spritspritdef check_gui_requirements():
- print("Checking requirements for gui")
+ #First, check requirements
# Define a command that tries to open a window
command = "python -c \"import tkinter; tkinter.Tk()\""
@@ -529,7 +533,7 @@ Raises
oktoproceed=True
else:
oktoproceed=False
- print("GUI window could not be created.")
+ print("GUI window cannot be created.")
return oktoproceed
@@ -571,7 +575,7 @@ Raises
-def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose=False)
+def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_selection='max', peak_freq_range=[1, 20], verbose=False)
Function to run tests on HVSR peaks to find best one and see if it passes quality checks
@@ -581,6 +585,10 @@ Parameters
Dictionary containing all the calculated information about the HVSR data (i.e., hvsr_out returned from process_hvsr)
hvsr_band : tuple or list, default=[0.4, 40]2-item tuple or list with lower and upper limit of frequencies to analyze
+peak_selection : str or numeric, default='max'How to select the "best" peak used in the analysis. For peak_selection="max" (default value), the highest peak within peak_freq_range is used.
+For peak_selection='scored', an algorithm is used to select the peak based in part on which peak passes the most SESAME criteria.
+If a numeric value is used (e.g., int or float), this should be a frequency value to manually select as the peak of interest.
peak_freq_range : tuple or list, default=[1, 20];The frequency range within which to check for peaks. If there is an HVSR curve with multiple peaks, this allows the full range of data to be processed while limiting peak picks to likely range.
verbose : bool, default=FalseReturns
Expand source code
-def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose=False):
+def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_selection='max', peak_freq_range=[1, 20], verbose=False):
"""Function to run tests on HVSR peaks to find best one and see if it passes quality checks
Parameters
@@ -605,6 +613,10 @@ Returns
Dictionary containing all the calculated information about the HVSR data (i.e., hvsr_out returned from process_hvsr)
hvsr_band : tuple or list, default=[0.4, 40]
2-item tuple or list with lower and upper limit of frequencies to analyze
+ peak_selection : str or numeric, default='max'
+ How to select the "best" peak used in the analysis. For peak_selection="max" (default value), the highest peak within peak_freq_range is used.
+ For peak_selection='scored', an algorithm is used to select the peak based in part on which peak passes the most SESAME criteria.
+ If a numeric value is used (e.g., int or float), this should be a frequency value to manually select as the peak of interest.
peak_freq_range : tuple or list, default=[1, 20];
The frequency range within which to check for peaks. If there is an HVSR curve with multiple peaks, this allows the full range of data to be processed while limiting peak picks to likely range.
verbose : bool, default=False
@@ -617,6 +629,25 @@ Returns
"""
orig_args = locals().copy() #Get the initial arguments
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'check_peaks' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['check_peaks'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(check_peaks).args[1:],
+ inspect.getfullargspec(check_peaks).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ hvsr_band = orig_args['hvsr_band']
+ peak_selection = orig_args['peak_selection']
+ peak_freq_range = orig_args['peak_freq_range']
+ verbose = orig_args['verbose']
+
+ hvsr_data['processing_parameters']['check_peaks'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['check_peaks'][key] = value
+
if (verbose and 'input_params' not in hvsr_data.keys()) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
pass
@@ -652,13 +683,38 @@ Returns
if hvsr_data['ProcessingStatus']['OverallStatus']:
if not hvsr_band:
hvsr_band = [0.4,40]
+
hvsr_data['hvsr_band'] = hvsr_band
anyK = list(hvsr_data['x_freqs'].keys())[0]
x = hvsr_data['x_freqs'][anyK] #Consistent for all curves
y = hvsr_data['hvsr_curve'] #Calculated based on "Use" column
- index_list = hvsr_data['hvsr_peak_indices'] #Calculated based on hvsr_curve
+
+ scorelist = ['score', 'scored', 'best', 's']
+ maxlist = ['max', 'highest', 'm']
+ # Convert peak_selection to numeric, get index of nearest value as list item for __init_peaks()
+ try:
+ peak_val = float(peak_selection)
+ index_list = [np.argmin(np.abs(x - peak_val))]
+ except:
+ # If score method is being used, get index list for __init_peaks()
+ if peak_selection in scorelist:
+ index_list = hvsr_data['hvsr_peak_indices'] #Calculated based on hvsr_curve
+ elif peak_selection in maxlist:
+ #Get max index as item in list for __init_peaks()
+ startInd = np.argmin(np.abs(x - peak_freq_range[0]))
+ endInd = np.argmin(np.abs(x - peak_freq_range[1]))
+ if startInd > endInd:
+ holder = startInd
+ startInd = endInd
+ endInd = holder
+ subArrayMax = np.argmax(y[startInd:endInd])
+
+ # If max val is in subarray, this will be the same as the max of curve
+ # Otherwise, it will be the index of the value that is max within peak_freq_range
+ index_list = [subArrayMax+startInd]
+
hvsrp = hvsr_data['hvsrp'] #Calculated based on "Use" column
hvsrm = hvsr_data['hvsrm'] #Calculated based on "Use" column
@@ -684,7 +740,7 @@ Returns
peakp = __init_peaks(x, hvsrp, index_p, hvsr_band, peak_freq_range)
peakp = __check_clarity(x, hvsrp, peakp, do_rank=True)
- #Do for hvsrm
+ # Do for hvsrm
# Find the relative extrema of hvsrm (hvsr - 1 standard deviation)
if not np.isnan(np.sum(hvsrm)):
index_m = __find_peaks(hvsrm)
@@ -694,6 +750,7 @@ Returns
peakm = __init_peaks(x, hvsrm, index_m, hvsr_band, peak_freq_range)
peakm = __check_clarity(x, hvsrm, peakm, do_rank=True)
+ # Get standard deviation of time peaks
stdf = __get_stdf(x, index_list, hvsrPeaks)
peak = __check_freq_stability(peak, peakm, peakp)
@@ -705,7 +762,7 @@ Returns
# Get the BestPeak based on the peak score
# Calculate whether each peak passes enough tests
curveTests = ['WindowLengthFreq.','SignificantCycles', 'LowCurveStDevOverTime']
- peakTests = ['PeakFreqClarityBelow', 'PeakFreqClarityAbove', 'PeakAmpClarity', 'FreqStability', 'PeakStability_FreqStD', 'PeakStability_AmpStD']
+ peakTests = ['PeakProminenceBelow', 'PeakProminenceAbove', 'PeakAmpClarity', 'FreqStability', 'PeakStability_FreqStD', 'PeakStability_AmpStD']
bestPeakScore = 0
for p in hvsr_data['PeakReport']:
@@ -739,6 +796,10 @@ Returns
else:
hvsr_data['BestPeak'] = {}
print(f"Processing Errors: No Best Peak identified for {hvsr_data['site']}")
+ try:
+ hvsr_data.plot()
+ except:
+ pass
return hvsr_data
@@ -903,6 +964,139 @@ Parameters
return
+
+def export_settings(hvsr_data, export_settings_path='default', export_settings_type='all', include_location=False, verbose=False)
+
+Save settings to json file
+Parameters
+
+export_settings_path : str, default="default"- Where to save the json file(s) containing the settings, by default 'default'.
+If "default," will save to sprit package resources. Otherwise, set a filepath location you would like for it to be saved to.
+If 'all' is selected, a directory should be supplied.
+Otherwise, it will save in the directory of the provided file, if it exists. Otherwise, defaults to the home directory.
+export_settings_type : str, {'all', 'instrument', 'processing'}- What kind of settings to save.
+If 'all', saves all possible types in their respective json files.
+If 'instrument', save the instrument settings to their respective file.
+If 'processing', saves the processing settings to their respective file. By default 'all'
+include_location : bool, default=False, input CRS- Whether to include the location parametersin the exported settings document.This includes xcoord, ycoord, elevation, elev_unit, and input_crs
+verbose : bool, default=True- Whether to print outputs and information to the terminal
+
+
+
+Expand source code
+
+def export_settings(hvsr_data, export_settings_path='default', export_settings_type='all', include_location=False, verbose=False):
+ """Save settings to json file
+
+ Parameters
+ ----------
+ export_settings_path : str, default="default"
+ Where to save the json file(s) containing the settings, by default 'default'.
+ If "default," will save to sprit package resources. Otherwise, set a filepath location you would like for it to be saved to.
+ If 'all' is selected, a directory should be supplied.
+ Otherwise, it will save in the directory of the provided file, if it exists. Otherwise, defaults to the home directory.
+ export_settings_type : str, {'all', 'instrument', 'processing'}
+ What kind of settings to save.
+ If 'all', saves all possible types in their respective json files.
+ If 'instrument', save the instrument settings to their respective file.
+ If 'processing', saves the processing settings to their respective file. By default 'all'
+ include_location : bool, default=False, input CRS
+ Whether to include the location parametersin the exported settings document.This includes xcoord, ycoord, elevation, elev_unit, and input_crs
+ verbose : bool, default=True
+ Whether to print outputs and information to the terminal
+
+ """
+ fnameDict = {}
+ fnameDict['instrument'] = "instrument_settings.json"
+ fnameDict['processing'] = "processing_settings.json"
+
+ if export_settings_path == 'default' or export_settings_path is True:
+ settingsPath = resource_dir.joinpath('settings')
+ else:
+ export_settings_path = pathlib.Path(export_settings_path)
+ if not export_settings_path.exists():
+ if not export_settings_path.parent.exists():
+ print(f'The provided value for export_settings_path ({export_settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}')
+ settingsPath = pathlib.Path.home()
+ else:
+ settingsPath = export_settings_path.parent
+
+ if export_settings_path.is_dir():
+ settingsPath = export_settings_path
+ elif export_settings_path.is_file():
+ settingsPath = export_settings_path.parent
+ fnameDict['instrument'] = export_settings_path.name+"_instrumentSettings.json"
+ fnameDict['processing'] = export_settings_path.name+"_processingSettings.json"
+
+ #Get final filepaths
+ instSetFPath = settingsPath.joinpath(fnameDict['instrument'])
+ procSetFPath = settingsPath.joinpath(fnameDict['processing'])
+
+ #Get settings values
+ instKeys = ["instrument", "net", "sta", "loc", "cha", "depth", "metapath", "hvsr_band"]
+ inst_location_keys = ['xcoord', 'ycoord', 'elevation', 'elev_unit', 'input_crs']
+ procFuncs = [fetch_data, remove_noise, generate_ppsds, process_hvsr, check_peaks, get_report]
+
+ instrument_settings_dict = {}
+ processing_settings_dict = {}
+
+ for k in instKeys:
+ if isinstance(hvsr_data[k], pathlib.PurePath):
+ #For those that are paths and cannot be serialized
+ instrument_settings_dict[k] = hvsr_data[k].as_posix()
+ else:
+ instrument_settings_dict[k] = hvsr_data[k]
+
+ if include_location:
+ for k in inst_location_keys:
+ if isinstance(hvsr_data[k], pathlib.PurePath):
+ #For those that are paths and cannot be serialized
+ instrument_settings_dict[k] = hvsr_data[k].as_posix()
+ else:
+ instrument_settings_dict[k] = hvsr_data[k]
+
+
+ for func in procFuncs:
+ funcName = func.__name__
+ processing_settings_dict[funcName] = {}
+ for arg in hvsr_data['processing_parameters'][funcName]:
+ if isinstance(hvsr_data['processing_parameters'][funcName][arg], (HVSRBatch, HVSRData)):
+ pass
+ else:
+ processing_settings_dict[funcName][arg] = hvsr_data['processing_parameters'][funcName][arg]
+
+ #Save settings files
+ if export_settings_type.lower()=='instrument' or export_settings_type.lower()=='all':
+ with open(instSetFPath.with_suffix('.inst').as_posix(), 'w') as instSetF:
+ jsonString = json.dumps(instrument_settings_dict, indent=2)
+ #Format output for readability
+ jsonString = jsonString.replace('\n ', ' ')
+ jsonString = jsonString.replace('[ ', '[')
+ jsonString = jsonString.replace('\n ]', ']')
+ #Export
+ instSetF.write(jsonString)
+ if export_settings_type.lower()=='processing' or export_settings_type.lower()=='all':
+ with open(procSetFPath.with_suffix('.proc').as_posix(), 'w') as procSetF:
+ jsonString = json.dumps(processing_settings_dict, indent=2)
+ #Format output for readability
+ jsonString = jsonString.replace('\n ', ' ')
+ jsonString = jsonString.replace('[ ', '[')
+ jsonString = jsonString.replace('\n ]', ']')
+ jsonString = jsonString.replace('\n },','\n\t\t},\n')
+ jsonString = jsonString.replace('{ "', '\n\t\t{\n\t\t"')
+ jsonString = jsonString.replace(', "', ',\n\t\t"')
+ jsonString = jsonString.replace('\n }', '\n\t\t}')
+ jsonString = jsonString.replace(': {', ':\n\t\t\t{')
+
+ #Export
+ procSetF.write(jsonString)
+
+
def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detrend='spline', detrend_order=2, update_metadata=True, plot_input_stream=False, verbose=False, **kwargs)
Parameters
Expand source code
def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detrend='spline', detrend_order=2, update_metadata=True, plot_input_stream=False, verbose=False, **kwargs):
- import warnings
+ #Get intput paramaters
+ orig_args = locals().copy()
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in params.keys():
+ if 'fetch_data' in params['processing_parameters'].keys():
+ defaultVDict = dict(zip(inspect.getfullargspec(fetch_data).args[1:],
+ inspect.getfullargspec(fetch_data).defaults))
+ defaultVDict['kwargs'] = kwargs
+ for k, v in params['processing_parameters']['fetch_data'].items():
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ #Update local variables, in case of previously-specified parameters
+ source=orig_args['source']
+ trim_dir=orig_args['trim_dir']
+ export_format=orig_args['export_format']
+ detrend=orig_args['detrend']
+ detrend_order=orig_args['detrend_order']
+ update_metadata=orig_args['update_metadata']
+ plot_input_stream=orig_args['plot_input_stream']
+ verbose=orig_args['verbose']
+ kwargs=orig_args['kwargs']
"""Fetch ambient seismic data from a source to read into obspy stream
@@ -962,7 +1179,9 @@ Parameters
date=params['acq_date']
#Cleanup for gui input
- if '}' in str(params['datapath']):
+ if isinstance(params['datapath'], (obspy.Stream, obspy.Trace)):
+ pass
+ elif '}' in str(params['datapath']):
params['datapath'] = params['datapath'].as_posix().replace('{','')
params['datapath'] = params['datapath'].split('}')
@@ -975,7 +1194,10 @@ Parameters
#Make sure datapath is pointing to an actual file
if isinstance(params['datapath'],list):
for i, d in enumerate(params['datapath']):
- params['datapath'][i] = sprit_utils.checkifpath(str(d).strip())
+ params['datapath'][i] = sprit_utils.checkifpath(str(d).strip(), sample_list=sampleList)
+ dPath = params['datapath']
+ elif isinstance(params['datapath'], (obspy.Stream, obspy.Trace)):
+ pass
else:
dPath = sprit_utils.checkifpath(params['datapath'], sample_list=sampleList)
@@ -1027,15 +1249,26 @@ Parameters
#Select which instrument we are reading from (requires different processes for each instrument)
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
+ trominoNameList = ['tromino', 'trom', 'tromino 3g', 'tromino 3g+', 'tr', 't']
+
+ #Get any kwargs that are included in obspy.read
+ obspyReadKwargs = {}
+ for argName in inspect.getfullargspec(obspy.read)[0]:
+ if argName in kwargs.keys():
+ obspyReadKwargs[argName] = kwargs[argName]
#Select how reading will be done
if source=='raw':
- if inst.lower() in raspShakeInstNameList:
- try:
+ try:
+ if inst.lower() in raspShakeInstNameList:
rawDataIN = __read_RS_file_struct(dPath, source, year, doy, inv, params, verbose=verbose)
- except:
- raise RuntimeError(f"Data not fetched for {params['site']}. Check input parameters or the data file.")
- return params
+
+ elif inst.lower() in trominoNameList:
+ rawDataIN = __read_tromino_files(dPath, params, verbose=verbose)
+ except:
+ raise RuntimeError(f"Data not fetched for {params['site']}. Check input parameters or the data file.")
+ elif source=='stream' or isinstance(params, (obspy.Stream, obspy.Trace)):
+ rawDataIN = params['datapath'].copy()
elif source=='dir':
if inst.lower() in raspShakeInstNameList:
rawDataIN = __read_RS_file_struct(dPath, source, year, doy, inv, params, verbose=verbose)
@@ -1046,28 +1279,27 @@ Parameters
for f in temp_file_glob:
currParams = params
currParams['datapath'] = f
+
curr_data = fetch_data(params, source='file', #all the same as input, except just reading the one file using the source='file'
- trim_dir=trim_dir, export_format=export_format, detrend=detrend, detrend_order=detrend_order, update_metadata=update_metadata, verbose=verbose, **kwargs),
+ trim_dir=trim_dir, export_format=export_format, detrend=detrend, detrend_order=detrend_order, update_metadata=update_metadata, verbose=verbose, **kwargs)
+ curr_data.merge()
obspyFiles[f.stem] = curr_data #Add path object to dict, with filepath's stem as the site name
return HVSRBatch(obspyFiles)
-
elif source=='file' and str(params['datapath']).lower() not in sampleList:
if isinstance(dPath, list) or isinstance(dPath, tuple):
rawStreams = []
for datafile in dPath:
- rawStream = obspy.read(datafile)
+ rawStream = obspy.read(datafile, **obspyReadKwargs)
rawStreams.append(rawStream) #These are actually streams, not traces
-
for i, stream in enumerate(rawStreams):
if i == 0:
rawDataIN = obspy.Stream(stream) #Just in case
else:
rawDataIN = rawDataIN + stream #This adds a stream/trace to the current stream object
-
elif str(dPath)[:6].lower()=='sample':
pass
else:
- rawDataIN = obspy.read(dPath)#, starttime=obspy.core.UTCDateTime(params['starttime']), endttime=obspy.core.UTCDateTime(params['endtime']), nearest_sample =True)
+ rawDataIN = obspy.read(dPath, **obspyReadKwargs)#, starttime=obspy.core.UTCDateTime(params['starttime']), endttime=obspy.core.UTCDateTime(params['endtime']), nearest_sample =True)
import warnings
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=UserWarning)
@@ -1114,12 +1346,15 @@ Parameters
except:
RuntimeError(f'source={source} not recognized, and datapath cannot be read using obspy.read()')
+ #Get metadata from the data itself, if not reading raw data
try:
dataIN = rawDataIN.copy()
if source!='raw':
#Use metadata from file for;
# site
if params['site'] == "HVSR Site":
+ if isinstance(dPath, (list, tuple)):
+ dPath = dPath[0]
params['site'] = dPath.stem
params['params']['site'] = dPath.stem
@@ -1156,11 +1391,13 @@ Parameters
today_Starttime = obspy.UTCDateTime(datetime.datetime(year=datetime.date.today().year, month=datetime.date.today().month,
day = datetime.date.today().day,
hour=0, minute=0, second=0, microsecond=0))
- maxStarttime = datetime.time(hour=0, minute=0, second=0, microsecond=0)
+ maxStarttime = datetime.datetime(year=params['acq_date'].year, month=params['acq_date'].month, day=params['acq_date'].day,
+ hour=0, minute=0, second=0, microsecond=0, tzinfo=datetime.timezone.utc)
if str(params['starttime']) == str(today_Starttime):
for tr in dataIN.merge():
- currTime = datetime.time(hour=tr.stats.starttime.hour, minute=tr.stats.starttime.minute,
- second=tr.stats.starttime.second, microsecond=tr.stats.starttime.microsecond)
+ currTime = datetime.datetime(year=tr.stats.starttime.year, month=tr.stats.starttime.month, day=tr.stats.starttime.day,
+ hour=tr.stats.starttime.hour, minute=tr.stats.starttime.minute,
+ second=tr.stats.starttime.second, microsecond=tr.stats.starttime.microsecond, tzinfo=datetime.timezone.utc)
if currTime > maxStarttime:
maxStarttime = currTime
@@ -1175,17 +1412,19 @@ Parameters
today_Endtime = obspy.UTCDateTime(datetime.datetime(year=datetime.date.today().year, month=datetime.date.today().month,
day = datetime.date.today().day,
hour=23, minute=59, second=59, microsecond=999999))
- minEndtime = datetime.time(hour=23, minute=59, second=59, microsecond=999999)
- if str(params['endtime']) == str(today_Endtime):
+ tomorrow_Endtime = today_Endtime + (60*60*24)
+ minEndtime = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc)#(hour=23, minute=59, second=59, microsecond=999999)
+ if str(params['endtime']) == str(today_Endtime) or str(params['endtime'])==tomorrow_Endtime:
for tr in dataIN.merge():
- currTime = datetime.time(hour=tr.stats.endtime.hour, minute=tr.stats.endtime.minute,
- second=tr.stats.endtime.second, microsecond=tr.stats.endtime.microsecond)
+ currTime = datetime.datetime(year=tr.stats.endtime.year, month=tr.stats.endtime.month, day=tr.stats.endtime.day,
+ hour=tr.stats.endtime.hour, minute=tr.stats.endtime.minute,
+ second=tr.stats.endtime.second, microsecond=tr.stats.endtime.microsecond, tzinfo=datetime.timezone.utc)
if currTime < minEndtime:
minEndtime = currTime
- newEndtime = obspy.UTCDateTime(datetime.datetime(year=params['acq_date'].year, month=params['acq_date'].month,
- day = params['acq_date'].day,
+ newEndtime = obspy.UTCDateTime(datetime.datetime(year=minEndtime.year, month=minEndtime.month,
+ day = minEndtime.day,
hour=minEndtime.hour, minute=minEndtime.minute,
- second=minEndtime.second, microsecond=minEndtime.microsecond))
+ second=minEndtime.second, microsecond=minEndtime.microsecond, tzinfo=datetime.timezone.utc))
params['endtime'] = newEndtime
params['params']['endtime'] = newEndtime
@@ -1225,10 +1464,12 @@ Parameters
#Remerge data
dataIN = dataIN.merge(method=1)
+ #Plot the input stream?
if plot_input_stream:
try:
params['InputPlot'] = _plot_specgram_stream(stream=dataIN, params=params, component='Z', stack_type='linear', detrend='mean', dbscale=True, fill_gaps=None, ylimstd=3, return_fig=True, fig=None, ax=None, show_plot=False)
- _get_removed_windows(input=dataIN, fig=params['InputPlot'][0], ax=params['InputPlot'][1], lineArtist =[], winArtist = [], existing_lineArtists=[], existing_xWindows=[], exist_win_format='matplotlib', keep_line_artists=True, time_type='matplotlib', show_plot=True)
+ #_get_removed_windows(input=dataIN, fig=params['InputPlot'][0], ax=params['InputPlot'][1], lineArtist =[], winArtist = [], existing_lineArtists=[], existing_xWindows=[], exist_win_format='matplotlib', keep_line_artists=True, time_type='matplotlib', show_plot=True)
+ plt.show()
except:
print('Error with default plotting method, falling back to internal obspy plotting method')
dataIN.plot(method='full', linewidth=0.25)
@@ -1239,6 +1480,7 @@ Parameters
else:
dataIN = _sort_channels(input=dataIN, source=source, verbose=verbose)
+ #Clean up the ends of the data unless explicitly specified to do otherwise (this is a kwarg, not a parameter)
if 'clean_ends' not in kwargs.keys():
clean_ends=True
else:
@@ -1274,6 +1516,14 @@ Parameters
params['batch'] = False #Set False by default, will get corrected later in batch mode
params['input_stream'] = dataIN.copy()
params['stream'] = dataIN.copy()
+
+ if 'processing_parameters' not in params.keys():
+ params['processing_parameters'] = {}
+ params['processing_parameters']['fetch_data'] = {}
+ for key, value in orig_args.items():
+ params['processing_parameters']['fetch_data'][key] = value
+
+
params['ProcessingStatus']['FetchDataStatus'] = True
if verbose and not isinstance(params, HVSRBatch):
dataINStr = dataIN.__str__().split('\n')
@@ -1546,6 +1796,8 @@ Returns
ppsd_kwargs_sprit_defaults['skip_on_gaps'] = True
if 'period_step_octaves' not in ppsd_kwargs:
ppsd_kwargs_sprit_defaults['period_step_octaves'] = 0.03125
+ if 'period_limits' not in ppsd_kwargs:
+ ppsd_kwargs_sprit_defaults['period_limits'] = [1/hvsr_data['hvsr_band'][1], 1/hvsr_data['hvsr_band'][0]]
#Get Probablistic power spectral densities (PPSDs)
#Get default args for function
@@ -1559,8 +1811,24 @@ Returns
ppsd_kwargs = get_default_args(PPSD)
ppsd_kwargs.update(ppsd_kwargs_sprit_defaults)#Update with sprit defaults, or user input
+ orig_args['ppsd_kwargs'] = ppsd_kwargs
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'generate_ppsds' in hvsr_data['processing_parameters'].keys():
+ defaultVDict = dict(zip(inspect.getfullargspec(generate_ppsds).args[1:],
+ inspect.getfullargspec(generate_ppsds).defaults))
+ defaultVDict['ppsd_kwargs'] = ppsd_kwargs
+ for k, v in hvsr_data['processing_parameters']['generate_ppsds'].items():
- orig_args['ppsd_kwargs'] = [ppsd_kwargs]
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ remove_outliers = orig_args['remove_outliers']
+ outlier_std = orig_args['outlier_std']
+ verbose = orig_args['verbose']
+ ppsd_kwargs = orig_args['ppsd_kwargs']
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
@@ -1594,7 +1862,7 @@ Returns
hvsr_data[site_name]['ProcessingStatus']['OverallStatus'] = False
return hvsr_data
else:
- paz=hvsr_data['paz']
+ paz = hvsr_data['paz']
stream = hvsr_data['stream']
#Get ppsds of e component
@@ -1729,10 +1997,18 @@ Returns
hvsrDF['TimesProcessed_MPLEnd'] = hvsrDF['TimesProcessed_MPL'] + (ppsd_kwargs['ppsd_length']/86400)
hvsrDF['Use'] = True
+ hvsrDF['Use']=hvsrDF['Use'].astype(bool)
for gap in hvsr_data['ppsds']['Z']['times_gaps']:
- hvsrDF['Use'] = (hvsrDF['TimesProcessed_Obspy'].gt(gap[0]) & hvsrDF['TimesProcessed_Obspy'].gt(gap[1]) )| \
- (hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[0]) & hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[1]))# | \
-
+ hvsrDF['Use'] = (hvsrDF['TimesProcessed_MPL'].gt(gap[1].matplotlib_date))| \
+ (hvsrDF['TimesProcessed_MPLEnd'].lt(gap[0].matplotlib_date))# | \
+
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+ if 'xwindows_out' in hvsr_data.keys():
+ for window in hvsr_data['xwindows_out']:
+ hvsrDF['Use'] = (hvsrDF['TimesProcessed_MPL'][hvsrDF['Use']].lt(window[0]) & hvsrDF['TimesProcessed_MPLEnd'][hvsrDF['Use']].lt(window[0]) )| \
+ (hvsrDF['TimesProcessed_MPL'][hvsrDF['Use']].gt(window[1]) & hvsrDF['TimesProcessed_MPLEnd'][hvsrDF['Use']].gt(window[1]))
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+
hvsrDF.set_index('TimesProcessed', inplace=True)
hvsr_data['hvsr_df'] = hvsrDF
#Create dict entry to keep track of how many outlier hvsr curves are removed (2-item list with [0]=current number, [1]=original number of curves)
@@ -1747,6 +2023,12 @@ Returns
hvsr_data = sprit_utils.make_it_classy(hvsr_data)
+ if 'processing_parameters' not in hvsr_data.keys():
+ hvsr_data['processing_parameters'] = {}
+ hvsr_data['processing_parameters']['generate_ppsds'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['generate_ppsds'][key] = value
+
hvsr_data['ProcessingStatus']['PPSDStatus'] = True
hvsr_data = _check_processing_status(hvsr_data)
return hvsr_data
@@ -1818,14 +2100,57 @@ Returns
params : dict
Modified input dictionary with additional key:value pair containing paz dictionary (key = "paz")
"""
-
invPath = params['metapath']
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
+ trominoNameList = ['tromino', 'trom', 'trm', 't']
if params['instrument'].lower() in raspShakeInstNameList:
if update_metadata:
params = _update_shake_metadata(filepath=invPath, params=params, write_path=write_path)
params = _read_RS_Metadata(params, source=source)
+ elif params['instrument'].lower() in trominoNameList:
+ params['paz'] = {'Z':{}, 'E':{}, 'N':{}}
+ #ALL THESE VALUES ARE PLACEHOLDERS, taken from RASPBERRY SHAKE! (Needed for PPSDs)
+ params['paz']['Z'] = {'sensitivity': 360000000.0,
+ 'gain': 360000000.0,
+ 'poles': [(-1+0j), (-3.03+0j), (-3.03+0j), (-666.67+0j)],
+ 'zeros': [0j, 0j, 0j]}
+ params['paz']['E'] = params['paz']['Z']
+ params['paz']['N'] = params['paz']['Z']
+
+ channelObj_Z = obspy.core.inventory.channel.Channel(code='BHZ', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=0, dip=90, types=None, external_references=None,
+ sample_rate=None, sample_rate_ratio_number_samples=None, sample_rate_ratio_number_seconds=None,
+ storage_format=None, clock_drift_in_seconds_per_sample=None, calibration_units=None,
+ calibration_units_description=None, sensor=None, pre_amplifier=None, data_logger=None,
+ equipments=None, response=None, description=None, comments=None, start_date=None, end_date=None,
+ restricted_status=None, alternate_code=None, historical_code=None, data_availability=None,
+ identifiers=None, water_level=None, source_id=None)
+ channelObj_E = obspy.core.inventory.channel.Channel(code='BHE', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=90, dip=0)
+
+ channelObj_N = obspy.core.inventory.channel.Channel(code='BHN', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=0, dip=0)
+
+ siteObj = obspy.core.inventory.util.Site(name=params['params']['site'], description=None, town=None, county=None, region=None, country=None)
+ stationObj = obspy.core.inventory.station.Station(code='TZ', latitude=params['params']['latitude'], longitude=params['params']['longitude'],
+ elevation=params['params']['elevation'], channels=[channelObj_Z, channelObj_E, channelObj_N], site=siteObj,
+ vault=None, geology=None, equipments=None, operators=None, creation_date=datetime.datetime.today(),
+ termination_date=None, total_number_of_channels=None,
+ selected_number_of_channels=None, description='Estimated data for Tromino, this is NOT from the manufacturer',
+ comments=None, start_date=None,
+ end_date=None, restricted_status=None, alternate_code=None, historical_code=None,
+ data_availability=None, identifiers=None, water_level=None, source_id=None)
+
+ network = [obspy.core.inventory.network.Network(code='TROM', stations=[stationObj], total_number_of_stations=None,
+ selected_number_of_stations=None, description=None, comments=None, start_date=None,
+ end_date=None, restricted_status=None, alternate_code=None, historical_code=None,
+ data_availability=None, identifiers=None, operators=None, source_id=None)]
+
+ params['inv'] = obspy.Inventory(networks=network)
else:
if not invPath:
pass #if invPath is None
@@ -1926,11 +2251,30 @@ Returns
list/tuple - a list or tuple of the above objects, in the same order they are in the report_format list
"""
- #print statement
- #Check if results are good
- #Curve pass?
orig_args = locals().copy() #Get the initial arguments
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_results.keys():
+ if 'get_report' in hvsr_results['processing_parameters'].keys():
+ for k, v in hvsr_results['processing_parameters']['get_report'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(get_report).args[1:],
+ inspect.getfullargspec(get_report).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ report_format = orig_args['report_format']
+ plot_type = orig_args['plot_type']
+ export_path = orig_args['export_path']
+ return_results = orig_args['return_results']
+ csv_overwrite_opt = orig_args['csv_overwrite_opt']
+ no_output = orig_args['no_output']
+ verbose = orig_args['verbose']
+
+ hvsr_results['processing_parameters']['get_report'] = {}
+ for key, value in orig_args.items():
+ hvsr_results['processing_parameters']['get_report'][key] = value
+
if (verbose and isinstance(hvsr_results, HVSRBatch)) or (verbose and not hvsr_results['batch']):
if isinstance(hvsr_results, HVSRData) and hvsr_results['batch']:
pass
@@ -2000,8 +2344,8 @@ Returns
curvePass = curvTestsPassed > 2
#Peak Pass?
