options["specdir"]))
# loop through decimation levels
for iDec in range(0, numLevels):
# open the spectra file for the current decimation level
check = specReader.openBinaryForReading("spectra", iDec)
if not check:
# probably because this decimation level not calculated
continue
specReader.printInfo()
# get windows
refTime = specReader.getReferenceTime()
winSize = specReader.getWindowSize()
winOlap = specReader.getWindowOverlap()
numWindows = specReader.getNumWindows()
evalFreq = decParams.getEvalFrequenciesForLevel(iDec)
sampleFreqDec = specReader.getSampleFreq()
globalOffset = specReader.getGlobalOffset()
fArray = specReader.getFrequencyArray()
statHandlers = {}
# create the statistic handlers
for stat in options["stats"]:
statElements = getStatElements(stat)
statHandlers[stat] = StatisticData(stat, refTime,
sampleFreqDec, winSize,
winOlap)
statHandlers[stat].setStatParams(numWindows, statElements,
evalFreq)
statHandlers[stat].comments = specReader.getComments()
statHandlers[stat].addComment(
projData.config.getConfigComment())
statHandlers[stat].addComment(
"Calculating statistic: {}".format(stat))
statHandlers[stat].addComment(
"Statistic components: {}".format(
listToString(statElements)))
# loop over windows and calculate the relevant statistics
for iW in range(0, numWindows):
# get data
winData = specReader.readBinaryWindowLocal(iW)
globalIndex = iW + globalOffset
# give the statistic calculator the spectra
statCalculator.setSpectra(fArray, winData, evalFreq)
# get the desired statistics
for sH in statHandlers:
data = statCalculator.getDataForStatName(sH)
statHandlers[sH].addStat(iW, globalIndex, data)
# save statistic
for sH in statHandlers:
statIO.setDatapath(
os.path.join(siteData.getMeasurementStatPath(meas),
options["specdir"]))
statIO.write(statHandlers[sH], iDec)
def chatterjeeMachlerMod(A, y, **kwargs):
# using the weights in chaterjeeMachler means that min resids val in median(resids)
# instead, use M estimate weights with a modified residual which includes a measure of leverage
# for this, use residuals / (1-p)^2
# I wonder if this will have a divide by zero bug
# now calculate p and n
n = A.shape[0]
p = A.shape[1]
pnRatio = 1.0 * p / n
# calculate the projection matrix
q, r = linalg.qr(A)
Pdiag = np.empty(shape=(n), dtype="float")
for i in range(0, n):
Pdiag[i] = np.absolute(np.sum(q[i, :] * np.conjugate(q[i, :]))).real
del q, r
Pdiag = Pdiag / (np.max(Pdiag) + 0.0000000001)
locP = np.median(Pdiag)
scaleP = sampleMAD(Pdiag)
# bound = locP + 6*scaleP
bound = locP + 6 * scaleP
indices = np.where(Pdiag > bound)
Pdiag[indices] = 0.99999
leverageMeas = np.power(1.0 - Pdiag, 2)
# weights for the first iteration
# this is purely based on the leverage
tmp = np.ones(shape=(n), dtype="float") * pnRatio
tmp = np.maximum(Pdiag, tmp)
weights = np.reciprocal(tmp)
# get options
options = parseKeywords(defaultDictionary(), kwargs, printkw=False)
# generalPrint("S-Estimate", "Using weight function = {}".format(weightFnc))
if options["intercept"] == True:
# add column of ones for constant term
A = np.hstack((np.ones(shape=(A.shape[0], 1), dtype="complex"), A))
# iteratively weighted least squares
iteration = 0
while iteration < options["maxiter"]:
# do the weighted least-squares
Anew, ynew = weightLS(A, y, weights)
paramsNew, squareResidNew, rankNew, sNew = linalg.lstsq(Anew, ynew)
residsNew = y - np.dot(A, paramsNew)
# check residsNew to make sure not all zeros (i.e. will happen in undetermined or equally determined system)
if np.sum(np.absolute(residsNew)) < eps():
# then return everything here
return paramsNew, residsNew, weights
residsNew = residsNew / leverageMeas
scale = sampleMAD0(residsNew)
# standardise and calculate weights
residsNew = residsNew / scale
weightsNew = getRobustLocationWeights(residsNew, "huber")
# increment iteration
iteration = iteration + 1
weights = weightsNew
params = paramsNew
if iteration > 1:
# check to see whether the change is smaller than the tolerance
changeResids = linalg.norm(residsNew -
resids) / linalg.norm(residsNew)
if changeResids < eps():
# update resids
resids = residsNew
break
# update resids
resids = residsNew
# now do the same again, but with a different function
# do the least squares solution
params, resids, squareResid, rank, s = olsModel(A, y)
resids = resids / leverageMeas
resids = resids / scale
weights = getRobustLocationWeights(resids, "trimmedMean")
# iteratively weighted least squares
iteration = 0
while iteration < options["maxiter"]:
# do the weighted least-squares
Anew, ynew = weightLS(A, y, weights)
paramsNew, squareResidNew, rankNew, sNew = linalg.lstsq(Anew, ynew)
residsNew = y - np.dot(A, paramsNew)
