def multiSpectra( ncores: int, timeDataList: List[TimeData], sampleFreq: float, windowSize: int, config: Dict[str, None] = None, ): """Multiprocessing of spectra Parameters ---------- ncores: int The number of cores for multiprocessing timeDataList : List[TimeData] A list of TimeData objects sampleFreq : float The sampling frequency of the TimeData windowSize : int The number of samples in the window Returns ------- specDataList : List[SpectrumData] A list of spectra data """ import multiprocessing as mp # separate time data into batches numWindows = len(timeDataList) batchSize = int(np.ceil(numWindows / ncores)) batches = [] sizes = [] for iB in range(0, ncores): batchStartWin = iB * batchSize if batchStartWin >= numWindows: break batchEndWin = batchStartWin + batchSize if batchEndWin > numWindows: batchEndWin = numWindows batch = [] for iW in range(batchStartWin, batchEndWin): batch.append(timeDataList[iW]) batches.append(batch) sizes.append(str(len(batch))) # set up tuples multiTuples = [(batch, sampleFreq, windowSize, config) for batch in batches] # multiprocess projectText("Running spectra calculations on {} cores".format(ncores)) projectText("{} windows being run in {} batches with sizes {}".format( numWindows, len(batches), ", ".join(sizes))) with mp.Pool(ncores) as pool: out = pool.starmap(calculateWindowSpectra, multiTuples) # format the output into a single list specDataList = [] for outBatch in out: specDataList = specDataList + outBatch return specDataList
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 """ from resistics.transfunc.io import TransferFunctionReader 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 multiStatistics( ncores: int, spectraData: List[SpectrumData], evalFreq: np.ndarray, stats: List[str], remoteData: Union[List[SpectrumData], None] = None, ): """Multiprocessing of statistics Parameters ---------- ncores : int The number of cores to use spectraData : List[SpectrumData] List of spectrum data to process evalFreq : np.ndarray The evaluation frequencies stats : List[str] The statistics to calculate remoteData : None, List[SpectrumData] Remote data in case of remote reference statistics. None is default Returns ------- List[Dict[str, Any]] The statistic data returned as a list with an entry for every window. The dictionary maps the statistic name to the data for that window. """ import multiprocessing as mp # prepare the lists if remoteData is None: multiTuples = [(winSpecData, evalFreq, stats) for winSpecData in spectraData] else: multiTuples = [ (winSpecData, evalFreq, stats, remoteSpecData) for winSpecData, remoteSpecData in zip(spectraData, remoteData) ] # multiprocess projectText("Running statistic calculations on {} cores".format(ncores)) with mp.Pool(ncores) as pool: out = pool.starmap(calculateWindowStatistics, multiTuples) return out
def loadProject(projectPath: str, configFile: str = "") -> ProjectData: """Load an existing project Parameters ---------- projectPath : str Path for the project directory configFile : str Path to a configuration file Returns ------- ProjectData A project data object """ # search for the .prj file (hopefully only one) gl = glob.glob(os.path.join(projectPath, "*.prj")) if len(gl) == 0: projectError( "Unable to find project file in path: {}".format(projectPath)) projectFile: str = os.path.basename(gl[0]) projectText("Loading project file: {}".format( os.path.join(projectPath, projectFile))) projectPaths = loadProjectFile(os.path.join(projectPath, projectFile)) # check the configuration file config = ConfigData(configFile) proj = ProjectData( projectFile, projectPaths["refTime"], projectPaths["calPath"], projectPaths["timePath"], projectPaths["specPath"], projectPaths["statPath"], projectPaths["maskPath"], projectPaths["transFuncPath"], projectPaths["imagePath"], config=config, ) proj.printInfo() proj.config.printInfo() return proj
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 ncores : int, optional The number of cores to run the transfer function calculations on """ from resistics.spectra.io import SpectrumWriter from resistics.decimate.decimator import Decimator from resistics.window.windower import Windower from resistics.project.shortcuts import ( getCalibrator, getDecimationParameters, getWindowParameters, ) from resistics.project.preprocess import ( applyPolarisationReversalOptions, applyScaleOptions, applyCalibrationOptions, applyFilterOptions, applyNotchOptions, ) 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["ncores"] = projData.config.getSpectraCores() 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) 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 declevel 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(declevel), winParams.getOverlap(declevel), ) if win.numWindows < 2: break # do no more decimation # print information and add some comments projectText( "Calculating spectra for decimation level {}".format( declevel)) timeData.addComment( "Evaluation