- peakTestsPassed = ( hvsr_results['BestPeak']['PassList']['PeakFreqClarityBelow'] +
- hvsr_results['BestPeak']['PassList']['PeakFreqClarityAbove']+
+ peakTestsPassed = ( hvsr_results['BestPeak']['PassList']['PeakProminenceBelow'] +
+ hvsr_results['BestPeak']['PassList']['PeakProminenceAbove']+
hvsr_results['BestPeak']['PassList']['PeakAmpClarity']+
hvsr_results['BestPeak']['PassList']['FreqStability']+
hvsr_results['BestPeak']['PassList']['PeakStability_FreqStD']+
@@ -2136,24 +2480,25 @@ Returns
report_string_list.append(internalSeparator)
report_string_list.append('')
+ justSize=34
#Print individual results
report_string_list.append('\tCurve Tests: {}/3 passed (3/3 needed)'.format(curvTestsPassed))
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Lw'][-1]} Length of processing windows: {hvsr_results['BestPeak']['Report']['Lw']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Nc'][-1]} Number of significant cycles: {hvsr_results['BestPeak']['Report']['Nc']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['σ_A(f)'][-1]} Low StDev. of H/V Curve over time: {hvsr_results['BestPeak']['Report']['σ_A(f)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Lw'][-1]}"+" Length of processing windows".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Lw']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Nc'][-1]}"+" Number of significant cycles".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Nc']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['σ_A(f)'][-1]}"+" Small H/V StDev over time".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['σ_A(f)']}")
report_string_list.append('')
report_string_list.append("\tPeak Tests: {}/6 passed (5/6 needed)".format(peakTestsPassed))
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f-)'][-1]} Clarity Below Peak Frequency: {hvsr_results['BestPeak']['Report']['A(f-)']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f+)'][-1]} Clarity Above Peak Frequency: {hvsr_results['BestPeak']['Report']['A(f+)']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A0'][-1]} Clarity of Peak Amplitude: {hvsr_results['BestPeak']['Report']['A0']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f-)'][-1]}"+" Peak is prominent below".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A(f-)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f+)'][-1]}"+" Peak is prominent above".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A(f+)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A0'][-1]}"+" Peak is large".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A0']}")
if hvsr_results['BestPeak']['PassList']['FreqStability']:
res = sprit_utils.check_mark()
else:
res = sprit_utils.x_mark()
- report_string_list.append(f"\t\t {res} Stability of Peak Freq. Over time: {hvsr_results['BestPeak']['Report']['P-'][:5]} and {hvsr_results['BestPeak']['Report']['P+'][:-1]} {res}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sf'][-1]} Stability of Peak (Freq. StDev): {hvsr_results['BestPeak']['Report']['Sf']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sa'][-1]} Stability of Peak (Amp. StDev): {hvsr_results['BestPeak']['Report']['Sa']}")
+ report_string_list.append(f"\t\t {res}"+ " Peak freq. is stable over time".ljust(justSize)+ f"{hvsr_results['BestPeak']['Report']['P-'][:5]} and {hvsr_results['BestPeak']['Report']['P+'][:-1]} {res}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sf'][-1]}"+" Stability of peak (Freq. StDev)".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Sf']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sa'][-1]}"+" Stability of peak (Amp. StDev)".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Sa']}")
report_string_list.append('')
report_string_list.append(f"Calculated using {hvsr_results['hvsr_df']['Use'].sum()}/{hvsr_results['hvsr_df']['Use'].count()} time windows".rjust(sepLen-1))
report_string_list.append(extSiteSeparator)
@@ -2177,7 +2522,7 @@ Returns
import pandas as pd
pdCols = ['Site Name', 'Acq_Date', 'Longitude', 'Latitide', 'Elevation', 'PeakFrequency',
'WindowLengthFreq.','SignificantCycles','LowCurveStDevOverTime',
- 'PeakFreqClarityBelow','PeakFreqClarityAbove','PeakAmpClarity','FreqStability', 'PeakStability_FreqStD','PeakStability_AmpStD', 'PeakPasses']
+ 'PeakProminenceBelow','PeakProminenceAbove','PeakAmpClarity','FreqStability', 'PeakStability_FreqStD','PeakStability_AmpStD', 'PeakPasses']
d = hvsr_results
criteriaList = []
for p in hvsr_results['BestPeak']["PassList"]:
@@ -2364,8 +2709,39 @@ Returns
return dataIN
+
+def import_settings(settings_import_path, settings_import_type='instrument', verbose=False)
+
+
+
+
+Expand source code
+
+def import_settings(settings_import_path, settings_import_type='instrument', verbose=False):
+
+ allList = ['all', ':', 'both', 'any']
+ if settings_import_type.lower() not in allList:
+ # if just a single settings dict is desired
+ with open(settings_import_path, 'r') as f:
+ settingsDict = json.load(f)
+ else:
+ # Either a directory or list
+ if isinstance(settings_import_path, (list, tuple)):
+ for setPath in settings_import_path:
+ pass
+ else:
+ settings_import_path = sprit_utils.checkifpath(settings_import_path)
+ if not settings_import_path.is_dir():
+ raise RuntimeError(f'settings_import_type={settings_import_type}, but settings_import_path is not list/tuple or filepath to directory')
+ else:
+ instFile = settings_import_path.glob('*.inst')
+ procFile = settings_import_path.glob('*.proc')
+ return settingsDict
+
+
-def input_params(datapath, site='HVSR Site', network='AM', station='RAC84', loc='00', channels=['EHZ', 'EHN', 'EHE'], acq_date='2023-10-19', starttime='00:00:00.00', endtime='23:59:59.999999', tzone='UTC', xcoord=-88.2290526, ycoord=40.1012122, elevation=755, input_crs='EPSG:4326', output_crs='EPSG:4326', elev_unit='feet', depth=0, instrument='Raspberry Shake', metapath='', hvsr_band=[0.4, 40], peak_freq_range=[0.4, 40], verbose=False)
+def input_params(datapath, site='HVSR Site', network='AM', station='RAC84', loc='00', channels=['EHZ', 'EHN', 'EHE'], acq_date='2023-10-30', starttime='00:00:00.00', endtime='23:59:59.999999', tzone='UTC', xcoord=-88.2290526, ycoord=40.1012122, elevation=755, input_crs='EPSG:4326', output_crs='EPSG:4326', elev_unit='feet', depth=0, instrument='Raspberry Shake', metapath=None, hvsr_band=[0.4, 40], peak_freq_range=[0.4, 40], processing_parameters={}, verbose=False)
Function for designating input parameters for reading in and processing data
@@ -2409,12 +2785,19 @@ Parameters
Depth of seismometer. Not currently used, but will likely be used in the future.
instrument : str or list {'Raspberry Shake')Instrument from which the data was acquired.
-metapath : str or pathlib.Path object, default=''Filepath of metadata, in format supported by obspy.read_inventory. If default value of '', will read from resources folder of repository (only supported for Raspberry Shake).
+metapath : str or pathlib.Path object, default=NoneFilepath of metadata, in format supported by obspy.read_inventory. If default value of None, will read from resources folder of repository (only supported for Raspberry Shake).
hvsr_band : list, default=[0.4, 40]Two-element list containing low and high "corner" frequencies (in Hz) for processing. This can specified again later.
peak_freq_range : list or tuple, default=[0.4, 40]Two-element list or tuple containing low and high frequencies (in Hz) that are used to check for HVSR Peaks. This can be a tigher range than hvsr_band, but if larger, it will still only use the hvsr_band range.
+processing_parameters={} : dict or filepath, default={}
+If filepath, should point to a .proc json file with processing parameters (i.e, an output from sprit.export_settings()).
+Note that this only applies to parameters for the functions: 'fetch_data', 'remove_noise', 'generate_ppsds', 'process_hvsr', 'check_peaks', and 'get_report.'
+If dictionary, dictionary containing nested dictionaries of function names as they key, and the parameter names/values as key/value pairs for each key.
+If a function name is not present, or if a parameter name is not present, default values will be used.
+For example:
+{ 'fetch_data' : {'source':'batch', 'trim_dir':"/path/to/trimmed/data", 'export_format':'mseed', 'detrend':'spline', 'plot_input_stream':True, 'verbose':False, kwargs:{'kwargskey':'kwargsvalue'}} }verbose : bool, default=FalseWhether to print output and results to terminal
@@ -2445,9 +2828,10 @@ Returns
elev_unit = 'feet',
depth = 0,
instrument = 'Raspberry Shake',
- metapath = '',
+ metapath = None,
hvsr_band = [0.4, 40],
peak_freq_range=[0.4, 40],
+ processing_parameters={},
verbose=False
):
"""Function for designating input parameters for reading in and processing data
@@ -2492,12 +2876,19 @@ Returns
Depth of seismometer. Not currently used, but will likely be used in the future.
instrument : str or list {'Raspberry Shake')
Instrument from which the data was acquired.
- metapath : str or pathlib.Path object, default=''
- Filepath of metadata, in format supported by obspy.read_inventory. If default value of '', will read from resources folder of repository (only supported for Raspberry Shake).
+ metapath : str or pathlib.Path object, default=None
+ Filepath of metadata, in format supported by obspy.read_inventory. If default value of None, will read from resources folder of repository (only supported for Raspberry Shake).
hvsr_band : list, default=[0.4, 40]
Two-element list containing low and high "corner" frequencies (in Hz) for processing. This can specified again later.
peak_freq_range : list or tuple, default=[0.4, 40]
Two-element list or tuple containing low and high frequencies (in Hz) that are used to check for HVSR Peaks. This can be a tigher range than hvsr_band, but if larger, it will still only use the hvsr_band range.
+ processing_parameters={} : dict or filepath, default={}
+ If filepath, should point to a .proc json file with processing parameters (i.e, an output from sprit.export_settings()).
+ Note that this only applies to parameters for the functions: 'fetch_data', 'remove_noise', 'generate_ppsds', 'process_hvsr', 'check_peaks', and 'get_report.'
+ If dictionary, dictionary containing nested dictionaries of function names as they key, and the parameter names/values as key/value pairs for each key.
+ If a function name is not present, or if a parameter name is not present, default values will be used.
+ For example:
+ `{ 'fetch_data' : {'source':'batch', 'trim_dir':"/path/to/trimmed/data", 'export_format':'mseed', 'detrend':'spline', 'plot_input_stream':True, 'verbose':False, kwargs:{'kwargskey':'kwargsvalue'}} }`
verbose : bool, default=False
Whether to print output and results to terminal
@@ -2509,30 +2900,6 @@ Returns
"""
orig_args = locals().copy() #Get the initial arguments
- #Declare obspy here instead of at top of file for (for example) colab, where obspy first needs to be installed on environment
- global obspy
- import obspy
- if verbose:
- print('Gathering input parameters (input_params())')
- for key, value in orig_args.items():
- print('\t {}={}'.format(key, value))
- print()
-
- #Make Sure metapath is all good
- if not pathlib.Path(metapath).exists() or metapath=='':
- if metapath == '':
- pass
- else:
- print('Specified metadata file cannot be read!')
- repoDir = pathlib.Path(os.path.dirname(__file__))
- metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv5plus_metadata.inv'))
- #print('Using default metadata file for Raspberry Shake v.7 distributed with package')
- else:
- if isinstance(metapath, pathlib.PurePath):
- metapath = metapath.as_posix()
- else:
- metapath = pathlib.Path(metapath).as_posix()
-
#Reformat times
if type(acq_date) is datetime.datetime:
date = str(acq_date.date())
@@ -2615,12 +2982,6 @@ Returns
acq_date = datetime.date(year=int(date.split('-')[0]), month=int(date.split('-')[1]), day=int(date.split('-')[2]))
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
- #Raspberry shake stationxml is in the resources folder, double check we have right path
- if instrument.lower() in raspShakeInstNameList:
- if metapath == r'resources/rs3dv7_metadata.inv':
- metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv7_metadata.inv'))
- #metapath = pathlib.Path(os.path.realpath(__file__)).parent.joinpath('/resources/rs3dv7_metadata.inv')
-
if output_crs is None:
output_crs='EPSG:4326'
@@ -2641,6 +3002,43 @@ Returns
'ProcessingStatus':{'InputStatus':True, 'OverallStatus':True}
}
+ #Replace any default parameter settings with those from json file of interest, potentially
+ instrument_settings_dict = {}
+ if pathlib.Path(instrument).exists():
+ instrument_settings = import_settings(settings_import_path=instrument, settings_import_type='instrument', verbose=verbose)
+ input_params_args = inspect.getfullargspec(input_params).args
+ input_params_args.append('net')
+ input_params_args.append('sta')
+ for k, settings_value in instrument_settings.items():
+ if k in input_params_args:
+ instrument_settings_dict[k] = settings_value
+ inputParamDict['instrument_settings'] = inputParamDict['instrument']
+ inputParamDict.update(instrument_settings_dict)
+
+ if instrument.lower() in raspShakeInstNameList:
+ if metapath is None:
+ metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv5plus_metadata.inv')).as_posix()
+ inputParamDict['metapath'] = metapath
+ #metapath = pathlib.Path(os.path.realpath(__file__)).parent.joinpath('/resources/rs3dv7_metadata.inv')
+
+ for settingName in instrument_settings_dict.keys():
+ if settingName in inputParamDict.keys():
+ inputParamDict[settingName] = instrument_settings_dict[settingName]
+
+ #Declare obspy here instead of at top of file for (for example) colab, where obspy first needs to be installed on environment
+ if verbose:
+ print('Gathering input parameters (input_params())')
+ for key, value in inputParamDict.items():
+ print('\t {}={}'.format(key, value))
+ print()
+
+ if isinstance(processing_parameters, dict):
+ inputParamDict['processing_parameters'] = processing_parameters
+ else:
+ processing_parameters = sprit_utils.checkifpath(processing_parameters)
+ inputParamDict['processing_parameters'] = import_settings(processing_parameters, settings_import_type='processing', verbose=verbose)
+
+ #Format everything nicely
params = sprit_utils.make_it_classy(inputParamDict)
params['ProcessingStatus']['InputParams'] = True
params = _check_processing_status(params)
@@ -2658,6 +3056,16 @@ Returns
def make_it_classy(input_data, verbose=False):
if isinstance(input_data, (sprit_hvsr.HVSRData, sprit_hvsr.HVSRBatch)):
+ for k, v in input_data.items():
+ if k=='input_params':
+ for kin in input_data['input_params'].keys():
+ if kin not in input_data.keys():
+ input_data[kin] = input_data['input_params'][kin]
+ if k=='params':
+ for kin in input_data['params'].keys():
+ print(kin)
+ if kin not in input_data.keys():
+ input_data[kin] = input_data['params'][kin]
output_class = input_data
else:
output_class = sprit_hvsr.HVSRData(input_data)
@@ -2667,7 +3075,7 @@ Returns
-def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, xtype='freq', fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True, **kwargs)
+def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True, **kwargs)
Function to plot HVSR data
@@ -2676,29 +3084,25 @@ Parameters
hvsr_data : dict
Dictionary containing output from process_hvsr function
-plot_type : str='HVSR' or list
-plot_type : str or list, default = 'HVSR ann p C+ ann p SPEC'The plot_type of plot(s) to plot. If list, will plot all plots listed
-'HVSR' : Standard HVSR plot, including standard deviation
-- '[HVSR] p' : HVSR plot with BestPeaks shown
-- '[HVSR] p' : HVSR plot with best picked peak shown
-
-- '[HVSR] p all' : HVSR plot with all picked peaks shown
-
-- '[HVSR] p t' : HVSR plot with peaks from all time steps in background
-
-- '[HVSR p* ann] : Annotates plot with peaks
-- '[HVSR] -s' : HVSR plots don't show standard deviation
-- '[HVSR] t' : HVSR plot with individual hv curves for each time step shown
-- '[HVSR] c' : HVSR plot with each components' spectra. Recommended to do this last (or just before 'specgram'), since doing c+ can make the component chart its own chart
-'Specgram' : Combined spectrogram of all components
-- '[spec]' : basic spectrogram plot of H/V curve
+- 'HVSR' - Standard HVSR plot, including standard deviation. Options are included below:
+- 'p' shows a vertical dotted line at frequency of the "best" peak
+- 'ann' annotates the frequency value of of the "best" peak
+- 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+- 't' shows the H/V curve for all time windows
+-'tp' shows all the peaks from the H/V curves of all the time windows
+- 'COMP' - plot of the PPSD curves for each individual component ("C" also works)
+- '+' (as a suffix in 'C+' or 'COMP+') plots C on a plot separate from HVSR (C+ is default, but without + will plot on the same plot as HVSR)
+- 'p' shows a vertical dotted line at frequency of the "best" peak
+- 'ann' annotates the frequency value of of the "best" peak
+- 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+- 't' shows the H/V curve for all time windows
+- 'SPEC' - spectrogram style plot of the H/V curve over time
+- 'p' shows a horizontal dotted line at the frequency of the "best" peak
+- 'ann' annotates the frequency value of the "best" peak
use_subplots : bool, default = TrueWhether to output the plots as subplots (True) or as separate plots (False)
-xtype : str, default = 'freq'String for what to use, between frequency or period
-For frequency, the following are accepted (case does not matter): 'f', 'Hz', 'freq', 'frequency'
-For period, the following are accepted (case does not matter): 'p', 'T', 's', 'sec', 'second', 'per', 'period'
fig : matplotlib.Figure, default = NoneIf not None, matplotlib figure on which plot is plotted
ax : matplotlib.Axis, default = NoneReturns
Expand source code
-def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, xtype='freq', fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True,**kwargs):
+def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True,**kwargs):
"""Function to plot HVSR data
Parameters
----------
hvsr_data : dict
Dictionary containing output from process_hvsr function
- plot_type : str='HVSR' or list
+ plot_type : str or list, default = 'HVSR ann p C+ ann p SPEC'
The plot_type of plot(s) to plot. If list, will plot all plots listed
- 'HVSR' : Standard HVSR plot, including standard deviation
- - '[HVSR] p' : HVSR plot with BestPeaks shown
- - '[HVSR] p' : HVSR plot with best picked peak shown
- - '[HVSR] p* all' : HVSR plot with all picked peaks shown
- - '[HVSR] p* t' : HVSR plot with peaks from all time steps in background
- - '[HVSR p* ann] : Annotates plot with peaks
- - '[HVSR] -s' : HVSR plots don't show standard deviation
- - '[HVSR] t' : HVSR plot with individual hv curves for each time step shown
- - '[HVSR] c' : HVSR plot with each components' spectra. Recommended to do this last (or just before 'specgram'), since doing c+ can make the component chart its own chart
- 'Specgram' : Combined spectrogram of all components
- - '[spec]' : basic spectrogram plot of H/V curve
+ - 'HVSR' - Standard HVSR plot, including standard deviation. Options are included below:
+ - 'p' shows a vertical dotted line at frequency of the "best" peak
+ - 'ann' annotates the frequency value of of the "best" peak
+ - 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+ - 't' shows the H/V curve for all time windows
+ -'tp' shows all the peaks from the H/V curves of all the time windows
+ - 'COMP' - plot of the PPSD curves for each individual component ("C" also works)
+ - '+' (as a suffix in 'C+' or 'COMP+') plots C on a plot separate from HVSR (C+ is default, but without + will plot on the same plot as HVSR)
+ - 'p' shows a vertical dotted line at frequency of the "best" peak
+ - 'ann' annotates the frequency value of of the "best" peak
+ - 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+ - 't' shows the H/V curve for all time windows
+ - 'SPEC' - spectrogram style plot of the H/V curve over time
+ - 'p' shows a horizontal dotted line at the frequency of the "best" peak
+ - 'ann' annotates the frequency value of the "best" peak
use_subplots : bool, default = True
Whether to output the plots as subplots (True) or as separate plots (False)
- xtype : str, default = 'freq'
- String for what to use, between frequency or period
- For frequency, the following are accepted (case does not matter): 'f', 'Hz', 'freq', 'frequency'
- For period, the following are accepted (case does not matter): 'p', 'T', 's', 'sec', 'second', 'per', 'period'
fig : matplotlib.Figure, default = None
If not None, matplotlib figure on which plot is plotted
ax : matplotlib.Axis, default = None
@@ -2873,12 +3277,16 @@ Returns
fig, axis = plt.subplots()
if p == 'hvsr':
- _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax)
+ _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax, **kwargs)
elif p=='comp':
plotComponents[0] = plotComponents[0][:-1]
- _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax)
+ _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax, **kwargs)
elif p=='spec':
- _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False)
+ plottypeKwargs = {}
+ for c in plotComponents:
+ plottypeKwargs[c] = True
+ kwargs.update(plottypeKwargs)
+ _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False, **kwargs)
else:
warnings.warn('Plot type {p} not recognized', UserWarning)
@@ -2998,6 +3406,27 @@ Returns
"""
orig_args = locals().copy() #Get the initial arguments
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'process_hvsr' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['process_hvsr'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(process_hvsr).args[1:],
+ inspect.getfullargspec(process_hvsr).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ method = orig_args['method']
+ smooth = orig_args['smooth']
+ freq_smooth = orig_args['freq_smooth']
+ f_smooth_width = orig_args['f_smooth_width']
+ resample = orig_args['resample']
+ outlier_curve_std = orig_args['outlier_curve_std']
+ verbose = orig_args['verbose']
+
+
+
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
pass
@@ -3062,10 +3491,8 @@ Returns
xValMin = min(ppsds[k]['period_bin_centers'])
xValMax = max(ppsds[k]['period_bin_centers'])
-
#Resample period bin values
x_periods[k] = np.logspace(np.log10(xValMin), np.log10(xValMax), num=resample)
-
if smooth or type(smooth) is int:
if smooth:
smooth = 51 #Default smoothing window
@@ -3093,7 +3520,6 @@ Returns
#Get average psd value across time for each channel (used to calc main H/V curve)
psdValsTAvg[k] = np.nanmean(np.array(psdRaw[k]), axis=0)
x_freqs[k] = np.divide(np.ones_like(x_periods[k]), x_periods[k])
-
stDev[k] = np.std(psdRaw[k], axis=0)
stDevValsM[k] = np.array(psdValsTAvg[k] - stDev[k])
stDevValsP[k] = np.array(psdValsTAvg[k] + stDev[k])
@@ -3111,9 +3537,8 @@ Returns
anyK = list(x_freqs.keys())[0]
hvsr_curve, _ = __get_hvsr_curve(x=x_freqs[anyK], psd=psdValsTAvg, method=methodInt, hvsr_data=hvsr_data, verbose=verbose)
origPPSD = hvsr_data['ppsds_obspy'].copy()
-
#Add some other variables to our output dictionary
- hvsr_data = {'input_params':hvsr_data,
+ hvsr_dataUpdate = {'input_params':hvsr_data,
'x_freqs':x_freqs,
'hvsr_curve':hvsr_curve,
'x_period':x_periods,
@@ -3130,7 +3555,7 @@ Returns
'hvsr_df':hvsr_data['hvsr_df']
}
- hvsr_out = HVSRData(hvsr_data)
+ hvsr_out = HVSRData(hvsr_dataUpdate)
#This is if manual editing was used (should probably be updated at some point to just use masks)
if 'xwindows_out' in hvsr_data.keys():
@@ -3204,7 +3629,6 @@ Returns
bool_col='Use'
eval_col='HV_Curves'
- testCol = hvsr_out['hvsr_df'].loc[hvsr_out['hvsr_df'][bool_col], eval_col].apply(np.nanstd).gt((avg_stdT + (std_stdT * outlier_curve_std)))
low_std_val = avg_stdT - (std_stdT * outlier_curve_std)
hi_std_val = avg_stdT + (std_stdT * outlier_curve_std)
@@ -3258,16 +3682,15 @@ Returns
hvsr_out = __gethvsrparams(hvsr_out)
#Include the original obspy stream in the output
- #print(hvsr_data.keys())
- #print(type(hvsr_data))
- #print(hvsr_data['input_params'].keys())
- hvsr_out['input_stream'] = hvsr_data['input_params']['input_stream'] #input_stream
-
+ hvsr_out['input_stream'] = hvsr_dataUpdate['input_params']['input_stream'] #input_stream
hvsr_out = sprit_utils.make_it_classy(hvsr_out)
-
hvsr_out['ProcessingStatus']['HVStatus'] = True
hvsr_out = _check_processing_status(hvsr_out)
+ hvsr_data['processing_parameters']['process_hvsr'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['process_hvsr'][key] = value
+
return hvsr_out
@@ -3449,7 +3872,30 @@ Returns
output : dict
Dictionary similar to hvsr_data, but containing modified data with 'noise' removed
"""
- orig_args = locals().copy() #Get the initial arguments
+ #Get intput paramaters
+ orig_args = locals().copy()
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'remove_noise' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['remove_noise'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(remove_noise).args[1:],
+ inspect.getfullargspec(remove_noise).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ remove_method = orig_args['remove_method']
+ sat_percent = orig_args['sat_percent']
+ noise_percent = orig_args['noise_percent']
+ sta = orig_args['sta']
+ lta = orig_args['lta']
+ stalta_thresh = orig_args['stalta_thresh']
+ warmup_time = orig_args['warmup_time']
+ cooldown_time = orig_args['cooldown_time']
+ min_win_size = orig_args['min_win_size']
+ remove_raw_noise = orig_args['remove_raw_noise']
+ verbose = orig_args['verbose']
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
@@ -3566,12 +4012,22 @@ Returns
#Add output
if isinstance(output, (HVSRData, dict)):
- output['stream'] = outStream
+ if isinstance(outStream, (obspy.Stream, obspy.Trace)):
+ output['stream'] = outStream
+ else:
+ output['stream'] = outStream['stream']
output['input_stream'] = hvsr_data['input_stream']
+
+ if 'processing_parameters' not in output.keys():
+ output['processing_parameters'] = {}
+ output['processing_parameters']['remove_noise'] = {}
+ for key, value in orig_args.items():
+ output['processing_parameters']['remove_noise'][key] = value
+
output['ProcessingStatus']['RemoveNoiseStatus'] = True
output = _check_processing_status(output)
- if 'hvsr_df' in output.keys():
+ if 'hvsr_df' in output.keys() or ('params' in output.keys() and 'hvsr_df' in output['params'].keys())or ('input_params' in output.keys() and 'hvsr_df' in output['input_params'].keys()):
hvsrDF = output['hvsr_df']
outStream = output['stream'].split()
@@ -3585,20 +4041,27 @@ Returns
if trEndTime < trStartTime and comp_end==comp_start:
gap = [trEndTime,trStartTime]
+
output['hvsr_df']['Use'] = (hvsrDF['TimesProcessed_Obspy'].gt(gap[0]) & hvsrDF['TimesProcessed_Obspy'].gt(gap[1]) )| \
(hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[0]) & hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[1]))# | \
+ output['hvsr_df']['Use'] = output['hvsr_df']['Use'].astype(bool)
trEndTime = trace.stats.endtime
outStream.merge()
- output['stream'] = outStream
+ output['stream'] = outStream
+
elif isinstance(hvsr_data, obspy.core.stream.Stream) or isinstance(hvsr_data, obspy.core.trace.Trace):
output = outStream
else:
warnings.warn(f"Output of type {type(output)} for this function will likely result in errors in other processing steps. Returning hvsr_data data.")
return hvsr_data
+
-
+
+ output = sprit_utils.make_it_classy(output)
+ if 'xwindows_out' not in output.keys():
+ output['xwindows_out'] = []
return output
@@ -3697,8 +4160,14 @@ Raises
RuntimeError
If the data being processed is a single file, an error will be raised if generate_ppsds() does not work correctly. No errors are raised for remove_noise() errors (since that is an optional step) and the process_hvsr() step (since that is the last processing step) .