# check residsNew to make sure not all zeros (i.e. will happen in undetermined or equally determined system)
if np.sum(np.absolute(residsNew)) < eps():
# then return everything here
return paramsNew, residsNew, weights
residsNew = residsNew / leverageMeas
scale = sampleMAD0(residsNew)
# standardise and calculate weights
residsNew = residsNew / scale
weightsNew = getRobustLocationWeights(residsNew, options["weights"])
# increment iteration
iteration = iteration + 1
weights = weightsNew
params = paramsNew
# check to see whether the change is smaller than the tolerance
changeResids = linalg.norm(residsNew - resids) / linalg.norm(residsNew)
if changeResids < eps():
# update resids
resids = residsNew
break
# update resids
resids = residsNew
# at the end, return the components
return params, resids, weights
def calculateRemoteStatistics(projData: ProjectData, remoteSite: str,
**kwargs):
"""Calculate statistics involving a remote reference site
Parameters
----------
projData : ProjectData
A project data instance
remoteSite : str
The name of the site to use as the remote site
sites : List[str], optional
A list of sites to calculate statistics for
sampleFreqs : List[float], optional
List of sampling frequencies for which to calculate statistics
chans : List[str], optional
List of data channels to use
specdir : str, optional
The spectra directory for which to calculate statistics
remotestats : List[str], optional
The statistics to calculate out. Acceptable statistics are: "RR_coherence", "RR_coherenceEqn", "RR_absvalEqn", "RR_transferFunction", "RR_resPhase". Configuration file values are used by default.
"""
options = {}
options["sites"] = projData.getSites()
options["sampleFreqs"] = projData.getSampleFreqs()
options["chans"] = []
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options["remotestats"] = projData.config.configParams["Statistics"][
"remotestats"]
options = parseKeywords(options, kwargs)
projectText(
"Calculating stats: {} for sites: {} with remote site {}".format(
listToString(options["remotestats"]),
listToString(options["sites"]),
remoteSite,
))
# create the statistic calculator and IO object
statCalculator = StatisticCalculator()
statIO = StatisticIO()
# loop over sites
for site in options["sites"]:
siteData = projData.getSiteData(site)
measurements = siteData.getMeasurements()
for meas in measurements:
sampleFreq = siteData.getMeasurementSampleFreq(meas)
if sampleFreq not in options["sampleFreqs"]:
# don't need to calculate statistics for this sampling frequency
continue
projectText(
"Calculating stats for site {}, measurement {} with reference {}"
.format(site, meas, remoteSite))
# decimation and window parameters
decParams = getDecimationParameters(sampleFreq, projData.config)
decParams.printInfo()
numLevels = decParams.numLevels
winParams = getWindowParameters(decParams, projData.config)
# create the window selector and find the shared windows
winSelector = getWindowSelector(projData, decParams, winParams)
winSelector.setSites([site, remoteSite])
# calc shared windows between site and remote
winSelector.calcSharedWindows()
# create the spectrum reader
specReader = SpectrumReader(
os.path.join(siteData.getMeasurementSpecPath(meas),
options["specdir"]))
# loop through decimation levels
for iDec in range(0, numLevels):
# open the spectra file for the current decimation level
check = specReader.openBinaryForReading("spectra", iDec)
if not check:
# probably because this decimation level not calculated
continue
specReader.printInfo()
# get a set of the shared windows at this decimation level
# these are the global indices
sharedWindows = winSelector.getSharedWindowsLevel(iDec)
# get other information regarding only this spectra file
refTime = specReader.getReferenceTime()
winSize = specReader.getWindowSize()
winOlap = specReader.getWindowOverlap()
numWindows = specReader.getNumWindows()
evalFreq = decParams.getEvalFrequenciesForLevel(iDec)
sampleFreqDec = specReader.getSampleFreq()
globalOffset = specReader.getGlobalOffset()
fArray = specReader.getFrequencyArray()
# now want to find the size of the intersection between the windows in this file and the shared windows
sharedWindowsMeas = sharedWindows.intersection(
set(np.arange(globalOffset, globalOffset + numWindows)))
sharedWindowsMeas = sorted(list(sharedWindowsMeas))
numSharedWindows = len(sharedWindowsMeas)
statHandlers = {}
# create the statistic handlers
for stat in options["remotestats"]:
statElements = getStatElements(stat)
statHandlers[stat] = StatisticData(stat, refTime,
sampleFreqDec, winSize,
winOlap)