frequencies for this level {}".format( listToString( decParams.getEvalFrequenciesForLevel( declevel)))) timeData.addComment( "Windowing with window size {} samples and overlap {} samples" .format( winParams.getWindowSize(declevel), winParams.getOverlap(declevel), )) if projData.config.configParams["Spectra"]["applywindow"]: timeData.addComment( "Performing fourier transform with window function {}" .format(projData.config.configParams["Spectra"] ["windowfunc"])) else: timeData.addComment( "Performing fourier transform with no window function" ) # collect time data timeDataList = [] for iW in range(0, win.numWindows): timeDataList.append(win.getData(iW)) # open spectra file for saving specPath = os.path.join( siteData.getMeasurementSpecPath(meas), options["specdir"]) specWrite = SpectrumWriter(specPath, datetimeRef) specWrite.openBinaryForWriting( "spectra", declevel, sampleFreqDec, winParams.getWindowSize(declevel), winParams.getOverlap(declevel), win.winOffset, win.numWindows, dataChans, ) if options["ncores"] > 0: specDataList = multiSpectra( options["ncores"], timeDataList, sampleFreqDec, winParams.getWindowSize(declevel), projData.config.configParams, ) else: specDataList = calculateWindowSpectra( timeDataList, sampleFreqDec, winParams.getWindowSize(declevel), projData.config.configParams, ) # write out to spectra file for iW in range(0, win.numWindows): specWrite.writeBinary(specDataList[iW]) specWrite.writeCommentsFile(timeData.getComments()) specWrite.closeFile()
def viewSpectraStack(projData: ProjectData, site: str, meas: str, **kwargs) -> Union[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. If no data was found, then None is returned. """ from resistics.common.plot import savePlot, plotOptionsSpec, colorbarMultiline 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) if specReader is None: return None # 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"])) if stackSize == 0: projectWarning("Too few windows for number of stacks {}".format( options["numstacks"])) options["numstacks"] = numWindows stackSize = 1 projectWarning("Number of stacks changed to {}".format( 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 viewSpectraSection(projData: ProjectData, site: str, meas: str, **kwargs) -> Union[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. If no data was found, then None is returned. """ from matplotlib.colors import LogNorm from resistics.common.plot import savePlot, plotOptionsSpec, colorbar2dSpectra 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) if specReader is None: return None # channels dataChans = specReader.getChannels() if len(options["chans"]) > 0: dataChans = options["chans"] # get windows numWindows = specReader.getNumWindows() sampleFreqDec = specReader.getSampleFreq() f = specReader.getFrequencyArray() # if plotting a section, ignore plotwindow if numWindows > 250: windows = list( np.linspace(0, numWindows, 250, endpoint=False, dtype=np.int32)) else: windows = np.arange(0, numWindows) # 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
def viewSpectra(projData: ProjectData, site: str, meas: str, **kwargs) -> Union[Figure, None]: """View spectra 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 plotwindow : int, str, Dict, optional Windows to plot (local). If int, the window with local index plotwindow will be plotted. If string and "all", all the windows will be plotted if there are less than 20 windows, otherwise 20 windows throughout the whole spectra dataset will be plotted. If a dictionary, needs to have start and stop to define a range. 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. If no data was found, then None is returned. """ from resistics.common.plot import savePlot, plotOptionsSpec, colorbarMultiline options = {} options["chans"]: List[str] = [] options["declevel"]: int = 0 options["plotwindow"]: Union[int, Dict, str] = [0] options["specdir"]: str = projData.config.configParams["Spectra"][ "specdir"] options["show"]: bool = True options["save"]: bool = False options["plotoptions"]: Dict = plotOptionsSpec() options = parseKeywords(options, kwargs) projectText("Plotting spectra for measurement {} and site {}".format( meas, site)) specReader = getSpecReader(projData, site, meas, **options) if specReader is None: return None # channels dataChans = specReader.getChannels() if len(options["chans"]) > 0: dataChans = options["chans"] numChans = len(dataChans) # get windows numWindows = specReader.getNumWindows() sampleFreqDec = specReader.getSampleFreq() # get the window data windows = options["plotwindow"] if isinstance(windows, str) and windows == "all": if numWindows > 20: windows = list( np.linspace(0, numWindows, 20, endpoint=False, dtype=np.int32)) else: windows = list(np.arange(0, numWindows)) elif isinstance(windows, int): windows = [windows] # if an integer, make it into a list elif isinstance(windows, dict): windows = list(np.arange(windows["start"], windows["stop"] + 1)) # create