"""
+ if 'hvsr_band' not in kwargs.keys():
+ kwargs['hvsr_band'] = inspect.signature(input_params).parameters['hvsr_band'].default
+ if 'peak_freq_range' not in kwargs.keys():
+ kwargs['peak_freq_range'] = inspect.signature(input_params).parameters['peak_freq_range'].default
+
#Get the input parameters
- input_params_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in input_params.__code__.co_varnames}
+ input_params_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(input_params).parameters.keys())}
+
try:
params = input_params(datapath=datapath, verbose=verbose, **input_params_kwargs)
except:
@@ -3711,15 +4180,14 @@ Raises
#Fetch Data
try:
- fetch_data_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in fetch_data.__code__.co_varnames}
+ fetch_data_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(fetch_data).parameters.keys())}
dataIN = fetch_data(params=params, source=source, verbose=verbose, **fetch_data_kwargs)
except:
#Even if batch, this is reading in data for all sites so we want to raise error, not just warn
raise RuntimeError('Data not read correctly, see sprit.fetch_data() function and parameters for more details.')
-
#Remove Noise
try:
- remove_noise_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in remove_noise.__code__.co_varnames}
+ remove_noise_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(remove_noise).parameters.keys())}
data_noiseRemoved = remove_noise(hvsr_data=dataIN, verbose=verbose,**remove_noise_kwargs)
except:
data_noiseRemoved = dataIN
@@ -3743,8 +4211,8 @@ Raises
#Generate PPSDs
try:
- generate_ppsds_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in generate_ppsds.__code__.co_varnames}
- PPSDkwargs = {k: v for k, v in locals()['kwargs'].items() if k in PPSD.__init__.__code__.co_varnames}
+ generate_ppsds_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(generate_ppsds).parameters.keys())}
+ PPSDkwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(PPSD).parameters.keys())}
generate_ppsds_kwargs.update(PPSDkwargs)
ppsd_data = generate_ppsds(hvsr_data=data_noiseRemoved, verbose=verbose,**generate_ppsds_kwargs)
except Exception as e:
@@ -3770,7 +4238,7 @@ Raises
#Process HVSR Curves
try:
- process_hvsr_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in process_hvsr.__code__.co_varnames}
+ process_hvsr_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(process_hvsr).parameters.keys())}
hvsr_results = process_hvsr(hvsr_data=ppsd_data, verbose=verbose,**process_hvsr_kwargs)
except Exception as e:
traceback.print_exception(sys.exc_info()[1])
@@ -3800,10 +4268,10 @@ Raises
#Final post-processing/reporting
#Check peaks
- check_peaks_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in check_peaks.__code__.co_varnames}
+ check_peaks_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(check_peaks).parameters.keys())}
hvsr_results = check_peaks(hvsr_data=hvsr_results, verbose=verbose, **check_peaks_kwargs)
- get_report_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in get_report.__code__.co_varnames}
+ get_report_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(get_report).parameters.keys())}
get_report(hvsr_results=hvsr_results, verbose=verbose, **get_report_kwargs)
if verbose:
@@ -3822,8 +4290,9 @@ Raises
#We do not need to plot another report if already plotted
pass
else:
- hvplot_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in plot_hvsr.__code__.co_varnames}
- hvsr_results['HV_Plot'] = plot_hvsr(hvsr_results, **hvplot_kwargs)
+ #hvplot_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in plot_hvsr.__code__.co_varnames}
+ #hvsr_results['HV_Plot'] = plot_hvsr(hvsr_results, return_fig=True, show=False, close_figs=True)
+ pass
else:
pass
@@ -4285,7 +4754,9 @@ Returns
Parameters
----------
export_path : filepath, default=True
- Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes). By default True. If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
+ Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes).
+ By default True.
+ If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
ext : str, optional
The extension to use for the output, by default 'hvsr'. This is still a pickle file that can be read with pickle.load(), but will have .hvsr extension.
"""
@@ -4607,7 +5078,9 @@ Parameters
Parameters
export_path : filepath, default=True- Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes). By default True. If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
+- Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes).
+By default True.
+If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
ext : str, optional- The extension to use for the output, by default 'hvsr'. This is still a pickle file that can be read with pickle.load(), but will have .hvsr extension.
@@ -4621,7 +5094,9 @@ Parameters
Parameters
----------
export_path : filepath, default=True
- Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes). By default True. If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
+ Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes).
+ By default True.
+ If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
ext : str, optional
The extension to use for the output, by default 'hvsr'. This is still a pickle file that can be read with pickle.load(), but will have .hvsr extension.
"""
@@ -4781,6 +5256,7 @@ Index
check_xvaluescheckifpathexport_dataexport_settingsfetch_dataformat_timegenerate_ppsdsIndex
guihas_required_channelsimport_dataimport_settingsinput_paramsmake_it_classyplot_hvsrModule sprit.sprit_gui
import datetime
import functools
import linecache
+import json
import os
import pathlib
import pkg_resources
@@ -63,6 +64,14 @@ Module sprit.sprit_gui
pass
global spritApp
+global current_theme_name
+
+resource_dir = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/'))
+settings_dir = resource_dir.joinpath('settings')
+gui_theme_file = settings_dir.joinpath('gui_theme.json')
+with open(gui_theme_file, 'r') as f:
+ curr_gui_dict = json.load(f)
+current_theme_name = curr_gui_dict['theme_name']
#Decorator that catches errors and warnings (to be modified later for gui)
def catch_errors(func):
@@ -180,10 +189,11 @@ Module sprit.sprit_gui
self.darkthemepath = pathlib.Path(pkg_resources.resource_filename(__name__, "resources/themes/forest-dark.tcl"))
self.lightthemepath = pathlib.Path(pkg_resources.resource_filename(__name__, "resources/themes/forest-light.tcl"))
+
+
# Create the style object
self.style = ttk.Style(master)
- self.master.tk.call('source', self.lightthemepath)
- #self.style.theme_use('default')
+ #
#self.style.theme_use('forest-light')
self.create_menubar()
@@ -193,6 +203,13 @@ Module sprit.sprit_gui
self.master.columnconfigure(0, weight=1)
+ if 'forest' in current_theme_name:
+ if 'light' in current_theme_name:
+ self.master.tk.call('source', self.lightthemepath)
+ else:
+ self.master.tk.call('source', self.darkthemepath)
+ else:
+ self.style.theme_use(current_theme_name)
# Create the dark theme
#self.style.theme_create("dark", parent="alt", settings={
# "TLabel": {"configure": {"background": "black", "foreground": "white"}},
@@ -240,7 +257,33 @@ Module sprit.sprit_gui
def on_theme_select():
# Set the theme based on the selected value
self.style = ttk.Style()
-
+
+ #Update the theme file so the new theme opens on reboot
+ prev_theme = curr_gui_dict['theme_name']
+ curr_gui_dict['theme_name'] = self.theme_var.get()
+ with open(gui_theme_file, 'w') as f:
+ json.dump(curr_gui_dict, f)
+
+ def apply_theme():
+ if 'forest' in self.theme_var.get():
+ if self.theme_var.get()=='forest-dark' and 'forest-dark' not in self.style.theme_names():
+ self.master.tk.call('source', self.darkthemepath)
+ elif self.theme_var.get()=='forest-light' and 'forest-light' not in self.style.theme_names():
+ self.master.tk.call('source', self.lightthemepath)
+ self.master.tk.call("ttk::style", "theme", "use", self.theme_var.get())
+
+ if curr_gui_dict['theme_name']=='forest-light' or curr_gui_dict['theme_name'] == 'forest-dark':
+ do_reboot = messagebox.askyesno('App Restart Required',
+ f"It is recommended to restart the SpRIT GUI at this time to apply this theme. If not, you may continue but theme errors may occur. Click No to retain current theme ({prev_theme}) \nReboot now?",
+ )
+ print(do_reboot)
+ if do_reboot:
+ reboot_app()
+ else:
+ self.theme_var.set(prev_theme)
+ else:
+ apply_theme()
+
"""An attempt to get the backgrounds right
def apply_to_all_children(widget, func):
Recursively apply a function to all child widgets of a given widget
@@ -258,12 +301,7 @@ Module sprit.sprit_gui
apply_to_all_children(self.master, change_background_color)
"""
- if 'forest' in self.theme_var.get():
- if self.theme_var.get()=='forest-dark' and 'forest-dark' not in self.style.theme_names():
- self.master.tk.call('source', self.darkthemepath)
- elif self.theme_var.get()=='forest-light' and 'forest-light' not in self.style.theme_names():
- self.master.tk.call('source', self.lightthemepath)
- self.master.tk.call("ttk::style", "theme", "use", self.theme_var.get())
+
#self.master.tk.call("ttk::setTheme", self.theme_var.get())
#self.style.theme_use(self.theme_var.get())
@@ -279,7 +317,7 @@ Module sprit.sprit_gui
filepath = filedialog.asksaveasfilename()
self.theme_menu = tk.Menu(self.menubar, tearoff=0)
- self.theme_var = tk.StringVar(value="Default")
+ self.theme_var = tk.StringVar(value=current_theme_name)
self.theme_menu.add_radiobutton(label="Default", variable=self.theme_var, value="default", command=on_theme_select)
self.theme_menu.add_radiobutton(label="Clam", variable=self.theme_var, value="clam", command=on_theme_select)
self.theme_menu.add_radiobutton(label="Alt", variable=self.theme_var, value="alt", command=on_theme_select)
@@ -524,8 +562,8 @@ Module sprit.sprit_gui
self.peakTest6ResultText.configure(text=hvsr_results['BestPeak']['Report']['Sa'][:-1])
self.peakTest6Result.configure(text=hvsr_results['BestPeak']['Report']['Sa'][-1])
- peakPass = (hvsr_results['BestPeak']['PassList']['PeakFreqClarityBelow'] +
- hvsr_results['BestPeak']['PassList']['PeakFreqClarityAbove']+
+ peakPass = (hvsr_results['BestPeak']['PassList']['PeakProminenceBelow'] +
+ hvsr_results['BestPeak']['PassList']['PeakProminenceAbove']+
hvsr_results['BestPeak']['PassList']['PeakAmpClarity']+
hvsr_results['BestPeak']['PassList']['FreqStability']+
hvsr_results['BestPeak']['PassList']['PeakStability_FreqStD']+
@@ -3065,6 +3103,13 @@ Module sprit.sprit_gui
root.destroy()
exit()
+def reboot_app():
+ """Restarts the current program.
+ Note: this function does not return. Any cleanup action (like
+ saving data) must be done before calling this function."""
+ python = sys.executable
+ os.execl(python, python, * sys.argv)
+
if __name__ == "__main__":
can_gui = sprit_utils.check_gui_requirements()
@@ -3226,6 +3271,25 @@ Functions
exit()
+
+def reboot_app()
+
+Restarts the current program.
+Note: this function does not return. Any cleanup action (like
+saving data) must be done before calling this function.
+
+
+Expand source code
+
+def reboot_app():
+ """Restarts the current program.
+ Note: this function does not return. Any cleanup action (like
+ saving data) must be done before calling this function."""
+ python = sys.executable
+ os.execl(python, python, * sys.argv)
+
+
@@ -3251,10 +3315,11 @@ Classes
self.darkthemepath = pathlib.Path(pkg_resources.resource_filename(__name__, "resources/themes/forest-dark.tcl"))
self.lightthemepath = pathlib.Path(pkg_resources.resource_filename(__name__, "resources/themes/forest-light.tcl"))
+
+
# Create the style object
self.style = ttk.Style(master)
- self.master.tk.call('source', self.lightthemepath)
- #self.style.theme_use('default')
+ #
#self.style.theme_use('forest-light')
self.create_menubar()
@@ -3264,6 +3329,13 @@ Classes
self.master.columnconfigure(0, weight=1)
+ if 'forest' in current_theme_name:
+ if 'light' in current_theme_name:
+ self.master.tk.call('source', self.lightthemepath)
+ else:
+ self.master.tk.call('source', self.darkthemepath)
+ else:
+ self.style.theme_use(current_theme_name)
# Create the dark theme
#self.style.theme_create("dark", parent="alt", settings={
# "TLabel": {"configure": {"background": "black", "foreground": "white"}},
@@ -3311,7 +3383,33 @@ Classes
def on_theme_select():
# Set the theme based on the selected value
self.style = ttk.Style()
-
+
+ #Update the theme file so the new theme opens on reboot
+ prev_theme = curr_gui_dict['theme_name']
+ curr_gui_dict['theme_name'] = self.theme_var.get()
+ with open(gui_theme_file, 'w') as f:
+ json.dump(curr_gui_dict, f)
+
+ def apply_theme():
+ if 'forest' in self.theme_var.get():
+ if self.theme_var.get()=='forest-dark' and 'forest-dark' not in self.style.theme_names():
+ self.master.tk.call('source', self.darkthemepath)
+ elif self.theme_var.get()=='forest-light' and 'forest-light' not in self.style.theme_names():
+ self.master.tk.call('source', self.lightthemepath)
+ self.master.tk.call("ttk::style", "theme", "use", self.theme_var.get())
+
+ if curr_gui_dict['theme_name']=='forest-light' or curr_gui_dict['theme_name'] == 'forest-dark':
+ do_reboot = messagebox.askyesno('App Restart Required',
+ f"It is recommended to restart the SpRIT GUI at this time to apply this theme. If not, you may continue but theme errors may occur. Click No to retain current theme ({prev_theme}) \nReboot now?",
+ )
+ print(do_reboot)
+ if do_reboot:
+ reboot_app()
+ else:
+ self.theme_var.set(prev_theme)
+ else:
+ apply_theme()
+
"""An attempt to get the backgrounds right
def apply_to_all_children(widget, func):
Recursively apply a function to all child widgets of a given widget
@@ -3329,12 +3427,7 @@ Classes
apply_to_all_children(self.master, change_background_color)
"""
- if 'forest' in self.theme_var.get():
- if self.theme_var.get()=='forest-dark' and 'forest-dark' not in self.style.theme_names():
- self.master.tk.call('source', self.darkthemepath)
- elif self.theme_var.get()=='forest-light' and 'forest-light' not in self.style.theme_names():
- self.master.tk.call('source', self.lightthemepath)
- self.master.tk.call("ttk::style", "theme", "use", self.theme_var.get())
+
#self.master.tk.call("ttk::setTheme", self.theme_var.get())
#self.style.theme_use(self.theme_var.get())
@@ -3350,7 +3443,7 @@ Classes
filepath = filedialog.asksaveasfilename()
self.theme_menu = tk.Menu(self.menubar, tearoff=0)
- self.theme_var = tk.StringVar(value="Default")
+ self.theme_var = tk.StringVar(value=current_theme_name)
self.theme_menu.add_radiobutton(label="Default", variable=self.theme_var, value="default", command=on_theme_select)
self.theme_menu.add_radiobutton(label="Clam", variable=self.theme_var, value="clam", command=on_theme_select)
self.theme_menu.add_radiobutton(label="Alt", variable=self.theme_var, value="alt", command=on_theme_select)
@@ -3595,8 +3688,8 @@ Classes
self.peakTest6ResultText.configure(text=hvsr_results['BestPeak']['Report']['Sa'][:-1])
self.peakTest6Result.configure(text=hvsr_results['BestPeak']['Report']['Sa'][-1])
- peakPass = (hvsr_results['BestPeak']['PassList']['PeakFreqClarityBelow'] +
- hvsr_results['BestPeak']['PassList']['PeakFreqClarityAbove']+
+ peakPass = (hvsr_results['BestPeak']['PassList']['PeakProminenceBelow'] +
+ hvsr_results['BestPeak']['PassList']['PeakProminenceAbove']+
hvsr_results['BestPeak']['PassList']['PeakAmpClarity']+
hvsr_results['BestPeak']['PassList']['FreqStability']+
hvsr_results['BestPeak']['PassList']['PeakStability_FreqStD']+
@@ -6151,7 +6244,33 @@ Methods
def on_theme_select():
# Set the theme based on the selected value
self.style = ttk.Style()
-
+
+ #Update the theme file so the new theme opens on reboot
+ prev_theme = curr_gui_dict['theme_name']
+ curr_gui_dict['theme_name'] = self.theme_var.get()
+ with open(gui_theme_file, 'w') as f:
+ json.dump(curr_gui_dict, f)
+
+ def apply_theme():
+ if 'forest' in self.theme_var.get():
+ if self.theme_var.get()=='forest-dark' and 'forest-dark' not in self.style.theme_names():
+ self.master.tk.call('source', self.darkthemepath)
+ elif self.theme_var.get()=='forest-light' and 'forest-light' not in self.style.theme_names():
+ self.master.tk.call('source', self.lightthemepath)
+ self.master.tk.call("ttk::style", "theme", "use", self.theme_var.get())
+
+ if curr_gui_dict['theme_name']=='forest-light' or curr_gui_dict['theme_name'] == 'forest-dark':
+ do_reboot = messagebox.askyesno('App Restart Required',
+ f"It is recommended to restart the SpRIT GUI at this time to apply this theme. If not, you may continue but theme errors may occur. Click No to retain current theme ({prev_theme}) \nReboot now?",
+ )
+ print(do_reboot)
+ if do_reboot:
+ reboot_app()
+ else:
+ self.theme_var.set(prev_theme)
+ else:
+ apply_theme()
+
"""An attempt to get the backgrounds right
def apply_to_all_children(widget, func):
Recursively apply a function to all child widgets of a given widget
@@ -6169,12 +6288,7 @@ Methods
apply_to_all_children(self.master, change_background_color)
"""
- if 'forest' in self.theme_var.get():
- if self.theme_var.get()=='forest-dark' and 'forest-dark' not in self.style.theme_names():
- self.master.tk.call('source', self.darkthemepath)
- elif self.theme_var.get()=='forest-light' and 'forest-light' not in self.style.theme_names():
- self.master.tk.call('source', self.lightthemepath)
- self.master.tk.call("ttk::style", "theme", "use", self.theme_var.get())
+
#self.master.tk.call("ttk::setTheme", self.theme_var.get())
#self.style.theme_use(self.theme_var.get())
@@ -6190,7 +6304,7 @@ Methods
filepath = filedialog.asksaveasfilename()
self.theme_menu = tk.Menu(self.menubar, tearoff=0)
- self.theme_var = tk.StringVar(value="Default")
+ self.theme_var = tk.StringVar(value=current_theme_name)
self.theme_menu.add_radiobutton(label="Default", variable=self.theme_var, value="default", command=on_theme_select)
self.theme_menu.add_radiobutton(label="Clam", variable=self.theme_var, value="clam", command=on_theme_select)
self.theme_menu.add_radiobutton(label="Alt", variable=self.theme_var, value="alt", command=on_theme_select)
@@ -6445,8 +6559,8 @@ Methods
self.peakTest6ResultText.configure(text=hvsr_results['BestPeak']['Report']['Sa'][:-1])
self.peakTest6Result.configure(text=hvsr_results['BestPeak']['Report']['Sa'][-1])
- peakPass = (hvsr_results['BestPeak']['PassList']['PeakFreqClarityBelow'] +
- hvsr_results['BestPeak']['PassList']['PeakFreqClarityAbove']+
+ peakPass = (hvsr_results['BestPeak']['PassList']['PeakProminenceBelow'] +
+ hvsr_results['BestPeak']['PassList']['PeakProminenceAbove']+
hvsr_results['BestPeak']['PassList']['PeakAmpClarity']+
hvsr_results['BestPeak']['PassList']['FreqStability']+
hvsr_results['BestPeak']['PassList']['PeakStability_FreqStD']+
@@ -9044,6 +9158,7 @@ Index
Classes
diff --git a/docs/sprit_hvsr.html b/docs/sprit_hvsr.html
index 46374ff..85f70b8 100644
--- a/docs/sprit_hvsr.html
+++ b/docs/sprit_hvsr.html
@@ -45,11 +45,12 @@ Module sprit.sprit_hvsr
import pathlib
import pickle
import pkg_resources
+import struct
+import sys
import tempfile
import traceback
import warnings
import xml.etree.ElementTree as ET
-import sys
import matplotlib
from matplotlib.backend_bases import MouseButton
@@ -79,7 +80,11 @@ Module sprit.sprit_hvsr
max_rank = 0
plotRows = 4
-sample_data_dir = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/sample_data/'))
+#Get the main resources directory path, and the other paths as well
+resource_dir = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/'))
+sample_data_dir = resource_dir.joinpath('sample_data')
+settings_dir = resource_dir.joinpath('settings')
+
sampleFileKeyMap = {'1':sample_data_dir.joinpath('SampleHVSRSite1_AM.RAC84.00.2023.046_2023-02-15_1704-1734.MSEED'),
'2':sample_data_dir.joinpath('SampleHVSRSite2_AM.RAC84.00.2023-02-15_2132-2200.MSEED'),
'3':sample_data_dir.joinpath('SampleHVSRSite3_AM.RAC84.00.2023.199_2023-07-18_1432-1455.MSEED'),
@@ -313,7 +318,9 @@ Module sprit.sprit_hvsr
Parameters
----------
export_path : filepath, default=True
- Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes). By default True. If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
+ Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes).
+ By default True.
+ If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
ext : str, optional
The extension to use for the output, by default 'hvsr'. This is still a pickle file that can be read with pickle.load(), but will have .hvsr extension.
"""
@@ -576,8 +583,14 @@ Module sprit.sprit_hvsr
RuntimeError
If the data being processed is a single file, an error will be raised if generate_ppsds() does not work correctly. No errors are raised for remove_noise() errors (since that is an optional step) and the process_hvsr() step (since that is the last processing step) .
"""
+ if 'hvsr_band' not in kwargs.keys():
+ kwargs['hvsr_band'] = inspect.signature(input_params).parameters['hvsr_band'].default
+ if 'peak_freq_range' not in kwargs.keys():
+ kwargs['peak_freq_range'] = inspect.signature(input_params).parameters['peak_freq_range'].default
+
#Get the input parameters
- input_params_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in input_params.__code__.co_varnames}
+ input_params_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(input_params).parameters.keys())}
+
try:
params = input_params(datapath=datapath, verbose=verbose, **input_params_kwargs)
except:
@@ -590,15 +603,14 @@ Module sprit.sprit_hvsr
#Fetch Data
try:
- fetch_data_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in fetch_data.__code__.co_varnames}
+ fetch_data_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(fetch_data).parameters.keys())}
dataIN = fetch_data(params=params, source=source, verbose=verbose, **fetch_data_kwargs)
except:
#Even if batch, this is reading in data for all sites so we want to raise error, not just warn
raise RuntimeError('Data not read correctly, see sprit.fetch_data() function and parameters for more details.')
-
#Remove Noise
try:
- remove_noise_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in remove_noise.__code__.co_varnames}
+ remove_noise_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(remove_noise).parameters.keys())}
data_noiseRemoved = remove_noise(hvsr_data=dataIN, verbose=verbose,**remove_noise_kwargs)
except:
data_noiseRemoved = dataIN
@@ -622,8 +634,8 @@ Module sprit.sprit_hvsr
#Generate PPSDs
try:
- generate_ppsds_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in generate_ppsds.__code__.co_varnames}
- PPSDkwargs = {k: v for k, v in locals()['kwargs'].items() if k in PPSD.__init__.__code__.co_varnames}
+ generate_ppsds_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(generate_ppsds).parameters.keys())}
+ PPSDkwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(PPSD).parameters.keys())}
generate_ppsds_kwargs.update(PPSDkwargs)
ppsd_data = generate_ppsds(hvsr_data=data_noiseRemoved, verbose=verbose,**generate_ppsds_kwargs)
except Exception as e:
@@ -649,7 +661,7 @@ Module sprit.sprit_hvsr
#Process HVSR Curves
try:
- process_hvsr_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in process_hvsr.__code__.co_varnames}
+ process_hvsr_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(process_hvsr).parameters.keys())}
hvsr_results = process_hvsr(hvsr_data=ppsd_data, verbose=verbose,**process_hvsr_kwargs)
except Exception as e:
traceback.print_exception(sys.exc_info()[1])
@@ -679,10 +691,10 @@ Module sprit.sprit_hvsr
#Final post-processing/reporting
#Check peaks
- check_peaks_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in check_peaks.__code__.co_varnames}
+ check_peaks_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(check_peaks).parameters.keys())}
hvsr_results = check_peaks(hvsr_data=hvsr_results, verbose=verbose, **check_peaks_kwargs)
- get_report_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in get_report.__code__.co_varnames}
+ get_report_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(get_report).parameters.keys())}
get_report(hvsr_results=hvsr_results, verbose=verbose, **get_report_kwargs)
if verbose:
@@ -701,8 +713,9 @@ Module sprit.sprit_hvsr
#We do not need to plot another report if already plotted
pass
else:
- hvplot_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in plot_hvsr.__code__.co_varnames}
- hvsr_results['HV_Plot'] = plot_hvsr(hvsr_results, **hvplot_kwargs)
+ #hvplot_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in plot_hvsr.__code__.co_varnames}
+ #hvsr_results['HV_Plot'] = plot_hvsr(hvsr_results, return_fig=True, show=False, close_figs=True)
+ pass
else:
pass
@@ -721,7 +734,7 @@ Module sprit.sprit_hvsr
#Quality checks, stability tests, clarity tests
#def check_peaks(hvsr, x, y, index_list, peak, peakm, peakp, hvsr_peaks, stdf, hvsr_log_std, rank, hvsr_band=[0.4, 40], do_rank=False):
-def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose=False):
+def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_selection='max', peak_freq_range=[1, 20], verbose=False):
"""Function to run tests on HVSR peaks to find best one and see if it passes quality checks
Parameters
@@ -730,6 +743,10 @@ Module sprit.sprit_hvsr
Dictionary containing all the calculated information about the HVSR data (i.e., hvsr_out returned from process_hvsr)
hvsr_band : tuple or list, default=[0.4, 40]
2-item tuple or list with lower and upper limit of frequencies to analyze
+ peak_selection : str or numeric, default='max'
+ How to select the "best" peak used in the analysis. For peak_selection="max" (default value), the highest peak within peak_freq_range is used.
+ For peak_selection='scored', an algorithm is used to select the peak based in part on which peak passes the most SESAME criteria.
+ If a numeric value is used (e.g., int or float), this should be a frequency value to manually select as the peak of interest.
peak_freq_range : tuple or list, default=[1, 20];
The frequency range within which to check for peaks. If there is an HVSR curve with multiple peaks, this allows the full range of data to be processed while limiting peak picks to likely range.
verbose : bool, default=False
@@ -742,6 +759,25 @@ Module sprit.sprit_hvsr
"""
orig_args = locals().copy() #Get the initial arguments
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'check_peaks' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['check_peaks'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(check_peaks).args[1:],
+ inspect.getfullargspec(check_peaks).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ hvsr_band = orig_args['hvsr_band']
+ peak_selection = orig_args['peak_selection']
+ peak_freq_range = orig_args['peak_freq_range']
+ verbose = orig_args['verbose']
+
+ hvsr_data['processing_parameters']['check_peaks'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['check_peaks'][key] = value
+
if (verbose and 'input_params' not in hvsr_data.keys()) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
pass
@@ -777,13 +813,38 @@ Module sprit.sprit_hvsr
if hvsr_data['ProcessingStatus']['OverallStatus']:
if not hvsr_band:
hvsr_band = [0.4,40]
+
hvsr_data['hvsr_band'] = hvsr_band
anyK = list(hvsr_data['x_freqs'].keys())[0]
x = hvsr_data['x_freqs'][anyK] #Consistent for all curves
y = hvsr_data['hvsr_curve'] #Calculated based on "Use" column
- index_list = hvsr_data['hvsr_peak_indices'] #Calculated based on hvsr_curve
+
+ scorelist = ['score', 'scored', 'best', 's']
+ maxlist = ['max', 'highest', 'm']
+ # Convert peak_selection to numeric, get index of nearest value as list item for __init_peaks()
+ try:
+ peak_val = float(peak_selection)
+ index_list = [np.argmin(np.abs(x - peak_val))]
+ except:
+ # If score method is being used, get index list for __init_peaks()
+ if peak_selection in scorelist:
+ index_list = hvsr_data['hvsr_peak_indices'] #Calculated based on hvsr_curve
+ elif peak_selection in maxlist:
+ #Get max index as item in list for __init_peaks()
+ startInd = np.argmin(np.abs(x - peak_freq_range[0]))
+ endInd = np.argmin(np.abs(x - peak_freq_range[1]))
+ if startInd > endInd:
+ holder = startInd
+ startInd = endInd
+ endInd = holder
+ subArrayMax = np.argmax(y[startInd:endInd])
+
+ # If max val is in subarray, this will be the same as the max of curve
+ # Otherwise, it will be the index of the value that is max within peak_freq_range
+ index_list = [subArrayMax+startInd]
+
hvsrp = hvsr_data['hvsrp'] #Calculated based on "Use" column
hvsrm = hvsr_data['hvsrm'] #Calculated based on "Use" column
@@ -809,7 +870,7 @@ Module sprit.sprit_hvsr
peakp = __init_peaks(x, hvsrp, index_p, hvsr_band, peak_freq_range)
peakp = __check_clarity(x, hvsrp, peakp, do_rank=True)
- #Do for hvsrm
+ # Do for hvsrm
# Find the relative extrema of hvsrm (hvsr - 1 standard deviation)
if not np.isnan(np.sum(hvsrm)):
index_m = __find_peaks(hvsrm)
@@ -819,6 +880,7 @@ Module sprit.sprit_hvsr
peakm = __init_peaks(x, hvsrm, index_m, hvsr_band, peak_freq_range)
peakm = __check_clarity(x, hvsrm, peakm, do_rank=True)
+ # Get standard deviation of time peaks
stdf = __get_stdf(x, index_list, hvsrPeaks)
peak = __check_freq_stability(peak, peakm, peakp)
@@ -830,7 +892,7 @@ Module sprit.sprit_hvsr
# Get the BestPeak based on the peak score
# Calculate whether each peak passes enough tests
curveTests = ['WindowLengthFreq.','SignificantCycles', 'LowCurveStDevOverTime']
- peakTests = ['PeakFreqClarityBelow', 'PeakFreqClarityAbove', 'PeakAmpClarity', 'FreqStability', 'PeakStability_FreqStD', 'PeakStability_AmpStD']
+ peakTests = ['PeakProminenceBelow', 'PeakProminenceAbove', 'PeakAmpClarity', 'FreqStability', 'PeakStability_FreqStD', 'PeakStability_AmpStD']
bestPeakScore = 0
for p in hvsr_data['PeakReport']:
@@ -864,6 +926,10 @@ Module sprit.sprit_hvsr
else:
hvsr_data['BestPeak'] = {}
print(f"Processing Errors: No Best Peak identified for {hvsr_data['site']}")
+ try:
+ hvsr_data.plot()
+ except:
+ pass
return hvsr_data
@@ -908,9 +974,140 @@ Module sprit.sprit_hvsr
print("Error in data export. Data must be either of type sprit.HVSRData or sprit.HVSRBatch")
return
+###WORKING ON THIS
+#Save default instrument and processing settings to json file(s)
+def export_settings(hvsr_data, export_settings_path='default', export_settings_type='all', include_location=False, verbose=False):
+ """Save settings to json file
+
+ Parameters
+ ----------
+ export_settings_path : str, default="default"
+ Where to save the json file(s) containing the settings, by default 'default'.