# remember, this is with the remote reference, so the number of windows is number of shared windows
statHandlers[stat].setStatParams(numSharedWindows,
statElements, evalFreq)
statHandlers[stat].comments = specReader.getComments()
statHandlers[stat].addComment(
projData.config.getConfigComment())
statHandlers[stat].addComment(
"Calculating remote statistic: {}".format(stat))
statHandlers[stat].addComment(
"Statistic components: {}".format(
listToString(statElements)))
# loop over the shared windows between the remote station and local station
for iW, globalWindow in enumerate(sharedWindowsMeas):
# get data and set in the statCalculator
winData = specReader.readBinaryWindowGlobal(globalWindow)
statCalculator.setSpectra(fArray, winData, evalFreq)
# for the remote site, use the reader in win selector
remoteSF, remoteReader = winSelector.getSpecReaderForWindow(
remoteSite, iDec, globalWindow)
winDataRR = remoteReader.readBinaryWindowGlobal(
globalWindow)
statCalculator.addRemoteSpec(winDataRR)
for sH in statHandlers:
data = statCalculator.getDataForStatName(sH)
statHandlers[sH].addStat(iW, globalWindow, data)
# save statistic
for sH in statHandlers:
statIO.setDatapath(
os.path.join(siteData.getMeasurementStatPath(meas),
options["specdir"]))
statIO.write(statHandlers[sH], iDec)
def viewSpectraSection(
projData: ProjectData, site: str, meas: str, **kwargs
) -> Union[plt.figure, None]:
"""View spectra section for a measurement
Parameters
----------
projData : projecData
The project data
site : str
The site to view
meas: str
The measurement of the site to view
chans : List[str], optional
Channels to plot
declevel : int, optional
Decimation level to plot
specdir : str, optional
String that specifies spectra directory for the measurement
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["chans"] = []
options["declevel"] = 0
options["specdir"] = projData.config.configParams["Spectra"]["specdir"]
options["show"] = True
options["save"] = False
options["plotoptions"] = plotOptionsSpec()
options = parseKeywords(options, kwargs)
projectText(
"Plotting spectra section for measurement {} and site {}".format(meas, site)
)
specReader = getSpecReader(projData, site, meas, **options)
# channels
dataChans = specReader.getChannels()
if len(options["chans"]) > 0:
dataChans = options["chans"]
# get windows
numWindows = specReader.getNumWindows()
sampleFreqDec = specReader.getSampleFreq()
# freq array
f = specReader.getFrequencyArray()
# now if plotting a section, ignore plotwindow for now
if numWindows > 250:
windows = list(np.linspace(0, numWindows, 250, endpoint=False, dtype=np.int32))
else:
windows = np.arange(0, 250)
# create figure
plotfonts = options["plotoptions"]["plotfonts"]
fig = plt.figure(figsize=options["plotoptions"]["figsize"])
st = fig.suptitle(
"Spectra section, site = {}, meas = {}, fs = {:.2f} [Hz], decimation level = {:2d}, windows = {:d}, {} to {}".format(
site,
meas,
sampleFreqDec,
options["declevel"],
len(windows),
windows[0],
windows[-1],
),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# collect the data
specData = np.empty(
shape=(len(windows), len(dataChans), specReader.getDataSize()), dtype="complex"
)
dates = []
for idx, iW in enumerate(windows):
winData = specReader.readBinaryWindowLocal(iW)
for cIdx, chan in enumerate(dataChans):
specData[idx, cIdx, :] = winData.data[chan]
dates.append(winData.startTime)
ampLim = options["plotoptions"]["amplim"]
for idx, chan in enumerate(dataChans):
ax = plt.subplot(1, len(dataChans), idx + 1)
plotData = np.transpose(np.absolute(np.squeeze(specData[:, idx, :])))
if len(ampLim) == 2:
plt.pcolor(
dates,
f,
plotData,
norm=LogNorm(vmin=ampLim[0], vmax=ampLim[1]),
cmap=colorbar2dSpectra(),
)
else:
plt.pcolor(
dates,
f,
plotData,
norm=LogNorm(vmin=plotData.min(), vmax=plotData.max()),
cmap=colorbar2dSpectra(),
)
cb = plt.colorbar()
cb.ax.tick_params(labelsize=plotfonts["axisTicks"])
# set axis limits
ax.set_ylim(0, specReader.getSampleFreq() / 2.0)
ax.set_xlim([dates[0], dates[-1]])
if isMagnetic(chan):
plt.title("Amplitude {} [nT]".format(chan), fontsize=plotfonts["title"])
else:
plt.title("Amplitude {} [mV/km]".format(chan), fontsize=plotfonts["title"])
ax.set_ylabel("Frequency [Hz]", fontsize=plotfonts["axisLabel"])
ax.set_xlabel("Time", fontsize=plotfonts["axisLabel"])
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
plt.grid(True)
# plot format
fig.autofmt_xdate(rotation=90, ha="center")
fig.tight_layout(rect=[0.02, 0.02, 0.96, 0.92])
# plot show and save
if options["save"]:
impath = projData.imagePath
filename = "spectraSection_{}_{}_dec{}_{}".format(
site, meas, options["declevel"], options["specdir"]
)
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