a figure plotfonts = options["plotoptions"]["plotfonts"] cmap = colorbarMultiline() fig = plt.figure(figsize=options["plotoptions"]["figsize"]) for iW in windows: if iW >= numWindows: break color = cmap(iW / numWindows) winData = specReader.readBinaryWindowLocal(iW) winData.view( fig=fig, chans=dataChans, label="{} to {}".format( winData.startTime.strftime("%m-%d %H:%M:%S"), winData.stopTime.strftime("%m-%d %H:%M:%S"), ), plotfonts=plotfonts, color=color, ) st = fig.suptitle( "Spectra plot, site = {}, meas = {}, fs = {:.2f} [Hz], decimation level = {:2d}" .format(site, meas, sampleFreqDec, options["declevel"]), fontsize=plotfonts["suptitle"], ) st.set_y(0.98) # put on axis labels etc for idx, chan in enumerate(dataChans): ax = plt.subplot(numChans, 1, idx + 1) plt.title("Amplitude {}".format(chan), fontsize=plotfonts["title"]) if len(options["plotoptions"]["amplim"]) == 2: ax.set_ylim(options["plotoptions"]["amplim"]) ax.set_xlim(0, specReader.getSampleFreq() / 2.0) plt.grid(True) # fig legend and formatting ax = plt.gca() h, l = ax.get_legend_handles_labels() fig.tight_layout(rect=[0.02, 0.02, 0.77, 0.92]) # legend axis legax = plt.axes(position=[0.77, 0.02, 0.23, 0.88], in_layout=False) plt.tick_params(left=False, labelleft=False, bottom=False, labelbottom="False") plt.box(False) legax.legend(h, l, loc="upper left", fontsize=plotfonts["legend"]) # plot show and save if options["save"]: impath = projData.imagePath filename = "spectraData_{}_{}_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 viewStatisticDensityplot( projData: ProjectData, site: str, sampleFreq: Union[int, float], stat: str, crossplots: List[List[str]], **kwargs ) -> Union[Figure, None]: """View statistic data as a density plot 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. If no data was found, None. """ from resistics.common.plot import ( savePlot, plotOptionsSpec, getPlotRowsAndCols, colorbar2dSpectra, ) 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 density plot 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()) if len(statMeas) == 0: projectWarning( "No statistic files for site {}, sampling frequency {}, statistic {} and decimation level {}".format( site, sampleFreq, stat, options["declevel"] ) ) return None # 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( "{} density plots for {}, sampling frequency {} Hz,\ndecimation 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"]) plotAll1 = [] plotAll2 = [] 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]) # add to all data if plotData1.size == 0: continue if plotData1.size == 1: plotAll1 = plotAll1 + [float(plotData1)] plotAll2 = plotAll2 + [float(plotData2)] else: plotAll1 = plotAll1 + plotData1.tolist() plotAll2 = plotAll2 + plotData2.tolist() plotAll1 = np.array(plotAll1) plotAll2 = np.array(plotAll2) nbins = 200 if len(options["xlim"]) > 0: plt.xlim(options["xlim"]) rangex = options["xlim"] else: minx = np.percentile(plotAll1, 2) maxx = np.percentile(plotAll1, 98) ax.set_xlim(minx, maxx) rangex = [minx, maxx] if len(options["ylim"]) > 0: plt.ylim(options["ylim"]) rangey = options["ylim"] else: miny = np.percentile(plotAll2, 2) maxy = np.percentile(plotAll2, 98) ax.set_ylim(miny, maxy) rangey = [miny, maxy] plt.hist2d( plotAll1, plotAll2, bins=(nbins, nbins), range=[rangex, rangey], cmap=plt.cm.inferno, ) # axis format 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"]) # 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 = "statDensityplot_{: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 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 ncores : int, optional The number of cores to run the transfer function calculations on """ from resistics.decimate.decimator import Decimator from resistics.window.selector import WindowSelector from resistics.project.shortcuts import ( getDecimationParameters, getWindowParameters, getWindowSelector, getLocalRegressor, getRemoteRegressor, ) 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["ncores"] = projData.config.getSolverCores() 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, options["specdir"]) # 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 = getRemoteRegressor(winSelector, outPath, projData.config) processor.setRemote(options["remotesite"], options["remotechans"]) else: projectText( "Single site processing with sites: in = {}, out = {}".format( options["inputsite"], site)) processor = getLocalRegressor(winSelector, outPath, projData.config) # add the input and output site processor.setCores(options["ncores"]) 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() projectText("Processing data using {} cores".format(options["ncores"])) processor.process()