+ If "default," will save to sprit package resources. Otherwise, set a filepath location you would like for it to be saved to.
+ If 'all' is selected, a directory should be supplied.
+ Otherwise, it will save in the directory of the provided file, if it exists. Otherwise, defaults to the home directory.
+ export_settings_type : str, {'all', 'instrument', 'processing'}
+ What kind of settings to save.
+ If 'all', saves all possible types in their respective json files.
+ If 'instrument', save the instrument settings to their respective file.
+ If 'processing', saves the processing settings to their respective file. By default 'all'
+ include_location : bool, default=False, input CRS
+ Whether to include the location parametersin the exported settings document.This includes xcoord, ycoord, elevation, elev_unit, and input_crs
+ verbose : bool, default=True
+ Whether to print outputs and information to the terminal
+
+ """
+ fnameDict = {}
+ fnameDict['instrument'] = "instrument_settings.json"
+ fnameDict['processing'] = "processing_settings.json"
+
+ if export_settings_path == 'default' or export_settings_path is True:
+ settingsPath = resource_dir.joinpath('settings')
+ else:
+ export_settings_path = pathlib.Path(export_settings_path)
+ if not export_settings_path.exists():
+ if not export_settings_path.parent.exists():
+ print(f'The provided value for export_settings_path ({export_settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}')
+ settingsPath = pathlib.Path.home()
+ else:
+ settingsPath = export_settings_path.parent
+
+ if export_settings_path.is_dir():
+ settingsPath = export_settings_path
+ elif export_settings_path.is_file():
+ settingsPath = export_settings_path.parent
+ fnameDict['instrument'] = export_settings_path.name+"_instrumentSettings.json"
+ fnameDict['processing'] = export_settings_path.name+"_processingSettings.json"
+
+ #Get final filepaths
+ instSetFPath = settingsPath.joinpath(fnameDict['instrument'])
+ procSetFPath = settingsPath.joinpath(fnameDict['processing'])
+
+ #Get settings values
+ instKeys = ["instrument", "net", "sta", "loc", "cha", "depth", "metapath", "hvsr_band"]
+ inst_location_keys = ['xcoord', 'ycoord', 'elevation', 'elev_unit', 'input_crs']
+ procFuncs = [fetch_data, remove_noise, generate_ppsds, process_hvsr, check_peaks, get_report]
+
+ instrument_settings_dict = {}
+ processing_settings_dict = {}
+
+ for k in instKeys:
+ if isinstance(hvsr_data[k], pathlib.PurePath):
+ #For those that are paths and cannot be serialized
+ instrument_settings_dict[k] = hvsr_data[k].as_posix()
+ else:
+ instrument_settings_dict[k] = hvsr_data[k]
+
+ if include_location:
+ for k in inst_location_keys:
+ if isinstance(hvsr_data[k], pathlib.PurePath):
+ #For those that are paths and cannot be serialized
+ instrument_settings_dict[k] = hvsr_data[k].as_posix()
+ else:
+ instrument_settings_dict[k] = hvsr_data[k]
+
+
+ for func in procFuncs:
+ funcName = func.__name__
+ processing_settings_dict[funcName] = {}
+ for arg in hvsr_data['processing_parameters'][funcName]:
+ if isinstance(hvsr_data['processing_parameters'][funcName][arg], (HVSRBatch, HVSRData)):
+ pass
+ else:
+ processing_settings_dict[funcName][arg] = hvsr_data['processing_parameters'][funcName][arg]
+
+ #Save settings files
+ if export_settings_type.lower()=='instrument' or export_settings_type.lower()=='all':
+ with open(instSetFPath.with_suffix('.inst').as_posix(), 'w') as instSetF:
+ jsonString = json.dumps(instrument_settings_dict, indent=2)
+ #Format output for readability
+ jsonString = jsonString.replace('\n ', ' ')
+ jsonString = jsonString.replace('[ ', '[')
+ jsonString = jsonString.replace('\n ]', ']')
+ #Export
+ instSetF.write(jsonString)
+ if export_settings_type.lower()=='processing' or export_settings_type.lower()=='all':
+ with open(procSetFPath.with_suffix('.proc').as_posix(), 'w') as procSetF:
+ jsonString = json.dumps(processing_settings_dict, indent=2)
+ #Format output for readability
+ jsonString = jsonString.replace('\n ', ' ')
+ jsonString = jsonString.replace('[ ', '[')
+ jsonString = jsonString.replace('\n ]', ']')
+ jsonString = jsonString.replace('\n },','\n\t\t},\n')
+ jsonString = jsonString.replace('{ "', '\n\t\t{\n\t\t"')
+ jsonString = jsonString.replace(', "', ',\n\t\t"')
+ jsonString = jsonString.replace('\n }', '\n\t\t}')
+ jsonString = jsonString.replace(': {', ':\n\t\t\t{')
+
+ #Export
+ procSetF.write(jsonString)
+
#Reads in traces to obspy stream
def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detrend='spline', detrend_order=2, update_metadata=True, plot_input_stream=False, verbose=False, **kwargs):
- import warnings
+ #Get intput paramaters
+ orig_args = locals().copy()
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in params.keys():
+ if 'fetch_data' in params['processing_parameters'].keys():
+ defaultVDict = dict(zip(inspect.getfullargspec(fetch_data).args[1:],
+ inspect.getfullargspec(fetch_data).defaults))
+ defaultVDict['kwargs'] = kwargs
+ for k, v in params['processing_parameters']['fetch_data'].items():
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ #Update local variables, in case of previously-specified parameters
+ source=orig_args['source']
+ trim_dir=orig_args['trim_dir']
+ export_format=orig_args['export_format']
+ detrend=orig_args['detrend']
+ detrend_order=orig_args['detrend_order']
+ update_metadata=orig_args['update_metadata']
+ plot_input_stream=orig_args['plot_input_stream']
+ verbose=orig_args['verbose']
+ kwargs=orig_args['kwargs']
"""Fetch ambient seismic data from a source to read into obspy stream
@@ -959,7 +1156,9 @@ Module sprit.sprit_hvsr
date=params['acq_date']
#Cleanup for gui input
- if '}' in str(params['datapath']):
+ if isinstance(params['datapath'], (obspy.Stream, obspy.Trace)):
+ pass
+ elif '}' in str(params['datapath']):
params['datapath'] = params['datapath'].as_posix().replace('{','')
params['datapath'] = params['datapath'].split('}')
@@ -972,7 +1171,10 @@ Module sprit.sprit_hvsr
#Make sure datapath is pointing to an actual file
if isinstance(params['datapath'],list):
for i, d in enumerate(params['datapath']):
- params['datapath'][i] = sprit_utils.checkifpath(str(d).strip())
+ params['datapath'][i] = sprit_utils.checkifpath(str(d).strip(), sample_list=sampleList)
+ dPath = params['datapath']
+ elif isinstance(params['datapath'], (obspy.Stream, obspy.Trace)):
+ pass
else:
dPath = sprit_utils.checkifpath(params['datapath'], sample_list=sampleList)
@@ -1024,15 +1226,26 @@ Module sprit.sprit_hvsr
#Select which instrument we are reading from (requires different processes for each instrument)
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
+ trominoNameList = ['tromino', 'trom', 'tromino 3g', 'tromino 3g+', 'tr', 't']
+
+ #Get any kwargs that are included in obspy.read
+ obspyReadKwargs = {}
+ for argName in inspect.getfullargspec(obspy.read)[0]:
+ if argName in kwargs.keys():
+ obspyReadKwargs[argName] = kwargs[argName]
#Select how reading will be done
if source=='raw':
- if inst.lower() in raspShakeInstNameList:
- try:
+ try:
+ if inst.lower() in raspShakeInstNameList:
rawDataIN = __read_RS_file_struct(dPath, source, year, doy, inv, params, verbose=verbose)
- except:
- raise RuntimeError(f"Data not fetched for {params['site']}. Check input parameters or the data file.")
- return params
+
+ elif inst.lower() in trominoNameList:
+ rawDataIN = __read_tromino_files(dPath, params, verbose=verbose)
+ except:
+ raise RuntimeError(f"Data not fetched for {params['site']}. Check input parameters or the data file.")
+ elif source=='stream' or isinstance(params, (obspy.Stream, obspy.Trace)):
+ rawDataIN = params['datapath'].copy()
elif source=='dir':
if inst.lower() in raspShakeInstNameList:
rawDataIN = __read_RS_file_struct(dPath, source, year, doy, inv, params, verbose=verbose)
@@ -1043,28 +1256,27 @@ Module sprit.sprit_hvsr
for f in temp_file_glob:
currParams = params
currParams['datapath'] = f
+
curr_data = fetch_data(params, source='file', #all the same as input, except just reading the one file using the source='file'
- trim_dir=trim_dir, export_format=export_format, detrend=detrend, detrend_order=detrend_order, update_metadata=update_metadata, verbose=verbose, **kwargs),
+ trim_dir=trim_dir, export_format=export_format, detrend=detrend, detrend_order=detrend_order, update_metadata=update_metadata, verbose=verbose, **kwargs)
+ curr_data.merge()
obspyFiles[f.stem] = curr_data #Add path object to dict, with filepath's stem as the site name
return HVSRBatch(obspyFiles)
-
elif source=='file' and str(params['datapath']).lower() not in sampleList:
if isinstance(dPath, list) or isinstance(dPath, tuple):
rawStreams = []
for datafile in dPath:
- rawStream = obspy.read(datafile)
+ rawStream = obspy.read(datafile, **obspyReadKwargs)
rawStreams.append(rawStream) #These are actually streams, not traces
-
for i, stream in enumerate(rawStreams):
if i == 0:
rawDataIN = obspy.Stream(stream) #Just in case
else:
rawDataIN = rawDataIN + stream #This adds a stream/trace to the current stream object
-
elif str(dPath)[:6].lower()=='sample':
pass
else:
- rawDataIN = obspy.read(dPath)#, starttime=obspy.core.UTCDateTime(params['starttime']), endttime=obspy.core.UTCDateTime(params['endtime']), nearest_sample =True)
+ rawDataIN = obspy.read(dPath, **obspyReadKwargs)#, starttime=obspy.core.UTCDateTime(params['starttime']), endttime=obspy.core.UTCDateTime(params['endtime']), nearest_sample =True)
import warnings
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=UserWarning)
@@ -1111,12 +1323,15 @@ Module sprit.sprit_hvsr
except:
RuntimeError(f'source={source} not recognized, and datapath cannot be read using obspy.read()')
+ #Get metadata from the data itself, if not reading raw data
try:
dataIN = rawDataIN.copy()
if source!='raw':
#Use metadata from file for;
# site
if params['site'] == "HVSR Site":
+ if isinstance(dPath, (list, tuple)):
+ dPath = dPath[0]
params['site'] = dPath.stem
params['params']['site'] = dPath.stem
@@ -1153,11 +1368,13 @@ Module sprit.sprit_hvsr
today_Starttime = obspy.UTCDateTime(datetime.datetime(year=datetime.date.today().year, month=datetime.date.today().month,
day = datetime.date.today().day,
hour=0, minute=0, second=0, microsecond=0))
- maxStarttime = datetime.time(hour=0, minute=0, second=0, microsecond=0)
+ maxStarttime = datetime.datetime(year=params['acq_date'].year, month=params['acq_date'].month, day=params['acq_date'].day,
+ hour=0, minute=0, second=0, microsecond=0, tzinfo=datetime.timezone.utc)
if str(params['starttime']) == str(today_Starttime):
for tr in dataIN.merge():
- currTime = datetime.time(hour=tr.stats.starttime.hour, minute=tr.stats.starttime.minute,
- second=tr.stats.starttime.second, microsecond=tr.stats.starttime.microsecond)
+ currTime = datetime.datetime(year=tr.stats.starttime.year, month=tr.stats.starttime.month, day=tr.stats.starttime.day,
+ hour=tr.stats.starttime.hour, minute=tr.stats.starttime.minute,
+ second=tr.stats.starttime.second, microsecond=tr.stats.starttime.microsecond, tzinfo=datetime.timezone.utc)
if currTime > maxStarttime:
maxStarttime = currTime
@@ -1172,17 +1389,19 @@ Module sprit.sprit_hvsr
today_Endtime = obspy.UTCDateTime(datetime.datetime(year=datetime.date.today().year, month=datetime.date.today().month,
day = datetime.date.today().day,
hour=23, minute=59, second=59, microsecond=999999))
- minEndtime = datetime.time(hour=23, minute=59, second=59, microsecond=999999)
- if str(params['endtime']) == str(today_Endtime):
+ tomorrow_Endtime = today_Endtime + (60*60*24)
+ minEndtime = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc)#(hour=23, minute=59, second=59, microsecond=999999)
+ if str(params['endtime']) == str(today_Endtime) or str(params['endtime'])==tomorrow_Endtime:
for tr in dataIN.merge():
- currTime = datetime.time(hour=tr.stats.endtime.hour, minute=tr.stats.endtime.minute,
- second=tr.stats.endtime.second, microsecond=tr.stats.endtime.microsecond)
+ currTime = datetime.datetime(year=tr.stats.endtime.year, month=tr.stats.endtime.month, day=tr.stats.endtime.day,
+ hour=tr.stats.endtime.hour, minute=tr.stats.endtime.minute,
+ second=tr.stats.endtime.second, microsecond=tr.stats.endtime.microsecond, tzinfo=datetime.timezone.utc)
if currTime < minEndtime:
minEndtime = currTime
- newEndtime = obspy.UTCDateTime(datetime.datetime(year=params['acq_date'].year, month=params['acq_date'].month,
- day = params['acq_date'].day,
+ newEndtime = obspy.UTCDateTime(datetime.datetime(year=minEndtime.year, month=minEndtime.month,
+ day = minEndtime.day,
hour=minEndtime.hour, minute=minEndtime.minute,
- second=minEndtime.second, microsecond=minEndtime.microsecond))
+ second=minEndtime.second, microsecond=minEndtime.microsecond, tzinfo=datetime.timezone.utc))
params['endtime'] = newEndtime
params['params']['endtime'] = newEndtime
@@ -1222,10 +1441,12 @@ Module sprit.sprit_hvsr
#Remerge data
dataIN = dataIN.merge(method=1)
+ #Plot the input stream?
if plot_input_stream:
try:
params['InputPlot'] = _plot_specgram_stream(stream=dataIN, params=params, component='Z', stack_type='linear', detrend='mean', dbscale=True, fill_gaps=None, ylimstd=3, return_fig=True, fig=None, ax=None, show_plot=False)
- _get_removed_windows(input=dataIN, fig=params['InputPlot'][0], ax=params['InputPlot'][1], lineArtist =[], winArtist = [], existing_lineArtists=[], existing_xWindows=[], exist_win_format='matplotlib', keep_line_artists=True, time_type='matplotlib', show_plot=True)
+ #_get_removed_windows(input=dataIN, fig=params['InputPlot'][0], ax=params['InputPlot'][1], lineArtist =[], winArtist = [], existing_lineArtists=[], existing_xWindows=[], exist_win_format='matplotlib', keep_line_artists=True, time_type='matplotlib', show_plot=True)
+ plt.show()
except:
print('Error with default plotting method, falling back to internal obspy plotting method')
dataIN.plot(method='full', linewidth=0.25)
@@ -1236,6 +1457,7 @@ Module sprit.sprit_hvsr
else:
dataIN = _sort_channels(input=dataIN, source=source, verbose=verbose)
+ #Clean up the ends of the data unless explicitly specified to do otherwise (this is a kwarg, not a parameter)
if 'clean_ends' not in kwargs.keys():
clean_ends=True
else:
@@ -1271,6 +1493,14 @@ Module sprit.sprit_hvsr
params['batch'] = False #Set False by default, will get corrected later in batch mode
params['input_stream'] = dataIN.copy()
params['stream'] = dataIN.copy()
+
+ if 'processing_parameters' not in params.keys():
+ params['processing_parameters'] = {}
+ params['processing_parameters']['fetch_data'] = {}
+ for key, value in orig_args.items():
+ params['processing_parameters']['fetch_data'][key] = value
+
+
params['ProcessingStatus']['FetchDataStatus'] = True
if verbose and not isinstance(params, HVSRBatch):
dataINStr = dataIN.__str__().split('\n')
@@ -1322,6 +1552,8 @@ Module sprit.sprit_hvsr
ppsd_kwargs_sprit_defaults['skip_on_gaps'] = True
if 'period_step_octaves' not in ppsd_kwargs:
ppsd_kwargs_sprit_defaults['period_step_octaves'] = 0.03125
+ if 'period_limits' not in ppsd_kwargs:
+ ppsd_kwargs_sprit_defaults['period_limits'] = [1/hvsr_data['hvsr_band'][1], 1/hvsr_data['hvsr_band'][0]]
#Get Probablistic power spectral densities (PPSDs)
#Get default args for function
@@ -1335,8 +1567,24 @@ Module sprit.sprit_hvsr
ppsd_kwargs = get_default_args(PPSD)
ppsd_kwargs.update(ppsd_kwargs_sprit_defaults)#Update with sprit defaults, or user input
+ orig_args['ppsd_kwargs'] = ppsd_kwargs
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'generate_ppsds' in hvsr_data['processing_parameters'].keys():
+ defaultVDict = dict(zip(inspect.getfullargspec(generate_ppsds).args[1:],
+ inspect.getfullargspec(generate_ppsds).defaults))
+ defaultVDict['ppsd_kwargs'] = ppsd_kwargs
+ for k, v in hvsr_data['processing_parameters']['generate_ppsds'].items():
- orig_args['ppsd_kwargs'] = [ppsd_kwargs]
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ remove_outliers = orig_args['remove_outliers']
+ outlier_std = orig_args['outlier_std']
+ verbose = orig_args['verbose']
+ ppsd_kwargs = orig_args['ppsd_kwargs']
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
@@ -1370,7 +1618,7 @@ Module sprit.sprit_hvsr
hvsr_data[site_name]['ProcessingStatus']['OverallStatus'] = False
return hvsr_data
else:
- paz=hvsr_data['paz']
+ paz = hvsr_data['paz']
stream = hvsr_data['stream']
#Get ppsds of e component
@@ -1505,10 +1753,18 @@ Module sprit.sprit_hvsr
hvsrDF['TimesProcessed_MPLEnd'] = hvsrDF['TimesProcessed_MPL'] + (ppsd_kwargs['ppsd_length']/86400)
hvsrDF['Use'] = True
+ hvsrDF['Use']=hvsrDF['Use'].astype(bool)
for gap in hvsr_data['ppsds']['Z']['times_gaps']:
- hvsrDF['Use'] = (hvsrDF['TimesProcessed_Obspy'].gt(gap[0]) & hvsrDF['TimesProcessed_Obspy'].gt(gap[1]) )| \
- (hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[0]) & hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[1]))# | \
-
+ hvsrDF['Use'] = (hvsrDF['TimesProcessed_MPL'].gt(gap[1].matplotlib_date))| \
+ (hvsrDF['TimesProcessed_MPLEnd'].lt(gap[0].matplotlib_date))# | \
+
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+ if 'xwindows_out' in hvsr_data.keys():
+ for window in hvsr_data['xwindows_out']:
+ hvsrDF['Use'] = (hvsrDF['TimesProcessed_MPL'][hvsrDF['Use']].lt(window[0]) & hvsrDF['TimesProcessed_MPLEnd'][hvsrDF['Use']].lt(window[0]) )| \
+ (hvsrDF['TimesProcessed_MPL'][hvsrDF['Use']].gt(window[1]) & hvsrDF['TimesProcessed_MPLEnd'][hvsrDF['Use']].gt(window[1]))
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+
hvsrDF.set_index('TimesProcessed', inplace=True)
hvsr_data['hvsr_df'] = hvsrDF
#Create dict entry to keep track of how many outlier hvsr curves are removed (2-item list with [0]=current number, [1]=original number of curves)
@@ -1523,6 +1779,12 @@ Module sprit.sprit_hvsr
hvsr_data = sprit_utils.make_it_classy(hvsr_data)
+ if 'processing_parameters' not in hvsr_data.keys():
+ hvsr_data['processing_parameters'] = {}
+ hvsr_data['processing_parameters']['generate_ppsds'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['generate_ppsds'][key] = value
+
hvsr_data['ProcessingStatus']['PPSDStatus'] = True
hvsr_data = _check_processing_status(hvsr_data)
return hvsr_data
@@ -1549,14 +1811,57 @@ Module sprit.sprit_hvsr
params : dict
Modified input dictionary with additional key:value pair containing paz dictionary (key = "paz")
"""
-
invPath = params['metapath']
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
+ trominoNameList = ['tromino', 'trom', 'trm', 't']
if params['instrument'].lower() in raspShakeInstNameList:
if update_metadata:
params = _update_shake_metadata(filepath=invPath, params=params, write_path=write_path)
params = _read_RS_Metadata(params, source=source)
+ elif params['instrument'].lower() in trominoNameList:
+ params['paz'] = {'Z':{}, 'E':{}, 'N':{}}
+ #ALL THESE VALUES ARE PLACEHOLDERS, taken from RASPBERRY SHAKE! (Needed for PPSDs)
+ params['paz']['Z'] = {'sensitivity': 360000000.0,
+ 'gain': 360000000.0,
+ 'poles': [(-1+0j), (-3.03+0j), (-3.03+0j), (-666.67+0j)],
+ 'zeros': [0j, 0j, 0j]}
+ params['paz']['E'] = params['paz']['Z']
+ params['paz']['N'] = params['paz']['Z']
+
+ channelObj_Z = obspy.core.inventory.channel.Channel(code='BHZ', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=0, dip=90, types=None, external_references=None,
+ sample_rate=None, sample_rate_ratio_number_samples=None, sample_rate_ratio_number_seconds=None,
+ storage_format=None, clock_drift_in_seconds_per_sample=None, calibration_units=None,
+ calibration_units_description=None, sensor=None, pre_amplifier=None, data_logger=None,
+ equipments=None, response=None, description=None, comments=None, start_date=None, end_date=None,
+ restricted_status=None, alternate_code=None, historical_code=None, data_availability=None,
+ identifiers=None, water_level=None, source_id=None)
+ channelObj_E = obspy.core.inventory.channel.Channel(code='BHE', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=90, dip=0)
+
+ channelObj_N = obspy.core.inventory.channel.Channel(code='BHN', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=0, dip=0)
+
+ siteObj = obspy.core.inventory.util.Site(name=params['params']['site'], description=None, town=None, county=None, region=None, country=None)
+ stationObj = obspy.core.inventory.station.Station(code='TZ', latitude=params['params']['latitude'], longitude=params['params']['longitude'],
+ elevation=params['params']['elevation'], channels=[channelObj_Z, channelObj_E, channelObj_N], site=siteObj,
+ vault=None, geology=None, equipments=None, operators=None, creation_date=datetime.datetime.today(),
+ termination_date=None, total_number_of_channels=None,
+ selected_number_of_channels=None, description='Estimated data for Tromino, this is NOT from the manufacturer',
+ comments=None, start_date=None,
+ end_date=None, restricted_status=None, alternate_code=None, historical_code=None,
+ data_availability=None, identifiers=None, water_level=None, source_id=None)
+
+ network = [obspy.core.inventory.network.Network(code='TROM', stations=[stationObj], total_number_of_stations=None,
+ selected_number_of_stations=None, description=None, comments=None, start_date=None,
+ end_date=None, restricted_status=None, alternate_code=None, historical_code=None,
+ data_availability=None, identifiers=None, operators=None, source_id=None)]
+
+ params['inv'] = obspy.Inventory(networks=network)
else:
if not invPath:
pass #if invPath is None
@@ -1613,11 +1918,30 @@ Module sprit.sprit_hvsr
list/tuple - a list or tuple of the above objects, in the same order they are in the report_format list
"""
- #print statement
- #Check if results are good
- #Curve pass?
orig_args = locals().copy() #Get the initial arguments
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_results.keys():
+ if 'get_report' in hvsr_results['processing_parameters'].keys():
+ for k, v in hvsr_results['processing_parameters']['get_report'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(get_report).args[1:],
+ inspect.getfullargspec(get_report).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ report_format = orig_args['report_format']
+ plot_type = orig_args['plot_type']
+ export_path = orig_args['export_path']
+ return_results = orig_args['return_results']
+ csv_overwrite_opt = orig_args['csv_overwrite_opt']
+ no_output = orig_args['no_output']
+ verbose = orig_args['verbose']
+
+ hvsr_results['processing_parameters']['get_report'] = {}
+ for key, value in orig_args.items():
+ hvsr_results['processing_parameters']['get_report'][key] = value
+
if (verbose and isinstance(hvsr_results, HVSRBatch)) or (verbose and not hvsr_results['batch']):
if isinstance(hvsr_results, HVSRData) and hvsr_results['batch']:
pass
@@ -1687,8 +2011,8 @@ Module sprit.sprit_hvsr
curvePass = curvTestsPassed > 2
#Peak Pass?
- peakTestsPassed = ( hvsr_results['BestPeak']['PassList']['PeakFreqClarityBelow'] +
- hvsr_results['BestPeak']['PassList']['PeakFreqClarityAbove']+
+ peakTestsPassed = ( hvsr_results['BestPeak']['PassList']['PeakProminenceBelow'] +
+ hvsr_results['BestPeak']['PassList']['PeakProminenceAbove']+
hvsr_results['BestPeak']['PassList']['PeakAmpClarity']+
hvsr_results['BestPeak']['PassList']['FreqStability']+
hvsr_results['BestPeak']['PassList']['PeakStability_FreqStD']+
@@ -1823,24 +2147,25 @@ Module sprit.sprit_hvsr
report_string_list.append(internalSeparator)
report_string_list.append('')
+ justSize=34
#Print individual results
report_string_list.append('\tCurve Tests: {}/3 passed (3/3 needed)'.format(curvTestsPassed))
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Lw'][-1]} Length of processing windows: {hvsr_results['BestPeak']['Report']['Lw']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Nc'][-1]} Number of significant cycles: {hvsr_results['BestPeak']['Report']['Nc']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['σ_A(f)'][-1]} Low StDev. of H/V Curve over time: {hvsr_results['BestPeak']['Report']['σ_A(f)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Lw'][-1]}"+" Length of processing windows".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Lw']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Nc'][-1]}"+" Number of significant cycles".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Nc']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['σ_A(f)'][-1]}"+" Small H/V StDev over time".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['σ_A(f)']}")
report_string_list.append('')
report_string_list.append("\tPeak Tests: {}/6 passed (5/6 needed)".format(peakTestsPassed))
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f-)'][-1]} Clarity Below Peak Frequency: {hvsr_results['BestPeak']['Report']['A(f-)']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f+)'][-1]} Clarity Above Peak Frequency: {hvsr_results['BestPeak']['Report']['A(f+)']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A0'][-1]} Clarity of Peak Amplitude: {hvsr_results['BestPeak']['Report']['A0']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f-)'][-1]}"+" Peak is prominent below".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A(f-)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f+)'][-1]}"+" Peak is prominent above".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A(f+)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A0'][-1]}"+" Peak is large".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A0']}")
if hvsr_results['BestPeak']['PassList']['FreqStability']:
res = sprit_utils.check_mark()
else:
res = sprit_utils.x_mark()
- report_string_list.append(f"\t\t {res} Stability of Peak Freq. Over time: {hvsr_results['BestPeak']['Report']['P-'][:5]} and {hvsr_results['BestPeak']['Report']['P+'][:-1]} {res}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sf'][-1]} Stability of Peak (Freq. StDev): {hvsr_results['BestPeak']['Report']['Sf']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sa'][-1]} Stability of Peak (Amp. StDev): {hvsr_results['BestPeak']['Report']['Sa']}")
+ report_string_list.append(f"\t\t {res}"+ " Peak freq. is stable over time".ljust(justSize)+ f"{hvsr_results['BestPeak']['Report']['P-'][:5]} and {hvsr_results['BestPeak']['Report']['P+'][:-1]} {res}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sf'][-1]}"+" Stability of peak (Freq. StDev)".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Sf']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sa'][-1]}"+" Stability of peak (Amp. StDev)".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Sa']}")
report_string_list.append('')
report_string_list.append(f"Calculated using {hvsr_results['hvsr_df']['Use'].sum()}/{hvsr_results['hvsr_df']['Use'].count()} time windows".rjust(sepLen-1))
report_string_list.append(extSiteSeparator)
@@ -1864,7 +2189,7 @@ Module sprit.sprit_hvsr
import pandas as pd
pdCols = ['Site Name', 'Acq_Date', 'Longitude', 'Latitide', 'Elevation', 'PeakFrequency',
'WindowLengthFreq.','SignificantCycles','LowCurveStDevOverTime',
- 'PeakFreqClarityBelow','PeakFreqClarityAbove','PeakAmpClarity','FreqStability', 'PeakStability_FreqStD','PeakStability_AmpStD', 'PeakPasses']
+ 'PeakProminenceBelow','PeakProminenceAbove','PeakAmpClarity','FreqStability', 'PeakStability_FreqStD','PeakStability_AmpStD', 'PeakPasses']
d = hvsr_results
criteriaList = []
for p in hvsr_results['BestPeak']["PassList"]:
@@ -1932,167 +2257,6 @@ Module sprit.sprit_hvsr
hvsr_results = report_output(_report_format=rep_form, _plot_type=plot_type, _return_results=return_results, _export_path=exp_path, _no_output=no_output, verbose=verbose)
return hvsr_results
-#Main function for plotting results
-def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, xtype='freq', fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True,**kwargs):
- """Function to plot HVSR data
-
- Parameters
- ----------
- hvsr_data : dict
- Dictionary containing output from process_hvsr function
- plot_type : str='HVSR' or list
- The plot_type of plot(s) to plot. If list, will plot all plots listed
- 'HVSR' : Standard HVSR plot, including standard deviation
- - '[HVSR] p' : HVSR plot with BestPeaks shown
- - '[HVSR] p' : HVSR plot with best picked peak shown
- - '[HVSR] p* all' : HVSR plot with all picked peaks shown
- - '[HVSR] p* t' : HVSR plot with peaks from all time steps in background
- - '[HVSR p* ann] : Annotates plot with peaks
- - '[HVSR] -s' : HVSR plots don't show standard deviation
- - '[HVSR] t' : HVSR plot with individual hv curves for each time step shown
- - '[HVSR] c' : HVSR plot with each components' spectra. Recommended to do this last (or just before 'specgram'), since doing c+ can make the component chart its own chart
- 'Specgram' : Combined spectrogram of all components
- - '[spec]' : basic spectrogram plot of H/V curve
- use_subplots : bool, default = True
- Whether to output the plots as subplots (True) or as separate plots (False)
- xtype : str, default = 'freq'
- String for what to use, between frequency or period
- For frequency, the following are accepted (case does not matter): 'f', 'Hz', 'freq', 'frequency'
- For period, the following are accepted (case does not matter): 'p', 'T', 's', 'sec', 'second', 'per', 'period'
- fig : matplotlib.Figure, default = None
- If not None, matplotlib figure on which plot is plotted
- ax : matplotlib.Axis, default = None
- If not None, matplotlib axis on which plot is plotted
- return_fig : bool
- Whether to return figure and axis objects
- save_dir : str or None
- Directory in which to save figures
- save_suffix : str
- Suffix to add to end of figure filename(s), if save_dir is used
- show_legend : bool, default=False
- Whether to show legend in plot
- show : bool
- Whether to show plot
- close_figs : bool, default=False
- Whether to close figures before plotting
- clear_fig : bool, default=True
- Whether to clear figures before plotting
- **kwargs : keyword arguments
- Keyword arguments for matplotlib.pyplot
-
- Returns
- -------
- fig, ax : matplotlib figure and axis objects
- Returns figure and axis matplotlib.pyplot objects if return_fig=True, otherwise, simply plots the figures
- """
- orig_args = locals().copy() #Get the initial arguments
- if isinstance(hvsr_data, HVSRBatch):
- #If running batch, we'll loop through each site
- for site_name in hvsr_data.keys():
- args = orig_args.copy() #Make a copy so we don't accidentally overwrite
- individual_params = hvsr_data[site_name] #Get what would normally be the "params" variable for each site
- args['hvsr_results'] = individual_params #reset the params parameter we originally read in to an individual site params
- if hvsr_data[site_name]['ProcessingStatus']['OverallStatus']:
- try:
- _hvsr_plot_batch(**args) #Call another function, that lets us run this function again
- except:
- print(f"{site_name} not able to be plotted.")
- else:
- if clear_fig and fig is not None and ax is not None: #Intended use for tkinter
- #Clear everything
- for key in ax:
- ax[key].clear()
- fig.clear()
- if close_figs:
- plt.close('all')
-
- compList = ['c', 'comp', 'component', 'components']
- specgramList = ['spec', 'specgram', 'spectrogram']
- hvsrList = ['hvsr', 'hv', 'h']
-
- hvsrInd = np.nan
- compInd = np.nan
- specInd = np.nan
-
- kList = plot_type.split(' ')
- for i, k in enumerate(kList):
- kList[i] = k.lower()
-
- #Get the plots in the right order, no matter how they were input (and ensure the right options go with the right plot)
- #HVSR index
- if len(set(hvsrList).intersection(kList)):
- for i, hv in enumerate(hvsrList):
- if hv in kList:
- hvsrInd = kList.index(hv)
- break
- #Component index
- #if len(set(compList).intersection(kList)):
- for i, c in enumerate(kList):
- if '+' in c and c[:-1] in compList:
- compInd = kList.index(c)
- break
-
- #Specgram index
- if len(set(specgramList).intersection(kList)):
- for i, sp in enumerate(specgramList):
- if sp in kList:
- specInd = kList.index(sp)
- break
-
- indList = [hvsrInd, compInd, specInd]
- indListCopy = indList.copy()
- plotTypeList = ['hvsr', 'comp', 'spec']
-
- plotTypeOrder = []
- plotIndOrder = []
-
- lastVal = 0
- while lastVal != 99:
- firstInd = np.nanargmin(indListCopy)
- plotTypeOrder.append(plotTypeList[firstInd])
- plotIndOrder.append(indList[firstInd])
- lastVal = indListCopy[firstInd]
- indListCopy[firstInd] = 99 #just a high number
-
- plotTypeOrder.pop()
- plotIndOrder[-1]=len(kList)
-
- for i, p in enumerate(plotTypeOrder):
- pStartInd = plotIndOrder[i]
- pEndInd = plotIndOrder[i+1]
- plotComponents = kList[pStartInd:pEndInd]
-
- if use_subplots and i==0 and fig is None and ax is None:
- mosaicPlots = []
- for pto in plotTypeOrder:
- mosaicPlots.append([pto])
- fig, ax = plt.subplot_mosaic(mosaicPlots, gridspec_kw={'hspace':0.3})
- axis = ax[p]
- elif use_subplots:
- with warnings.catch_warnings():
- warnings.simplefilter("ignore") #Often warns about xlim when it is not an issue
- ax[p].clear()
- axis = ax[p]
- else:
- fig, axis = plt.subplots()
-
- if p == 'hvsr':
- _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax)
- elif p=='comp':
- plotComponents[0] = plotComponents[0][:-1]
- _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax)
- elif p=='spec':
- _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False)
- else:
- warnings.warn('Plot type {p} not recognized', UserWarning)
-
- if show:
- fig.canvas.draw()
-
- if return_fig:
- return fig, ax
- return
-
#Import data
def import_data(import_filepath, data_format='pickle'):
"""Function to import .hvsr (or other extension) data exported using export_data() function
@@ -2115,6 +2279,28 @@ Module sprit.sprit_hvsr
dataIN = import_filepath
return dataIN
+#Import settings
+def import_settings(settings_import_path, settings_import_type='instrument', verbose=False):
+
+ allList = ['all', ':', 'both', 'any']
+ if settings_import_type.lower() not in allList:
+ # if just a single settings dict is desired
+ with open(settings_import_path, 'r') as f:
+ settingsDict = json.load(f)
+ else:
+ # Either a directory or list
+ if isinstance(settings_import_path, (list, tuple)):
+ for setPath in settings_import_path:
+ pass
+ else:
+ settings_import_path = sprit_utils.checkifpath(settings_import_path)
+ if not settings_import_path.is_dir():
+ raise RuntimeError(f'settings_import_type={settings_import_type}, but settings_import_path is not list/tuple or filepath to directory')
+ else:
+ instFile = settings_import_path.glob('*.inst')
+ procFile = settings_import_path.glob('*.proc')
+ return settingsDict
+
#Define input parameters
def input_params(datapath,
site='HVSR Site',
@@ -2134,9 +2320,10 @@ Module sprit.sprit_hvsr
elev_unit = 'feet',
depth = 0,
instrument = 'Raspberry Shake',
- metapath = '',
+ metapath = None,
hvsr_band = [0.4, 40],
peak_freq_range=[0.4, 40],
+ processing_parameters={},
verbose=False
):
"""Function for designating input parameters for reading in and processing data
@@ -2181,12 +2368,19 @@ Module sprit.sprit_hvsr
Depth of seismometer. Not currently used, but will likely be used in the future.
instrument : str or list {'Raspberry Shake')
Instrument from which the data was acquired.
- metapath : str or pathlib.Path object, default=''
- Filepath of metadata, in format supported by obspy.read_inventory. If default value of '', will read from resources folder of repository (only supported for Raspberry Shake).
+ metapath : str or pathlib.Path object, default=None
+ Filepath of metadata, in format supported by obspy.read_inventory. If default value of None, will read from resources folder of repository (only supported for Raspberry Shake).
hvsr_band : list, default=[0.4, 40]
Two-element list containing low and high "corner" frequencies (in Hz) for processing. This can specified again later.
peak_freq_range : list or tuple, default=[0.4, 40]
Two-element list or tuple containing low and high frequencies (in Hz) that are used to check for HVSR Peaks. This can be a tigher range than hvsr_band, but if larger, it will still only use the hvsr_band range.
+ processing_parameters={} : dict or filepath, default={}
+ If filepath, should point to a .proc json file with processing parameters (i.e, an output from sprit.export_settings()).
+ Note that this only applies to parameters for the functions: 'fetch_data', 'remove_noise', 'generate_ppsds', 'process_hvsr', 'check_peaks', and 'get_report.'
+ If dictionary, dictionary containing nested dictionaries of function names as they key, and the parameter names/values as key/value pairs for each key.
+ If a function name is not present, or if a parameter name is not present, default values will be used.
+ For example:
+ `{ 'fetch_data' : {'source':'batch', 'trim_dir':"/path/to/trimmed/data", 'export_format':'mseed', 'detrend':'spline', 'plot_input_stream':True, 'verbose':False, kwargs:{'kwargskey':'kwargsvalue'}} }`
verbose : bool, default=False
Whether to print output and results to terminal
@@ -2198,30 +2392,6 @@ Module sprit.sprit_hvsr
"""
orig_args = locals().copy() #Get the initial arguments
- #Declare obspy here instead of at top of file for (for example) colab, where obspy first needs to be installed on environment
- global obspy
- import obspy
- if verbose:
- print('Gathering input parameters (input_params())')
- for key, value in orig_args.items():
- print('\t {}={}'.format(key, value))
- print()
-
- #Make Sure metapath is all good
- if not pathlib.Path(metapath).exists() or metapath=='':
- if metapath == '':
- pass
- else:
- print('Specified metadata file cannot be read!')
- repoDir = pathlib.Path(os.path.dirname(__file__))
- metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv5plus_metadata.inv'))
- #print('Using default metadata file for Raspberry Shake v.7 distributed with package')
- else:
- if isinstance(metapath, pathlib.PurePath):
- metapath = metapath.as_posix()
- else:
- metapath = pathlib.Path(metapath).as_posix()
-
#Reformat times
if type(acq_date) is datetime.datetime:
date = str(acq_date.date())
@@ -2304,12 +2474,6 @@ Module sprit.sprit_hvsr
acq_date = datetime.date(year=int(date.split('-')[0]), month=int(date.split('-')[1]), day=int(date.split('-')[2]))
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
- #Raspberry shake stationxml is in the resources folder, double check we have right path
- if instrument.lower() in raspShakeInstNameList:
- if metapath == r'resources/rs3dv7_metadata.inv':
- metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv7_metadata.inv'))
- #metapath = pathlib.Path(os.path.realpath(__file__)).parent.joinpath('/resources/rs3dv7_metadata.inv')
-
if output_crs is None:
output_crs='EPSG:4326'
@@ -2322,18 +2486,220 @@ Module sprit.sprit_hvsr
coord_transformer = Transformer.from_crs(input_crs, output_crs, always_xy=True)
xcoord, ycoord = coord_transformer.transform(xcoord, ycoord)
- #Add key/values to input parameter dictionary
- inputParamDict = {'site':site, 'net':network,'sta':station, 'loc':loc, 'cha':channels, 'instrument':instrument,
- 'acq_date':acq_date,'starttime':starttime,'endtime':endtime, 'timezone':'UTC', #Will be in UTC by this point
- 'longitude':xcoord,'latitude':ycoord,'elevation':elevation,'input_crs':input_crs, 'output_crs':output_crs,
- 'depth':depth, 'datapath': datapath, 'metapath':metapath, 'hvsr_band':hvsr_band, 'peak_freq_range':peak_freq_range,
- 'ProcessingStatus':{'InputStatus':True, 'OverallStatus':True}
- }
-
- params = sprit_utils.make_it_classy(inputParamDict)
- params['ProcessingStatus']['InputParams'] = True
- params = _check_processing_status(params)
- return params
+ #Add key/values to input parameter dictionary
+ inputParamDict = {'site':site, 'net':network,'sta':station, 'loc':loc, 'cha':channels, 'instrument':instrument,
+ 'acq_date':acq_date,'starttime':starttime,'endtime':endtime, 'timezone':'UTC', #Will be in UTC by this point
+ 'longitude':xcoord,'latitude':ycoord,'elevation':elevation,'input_crs':input_crs, 'output_crs':output_crs,
+ 'depth':depth, 'datapath': datapath, 'metapath':metapath, 'hvsr_band':hvsr_band, 'peak_freq_range':peak_freq_range,
+ 'ProcessingStatus':{'InputStatus':True, 'OverallStatus':True}
+ }
+
+ #Replace any default parameter settings with those from json file of interest, potentially
+ instrument_settings_dict = {}
+ if pathlib.Path(instrument).exists():
+ instrument_settings = import_settings(settings_import_path=instrument, settings_import_type='instrument', verbose=verbose)
+ input_params_args = inspect.getfullargspec(input_params).args
+ input_params_args.append('net')
+ input_params_args.append('sta')
+ for k, settings_value in instrument_settings.items():
+ if k in input_params_args:
+ instrument_settings_dict[k] = settings_value
+ inputParamDict['instrument_settings'] = inputParamDict['instrument']
+ inputParamDict.update(instrument_settings_dict)
+
+ if instrument.lower() in raspShakeInstNameList:
+ if metapath is None:
+ metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv5plus_metadata.inv')).as_posix()
+ inputParamDict['metapath'] = metapath
+ #metapath = pathlib.Path(os.path.realpath(__file__)).parent.joinpath('/resources/rs3dv7_metadata.inv')
+
+ for settingName in instrument_settings_dict.keys():
+ if settingName in inputParamDict.keys():
+ inputParamDict[settingName] = instrument_settings_dict[settingName]
+
+ #Declare obspy here instead of at top of file for (for example) colab, where obspy first needs to be installed on environment
+ if verbose:
+ print('Gathering input parameters (input_params())')
+ for key, value in inputParamDict.items():
+ print('\t {}={}'.format(key, value))
+ print()
+
+ if isinstance(processing_parameters, dict):
+ inputParamDict['processing_parameters'] = processing_parameters
+ else:
+ processing_parameters = sprit_utils.checkifpath(processing_parameters)
+ inputParamDict['processing_parameters'] = import_settings(processing_parameters, settings_import_type='processing', verbose=verbose)
+
+ #Format everything nicely
+ params = sprit_utils.make_it_classy(inputParamDict)
+ params['ProcessingStatus']['InputParams'] = True
+ params = _check_processing_status(params)
+ return params
+
+#Main function for plotting results
+def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True,**kwargs):
+ """Function to plot HVSR data
+
+ Parameters
+ ----------
+ hvsr_data : dict
+ Dictionary containing output from process_hvsr function
+ plot_type : str or list, default = 'HVSR ann p C+ ann p SPEC'
+ The plot_type of plot(s) to plot. If list, will plot all plots listed
+ - 'HVSR' - Standard HVSR plot, including standard deviation. Options are included below:
+ - 'p' shows a vertical dotted line at frequency of the "best" peak
+ - 'ann' annotates the frequency value of of the "best" peak
+ - 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+ - 't' shows the H/V curve for all time windows
+ -'tp' shows all the peaks from the H/V curves of all the time windows
+ - 'COMP' - plot of the PPSD curves for each individual component ("C" also works)
+ - '+' (as a suffix in 'C+' or 'COMP+') plots C on a plot separate from HVSR (C+ is default, but without + will plot on the same plot as HVSR)
+ - 'p' shows a vertical dotted line at frequency of the "best" peak
+ - 'ann' annotates the frequency value of of the "best" peak
+ - 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+ - 't' shows the H/V curve for all time windows
+ - 'SPEC' - spectrogram style plot of the H/V curve over time
+ - 'p' shows a horizontal dotted line at the frequency of the "best" peak
+ - 'ann' annotates the frequency value of the "best" peak
+ use_subplots : bool, default = True
+ Whether to output the plots as subplots (True) or as separate plots (False)
+ fig : matplotlib.Figure, default = None
+ If not None, matplotlib figure on which plot is plotted
+ ax : matplotlib.Axis, default = None
+ If not None, matplotlib axis on which plot is plotted
+ return_fig : bool
+ Whether to return figure and axis objects
+ save_dir : str or None
+ Directory in which to save figures
+ save_suffix : str
+ Suffix to add to end of figure filename(s), if save_dir is used
+ show_legend : bool, default=False
+ Whether to show legend in plot
+ show : bool
+ Whether to show plot
+ close_figs : bool, default=False
+ Whether to close figures before plotting
+ clear_fig : bool, default=True
+ Whether to clear figures before plotting
+ **kwargs : keyword arguments
+ Keyword arguments for matplotlib.pyplot
+
+ Returns
+ -------
+ fig, ax : matplotlib figure and axis objects
+ Returns figure and axis matplotlib.pyplot objects if return_fig=True, otherwise, simply plots the figures
+ """
+ orig_args = locals().copy() #Get the initial arguments
+ if isinstance(hvsr_data, HVSRBatch):
+ #If running batch, we'll loop through each site
+ for site_name in hvsr_data.keys():
+ args = orig_args.copy() #Make a copy so we don't accidentally overwrite
+ individual_params = hvsr_data[site_name] #Get what would normally be the "params" variable for each site
+ args['hvsr_results'] = individual_params #reset the params parameter we originally read in to an individual site params
+ if hvsr_data[site_name]['ProcessingStatus']['OverallStatus']:
+ try:
+ _hvsr_plot_batch(**args) #Call another function, that lets us run this function again
+ except:
+ print(f"{site_name} not able to be plotted.")
+ else:
+ if clear_fig and fig is not None and ax is not None: #Intended use for tkinter
+ #Clear everything
+ for key in ax:
+ ax[key].clear()
+ fig.clear()
+ if close_figs:
+ plt.close('all')
+
+ compList = ['c', 'comp', 'component', 'components']
+ specgramList = ['spec', 'specgram', 'spectrogram']
+ hvsrList = ['hvsr', 'hv', 'h']
+
+ hvsrInd = np.nan
+ compInd = np.nan
+ specInd = np.nan
+
+ kList = plot_type.split(' ')
+ for i, k in enumerate(kList):
+ kList[i] = k.lower()
+
+ #Get the plots in the right order, no matter how they were input (and ensure the right options go with the right plot)
+ #HVSR index
+ if len(set(hvsrList).intersection(kList)):
+ for i, hv in enumerate(hvsrList):
+ if hv in kList:
+ hvsrInd = kList.index(hv)
+ break
+ #Component index
+ #if len(set(compList).intersection(kList)):
+ for i, c in enumerate(kList):
+ if '+' in c and c[:-1] in compList:
+ compInd = kList.index(c)
+ break
+
+ #Specgram index
+ if len(set(specgramList).intersection(kList)):
+ for i, sp in enumerate(specgramList):
+ if sp in kList:
+ specInd = kList.index(sp)
+ break
+
+ indList = [hvsrInd, compInd, specInd]
+ indListCopy = indList.copy()
+ plotTypeList = ['hvsr', 'comp', 'spec']
+
+ plotTypeOrder = []
+ plotIndOrder = []
+
+ lastVal = 0
+ while lastVal != 99:
+ firstInd = np.nanargmin(indListCopy)
+ plotTypeOrder.append(plotTypeList[firstInd])
+ plotIndOrder.append(indList[firstInd])
+ lastVal = indListCopy[firstInd]
+ indListCopy[firstInd] = 99 #just a high number
+
+ plotTypeOrder.pop()
+ plotIndOrder[-1]=len(kList)
+
+ for i, p in enumerate(plotTypeOrder):
+ pStartInd = plotIndOrder[i]
+ pEndInd = plotIndOrder[i+1]
+ plotComponents = kList[pStartInd:pEndInd]
+
+ if use_subplots and i==0 and fig is None and ax is None:
+ mosaicPlots = []
+ for pto in plotTypeOrder:
+ mosaicPlots.append([pto])
+ fig, ax = plt.subplot_mosaic(mosaicPlots, gridspec_kw={'hspace':0.3})
+ axis = ax[p]
+ elif use_subplots:
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore") #Often warns about xlim when it is not an issue
+ ax[p].clear()
+ axis = ax[p]
+ else:
+ fig, axis = plt.subplots()
+
+ if p == 'hvsr':
+ _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax, **kwargs)
+ elif p=='comp':
+ plotComponents[0] = plotComponents[0][:-1]
+ _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax, **kwargs)
+ elif p=='spec':
+ plottypeKwargs = {}
+ for c in plotComponents:
+ plottypeKwargs[c] = True
+ kwargs.update(plottypeKwargs)
+ _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False, **kwargs)
+ else:
+ warnings.warn('Plot type {p} not recognized', UserWarning)
+
+ if show:
+ fig.canvas.draw()
+
+ if return_fig:
+ return fig, ax
+ return
#Plot Obspy Trace in axis using matplotlib
def plot_stream(stream, params, fig=None, axes=None, show_plot=False, ylim_std=0.75, return_fig=True):
@@ -2521,6 +2887,27 @@ Module sprit.sprit_hvsr
"""
orig_args = locals().copy() #Get the initial arguments
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'process_hvsr' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['process_hvsr'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(process_hvsr).args[1:],
+ inspect.getfullargspec(process_hvsr).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ method = orig_args['method']
+ smooth = orig_args['smooth']
+ freq_smooth = orig_args['freq_smooth']
+ f_smooth_width = orig_args['f_smooth_width']
+ resample = orig_args['resample']
+ outlier_curve_std = orig_args['outlier_curve_std']
+ verbose = orig_args['verbose']
+
+
+
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
pass
@@ -2585,10 +2972,8 @@ Module sprit.sprit_hvsr
xValMin = min(ppsds[k]['period_bin_centers'])
xValMax = max(ppsds[k]['period_bin_centers'])
-
#Resample period bin values
x_periods[k] = np.logspace(np.log10(xValMin), np.log10(xValMax), num=resample)
-
if smooth or type(smooth) is int:
if smooth:
smooth = 51 #Default smoothing window
@@ -2616,7 +3001,6 @@ Module sprit.sprit_hvsr
#Get average psd value across time for each channel (used to calc main H/V curve)
psdValsTAvg[k] = np.nanmean(np.array(psdRaw[k]), axis=0)
x_freqs[k] = np.divide(np.ones_like(x_periods[k]), x_periods[k])
-
stDev[k] = np.std(psdRaw[k], axis=0)
stDevValsM[k] = np.array(psdValsTAvg[k] - stDev[k])
stDevValsP[k] = np.array(psdValsTAvg[k] + stDev[k])
@@ -2634,9 +3018,8 @@ Module sprit.sprit_hvsr
anyK = list(x_freqs.keys())[0]
hvsr_curve, _ = __get_hvsr_curve(x=x_freqs[anyK], psd=psdValsTAvg, method=methodInt, hvsr_data=hvsr_data, verbose=verbose)
origPPSD = hvsr_data['ppsds_obspy'].copy()
-
#Add some other variables to our output dictionary
- hvsr_data = {'input_params':hvsr_data,
+ hvsr_dataUpdate = {'input_params':hvsr_data,
'x_freqs':x_freqs,
'hvsr_curve':hvsr_curve,
'x_period':x_periods,
@@ -2653,7 +3036,7 @@ Module sprit.sprit_hvsr
'hvsr_df':hvsr_data['hvsr_df']
}
- hvsr_out = HVSRData(hvsr_data)
+ hvsr_out = HVSRData(hvsr_dataUpdate)
#This is if manual editing was used (should probably be updated at some point to just use masks)
if 'xwindows_out' in hvsr_data.keys():
@@ -2727,7 +3110,6 @@ Module sprit.sprit_hvsr
bool_col='Use'
eval_col='HV_Curves'
- testCol = hvsr_out['hvsr_df'].loc[hvsr_out['hvsr_df'][bool_col], eval_col].apply(np.nanstd).gt((avg_stdT + (std_stdT * outlier_curve_std)))
low_std_val = avg_stdT - (std_stdT * outlier_curve_std)
hi_std_val = avg_stdT + (std_stdT * outlier_curve_std)
@@ -2781,16 +3163,15 @@ Module sprit.sprit_hvsr
hvsr_out = __gethvsrparams(hvsr_out)
#Include the original obspy stream in the output
- #print(hvsr_data.keys())
- #print(type(hvsr_data))
- #print(hvsr_data['input_params'].keys())
- hvsr_out['input_stream'] = hvsr_data['input_params']['input_stream'] #input_stream
-
+ hvsr_out['input_stream'] = hvsr_dataUpdate['input_params']['input_stream'] #input_stream
hvsr_out = sprit_utils.make_it_classy(hvsr_out)
-
hvsr_out['ProcessingStatus']['HVStatus'] = True
hvsr_out = _check_processing_status(hvsr_out)
+ hvsr_data['processing_parameters']['process_hvsr'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['process_hvsr'][key] = value
+
return hvsr_out
#Function to remove noise windows from data
@@ -2839,7 +3220,30 @@ Module sprit.sprit_hvsr
output : dict
Dictionary similar to hvsr_data, but containing modified data with 'noise' removed
"""
- orig_args = locals().copy() #Get the initial arguments
+ #Get intput paramaters
+ orig_args = locals().copy()
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'remove_noise' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['remove_noise'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(remove_noise).args[1:],
+ inspect.getfullargspec(remove_noise).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ remove_method = orig_args['remove_method']
+ sat_percent = orig_args['sat_percent']
+ noise_percent = orig_args['noise_percent']
+ sta = orig_args['sta']
+ lta = orig_args['lta']
+ stalta_thresh = orig_args['stalta_thresh']
+ warmup_time = orig_args['warmup_time']
+ cooldown_time = orig_args['cooldown_time']
+ min_win_size = orig_args['min_win_size']
+ remove_raw_noise = orig_args['remove_raw_noise']
+ verbose = orig_args['verbose']
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
@@ -2956,12 +3360,22 @@ Module sprit.sprit_hvsr
#Add output
if isinstance(output, (HVSRData, dict)):
- output['stream'] = outStream
+ if isinstance(outStream, (obspy.Stream, obspy.Trace)):
+ output['stream'] = outStream
+ else:
+ output['stream'] = outStream['stream']
output['input_stream'] = hvsr_data['input_stream']
+
+ if 'processing_parameters' not in output.keys():
+ output['processing_parameters'] = {}
+ output['processing_parameters']['remove_noise'] = {}
+ for key, value in orig_args.items():
+ output['processing_parameters']['remove_noise'][key] = value
+
output['ProcessingStatus']['RemoveNoiseStatus'] = True
output = _check_processing_status(output)
- if 'hvsr_df' in output.keys():
+ if 'hvsr_df' in output.keys() or ('params' in output.keys() and 'hvsr_df' in output['params'].keys())or ('input_params' in output.keys() and 'hvsr_df' in output['input_params'].keys()):
hvsrDF = output['hvsr_df']
outStream = output['stream'].split()
@@ -2975,20 +3389,27 @@ Module sprit.sprit_hvsr
if trEndTime < trStartTime and comp_end==comp_start:
gap = [trEndTime,trStartTime]
+
output['hvsr_df']['Use'] = (hvsrDF['TimesProcessed_Obspy'].gt(gap[0]) & hvsrDF['TimesProcessed_Obspy'].gt(gap[1]) )| \
(hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[0]) & hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[1]))# | \
+ output['hvsr_df']['Use'] = output['hvsr_df']['Use'].astype(bool)
trEndTime = trace.stats.endtime
outStream.merge()
- output['stream'] = outStream
+ output['stream'] = outStream
+
elif isinstance(hvsr_data, obspy.core.stream.Stream) or isinstance(hvsr_data, obspy.core.trace.Trace):
output = outStream
else:
warnings.warn(f"Output of type {type(output)} for this function will likely result in errors in other processing steps. Returning hvsr_data data.")
return hvsr_data
+
-
+
+ output = sprit_utils.make_it_classy(output)
+ if 'xwindows_out' not in output.keys():
+ output['xwindows_out'] = []
return output
#Remove outlier ppsds
@@ -3053,16 +3474,19 @@ Module sprit.sprit_hvsr
psds_to_rid.append(i)
#Use dataframe
- hvsrDF = params['hvsr_df']
+ hvsrDF = params['hvsr_df'].copy()
psdVals = hvsrDF['psd_values_'+k]
- params['hvsr_df'][k+'_CurveMedian'] = psdVals.apply(np.nanmedian)
- params['hvsr_df'][k+'_CurveMean'] = psdVals.apply(np.nanmean)
+ hvsrDF[k+'_CurveMedian'] = psdVals.apply(np.nanmedian)
+ hvsrDF[k+'_CurveMean'] = psdVals.apply(np.nanmean)
- totMean = np.nanmean(params['hvsr_df'][k+'_CurveMean'])
- stds[k] = np.nanstd(params['hvsr_df'][k+'_CurveMean'])
-
- meanArr = params['hvsr_df'][k+'_CurveMean']
- params['hvsr_df']['Use'] = meanArr < (totMean + outlier_std * stds[k])
+ totMean = np.nanmean(hvsrDF[k+'_CurveMean'])
+ stds[k] = np.nanstd(hvsrDF[k+'_CurveMean'])
+
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+ #meanArr = hvsrDF[k+'_CurveMean'].loc[hvsrDF['Use']]
+ threshVal = totMean + outlier_std * stds[k]
+ hvsrDF['Use'] = hvsrDF[k+'_CurveMean'][hvsrDF['Use']].lt(threshVal)
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
psds_to_rid = np.unique(psds_to_rid)
@@ -3931,6 +4355,88 @@ Module sprit.sprit_hvsr
return rawDataIN
+#Read data from Tromino
+def __read_tromino_files(datapath, params, verbose=False):
+ """Function to read data from tromino. Specifically, this has been lightly tested on Tromino 3G+ machines
+
+ Parameters
+ ----------
+ datapath : str, pathlib.Path()
+ The input parameter _datapath_ from sprit.input_params()
+ params : HVSRData or HVSRBatch
+ The parameters as read in from input_params() and and fetch_data()
+ verbose : bool, optional
+ Whether to print results to terminal, by default False
+
+ Returns
+ -------
+ obspy.Stream
+ An obspy.Stream object containing the trace data from the Tromino instrument
+ """
+ dPath = datapath
+
+ strucSizes = {'c':1, 'b':1,'B':1, '?':1,
+ 'h':2,'H':2,'e':2,
+ 'i':4,'I':4,'l':4,'L':4,'f':4,
+ 'q':8,'Q':8,'d':8,
+ 'n':8,'N':8,'s':16,'p':16,'P':16,'x':16}
+
+ #H (pretty sure it's Q) I L or Q all seem to work (probably not Q?)