def viewStatistic( projData: ProjectData, site: str, sampleFreq: Union[int, float], stat: str, **kwargs ) -> Union[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. If no data was found, None. """ from resistics.common.plot import savePlot, plotOptionsSpec, getPlotRowsAndCols 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"], ) statMeas = list(statData.keys()) if len(statMeas) == 0: projectWarning( "No statistic files for site {}, sampling frequency {}, statistic {} and decimation level {}".format( site, sampleFreq, stat, options["declevel"] ) ) return None # 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 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 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 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. """ from resistics.statistics.io import StatisticIO from resistics.statistics.calculator import StatisticCalculator from resistics.project.shortcuts import ( getDecimationParameters, getWindowParameters, getWindowSelector, ) 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["ncores"] = projData.config.getStatisticCores() options = parseKeywords(options, kwargs) projectText( "Calculating stats: {} for sites: {} with remote site {}".format( listToString(options["remotestats"]), listToString(options["sites"]), remoteSite, ) ) statIO = StatisticIO() 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"]: continue projectText( "Calculating stats for site {}, measurement {} with reference {}".format( site, meas, remoteSite ) ) # decimation and window parameters decParams = getDecimationParameters(sampleFreq, projData.config) 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]) winSelector.calcSharedWindows() # create the spectrum reader specReader = SpectrumReader( os.path.join(siteData.getMeasurementSpecPath(meas), options["specdir"]) ) # calculate statistics for decimation level if spectra file exists for declevel in range(0, numLevels): check = specReader.openBinaryForReading("spectra", declevel) if not check: continue # information regarding only this spectra file refTime = specReader.getReferenceTime() winSize = specReader.getWindowSize() winOlap = specReader.getWindowOverlap() numWindows = specReader.getNumWindows() evalFreq = decParams.getEvalFrequenciesForLevel(declevel) sampleFreqDec = specReader.getSampleFreq() globalOffset = specReader.getGlobalOffset() # find size of the intersection between the windows in this spectra file and the shared windows sharedWindows = winSelector.getSharedWindowsLevel(declevel) sharedWindowsMeas = sharedWindows.intersection( set(np.arange(globalOffset, globalOffset + numWindows)) ) sharedWindowsMeas = sorted(list(sharedWindowsMeas)) numSharedWindows = len(sharedWindowsMeas) statData = {} # create the statistic handlers for stat in options["remotestats"]: statElements = getStatElements(stat) statData[stat] = StatisticData( stat, refTime, sampleFreqDec, winSize, winOlap ) # with remote reference the number of windows is number of shared windows statData[stat].setStatParams( numSharedWindows, statElements, evalFreq ) statData[stat].comments = specReader.getComments() statData[stat].addComment(projData.config.getConfigComment()) statData[stat].addComment( "Calculating remote statistic: {}".format(stat) ) statData[stat].addComment( "Statistic components: {}".format(listToString(statElements)) ) # collect the spectra data spectraData, _globalIndices = specReader.readBinaryBatchGlobal( sharedWindowsMeas ) remoteData = [] for globalWindow in sharedWindowsMeas: _, remoteReader = winSelector.getSpecReaderForWindow( remoteSite, declevel, globalWindow ) remoteData.append(remoteReader.readBinaryWindowGlobal(globalWindow)) # calculate if options["ncores"] > 0: out = multiStatistics( options["ncores"], spectraData, evalFreq, options["remotestats"], remoteData=remoteData, ) for iW, globalWindow in enumerate(sharedWindowsMeas): for stat in options["remotestats"]: statData[stat].addStat(iW, globalWindow, out[iW][stat]) else: statCalculator = StatisticCalculator() for iW, globalWindow in enumerate(sharedWindowsMeas): winStatData = calculateWindowStatistics( spectraData[iW], evalFreq, options["remotestats"], remoteSpecData=remoteData[iW], statCalculator=statCalculator, ) for stat in options["remotestats"]: statData[stat].addStat(iW, globalWindow, winStatData[stat]) # save statistic for stat in options["remotestats"]: statIO.setDatapath( os.path.join( siteData.getMeasurementStatPath(meas), options["specdir"] ) ) statIO.write(statData[stat], declevel)