+ structFormat = 'H'
+ structSize = strucSizes[structFormat]
+
+ dataList = []
+ with open(dPath, 'rb') as f:
+ while True:
+ data = f.read(structSize) # Read 4 bytes
+ if not data: # End of file
+ break
+ value = struct.unpack(structFormat, data)[0] # Interpret as a float
+ dataList.append(value)
+
+ import numpy as np
+ dataArr = np.array(dataList)
+ import matplotlib.pyplot as plt
+
+ medVal = np.nanmedian(dataArr[50000:100000])
+
+ startByte=24576
+ comp1 = dataArr[startByte::3] - medVal
+ comp2 = dataArr[startByte+1::3] - medVal
+ comp3 = dataArr[startByte+2::3] - medVal
+ headerBytes = dataArr[:startByte]
+
+ #fig, ax = plt.subplots(3, sharex=True, sharey=True)
+ #ax[0].plot(comp1, linewidth=0.1, c='k')
+ #ax[1].plot(comp2, linewidth=0.1, c='k')
+ #ax[2].plot(comp3, linewidth=0.1, c='k')
+
+ sTime = obspy.UTCDateTime(params['acq_date'].year, params['acq_date'].month, params['acq_date'].day,
+ params['starttime'].hour, params['starttime'].minute,
+ params['starttime'].second,params['starttime'].microsecond)
+ eTime = sTime + (((len(comp1))/128)/60)*60
+
+ traceHeader1 = {'sampling_rate':128,
+ 'calib' : 1,
+ 'npts':len(comp1),
+ 'network':'AM',
+ 'location':'00',
+ 'station' : 'TRMNO',
+ 'channel':'BHE',
+ 'starttime':sTime}
+
+ traceHeader2=traceHeader1.copy()
+ traceHeader3=traceHeader1.copy()
+ traceHeader2['channel'] = 'BHN'
+ traceHeader3['channel'] = 'BHZ'
+
+ trace1 = obspy.Trace(data=comp1, header=traceHeader1)
+ trace2 = obspy.Trace(data=comp2, header=traceHeader2)
+ trace3 = obspy.Trace(data=comp3, header=traceHeader3)
+
+ st = obspy.Stream([trace1, trace2, trace3])
+ return st
+
##Helper functions for remove_noise()
#Helper function for removing gaps
def __remove_gaps(stream, window_gaps_obspy):
@@ -4464,13 +4970,13 @@ Module sprit.sprit_hvsr
if 'hvsr_curve' in input.keys():
fig, ax = plot_hvsr(hvsr_data=input, plot_type='spec', returnfig=True, cmap='turbo')
else:
- params = input.copy()
- input = input['stream']
+ hvsr_data = input#.copy()
+ input_stream = hvsr_data['stream']
- if isinstance(input, obspy.core.stream.Stream):
- fig, ax = _plot_specgram_stream(input, component=['Z'])
- elif isinstance(input, obspy.core.trace.Trace):
- fig, ax = _plot_specgram_stream(input)
+ if isinstance(input_stream, obspy.core.stream.Stream):
+ fig, ax = _plot_specgram_stream(input_stream, component=['Z'])
+ elif isinstance(input_stream, obspy.core.trace.Trace):
+ fig, ax = _plot_specgram_stream(input_stream)
global lineArtist
global winArtist
@@ -4494,10 +5000,10 @@ Module sprit.sprit_hvsr
fig.canvas.mpl_connect('close_event', _on_fig_close)#(clickNo, xWindows, pathList, windowDrawn, winArtist, lineArtist, x0, fig, ax))
plt.pause(1)
- params['xwindows_out'] = xWindows
- params['fig'] = fig
- params['ax'] = ax
- return params
+ hvsr_data['xwindows_out'] = xWindows
+ hvsr_data['fig_noise'] = fig
+ hvsr_data['ax_noise'] = ax
+ return hvsr_data
#Support function to help select_windows run properly
def _on_fig_close(event):
@@ -5233,7 +5739,6 @@ Module sprit.sprit_hvsr
ax.set_ylabel('H/V Ratio'+'\n['+hvsr_data['method']+']')
ax.set_title(hvsr_data['input_params']['site'])
-
#print("label='comp'" in str(ax.__dict__['_axes']))
for k in plot_type:
if k=='p' and 'all' not in plot_type:
@@ -5286,7 +5791,7 @@ Module sprit.sprit_hvsr
plotSuff = plotSuff+'IndComponents_'
if 'c' not in plot_type[0]:#This is part of the hvsr axis
- fig.tight_layout()
+ #fig.tight_layout()
axis2 = ax.twinx()
compAxis = axis2
#axis2 = plt.gca()
@@ -5356,7 +5861,7 @@ Module sprit.sprit_hvsr
axisbox.append(float(i))
if kwargs['show_legend']:
- ax.legend(loc=legendLoc)
+ ax.legend(loc=legendLoc,bbox_to_anchor=(1.05, 1))
__plot_current_fig(save_dir=save_dir,
filename=filename,
@@ -5389,18 +5894,27 @@ Module sprit.sprit_hvsr
def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix='',**kwargs):
"""Private function for plotting average spectrogram of all three channels from ppsds
"""
+ # Get all input parameters
if fig is None and ax is None:
fig, ax = plt.subplots()
if 'kwargs' in kwargs.keys():
kwargs = kwargs['kwargs']
- if 'peak_plot' in kwargs.keys():
+ if 'spec' in kwargs.keys():
+ del kwargs['spec']
+
+ if 'p' in kwargs.keys():
peak_plot=True
- del kwargs['peak_plot']
+ del kwargs['p']
else:
peak_plot=False
-
+
+ if 'ann' in kwargs.keys():
+ annotate=True
+ del kwargs['ann']
+ else:
+ annotate=False
if 'grid' in kwargs.keys():
ax.grid(which=kwargs['grid'], alpha=0.25)
@@ -5433,25 +5947,22 @@ Module sprit.sprit_hvsr
else:
kwargs['cmap'] = 'turbo'
+ # Setup
ppsds = hvsr_data['ppsds']#[k]['current_times_used']
import matplotlib.dates as mdates
anyKey = list(ppsds.keys())[0]
-
- psdHList =[]
- psdZList =[]
- for k in hvsr_data['psd_raw']:
- if 'z' in k.lower():
- psdZList.append(hvsr_data['psd_raw'][k])
- else:
- psdHList.append(hvsr_data['psd_raw'][k])
- #if detrend:
- # psdArr = np.subtract(psdArr, np.median(psdArr, axis=0))
- psdArr = hvsr_data['ind_hvsr_curves'].T
+ # Get data
+ psdArr = np.stack(hvsr_data['hvsr_df']['HV_Curves'].apply(np.flip))
+ useArr = np.array(hvsr_data['hvsr_df']['Use'])
+ useArr = np.tile(useArr, (psdArr.shape[1], 1)).astype(int)
+ useArr = np.clip(useArr, a_min=0.15, a_max=1)
- xmin = min(hvsr_data['ppsds'][anyKey]['current_times_used'][:-1]).matplotlib_date
- xmax = max(hvsr_data['ppsds'][anyKey]['current_times_used'][:-1]).matplotlib_date
-
+ # Get times
+ xmin = hvsr_data['hvsr_df']['TimesProcessed_MPL'].min()
+ xmax = hvsr_data['hvsr_df']['TimesProcessed_MPL'].max()
+
+ #Format times
tTicks = mdates.MinuteLocator(byminute=range(0,60,5))
ax.xaxis.set_major_locator(tTicks)
tTicks_minor = mdates.SecondLocator(bysecond=[0])
@@ -5461,58 +5972,67 @@ Module sprit.sprit_hvsr
ax.xaxis.set_major_formatter(tLabels)
ax.tick_params(axis='x', labelsize=8)
- if hvsr_data['ppsds'][anyKey]['current_times_used'][0].date != hvsr_data['ppsds'][anyKey]['current_times_used'][-1].date:
- day = str(hvsr_data['ppsds'][anyKey]['current_times_used'][0].date)+' - '+str(hvsr_data['ppsds'][anyKey]['current_times_used'][1].date)
+ #Get day label for bottom of chart
+ if hvsr_data['hvsr_df'].index[0].date() != hvsr_data['hvsr_df'].index[-1].date():
+ day = str(hvsr_data['hvsr_df'].index[0].date())+' - '+str(hvsr_data['hvsr_df'].index[-1].date())
else:
- day = str(hvsr_data['ppsds'][anyKey]['current_times_used'][0].date)
+ day = str(hvsr_data['hvsr_df'].index[0].date())
+ #Get extents
ymin = hvsr_data['input_params']['hvsr_band'][0]
ymax = hvsr_data['input_params']['hvsr_band'][1]
- extList = [xmin, xmax, ymin, ymax]
-
- #ax = plt.gca()
- #fig = plt.gcf()
-
freqticks = np.flip(hvsr_data['x_freqs'][anyKey])
yminind = np.argmin(np.abs(ymin-freqticks))
ymaxind = np.argmin(np.abs(ymax-freqticks))
freqticks = freqticks[yminind:ymaxind]
+ freqticks = np.logspace(np.log10(freqticks[0]), np.log10(freqticks[-1]), num=999)
+
+ extList = [xmin, xmax, ymin, ymax]
+
+ #Set up axes
+ ax.set_facecolor([0,0,0]) #Create black background for transparency to look darker
+
+ # Interpolate into linear
+ new_indices = np.linspace(freqticks[0], freqticks[-1], len(freqticks))
+ linList = []
+ for row in psdArr:
+ row = row.astype(np.float16)
+ linList.append(np.interp(new_indices, freqticks, row))
+ linear_arr = np.stack(linList)
+
+ # Create chart
+ im = ax.imshow(linear_arr.T, origin='lower', extent=extList, aspect='auto', alpha=useArr, **kwargs)
+ ax.tick_params(left=True, right=True, top=True)
- #Set up axes, since data is already in semilog
- axy = ax.twinx()
- axy.set_yticks([])
- axy.zorder=0
- ax.zorder=1
- ax.set_facecolor('#ffffff00') #Create transparent background for front axis
- #plt.sca(axy)
- im = ax.imshow(psdArr, origin='lower', extent=extList, aspect='auto', interpolation='nearest', **kwargs)
- ax.tick_params(left=False, right=False)
- #plt.sca(ax)
if peak_plot:
- ax.hlines(hvsr_data['BestPeak']['f0'], xmin, xmax, colors='k', linestyles='dashed', alpha=0.5)
+ ax.axhline(hvsr_data['BestPeak']['f0'], c='k', linestyle='dotted', zorder=1000)
+
+ if annotate:
+ if float(hvsr_data['BestPeak']['f0']) < 1:
+ boxYPerc = 0.998
+ vertAlign = 'top'
+ else:
+ boxYPerc = 0.002
+ vertAlign = 'bottom'
+ xLocation = float(xmin) + (float(xmax)-float(xmin))*0.99
+ yLocation = hvsr_data['input_params']['hvsr_band'][0] + (hvsr_data['input_params']['hvsr_band'][1]-hvsr_data['input_params']['hvsr_band'][0])*(boxYPerc)
+ ann = ax.text(x=xLocation, y=yLocation, fontsize='small', s=f"Peak at {hvsr_data['BestPeak']['f0']:0.2f} Hz", ha='right', va=vertAlign,
+ bbox={'alpha':0.8, 'edgecolor':'w', 'fc':'w', 'pad':0.3})
- #FreqTicks =np.arange(1,np.round(max(hvsr_data['x_freqs'][anyKey]),0), 10)
- specTitle = ax.set_title(hvsr_data['input_params']['site']+': Spectrogram')
- bgClr = (fig.get_facecolor()[0], fig.get_facecolor()[1], fig.get_facecolor()[2], 0.1)
- specTitle.set_color(bgClr)
ax.set_xlabel('UTC Time \n'+day)
-
if colorbar:
- cbar = plt.colorbar(mappable=im)
+ cbar = plt.colorbar(mappable=im, orientation='horizontal')
cbar.set_label('H/V Ratio')
ax.set_ylabel(ylabel)
- ax.set_yticks(freqticks)
- ax.semilogy()
- ax.set_ylim(hvsr_data['input_params']['hvsr_band'])
+ ax.set_yscale('log')
- #fig.tight_layout()
+ #plt.sca(ax)
#plt.rcParams['figure.dpi'] = 500
#plt.rcParams['figure.figsize'] = (12,4)
fig.canvas.draw()
- #fig.tight_layout()
- #plt.show()
+
return fig, ax
#Plot spectrogram from stream
@@ -5831,6 +6351,7 @@ Module sprit.sprit_hvsr
window_num = np.array(hvsr_data['psd_raw'][anyKey]).shape[0]
for _i in range(len(_peak)):
+ # Test 1
peakFreq= _peak[_i]['f0']
test1 = peakFreq > 10/window_len
@@ -5840,38 +6361,37 @@ Module sprit.sprit_hvsr
halfF0 = peakFreq/2
doublef0 = peakFreq*2
+
test3 = True
failCount = 0
for i, freq in enumerate(hvsr_data['x_freqs'][anyKey][:-1]):
- ###IS THIS RIGHT???
if freq >= halfF0 and freq <doublef0:
+ compVal = 2
if peakFreq >= 0.5:
- if hvsr_data['hvsr_log_std'][i] >= 2:
+ if hvsr_data['hvsr_log_std'][i] >= compVal:
test3=False
failCount +=1
+
else: #if peak freq is less than 0.5
- if hvsr_data['hvsr_log_std'][i] >= 3:
+ compVal = 3
+ if hvsr_data['hvsr_log_std'][i] >= compVal:
test3=False
failCount +=1
if test1:
- _peak[_i]['Report']['Lw'] = '{} > 10 / {} {}'.format(round(peakFreq,3), int(window_len), sprit_utils.check_mark())
+ _peak[_i]['Report']['Lw'] = f'{round(peakFreq,3)} > {10/int(window_len):0.3} (10 / {int(window_len)}) {sprit_utils.check_mark()}'
else:
- _peak[_i]['Report']['Lw'] = '{} > 10 / {} {}'.format(round(peakFreq,3), int(window_len), '✘')
+ _peak[_i]['Report']['Lw'] = f'{round(peakFreq,3)} > {10/int(window_len):0.3} (10 / {int(window_len)}) {sprit_utils.x_mark()}'
if test2:
- _peak[_i]['Report']['Nc'] = '{} > 200 {}'.format(round(nc,0), sprit_utils.check_mark())
+ _peak[_i]['Report']['Nc'] = f'{int(nc)} > 200 {sprit_utils.check_mark()}'
else:
- _peak[_i]['Report']['Nc'] = '{} > 200 {}'.format(round(nc,0), '✘')
+ _peak[_i]['Report']['Nc'] = f'{int(nc)} > 200 {sprit_utils.x_mark()}'
if test3:
- if peakFreq >= 0.5:
- compVal = 2
- else:
- compVal = 3
- _peak[_i]['Report']['σ_A(f)'] = 'σ_A for all freqs {}-{} < {} {}'.format(round(peakFreq*0.5, 3), round(peakFreq*2, 3), compVal, sprit_utils.check_mark())
+ _peak[_i]['Report']['σ_A(f)'] = f'H/V Amp. St.Dev. for {peakFreq*0.5:0.3f}-{peakFreq*2:0.3f}Hz < {compVal} {sprit_utils.check_mark()}'
else:
- _peak[_i]['Report']['σ_A(f)'] = 'σ_A for all freqs {}-{} < {} {}'.format(round(peakFreq*0.5, 3), round(peakFreq*2, 3), compVal, '✘')
+ _peak[_i]['Report']['σ_A(f)'] = f'H/V Amp. St.Dev. for {peakFreq*0.5:0.3f}-{peakFreq*2:0.3f}Hz < {compVal} {sprit_utils.x_mark()}'
_peak[_i]['PassList']['WindowLengthFreq.'] = test1
_peak[_i]['PassList']['SignificantCycles'] = test2
@@ -5917,16 +6437,16 @@ Module sprit.sprit_hvsr
for _i in range(len(_peak)):
#Initialize as False
- _peak[_i]['f-'] = '✘'
- _peak[_i]['Report']['A(f-)'] = 'No A_h/v in freqs {}-{} < {} {}'.format(round(_peak[_i]['A0']/4, 3), round(_peak[_i]['A0'], 3), round(_peak[_i]['A0']/2, 3), '✘')
- _peak[_i]['PassList']['PeakFreqClarityBelow'] = False #Start with assumption that it is False until we find an instance where it is True
+ _peak[_i]['f-'] = sprit_utils.x_mark()
+ _peak[_i]['Report']['A(f-)'] = f"H/V curve > {_peak[_i]['A0']/2:0.2f} for all {_peak[_i]['A0']/4:0.2f} Hz-{_peak[_i]['A0']:0.3f} Hz {sprit_utils.x_mark()}"
+ _peak[_i]['PassList']['PeakProminenceBelow'] = False #Start with assumption that it is False until we find an instance where it is True
for _j in range(jstart, -1, -1):
# There exist one frequency f-, lying between f0/4 and f0, such that A0 / A(f-) > 2.
if (float(_peak[_i]['f0']) / 4.0 <= _x[_j] < float(_peak[_i]['f0'])) and float(_peak[_i]['A0']) / _y[_j] > 2.0:
_peak[_i]['Score'] += 1
_peak[_i]['f-'] = '%10.3f %1s' % (_x[_j], sprit_utils.check_mark())
- _peak[_i]['Report']['A(f-)'] = 'A({}): {} < {} {}'.format(round(_x[_j], 3), round(_y[_j], 3), round(_peak[_i]['A0']/2,3), sprit_utils.check_mark())
- _peak[_i]['PassList']['PeakFreqClarityBelow'] = True
+ _peak[_i]['Report']['A(f-)'] = f"Amp. of H/V Curve @{_x[_j]:0.3f}Hz ({_y[_j]:0.3f}) < {_peak[_i]['A0']/2:0.3f} {sprit_utils.check_mark()}"
+ _peak[_i]['PassList']['PeakProminenceBelow'] = True
break
else:
pass
@@ -5935,25 +6455,21 @@ Module sprit.sprit_hvsr
max_rank += 1
for _i in range(len(_peak)):
#Initialize as False
- _peak[_i]['f+'] = '✘'
- _peak[_i]['Report']['A(f+)'] = 'No A_h/v in freqs {}-{} < {} {}'.format(round(_peak[_i]['A0'], 3), round(_peak[_i]['A0']*4, 3), round(_peak[_i]['A0']/2, 3), '✘')
- _peak[_i]['PassList']['PeakFreqClarityAbove'] = False
+ _peak[_i]['f+'] = sprit_utils.x_mark()
+ _peak[_i]['Report']['A(f+)'] = f"H/V curve > {_peak[_i]['A0']/2:0.2f} for all {_peak[_i]['A0']:0.2f} Hz-{_peak[_i]['A0']*4:0.3f} Hz {sprit_utils.x_mark()}"
+ _peak[_i]['PassList']['PeakProminenceAbove'] = False
for _j in range(len(_x) - 1):
# There exist one frequency f+, lying between f0 and 4*f0, such that A0 / A(f+) > 2.
if float(_peak[_i]['f0']) * 4.0 >= _x[_j] > float(_peak[_i]['f0']) and \
float(_peak[_i]['A0']) / _y[_j] > 2.0:
_peak[_i]['Score'] += 1
- _peak[_i]['f+'] = '%10.3f %1s' % (_x[_j], sprit_utils.check_mark())
- _peak[_i]['Report']['A(f+)'] = 'A({}): {} < {} {}'.format(round(_x[_j], 3), round(_y[_j], 3), round(_peak[_i]['A0']/2,3), sprit_utils.check_mark())
- _peak[_i]['PassList']['PeakFreqClarityAbove'] = True
+ _peak[_i]['f+'] = f"{_x[_j]:0.3f} {sprit_utils.check_mark()}"
+ _peak[_i]['Report']['A(f+)'] = f"H/V Curve at {_x[_j]:0.2f} Hz: {_y[_j]:0.2f} < {_peak[_i]['A0']/2:0.2f} (f0/2) {sprit_utils.check_mark()}"
+ _peak[_i]['PassList']['PeakProminenceAbove'] = True
break
else:
pass
-# if False in clarityPass:
-# _peak[_i]['PassList']['PeakFreqClarityBelow'] = False
-# else:
-# _peak[_i]['PassList']['PeakFreqClarityAbove'] = True
#Amplitude Clarity test
# Only peaks with A0 > 2 pass
@@ -5963,11 +6479,11 @@ Module sprit.sprit_hvsr
for _i in range(len(_peak)):
if float(_peak[_i]['A0']) > _a0:
- _peak[_i]['Report']['A0'] = '%10.2f > %0.1f %1s' % (_peak[_i]['A0'], _a0, sprit_utils.check_mark())
+ _peak[_i]['Report']['A0'] = f"Amplitude of peak ({_peak[_i]['A0']:0.2f}) > {int(_a0)} {sprit_utils.check_mark()}"
_peak[_i]['Score'] += 1
_peak[_i]['PassList']['PeakAmpClarity'] = True
else:
- _peak[_i]['Report']['A0'] = '%10.2f > %0.1f %1s' % (_peak[_i]['A0'], _a0, '✘')
+ _peak[_i]['Report']['A0'] = '%0.2f > %0.1f %1s' % (_peak[_i]['A0'], _a0, sprit_utils.x_mark())
_peak[_i]['PassList']['PeakAmpClarity'] = False
return _peak
@@ -6004,19 +6520,17 @@ Module sprit.sprit_hvsr
for _i in range(len(_peak)):
_dx = 1000000.
_found_m.append(False)
- _peak[_i]['Report']['P-'] = '✘'
+ _peak[_i]['Report']['P-'] = sprit_utils.x_mark()
for _j in range(len(_peakm)):
if abs(_peakm[_j]['f0'] - _peak[_i]['f0']) < _dx:
_index = _j
_dx = abs(_peakm[_j]['f0'] - _peak[_i]['f0'])
if _peak[_i]['f0'] * 0.95 <= _peakm[_j]['f0'] <= _peak[_i]['f0'] * 1.05:
- _peak[_i]['Report']['P-'] = '%0.3f within ±5%s of %0.3f %1s' % (_peakm[_j]['f0'], '%',
- _peak[_i]['f0'], sprit_utils.check_mark())
+ _peak[_i]['Report']['P-'] = f"{_peakm[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.check_mark()}"
_found_m[_i] = True
break
- if _peak[_i]['Report']['P-'] == '✘':
- _peak[_i]['Report']['P-'] = '%0.3f within ±5%s of %0.3f %1s' % (_peakm[_j]['f0'], '%', ##changed i to j
- _peak[_i]['f0'], '✘')
+ if _peak[_i]['Report']['P-'] == sprit_utils.x_mark():
+ _peak[_i]['Report']['P-'] = f"{_peakm[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.x_mark()}"
# Then Check above
_found_p = list()
@@ -6030,25 +6544,21 @@ Module sprit.sprit_hvsr
_dx = abs(_peakp[_j]['f0'] - _peak[_i]['f0'])
if _peak[_i]['f0'] * 0.95 <= _peakp[_j]['f0'] <= _peak[_i]['f0'] * 1.05:
if _found_m[_i]:
- _peak[_i]['Report']['P+'] = '%0.3f within ±5%s of %0.3f %1s' % (
- _peakp[_j]['f0'], '%', _peak[_i]['f0'], sprit_utils.check_mark())
+ _peak[_i]['Report']['P+'] = f"{_peakp[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.check_mark()}"
_peak[_i]['Score'] += 1
_peak[_i]['PassList']['FreqStability'] = True
else:
- _peak[_i]['Report']['P+'] = '%0.3f within ±5%s of %0.3f %1s' % (
- _peakp[_j]['f0'], '%', _peak[_i]['f0'], '✘')
+ _peak[_i]['Report']['P+'] = f"{_peakp[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.x_mark()}"
_peak[_i]['PassList']['FreqStability'] = False
break
else:
- _peak[_i]['Report']['P+'] = '%0.3f within ±5%s of %0.3f %1s' % (_peakp[_j]['f0'], '%', _peak[_i]['f0'], '✘')
+ _peak[_i]['Report']['P+'] = f"{_peakp[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.x_mark()}"
_peak[_i]['PassList']['FreqStability'] = False
if _peak[_i]['Report']['P+'] == sprit_utils.x_mark() and len(_peakp) > 0:
- _peak[_i]['Report']['P+'] = '%0.3f within ±5%s of %0.3f %1s' % (
- _peakp[_j]['f0'], '%', _peak[_i]['f0'], sprit_utils.x_mark()) #changed i to j
+ _peak[_i]['Report']['P+'] = f"{_peakp[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.x_mark()}"
return _peak
-
# Check stability
def __check_stability(_stdf, _peak, _hvsr_log_std, rank):
"""Test peaks for satisfying stability conditions as outlined by SESAME 2004
@@ -6089,104 +6599,96 @@ Module sprit.sprit_hvsr
if _this_peak['f0'] < 0.2:
_e = 0.25
if _stdf[_i] < _e * _this_peak['f0']:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'],
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_FreqStD'] = True
-
else:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'], '✘')
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.x_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
_t = 0.48
if _hvsr_log_std[_i] < _t:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t,
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_AmpStD'] = True
else:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, '✘')
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['PassList']['PeakStability_AmpStD'] = False
elif 0.2 <= _this_peak['f0'] < 0.5:
_e = 0.2
if _stdf[_i] < _e * _this_peak['f0']:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'],
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_FreqStD'] = True
else:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'], '✘')
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.x_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
_t = 0.40
if _hvsr_log_std[_i] < _t:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t,
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_AmpStD'] = True
else:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, '✘')
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['PassList']['PeakStability_AmpStD'] = False
elif 0.5 <= _this_peak['f0'] < 1.0:
_e = 0.15
if _stdf[_i] < _e * _this_peak['f0']:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'],
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_FreqStD'] = True
else:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'], '✘')
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.x_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
_t = 0.3
if _hvsr_log_std[_i] < _t:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, sprit_utils.check_mark())
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_AmpStD'] = True
else:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, '✘')
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['PassList']['PeakStability_AmpStD'] = False
elif 1.0 <= _this_peak['f0'] <= 2.0:
_e = 0.1
if _stdf[_i] < _e * _this_peak['f0']:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'],
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_FreqStD'] = True
else:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s ' % (_stdf[_i], _e, _this_peak['f0'], '✘')
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.x_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
_t = 0.25
if _hvsr_log_std[_i] < _t:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, sprit_utils.check_mark())
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_AmpStD'] = True
else:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, '✘')
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['PassList']['PeakStability_AmpStD'] = False
elif _this_peak['f0'] > 0.2:
_e = 0.05
if _stdf[_i] < _e * _this_peak['f0']:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'],
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_FreqStD'] = True
else:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'], '✘')
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.x_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
_t = 0.2
if _hvsr_log_std[_i] < _t:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, sprit_utils.check_mark())
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_AmpStD'] = True
else:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, '✘')
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
return _peak
@@ -6498,7 +7000,7 @@ Raises
-def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose=False)
+def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_selection='max', peak_freq_range=[1, 20], verbose=False)
Function to run tests on HVSR peaks to find best one and see if it passes quality checks
@@ -6508,6 +7010,10 @@ Parameters
Dictionary containing all the calculated information about the HVSR data (i.e., hvsr_out returned from process_hvsr)
hvsr_band : tuple or list, default=[0.4, 40]2-item tuple or list with lower and upper limit of frequencies to analyze
+peak_selection : str or numeric, default='max'How to select the "best" peak used in the analysis. For peak_selection="max" (default value), the highest peak within peak_freq_range is used.