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 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. ncores : int, optional The number of cores to run the transfer function calculations on """ from resistics.statistics.io import StatisticIO from resistics.project.shortcuts import getDecimationParameters 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["ncores"] = projData.config.getStatisticCores() options = parseKeywords(options, kwargs) projectText( "Calculating stats: {} for sites: {}".format( listToString(options["stats"]), listToString(options["sites"]) ) ) # loop through sites and calculate statistics statIO = StatisticIO() 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"]: continue projectText( "Calculating stats for site {}, measurement {}".format(site, meas) ) decParams = getDecimationParameters(sampleFreq, projData.config) numLevels = decParams.numLevels specReader = SpectrumReader( os.path.join(siteData.getMeasurementSpecPath(meas), options["specdir"]) ) # calculate statistics for decimation level if spectra file exists for declevel in range(0, numLevels): check = specReader.openBinaryForReading("spectra", declevel) if not check: continue refTime = specReader.getReferenceTime() winSize = specReader.getWindowSize() winOlap = specReader.getWindowOverlap() numWindows = specReader.getNumWindows() sampleFreqDec = specReader.getSampleFreq() evalFreq = decParams.getEvalFrequenciesForLevel(declevel) # dictionary for saving statistic data statData = {} for stat in options["stats"]: statElements = getStatElements(stat) statData[stat] = StatisticData( stat, refTime, sampleFreqDec, winSize, winOlap ) statData[stat].setStatParams(numWindows, statElements, evalFreq) statData[stat].comments = specReader.getComments() statData[stat].addComment(projData.config.getConfigComment()) statData[stat].addComment("Calculating statistic: {}".format(stat)) statData[stat].addComment( "Statistic components: {}".format(listToString(statElements)) ) # get all the spectra data in batch and process all the windows spectraData, globalIndices = specReader.readBinaryBatchGlobal() if options["ncores"] > 0: out = multiStatistics( options["ncores"], spectraData, evalFreq, options["stats"] ) for iW in range(numWindows): for stat in options["stats"]: statData[stat].addStat(iW, globalIndices[iW], out[iW][stat]) else: statCalculator = StatisticCalculator() for iW in range(numWindows): winSpecData = spectraData[iW] winStatData = calculateWindowStatistics( winSpecData, evalFreq, options["stats"], statCalculator=statCalculator, ) for stat in options["stats"]: statData[stat].addStat( iW, globalIndices[iW], winStatData[stat] ) specReader.closeFile() # save statistic for stat in options["stats"]: statIO.setDatapath( os.path.join( siteData.getMeasurementStatPath(meas), options["specdir"] ) ) statIO.write(statData[stat], declevel)
def viewTipper(projData: ProjectData, **kwargs) -> List[Figure]: """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 """ from resistics.common.plot import ( savePlot, plotOptionsTipper, getTransferFunctionFigSize, transferFunctionColours, ) 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 figs = [] 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) figs.append(fig) 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"]) return figs
def viewTime(projData: ProjectData, startDate: str, endDate: str, **kwargs) -> Union[Figure, None]: """View timeseries in the project Parameters ---------- projData : ProjectData The project data instance 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 scale : Dict[str, float] Keys are channels and values are floats to multiply the channel data by 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. """ from resistics.project.shortcuts import getCalibrator from resistics.project.preprocess import ( applyPolarisationReversalOptions, applyScaleOptions, applyCalibrationOptions, applyFilterOptions, applyNormaliseOptions, applyNotchOptions, ) from resistics.common.plot import savePlot, plotOptionsTime 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["scale"]: Union[bool, Dict[str, float]] = 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) if isinstance(siteData, bool): # site does not exist continue 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) applyScaleOptions(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 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. """ from resistics.project.shortcuts import getCalibrator from resistics.project.preprocess import ( applyPolarisationReversalOptions, applyScaleOptions, applyCalibrationOptions, applyFilterOptions, applyInterpolationOptions, applyNormaliseOptions, applyNotchOptions, applyResampleOptions, ) 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 = getCalibrator(projData.calPath, projData.config) if options["calibrate"]: cal.printInfo() writer = TimeWriterInternal() # 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, _ = 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 = TimeWriterInternal() 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 viewStatisticHistogram( projData: ProjectData, site: str, sampleFreq: float, stat: str, **kwargs ) -> Union[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. If no data was found, None. """ from resistics.common.plot import savePlot, plotOptionsSpec, getPlotRowsAndCols 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()) if len(statMeas) == 0: projectWarning( "No statistic files for site {}, sampling frequency {}, statistic {} and decimation level {}".format( site, sampleFreq, stat, options["declevel"] ) ) return None # 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