+For peak_selection='scored', an algorithm is used to select the peak based in part on which peak passes the most SESAME criteria.
+If a numeric value is used (e.g., int or float), this should be a frequency value to manually select as the peak of interest.
peak_freq_range : tuple or list, default=[1, 20];The frequency range within which to check for peaks. If there is an HVSR curve with multiple peaks, this allows the full range of data to be processed while limiting peak picks to likely range.
verbose : bool, default=FalseReturns
Expand source code
-def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose=False):
+def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_selection='max', peak_freq_range=[1, 20], verbose=False):
"""Function to run tests on HVSR peaks to find best one and see if it passes quality checks
Parameters
@@ -6532,6 +7038,10 @@ Returns
Dictionary containing all the calculated information about the HVSR data (i.e., hvsr_out returned from process_hvsr)
hvsr_band : tuple or list, default=[0.4, 40]
2-item tuple or list with lower and upper limit of frequencies to analyze
+ peak_selection : str or numeric, default='max'
+ How to select the "best" peak used in the analysis. For peak_selection="max" (default value), the highest peak within peak_freq_range is used.
+ For peak_selection='scored', an algorithm is used to select the peak based in part on which peak passes the most SESAME criteria.
+ If a numeric value is used (e.g., int or float), this should be a frequency value to manually select as the peak of interest.
peak_freq_range : tuple or list, default=[1, 20];
The frequency range within which to check for peaks. If there is an HVSR curve with multiple peaks, this allows the full range of data to be processed while limiting peak picks to likely range.
verbose : bool, default=False
@@ -6544,6 +7054,25 @@ Returns
"""
orig_args = locals().copy() #Get the initial arguments
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'check_peaks' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['check_peaks'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(check_peaks).args[1:],
+ inspect.getfullargspec(check_peaks).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ hvsr_band = orig_args['hvsr_band']
+ peak_selection = orig_args['peak_selection']
+ peak_freq_range = orig_args['peak_freq_range']
+ verbose = orig_args['verbose']
+
+ hvsr_data['processing_parameters']['check_peaks'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['check_peaks'][key] = value
+
if (verbose and 'input_params' not in hvsr_data.keys()) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
pass
@@ -6579,13 +7108,38 @@ Returns
if hvsr_data['ProcessingStatus']['OverallStatus']:
if not hvsr_band:
hvsr_band = [0.4,40]
+
hvsr_data['hvsr_band'] = hvsr_band
anyK = list(hvsr_data['x_freqs'].keys())[0]
x = hvsr_data['x_freqs'][anyK] #Consistent for all curves
y = hvsr_data['hvsr_curve'] #Calculated based on "Use" column
- index_list = hvsr_data['hvsr_peak_indices'] #Calculated based on hvsr_curve
+
+ scorelist = ['score', 'scored', 'best', 's']
+ maxlist = ['max', 'highest', 'm']
+ # Convert peak_selection to numeric, get index of nearest value as list item for __init_peaks()
+ try:
+ peak_val = float(peak_selection)
+ index_list = [np.argmin(np.abs(x - peak_val))]
+ except:
+ # If score method is being used, get index list for __init_peaks()
+ if peak_selection in scorelist:
+ index_list = hvsr_data['hvsr_peak_indices'] #Calculated based on hvsr_curve
+ elif peak_selection in maxlist:
+ #Get max index as item in list for __init_peaks()
+ startInd = np.argmin(np.abs(x - peak_freq_range[0]))
+ endInd = np.argmin(np.abs(x - peak_freq_range[1]))
+ if startInd > endInd:
+ holder = startInd
+ startInd = endInd
+ endInd = holder
+ subArrayMax = np.argmax(y[startInd:endInd])
+
+ # If max val is in subarray, this will be the same as the max of curve
+ # Otherwise, it will be the index of the value that is max within peak_freq_range
+ index_list = [subArrayMax+startInd]
+
hvsrp = hvsr_data['hvsrp'] #Calculated based on "Use" column
hvsrm = hvsr_data['hvsrm'] #Calculated based on "Use" column
@@ -6611,7 +7165,7 @@ Returns
peakp = __init_peaks(x, hvsrp, index_p, hvsr_band, peak_freq_range)
peakp = __check_clarity(x, hvsrp, peakp, do_rank=True)
- #Do for hvsrm
+ # Do for hvsrm
# Find the relative extrema of hvsrm (hvsr - 1 standard deviation)
if not np.isnan(np.sum(hvsrm)):
index_m = __find_peaks(hvsrm)
@@ -6621,6 +7175,7 @@ Returns
peakm = __init_peaks(x, hvsrm, index_m, hvsr_band, peak_freq_range)
peakm = __check_clarity(x, hvsrm, peakm, do_rank=True)
+ # Get standard deviation of time peaks
stdf = __get_stdf(x, index_list, hvsrPeaks)
peak = __check_freq_stability(peak, peakm, peakp)
@@ -6632,7 +7187,7 @@ Returns
# Get the BestPeak based on the peak score
# Calculate whether each peak passes enough tests
curveTests = ['WindowLengthFreq.','SignificantCycles', 'LowCurveStDevOverTime']
- peakTests = ['PeakFreqClarityBelow', 'PeakFreqClarityAbove', 'PeakAmpClarity', 'FreqStability', 'PeakStability_FreqStD', 'PeakStability_AmpStD']
+ peakTests = ['PeakProminenceBelow', 'PeakProminenceAbove', 'PeakAmpClarity', 'FreqStability', 'PeakStability_FreqStD', 'PeakStability_AmpStD']
bestPeakScore = 0
for p in hvsr_data['PeakReport']:
@@ -6666,6 +7221,10 @@ Returns
else:
hvsr_data['BestPeak'] = {}
print(f"Processing Errors: No Best Peak identified for {hvsr_data['site']}")
+ try:
+ hvsr_data.plot()
+ except:
+ pass
return hvsr_data
@@ -6730,6 +7289,139 @@ Parameters
return
+
+def export_settings(hvsr_data, export_settings_path='default', export_settings_type='all', include_location=False, verbose=False)
+
+Save settings to json file
+Parameters
+
+export_settings_path : str, default="default"- Where to save the json file(s) containing the settings, by default 'default'.
+If "default," will save to sprit package resources. Otherwise, set a filepath location you would like for it to be saved to.
+If 'all' is selected, a directory should be supplied.
+Otherwise, it will save in the directory of the provided file, if it exists. Otherwise, defaults to the home directory.
+export_settings_type : str, {'all', 'instrument', 'processing'}- What kind of settings to save.
+If 'all', saves all possible types in their respective json files.
+If 'instrument', save the instrument settings to their respective file.
+If 'processing', saves the processing settings to their respective file. By default 'all'
+include_location : bool, default=False, input CRS- Whether to include the location parametersin the exported settings document.This includes xcoord, ycoord, elevation, elev_unit, and input_crs
+verbose : bool, default=True- Whether to print outputs and information to the terminal
+
+
+
+Expand source code
+
+def export_settings(hvsr_data, export_settings_path='default', export_settings_type='all', include_location=False, verbose=False):
+ """Save settings to json file
+
+ Parameters
+ ----------
+ export_settings_path : str, default="default"
+ Where to save the json file(s) containing the settings, by default 'default'.
+ If "default," will save to sprit package resources. Otherwise, set a filepath location you would like for it to be saved to.
+ If 'all' is selected, a directory should be supplied.
+ Otherwise, it will save in the directory of the provided file, if it exists. Otherwise, defaults to the home directory.
+ export_settings_type : str, {'all', 'instrument', 'processing'}
+ What kind of settings to save.
+ If 'all', saves all possible types in their respective json files.
+ If 'instrument', save the instrument settings to their respective file.
+ If 'processing', saves the processing settings to their respective file. By default 'all'
+ include_location : bool, default=False, input CRS
+ Whether to include the location parametersin the exported settings document.This includes xcoord, ycoord, elevation, elev_unit, and input_crs
+ verbose : bool, default=True
+ Whether to print outputs and information to the terminal
+
+ """
+ fnameDict = {}
+ fnameDict['instrument'] = "instrument_settings.json"
+ fnameDict['processing'] = "processing_settings.json"
+
+ if export_settings_path == 'default' or export_settings_path is True:
+ settingsPath = resource_dir.joinpath('settings')
+ else:
+ export_settings_path = pathlib.Path(export_settings_path)
+ if not export_settings_path.exists():
+ if not export_settings_path.parent.exists():
+ print(f'The provided value for export_settings_path ({export_settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}')
+ settingsPath = pathlib.Path.home()
+ else:
+ settingsPath = export_settings_path.parent
+
+ if export_settings_path.is_dir():
+ settingsPath = export_settings_path
+ elif export_settings_path.is_file():
+ settingsPath = export_settings_path.parent
+ fnameDict['instrument'] = export_settings_path.name+"_instrumentSettings.json"
+ fnameDict['processing'] = export_settings_path.name+"_processingSettings.json"
+
+ #Get final filepaths
+ instSetFPath = settingsPath.joinpath(fnameDict['instrument'])
+ procSetFPath = settingsPath.joinpath(fnameDict['processing'])
+
+ #Get settings values
+ instKeys = ["instrument", "net", "sta", "loc", "cha", "depth", "metapath", "hvsr_band"]
+ inst_location_keys = ['xcoord', 'ycoord', 'elevation', 'elev_unit', 'input_crs']
+ procFuncs = [fetch_data, remove_noise, generate_ppsds, process_hvsr, check_peaks, get_report]
+
+ instrument_settings_dict = {}
+ processing_settings_dict = {}
+
+ for k in instKeys:
+ if isinstance(hvsr_data[k], pathlib.PurePath):
+ #For those that are paths and cannot be serialized
+ instrument_settings_dict[k] = hvsr_data[k].as_posix()
+ else:
+ instrument_settings_dict[k] = hvsr_data[k]
+
+ if include_location:
+ for k in inst_location_keys:
+ if isinstance(hvsr_data[k], pathlib.PurePath):
+ #For those that are paths and cannot be serialized
+ instrument_settings_dict[k] = hvsr_data[k].as_posix()
+ else:
+ instrument_settings_dict[k] = hvsr_data[k]
+
+
+ for func in procFuncs:
+ funcName = func.__name__
+ processing_settings_dict[funcName] = {}
+ for arg in hvsr_data['processing_parameters'][funcName]:
+ if isinstance(hvsr_data['processing_parameters'][funcName][arg], (HVSRBatch, HVSRData)):
+ pass
+ else:
+ processing_settings_dict[funcName][arg] = hvsr_data['processing_parameters'][funcName][arg]
+
+ #Save settings files
+ if export_settings_type.lower()=='instrument' or export_settings_type.lower()=='all':
+ with open(instSetFPath.with_suffix('.inst').as_posix(), 'w') as instSetF:
+ jsonString = json.dumps(instrument_settings_dict, indent=2)
+ #Format output for readability
+ jsonString = jsonString.replace('\n ', ' ')
+ jsonString = jsonString.replace('[ ', '[')
+ jsonString = jsonString.replace('\n ]', ']')
+ #Export
+ instSetF.write(jsonString)
+ if export_settings_type.lower()=='processing' or export_settings_type.lower()=='all':
+ with open(procSetFPath.with_suffix('.proc').as_posix(), 'w') as procSetF:
+ jsonString = json.dumps(processing_settings_dict, indent=2)
+ #Format output for readability
+ jsonString = jsonString.replace('\n ', ' ')
+ jsonString = jsonString.replace('[ ', '[')
+ jsonString = jsonString.replace('\n ]', ']')
+ jsonString = jsonString.replace('\n },','\n\t\t},\n')
+ jsonString = jsonString.replace('{ "', '\n\t\t{\n\t\t"')
+ jsonString = jsonString.replace(', "', ',\n\t\t"')
+ jsonString = jsonString.replace('\n }', '\n\t\t}')
+ jsonString = jsonString.replace(': {', ':\n\t\t\t{')
+
+ #Export
+ procSetF.write(jsonString)
+
+
def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detrend='spline', detrend_order=2, update_metadata=True, plot_input_stream=False, verbose=False, **kwargs)
Parameters
Expand source code
def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detrend='spline', detrend_order=2, update_metadata=True, plot_input_stream=False, verbose=False, **kwargs):
- import warnings
+ #Get intput paramaters
+ orig_args = locals().copy()
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in params.keys():
+ if 'fetch_data' in params['processing_parameters'].keys():
+ defaultVDict = dict(zip(inspect.getfullargspec(fetch_data).args[1:],
+ inspect.getfullargspec(fetch_data).defaults))
+ defaultVDict['kwargs'] = kwargs
+ for k, v in params['processing_parameters']['fetch_data'].items():
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ #Update local variables, in case of previously-specified parameters
+ source=orig_args['source']
+ trim_dir=orig_args['trim_dir']
+ export_format=orig_args['export_format']
+ detrend=orig_args['detrend']
+ detrend_order=orig_args['detrend_order']
+ update_metadata=orig_args['update_metadata']
+ plot_input_stream=orig_args['plot_input_stream']
+ verbose=orig_args['verbose']
+ kwargs=orig_args['kwargs']
"""Fetch ambient seismic data from a source to read into obspy stream
@@ -6789,7 +7504,9 @@ Parameters
date=params['acq_date']
#Cleanup for gui input
- if '}' in str(params['datapath']):
+ if isinstance(params['datapath'], (obspy.Stream, obspy.Trace)):
+ pass
+ elif '}' in str(params['datapath']):
params['datapath'] = params['datapath'].as_posix().replace('{','')
params['datapath'] = params['datapath'].split('}')
@@ -6802,7 +7519,10 @@ Parameters
#Make sure datapath is pointing to an actual file
if isinstance(params['datapath'],list):
for i, d in enumerate(params['datapath']):
- params['datapath'][i] = sprit_utils.checkifpath(str(d).strip())
+ params['datapath'][i] = sprit_utils.checkifpath(str(d).strip(), sample_list=sampleList)
+ dPath = params['datapath']
+ elif isinstance(params['datapath'], (obspy.Stream, obspy.Trace)):
+ pass
else:
dPath = sprit_utils.checkifpath(params['datapath'], sample_list=sampleList)
@@ -6854,15 +7574,26 @@ Parameters
#Select which instrument we are reading from (requires different processes for each instrument)
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
+ trominoNameList = ['tromino', 'trom', 'tromino 3g', 'tromino 3g+', 'tr', 't']
+
+ #Get any kwargs that are included in obspy.read
+ obspyReadKwargs = {}
+ for argName in inspect.getfullargspec(obspy.read)[0]:
+ if argName in kwargs.keys():
+ obspyReadKwargs[argName] = kwargs[argName]
#Select how reading will be done
if source=='raw':
- if inst.lower() in raspShakeInstNameList:
- try:
+ try:
+ if inst.lower() in raspShakeInstNameList:
rawDataIN = __read_RS_file_struct(dPath, source, year, doy, inv, params, verbose=verbose)
- except:
- raise RuntimeError(f"Data not fetched for {params['site']}. Check input parameters or the data file.")
- return params
+
+ elif inst.lower() in trominoNameList:
+ rawDataIN = __read_tromino_files(dPath, params, verbose=verbose)
+ except:
+ raise RuntimeError(f"Data not fetched for {params['site']}. Check input parameters or the data file.")
+ elif source=='stream' or isinstance(params, (obspy.Stream, obspy.Trace)):
+ rawDataIN = params['datapath'].copy()
elif source=='dir':
if inst.lower() in raspShakeInstNameList:
rawDataIN = __read_RS_file_struct(dPath, source, year, doy, inv, params, verbose=verbose)
@@ -6873,28 +7604,27 @@ Parameters
for f in temp_file_glob:
currParams = params
currParams['datapath'] = f
+
curr_data = fetch_data(params, source='file', #all the same as input, except just reading the one file using the source='file'
- trim_dir=trim_dir, export_format=export_format, detrend=detrend, detrend_order=detrend_order, update_metadata=update_metadata, verbose=verbose, **kwargs),
+ trim_dir=trim_dir, export_format=export_format, detrend=detrend, detrend_order=detrend_order, update_metadata=update_metadata, verbose=verbose, **kwargs)
+ curr_data.merge()
obspyFiles[f.stem] = curr_data #Add path object to dict, with filepath's stem as the site name
return HVSRBatch(obspyFiles)
-
elif source=='file' and str(params['datapath']).lower() not in sampleList:
if isinstance(dPath, list) or isinstance(dPath, tuple):
rawStreams = []
for datafile in dPath:
- rawStream = obspy.read(datafile)
+ rawStream = obspy.read(datafile, **obspyReadKwargs)
rawStreams.append(rawStream) #These are actually streams, not traces
-
for i, stream in enumerate(rawStreams):
if i == 0:
rawDataIN = obspy.Stream(stream) #Just in case
else:
rawDataIN = rawDataIN + stream #This adds a stream/trace to the current stream object
-
elif str(dPath)[:6].lower()=='sample':
pass
else:
- rawDataIN = obspy.read(dPath)#, starttime=obspy.core.UTCDateTime(params['starttime']), endttime=obspy.core.UTCDateTime(params['endtime']), nearest_sample =True)
+ rawDataIN = obspy.read(dPath, **obspyReadKwargs)#, starttime=obspy.core.UTCDateTime(params['starttime']), endttime=obspy.core.UTCDateTime(params['endtime']), nearest_sample =True)
import warnings
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=UserWarning)
@@ -6941,12 +7671,15 @@ Parameters
except:
RuntimeError(f'source={source} not recognized, and datapath cannot be read using obspy.read()')
+ #Get metadata from the data itself, if not reading raw data
try:
dataIN = rawDataIN.copy()
if source!='raw':
#Use metadata from file for;
# site
if params['site'] == "HVSR Site":
+ if isinstance(dPath, (list, tuple)):
+ dPath = dPath[0]
params['site'] = dPath.stem
params['params']['site'] = dPath.stem
@@ -6983,11 +7716,13 @@ Parameters
today_Starttime = obspy.UTCDateTime(datetime.datetime(year=datetime.date.today().year, month=datetime.date.today().month,
day = datetime.date.today().day,
hour=0, minute=0, second=0, microsecond=0))
- maxStarttime = datetime.time(hour=0, minute=0, second=0, microsecond=0)
+ maxStarttime = datetime.datetime(year=params['acq_date'].year, month=params['acq_date'].month, day=params['acq_date'].day,
+ hour=0, minute=0, second=0, microsecond=0, tzinfo=datetime.timezone.utc)
if str(params['starttime']) == str(today_Starttime):
for tr in dataIN.merge():
- currTime = datetime.time(hour=tr.stats.starttime.hour, minute=tr.stats.starttime.minute,
- second=tr.stats.starttime.second, microsecond=tr.stats.starttime.microsecond)
+ currTime = datetime.datetime(year=tr.stats.starttime.year, month=tr.stats.starttime.month, day=tr.stats.starttime.day,
+ hour=tr.stats.starttime.hour, minute=tr.stats.starttime.minute,
+ second=tr.stats.starttime.second, microsecond=tr.stats.starttime.microsecond, tzinfo=datetime.timezone.utc)
if currTime > maxStarttime:
maxStarttime = currTime
@@ -7002,17 +7737,19 @@ Parameters
today_Endtime = obspy.UTCDateTime(datetime.datetime(year=datetime.date.today().year, month=datetime.date.today().month,
day = datetime.date.today().day,
hour=23, minute=59, second=59, microsecond=999999))
- minEndtime = datetime.time(hour=23, minute=59, second=59, microsecond=999999)
- if str(params['endtime']) == str(today_Endtime):
+ tomorrow_Endtime = today_Endtime + (60*60*24)
+ minEndtime = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc)#(hour=23, minute=59, second=59, microsecond=999999)
+ if str(params['endtime']) == str(today_Endtime) or str(params['endtime'])==tomorrow_Endtime:
for tr in dataIN.merge():
- currTime = datetime.time(hour=tr.stats.endtime.hour, minute=tr.stats.endtime.minute,
- second=tr.stats.endtime.second, microsecond=tr.stats.endtime.microsecond)
+ currTime = datetime.datetime(year=tr.stats.endtime.year, month=tr.stats.endtime.month, day=tr.stats.endtime.day,
+ hour=tr.stats.endtime.hour, minute=tr.stats.endtime.minute,
+ second=tr.stats.endtime.second, microsecond=tr.stats.endtime.microsecond, tzinfo=datetime.timezone.utc)
if currTime < minEndtime:
minEndtime = currTime
- newEndtime = obspy.UTCDateTime(datetime.datetime(year=params['acq_date'].year, month=params['acq_date'].month,
- day = params['acq_date'].day,
+ newEndtime = obspy.UTCDateTime(datetime.datetime(year=minEndtime.year, month=minEndtime.month,
+ day = minEndtime.day,
hour=minEndtime.hour, minute=minEndtime.minute,
- second=minEndtime.second, microsecond=minEndtime.microsecond))
+ second=minEndtime.second, microsecond=minEndtime.microsecond, tzinfo=datetime.timezone.utc))
params['endtime'] = newEndtime
params['params']['endtime'] = newEndtime
@@ -7052,10 +7789,12 @@ Parameters
#Remerge data
dataIN = dataIN.merge(method=1)
+ #Plot the input stream?
if plot_input_stream:
try:
params['InputPlot'] = _plot_specgram_stream(stream=dataIN, params=params, component='Z', stack_type='linear', detrend='mean', dbscale=True, fill_gaps=None, ylimstd=3, return_fig=True, fig=None, ax=None, show_plot=False)
- _get_removed_windows(input=dataIN, fig=params['InputPlot'][0], ax=params['InputPlot'][1], lineArtist =[], winArtist = [], existing_lineArtists=[], existing_xWindows=[], exist_win_format='matplotlib', keep_line_artists=True, time_type='matplotlib', show_plot=True)
+ #_get_removed_windows(input=dataIN, fig=params['InputPlot'][0], ax=params['InputPlot'][1], lineArtist =[], winArtist = [], existing_lineArtists=[], existing_xWindows=[], exist_win_format='matplotlib', keep_line_artists=True, time_type='matplotlib', show_plot=True)
+ plt.show()
except:
print('Error with default plotting method, falling back to internal obspy plotting method')
dataIN.plot(method='full', linewidth=0.25)
@@ -7066,6 +7805,7 @@ Parameters
else:
dataIN = _sort_channels(input=dataIN, source=source, verbose=verbose)
+ #Clean up the ends of the data unless explicitly specified to do otherwise (this is a kwarg, not a parameter)
if 'clean_ends' not in kwargs.keys():
clean_ends=True
else:
@@ -7101,6 +7841,14 @@ Parameters
params['batch'] = False #Set False by default, will get corrected later in batch mode
params['input_stream'] = dataIN.copy()
params['stream'] = dataIN.copy()
+
+ if 'processing_parameters' not in params.keys():
+ params['processing_parameters'] = {}
+ params['processing_parameters']['fetch_data'] = {}
+ for key, value in orig_args.items():
+ params['processing_parameters']['fetch_data'][key] = value
+
+
params['ProcessingStatus']['FetchDataStatus'] = True
if verbose and not isinstance(params, HVSRBatch):
dataINStr = dataIN.__str__().split('\n')
@@ -7185,6 +7933,8 @@ Returns
ppsd_kwargs_sprit_defaults['skip_on_gaps'] = True
if 'period_step_octaves' not in ppsd_kwargs:
ppsd_kwargs_sprit_defaults['period_step_octaves'] = 0.03125
+ if 'period_limits' not in ppsd_kwargs:
+ ppsd_kwargs_sprit_defaults['period_limits'] = [1/hvsr_data['hvsr_band'][1], 1/hvsr_data['hvsr_band'][0]]
#Get Probablistic power spectral densities (PPSDs)
#Get default args for function
@@ -7198,8 +7948,24 @@ Returns
ppsd_kwargs = get_default_args(PPSD)
ppsd_kwargs.update(ppsd_kwargs_sprit_defaults)#Update with sprit defaults, or user input
+ orig_args['ppsd_kwargs'] = ppsd_kwargs
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'generate_ppsds' in hvsr_data['processing_parameters'].keys():
+ defaultVDict = dict(zip(inspect.getfullargspec(generate_ppsds).args[1:],
+ inspect.getfullargspec(generate_ppsds).defaults))
+ defaultVDict['ppsd_kwargs'] = ppsd_kwargs
+ for k, v in hvsr_data['processing_parameters']['generate_ppsds'].items():
+
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
- orig_args['ppsd_kwargs'] = [ppsd_kwargs]
+ remove_outliers = orig_args['remove_outliers']
+ outlier_std = orig_args['outlier_std']
+ verbose = orig_args['verbose']
+ ppsd_kwargs = orig_args['ppsd_kwargs']
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
@@ -7233,7 +7999,7 @@ Returns
hvsr_data[site_name]['ProcessingStatus']['OverallStatus'] = False
return hvsr_data
else:
- paz=hvsr_data['paz']
+ paz = hvsr_data['paz']
stream = hvsr_data['stream']
#Get ppsds of e component
@@ -7368,10 +8134,18 @@ Returns
hvsrDF['TimesProcessed_MPLEnd'] = hvsrDF['TimesProcessed_MPL'] + (ppsd_kwargs['ppsd_length']/86400)
hvsrDF['Use'] = True
+ hvsrDF['Use']=hvsrDF['Use'].astype(bool)
for gap in hvsr_data['ppsds']['Z']['times_gaps']:
- hvsrDF['Use'] = (hvsrDF['TimesProcessed_Obspy'].gt(gap[0]) & hvsrDF['TimesProcessed_Obspy'].gt(gap[1]) )| \
- (hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[0]) & hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[1]))# | \
-
+ hvsrDF['Use'] = (hvsrDF['TimesProcessed_MPL'].gt(gap[1].matplotlib_date))| \
+ (hvsrDF['TimesProcessed_MPLEnd'].lt(gap[0].matplotlib_date))# | \
+
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+ if 'xwindows_out' in hvsr_data.keys():
+ for window in hvsr_data['xwindows_out']:
+ hvsrDF['Use'] = (hvsrDF['TimesProcessed_MPL'][hvsrDF['Use']].lt(window[0]) & hvsrDF['TimesProcessed_MPLEnd'][hvsrDF['Use']].lt(window[0]) )| \
+ (hvsrDF['TimesProcessed_MPL'][hvsrDF['Use']].gt(window[1]) & hvsrDF['TimesProcessed_MPLEnd'][hvsrDF['Use']].gt(window[1]))
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+
hvsrDF.set_index('TimesProcessed', inplace=True)
hvsr_data['hvsr_df'] = hvsrDF
#Create dict entry to keep track of how many outlier hvsr curves are removed (2-item list with [0]=current number, [1]=original number of curves)
@@ -7386,6 +8160,12 @@ Returns
hvsr_data = sprit_utils.make_it_classy(hvsr_data)
+ if 'processing_parameters' not in hvsr_data.keys():
+ hvsr_data['processing_parameters'] = {}
+ hvsr_data['processing_parameters']['generate_ppsds'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['generate_ppsds'][key] = value
+
hvsr_data['ProcessingStatus']['PPSDStatus'] = True
hvsr_data = _check_processing_status(hvsr_data)
return hvsr_data
@@ -7439,14 +8219,57 @@ Returns
params : dict
Modified input dictionary with additional key:value pair containing paz dictionary (key = "paz")
"""
-
invPath = params['metapath']
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
+ trominoNameList = ['tromino', 'trom', 'trm', 't']
if params['instrument'].lower() in raspShakeInstNameList:
if update_metadata:
params = _update_shake_metadata(filepath=invPath, params=params, write_path=write_path)
params = _read_RS_Metadata(params, source=source)
+ elif params['instrument'].lower() in trominoNameList:
+ params['paz'] = {'Z':{}, 'E':{}, 'N':{}}
+ #ALL THESE VALUES ARE PLACEHOLDERS, taken from RASPBERRY SHAKE! (Needed for PPSDs)
+ params['paz']['Z'] = {'sensitivity': 360000000.0,
+ 'gain': 360000000.0,
+ 'poles': [(-1+0j), (-3.03+0j), (-3.03+0j), (-666.67+0j)],
+ 'zeros': [0j, 0j, 0j]}
+ params['paz']['E'] = params['paz']['Z']
+ params['paz']['N'] = params['paz']['Z']
+
+ channelObj_Z = obspy.core.inventory.channel.Channel(code='BHZ', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=0, dip=90, types=None, external_references=None,
+ sample_rate=None, sample_rate_ratio_number_samples=None, sample_rate_ratio_number_seconds=None,
+ storage_format=None, clock_drift_in_seconds_per_sample=None, calibration_units=None,
+ calibration_units_description=None, sensor=None, pre_amplifier=None, data_logger=None,
+ equipments=None, response=None, description=None, comments=None, start_date=None, end_date=None,
+ restricted_status=None, alternate_code=None, historical_code=None, data_availability=None,
+ identifiers=None, water_level=None, source_id=None)
+ channelObj_E = obspy.core.inventory.channel.Channel(code='BHE', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=90, dip=0)
+
+ channelObj_N = obspy.core.inventory.channel.Channel(code='BHN', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=0, dip=0)
+
+ siteObj = obspy.core.inventory.util.Site(name=params['params']['site'], description=None, town=None, county=None, region=None, country=None)
+ stationObj = obspy.core.inventory.station.Station(code='TZ', latitude=params['params']['latitude'], longitude=params['params']['longitude'],
+ elevation=params['params']['elevation'], channels=[channelObj_Z, channelObj_E, channelObj_N], site=siteObj,
+ vault=None, geology=None, equipments=None, operators=None, creation_date=datetime.datetime.today(),
+ termination_date=None, total_number_of_channels=None,
+ selected_number_of_channels=None, description='Estimated data for Tromino, this is NOT from the manufacturer',
+ comments=None, start_date=None,
+ end_date=None, restricted_status=None, alternate_code=None, historical_code=None,
+ data_availability=None, identifiers=None, water_level=None, source_id=None)
+
+ network = [obspy.core.inventory.network.Network(code='TROM', stations=[stationObj], total_number_of_stations=None,
+ selected_number_of_stations=None, description=None, comments=None, start_date=None,
+ end_date=None, restricted_status=None, alternate_code=None, historical_code=None,
+ data_availability=None, identifiers=None, operators=None, source_id=None)]
+
+ params['inv'] = obspy.Inventory(networks=network)
else:
if not invPath:
pass #if invPath is None
@@ -7547,11 +8370,30 @@ Returns
list/tuple - a list or tuple of the above objects, in the same order they are in the report_format list
"""
- #print statement
- #Check if results are good
- #Curve pass?
orig_args = locals().copy() #Get the initial arguments
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_results.keys():
+ if 'get_report' in hvsr_results['processing_parameters'].keys():
+ for k, v in hvsr_results['processing_parameters']['get_report'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(get_report).args[1:],
+ inspect.getfullargspec(get_report).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ report_format = orig_args['report_format']
+ plot_type = orig_args['plot_type']
+ export_path = orig_args['export_path']
+ return_results = orig_args['return_results']
+ csv_overwrite_opt = orig_args['csv_overwrite_opt']
+ no_output = orig_args['no_output']
+ verbose = orig_args['verbose']
+
+ hvsr_results['processing_parameters']['get_report'] = {}
+ for key, value in orig_args.items():
+ hvsr_results['processing_parameters']['get_report'][key] = value
+
if (verbose and isinstance(hvsr_results, HVSRBatch)) or (verbose and not hvsr_results['batch']):
if isinstance(hvsr_results, HVSRData) and hvsr_results['batch']:
pass
@@ -7621,8 +8463,8 @@ Returns
curvePass = curvTestsPassed > 2
#Peak Pass?
- peakTestsPassed = ( hvsr_results['BestPeak']['PassList']['PeakFreqClarityBelow'] +
- hvsr_results['BestPeak']['PassList']['PeakFreqClarityAbove']+
+ peakTestsPassed = ( hvsr_results['BestPeak']['PassList']['PeakProminenceBelow'] +
+ hvsr_results['BestPeak']['PassList']['PeakProminenceAbove']+
hvsr_results['BestPeak']['PassList']['PeakAmpClarity']+
hvsr_results['BestPeak']['PassList']['FreqStability']+
hvsr_results['BestPeak']['PassList']['PeakStability_FreqStD']+
@@ -7757,24 +8599,25 @@ Returns
report_string_list.append(internalSeparator)
report_string_list.append('')
+ justSize=34
#Print individual results
report_string_list.append('\tCurve Tests: {}/3 passed (3/3 needed)'.format(curvTestsPassed))
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Lw'][-1]} Length of processing windows: {hvsr_results['BestPeak']['Report']['Lw']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Nc'][-1]} Number of significant cycles: {hvsr_results['BestPeak']['Report']['Nc']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['σ_A(f)'][-1]} Low StDev. of H/V Curve over time: {hvsr_results['BestPeak']['Report']['σ_A(f)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Lw'][-1]}"+" Length of processing windows".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Lw']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Nc'][-1]}"+" Number of significant cycles".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Nc']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['σ_A(f)'][-1]}"+" Small H/V StDev over time".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['σ_A(f)']}")
report_string_list.append('')
report_string_list.append("\tPeak Tests: {}/6 passed (5/6 needed)".format(peakTestsPassed))
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f-)'][-1]} Clarity Below Peak Frequency: {hvsr_results['BestPeak']['Report']['A(f-)']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f+)'][-1]} Clarity Above Peak Frequency: {hvsr_results['BestPeak']['Report']['A(f+)']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A0'][-1]} Clarity of Peak Amplitude: {hvsr_results['BestPeak']['Report']['A0']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f-)'][-1]}"+" Peak is prominent below".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A(f-)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f+)'][-1]}"+" Peak is prominent above".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A(f+)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A0'][-1]}"+" Peak is large".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A0']}")
if hvsr_results['BestPeak']['PassList']['FreqStability']:
res = sprit_utils.check_mark()
else:
res = sprit_utils.x_mark()
- report_string_list.append(f"\t\t {res} Stability of Peak Freq. Over time: {hvsr_results['BestPeak']['Report']['P-'][:5]} and {hvsr_results['BestPeak']['Report']['P+'][:-1]} {res}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sf'][-1]} Stability of Peak (Freq. StDev): {hvsr_results['BestPeak']['Report']['Sf']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sa'][-1]} Stability of Peak (Amp. StDev): {hvsr_results['BestPeak']['Report']['Sa']}")
+ report_string_list.append(f"\t\t {res}"+ " Peak freq. is stable over time".ljust(justSize)+ f"{hvsr_results['BestPeak']['Report']['P-'][:5]} and {hvsr_results['BestPeak']['Report']['P+'][:-1]} {res}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sf'][-1]}"+" Stability of peak (Freq. StDev)".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Sf']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sa'][-1]}"+" Stability of peak (Amp. StDev)".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Sa']}")
report_string_list.append('')
report_string_list.append(f"Calculated using {hvsr_results['hvsr_df']['Use'].sum()}/{hvsr_results['hvsr_df']['Use'].count()} time windows".rjust(sepLen-1))
report_string_list.append(extSiteSeparator)
@@ -7798,7 +8641,7 @@ Returns
import pandas as pd
pdCols = ['Site Name', 'Acq_Date', 'Longitude', 'Latitide', 'Elevation', 'PeakFrequency',
'WindowLengthFreq.','SignificantCycles','LowCurveStDevOverTime',
- 'PeakFreqClarityBelow','PeakFreqClarityAbove','PeakAmpClarity','FreqStability', 'PeakStability_FreqStD','PeakStability_AmpStD', 'PeakPasses']
+ 'PeakProminenceBelow','PeakProminenceAbove','PeakAmpClarity','FreqStability', 'PeakStability_FreqStD','PeakStability_AmpStD', 'PeakPasses']
d = hvsr_results
criteriaList = []
for p in hvsr_results['BestPeak']["PassList"]:
@@ -7965,8 +8808,39 @@ Returns
return dataIN
+
+def import_settings(settings_import_path, settings_import_type='instrument', verbose=False)
+
+
+
+
+Expand source code
+
+def import_settings(settings_import_path, settings_import_type='instrument', verbose=False):
+
+ allList = ['all', ':', 'both', 'any']
+ if settings_import_type.lower() not in allList:
+ # if just a single settings dict is desired
+ with open(settings_import_path, 'r') as f:
+ settingsDict = json.load(f)
+ else:
+ # Either a directory or list
+ if isinstance(settings_import_path, (list, tuple)):
+ for setPath in settings_import_path:
+ pass
+ else:
+ settings_import_path = sprit_utils.checkifpath(settings_import_path)
+ if not settings_import_path.is_dir():
+ raise RuntimeError(f'settings_import_type={settings_import_type}, but settings_import_path is not list/tuple or filepath to directory')
+ else:
+ instFile = settings_import_path.glob('*.inst')
+ procFile = settings_import_path.glob('*.proc')
+ return settingsDict
+
+
-def input_params(datapath, site='HVSR Site', network='AM', station='RAC84', loc='00', channels=['EHZ', 'EHN', 'EHE'], acq_date='2023-10-19', starttime='00:00:00.00', endtime='23:59:59.999999', tzone='UTC', xcoord=-88.2290526, ycoord=40.1012122, elevation=755, input_crs='EPSG:4326', output_crs='EPSG:4326', elev_unit='feet', depth=0, instrument='Raspberry Shake', metapath='', hvsr_band=[0.4, 40], peak_freq_range=[0.4, 40], verbose=False)
+def input_params(datapath, site='HVSR Site', network='AM', station='RAC84', loc='00', channels=['EHZ', 'EHN', 'EHE'], acq_date='2023-10-30', starttime='00:00:00.00', endtime='23:59:59.999999', tzone='UTC', xcoord=-88.2290526, ycoord=40.1012122, elevation=755, input_crs='EPSG:4326', output_crs='EPSG:4326', elev_unit='feet', depth=0, instrument='Raspberry Shake', metapath=None, hvsr_band=[0.4, 40], peak_freq_range=[0.4, 40], processing_parameters={}, verbose=False)
Function for designating input parameters for reading in and processing data
@@ -8010,12 +8884,19 @@ Parameters
Depth of seismometer. Not currently used, but will likely be used in the future.
instrument : str or list {'Raspberry Shake')Instrument from which the data was acquired.
-metapath : str or pathlib.Path object, default=''Filepath of metadata, in format supported by obspy.read_inventory. If default value of '', will read from resources folder of repository (only supported for Raspberry Shake).
+metapath : str or pathlib.Path object, default=NoneFilepath of metadata, in format supported by obspy.read_inventory. If default value of None, will read from resources folder of repository (only supported for Raspberry Shake).
hvsr_band : list, default=[0.4, 40]Two-element list containing low and high "corner" frequencies (in Hz) for processing. This can specified again later.
peak_freq_range : list or tuple, default=[0.4, 40]Two-element list or tuple containing low and high frequencies (in Hz) that are used to check for HVSR Peaks. This can be a tigher range than hvsr_band, but if larger, it will still only use the hvsr_band range.
+processing_parameters={} : dict or filepath, default={}
+If filepath, should point to a .proc json file with processing parameters (i.e, an output from sprit.export_settings()).
+Note that this only applies to parameters for the functions: 'fetch_data', 'remove_noise', 'generate_ppsds', 'process_hvsr', 'check_peaks', and 'get_report.'
+If dictionary, dictionary containing nested dictionaries of function names as they key, and the parameter names/values as key/value pairs for each key.
+If a function name is not present, or if a parameter name is not present, default values will be used.
+For example:
+{ 'fetch_data' : {'source':'batch', 'trim_dir':"/path/to/trimmed/data", 'export_format':'mseed', 'detrend':'spline', 'plot_input_stream':True, 'verbose':False, kwargs:{'kwargskey':'kwargsvalue'}} }verbose : bool, default=FalseWhether to print output and results to terminal
@@ -8046,9 +8927,10 @@ Returns
elev_unit = 'feet',
depth = 0,
instrument = 'Raspberry Shake',
- metapath = '',
+ metapath = None,
hvsr_band = [0.4, 40],
peak_freq_range=[0.4, 40],
+ processing_parameters={},
verbose=False
):
"""Function for designating input parameters for reading in and processing data
@@ -8093,12 +8975,19 @@ Returns
Depth of seismometer. Not currently used, but will likely be used in the future.
instrument : str or list {'Raspberry Shake')
Instrument from which the data was acquired.
- metapath : str or pathlib.Path object, default=''
- Filepath of metadata, in format supported by obspy.read_inventory. If default value of '', will read from resources folder of repository (only supported for Raspberry Shake).
+ metapath : str or pathlib.Path object, default=None
+ Filepath of metadata, in format supported by obspy.read_inventory. If default value of None, will read from resources folder of repository (only supported for Raspberry Shake).
hvsr_band : list, default=[0.4, 40]
Two-element list containing low and high "corner" frequencies (in Hz) for processing. This can specified again later.
peak_freq_range : list or tuple, default=[0.4, 40]
Two-element list or tuple containing low and high frequencies (in Hz) that are used to check for HVSR Peaks. This can be a tigher range than hvsr_band, but if larger, it will still only use the hvsr_band range.
+ processing_parameters={} : dict or filepath, default={}
+ If filepath, should point to a .proc json file with processing parameters (i.e, an output from sprit.export_settings()).
+ Note that this only applies to parameters for the functions: 'fetch_data', 'remove_noise', 'generate_ppsds', 'process_hvsr', 'check_peaks', and 'get_report.'
+ If dictionary, dictionary containing nested dictionaries of function names as they key, and the parameter names/values as key/value pairs for each key.
+ If a function name is not present, or if a parameter name is not present, default values will be used.
+ For example:
+ `{ 'fetch_data' : {'source':'batch', 'trim_dir':"/path/to/trimmed/data", 'export_format':'mseed', 'detrend':'spline', 'plot_input_stream':True, 'verbose':False, kwargs:{'kwargskey':'kwargsvalue'}} }`
verbose : bool, default=False
Whether to print output and results to terminal
@@ -8110,30 +8999,6 @@ Returns
"""
orig_args = locals().copy() #Get the initial arguments
- #Declare obspy here instead of at top of file for (for example) colab, where obspy first needs to be installed on environment
- global obspy
- import obspy
- if verbose:
- print('Gathering input parameters (input_params())')
- for key, value in orig_args.items():
- print('\t {}={}'.format(key, value))
- print()
-
- #Make Sure metapath is all good
- if not pathlib.Path(metapath).exists() or metapath=='':
- if metapath == '':
- pass
- else:
- print('Specified metadata file cannot be read!')
- repoDir = pathlib.Path(os.path.dirname(__file__))
- metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv5plus_metadata.inv'))
- #print('Using default metadata file for Raspberry Shake v.7 distributed with package')
- else:
- if isinstance(metapath, pathlib.PurePath):
- metapath = metapath.as_posix()
- else:
- metapath = pathlib.Path(metapath).as_posix()
-
#Reformat times
if type(acq_date) is datetime.datetime:
date = str(acq_date.date())
@@ -8216,12 +9081,6 @@ Returns
acq_date = datetime.date(year=int(date.split('-')[0]), month=int(date.split('-')[1]), day=int(date.split('-')[2]))
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
- #Raspberry shake stationxml is in the resources folder, double check we have right path
- if instrument.lower() in raspShakeInstNameList:
- if metapath == r'resources/rs3dv7_metadata.inv':
- metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv7_metadata.inv'))
- #metapath = pathlib.Path(os.path.realpath(__file__)).parent.joinpath('/resources/rs3dv7_metadata.inv')
-
if output_crs is None:
output_crs='EPSG:4326'
@@ -8242,6 +9101,43 @@ Returns
'ProcessingStatus':{'InputStatus':True, 'OverallStatus':True}
}
+ #Replace any default parameter settings with those from json file of interest, potentially
+ instrument_settings_dict = {}
+ if pathlib.Path(instrument).exists():
+ instrument_settings = import_settings(settings_import_path=instrument, settings_import_type='instrument', verbose=verbose)
+ input_params_args = inspect.getfullargspec(input_params).args
+ input_params_args.append('net')
+ input_params_args.append('sta')
+ for k, settings_value in instrument_settings.items():
+ if k in input_params_args:
+ instrument_settings_dict[k] = settings_value
+ inputParamDict['instrument_settings'] = inputParamDict['instrument']
+ inputParamDict.update(instrument_settings_dict)
+
+ if instrument.lower() in raspShakeInstNameList:
+ if metapath is None:
+ metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv5plus_metadata.inv')).as_posix()
+ inputParamDict['metapath'] = metapath
+ #metapath = pathlib.Path(os.path.realpath(__file__)).parent.joinpath('/resources/rs3dv7_metadata.inv')
+
+ for settingName in instrument_settings_dict.keys():
+ if settingName in inputParamDict.keys():
+ inputParamDict[settingName] = instrument_settings_dict[settingName]
+
+ #Declare obspy here instead of at top of file for (for example) colab, where obspy first needs to be installed on environment
+ if verbose:
+ print('Gathering input parameters (input_params())')
+ for key, value in inputParamDict.items():
+ print('\t {}={}'.format(key, value))
+ print()
+
+ if isinstance(processing_parameters, dict):
+ inputParamDict['processing_parameters'] = processing_parameters
+ else:
+ processing_parameters = sprit_utils.checkifpath(processing_parameters)
+ inputParamDict['processing_parameters'] = import_settings(processing_parameters, settings_import_type='processing', verbose=verbose)
+
+ #Format everything nicely
params = sprit_utils.make_it_classy(inputParamDict)
params['ProcessingStatus']['InputParams'] = True
params = _check_processing_status(params)
@@ -8249,7 +9145,7 @@ Returns
-def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, xtype='freq', fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True, **kwargs)
+def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True, **kwargs)
Function to plot HVSR data
@@ -8258,29 +9154,25 @@ Parameters
hvsr_data : dict
Dictionary containing output from process_hvsr function
-plot_type : str='HVSR' or list
-plot_type : str or list, default = 'HVSR ann p C+ ann p SPEC'The plot_type of plot(s) to plot. If list, will plot all plots listed
-'HVSR' : Standard HVSR plot, including standard deviation
-- '[HVSR] p' : HVSR plot with BestPeaks shown
-- '[HVSR] p' : HVSR plot with best picked peak shown
-
-- '[HVSR] p all' : HVSR plot with all picked peaks shown
-
-- '[HVSR] p t' : HVSR plot with peaks from all time steps in background
-
-- '[HVSR p* ann] : Annotates plot with peaks
-- '[HVSR] -s' : HVSR plots don't show standard deviation
-- '[HVSR] t' : HVSR plot with individual hv curves for each time step shown
-- '[HVSR] c' : HVSR plot with each components' spectra. Recommended to do this last (or just before 'specgram'), since doing c+ can make the component chart its own chart
-'Specgram' : Combined spectrogram of all components
-- '[spec]' : basic spectrogram plot of H/V curve
+- 'HVSR' - Standard HVSR plot, including standard deviation. Options are included below:
+- 'p' shows a vertical dotted line at frequency of the "best" peak
+- 'ann' annotates the frequency value of of the "best" peak
+- 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+- 't' shows the H/V curve for all time windows
+-'tp' shows all the peaks from the H/V curves of all the time windows
+- 'COMP' - plot of the PPSD curves for each individual component ("C" also works)
+- '+' (as a suffix in 'C+' or 'COMP+') plots C on a plot separate from HVSR (C+ is default, but without + will plot on the same plot as HVSR)
+- 'p' shows a vertical dotted line at frequency of the "best" peak
+- 'ann' annotates the frequency value of of the "best" peak
+- 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+- 't' shows the H/V curve for all time windows
+- 'SPEC' - spectrogram style plot of the H/V curve over time
+- 'p' shows a horizontal dotted line at the frequency of the "best" peak
+- 'ann' annotates the frequency value of the "best" peak
use_subplots : bool, default = TrueWhether to output the plots as subplots (True) or as separate plots (False)
-xtype : str, default = 'freq'String for what to use, between frequency or period
-For frequency, the following are accepted (case does not matter): 'f', 'Hz', 'freq', 'frequency'
-For period, the following are accepted (case does not matter): 'p', 'T', 's', 'sec', 'second', 'per', 'period'
fig : matplotlib.Figure, default = NoneIf not None, matplotlib figure on which plot is plotted
ax : matplotlib.Axis, default = NoneReturns
Expand source code
-def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, xtype='freq', fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True,**kwargs):
+def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True,**kwargs):
"""Function to plot HVSR data
Parameters
----------
hvsr_data : dict
Dictionary containing output from process_hvsr function
- plot_type : str='HVSR' or list
+ plot_type : str or list, default = 'HVSR ann p C+ ann p SPEC'
The plot_type of plot(s) to plot. If list, will plot all plots listed
- 'HVSR' : Standard HVSR plot, including standard deviation
- - '[HVSR] p' : HVSR plot with BestPeaks shown
- - '[HVSR] p' : HVSR plot with best picked peak shown
- - '[HVSR] p* all' : HVSR plot with all picked peaks shown
- - '[HVSR] p* t' : HVSR plot with peaks from all time steps in background
- - '[HVSR p* ann] : Annotates plot with peaks
- - '[HVSR] -s' : HVSR plots don't show standard deviation
- - '[HVSR] t' : HVSR plot with individual hv curves for each time step shown
- - '[HVSR] c' : HVSR plot with each components' spectra. Recommended to do this last (or just before 'specgram'), since doing c+ can make the component chart its own chart
- 'Specgram' : Combined spectrogram of all components
- - '[spec]' : basic spectrogram plot of H/V curve
+ - 'HVSR' - Standard HVSR plot, including standard deviation. Options are included below:
+ - 'p' shows a vertical dotted line at frequency of the "best" peak
+ - 'ann' annotates the frequency value of of the "best" peak
+ - 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+ - 't' shows the H/V curve for all time windows
+ -'tp' shows all the peaks from the H/V curves of all the time windows
+ - 'COMP' - plot of the PPSD curves for each individual component ("C" also works)
+ - '+' (as a suffix in 'C+' or 'COMP+') plots C on a plot separate from HVSR (C+ is default, but without + will plot on the same plot as HVSR)
+ - 'p' shows a vertical dotted line at frequency of the "best" peak
+ - 'ann' annotates the frequency value of of the "best" peak
+ - 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+ - 't' shows the H/V curve for all time windows
+ - 'SPEC' - spectrogram style plot of the H/V curve over time
+ - 'p' shows a horizontal dotted line at the frequency of the "best" peak
+ - 'ann' annotates the frequency value of the "best" peak
use_subplots : bool, default = True
Whether to output the plots as subplots (True) or as separate plots (False)
- xtype : str, default = 'freq'
- String for what to use, between frequency or period
- For frequency, the following are accepted (case does not matter): 'f', 'Hz', 'freq', 'frequency'
- For period, the following are accepted (case does not matter): 'p', 'T', 's', 'sec', 'second', 'per', 'period'
fig : matplotlib.Figure, default = None
If not None, matplotlib figure on which plot is plotted
ax : matplotlib.Axis, default = None
@@ -8455,12 +9347,16 @@ Returns
fig, axis = plt.subplots()
if p == 'hvsr':
- _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax)
+ _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax, **kwargs)
elif p=='comp':
plotComponents[0] = plotComponents[0][:-1]
- _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax)
+ _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax, **kwargs)
elif p=='spec':
- _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False)
+ plottypeKwargs = {}
+ for c in plotComponents:
+ plottypeKwargs[c] = True
+ kwargs.update(plottypeKwargs)
+ _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False, **kwargs)
else:
warnings.warn('Plot type {p} not recognized', UserWarning)
@@ -8746,6 +9642,27 @@ Returns
"""
orig_args = locals().copy() #Get the initial arguments
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'process_hvsr' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['process_hvsr'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(process_hvsr).args[1:],
+ inspect.getfullargspec(process_hvsr).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ method = orig_args['method']
+ smooth = orig_args['smooth']
+ freq_smooth = orig_args['freq_smooth']
+ f_smooth_width = orig_args['f_smooth_width']
+ resample = orig_args['resample']
+ outlier_curve_std = orig_args['outlier_curve_std']
+ verbose = orig_args['verbose']
+
+
+
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
pass
@@ -8810,10 +9727,8 @@ Returns
xValMin = min(ppsds[k]['period_bin_centers'])
xValMax = max(ppsds[k]['period_bin_centers'])
-
#Resample period bin values
x_periods[k] = np.logspace(np.log10(xValMin), np.log10(xValMax), num=resample)
-
if smooth or type(smooth) is int:
if smooth:
smooth = 51 #Default smoothing window
@@ -8841,7 +9756,6 @@ Returns
#Get average psd value across time for each channel (used to calc main H/V curve)
psdValsTAvg[k] = np.nanmean(np.array(psdRaw[k]), axis=0)
x_freqs[k] = np.divide(np.ones_like(x_periods[k]), x_periods[k])
-
stDev[k] = np.std(psdRaw[k], axis=0)
stDevValsM[k] = np.array(psdValsTAvg[k] - stDev[k])
stDevValsP[k] = np.array(psdValsTAvg[k] + stDev[k])
@@ -8859,9 +9773,8 @@ Returns
anyK = list(x_freqs.keys())[0]
hvsr_curve, _ = __get_hvsr_curve(x=x_freqs[anyK], psd=psdValsTAvg, method=methodInt, hvsr_data=hvsr_data, verbose=verbose)
origPPSD = hvsr_data['ppsds_obspy'].copy()
-
#Add some other variables to our output dictionary
- hvsr_data = {'input_params':hvsr_data,
+ hvsr_dataUpdate = {'input_params':hvsr_data,
'x_freqs':x_freqs,
'hvsr_curve':hvsr_curve,
'x_period':x_periods,
@@ -8878,7 +9791,7 @@ Returns
'hvsr_df':hvsr_data['hvsr_df']
}
- hvsr_out = HVSRData(hvsr_data)
+ hvsr_out = HVSRData(hvsr_dataUpdate)
#This is if manual editing was used (should probably be updated at some point to just use masks)
if 'xwindows_out' in hvsr_data.keys():
@@ -8952,7 +9865,6 @@ Returns
bool_col='Use'
eval_col='HV_Curves'
- testCol = hvsr_out['hvsr_df'].loc[hvsr_out['hvsr_df'][bool_col], eval_col].apply(np.nanstd).gt((avg_stdT + (std_stdT * outlier_curve_std)))
low_std_val = avg_stdT - (std_stdT * outlier_curve_std)
hi_std_val = avg_stdT + (std_stdT * outlier_curve_std)
@@ -9006,16 +9918,15 @@ Returns
hvsr_out = __gethvsrparams(hvsr_out)
#Include the original obspy stream in the output
- #print(hvsr_data.keys())
- #print(type(hvsr_data))
- #print(hvsr_data['input_params'].keys())
- hvsr_out['input_stream'] = hvsr_data['input_params']['input_stream'] #input_stream
-
+ hvsr_out['input_stream'] = hvsr_dataUpdate['input_params']['input_stream'] #input_stream
hvsr_out = sprit_utils.make_it_classy(hvsr_out)
-
hvsr_out['ProcessingStatus']['HVStatus'] = True
hvsr_out = _check_processing_status(hvsr_out)
+ hvsr_data['processing_parameters']['process_hvsr'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['process_hvsr'][key] = value
+
return hvsr_out
@@ -9114,7 +10025,30 @@ Returns
output : dict
Dictionary similar to hvsr_data, but containing modified data with 'noise' removed
"""
- orig_args = locals().copy() #Get the initial arguments
+ #Get intput paramaters
+ orig_args = locals().copy()
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'remove_noise' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['remove_noise'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(remove_noise).args[1:],
+ inspect.getfullargspec(remove_noise).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ remove_method = orig_args['remove_method']
+ sat_percent = orig_args['sat_percent']
+ noise_percent = orig_args['noise_percent']
+ sta = orig_args['sta']
+ lta = orig_args['lta']
+ stalta_thresh = orig_args['stalta_thresh']
+ warmup_time = orig_args['warmup_time']
+ cooldown_time = orig_args['cooldown_time']
+ min_win_size = orig_args['min_win_size']
+ remove_raw_noise = orig_args['remove_raw_noise']
+ verbose = orig_args['verbose']
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
@@ -9231,12 +10165,22 @@ Returns
#Add output
if isinstance(output, (HVSRData, dict)):
- output['stream'] = outStream
+ if isinstance(outStream, (obspy.Stream, obspy.Trace)):
+ output['stream'] = outStream
+ else:
+ output['stream'] = outStream['stream']
output['input_stream'] = hvsr_data['input_stream']
+
+ if 'processing_parameters' not in output.keys():
+ output['processing_parameters'] = {}
+ output['processing_parameters']['remove_noise'] = {}
+ for key, value in orig_args.items():
+ output['processing_parameters']['remove_noise'][key] = value
+
output['ProcessingStatus']['RemoveNoiseStatus'] = True
output = _check_processing_status(output)
- if 'hvsr_df' in output.keys():
+ if 'hvsr_df' in output.keys() or ('params' in output.keys() and 'hvsr_df' in output['params'].keys())or ('input_params' in output.keys() and 'hvsr_df' in output['input_params'].keys()):
hvsrDF = output['hvsr_df']
outStream = output['stream'].split()
@@ -9250,20 +10194,27 @@ Returns
if trEndTime < trStartTime and comp_end==comp_start:
gap = [trEndTime,trStartTime]
+
output['hvsr_df']['Use'] = (hvsrDF['TimesProcessed_Obspy'].gt(gap[0]) & hvsrDF['TimesProcessed_Obspy'].gt(gap[1]) )| \
(hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[0]) & hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[1]))# | \
+ output['hvsr_df']['Use'] = output['hvsr_df']['Use'].astype(bool)
trEndTime = trace.stats.endtime
outStream.merge()
- output['stream'] = outStream
+ output['stream'] = outStream
+
elif isinstance(hvsr_data, obspy.core.stream.Stream) or isinstance(hvsr_data, obspy.core.trace.Trace):
output = outStream
else:
warnings.warn(f"Output of type {type(output)} for this function will likely result in errors in other processing steps. Returning hvsr_data data.")
return hvsr_data
+
-
+
+ output = sprit_utils.make_it_classy(output)
+ if 'xwindows_out' not in output.keys():
+ output['xwindows_out'] = []
return output
@@ -9354,16 +10305,19 @@ Returns
psds_to_rid.append(i)
#Use dataframe
- hvsrDF = params['hvsr_df']
+ hvsrDF = params['hvsr_df'].copy()
psdVals = hvsrDF['psd_values_'+k]
- params['hvsr_df'][k+'_CurveMedian'] = psdVals.apply(np.nanmedian)
- params['hvsr_df'][k+'_CurveMean'] = psdVals.apply(np.nanmean)
-
- totMean = np.nanmean(params['hvsr_df'][k+'_CurveMean'])
- stds[k] = np.nanstd(params['hvsr_df'][k+'_CurveMean'])
+ hvsrDF[k+'_CurveMedian'] = psdVals.apply(np.nanmedian)
+ hvsrDF[k+'_CurveMean'] = psdVals.apply(np.nanmean)
- meanArr = params['hvsr_df'][k+'_CurveMean']
- params['hvsr_df']['Use'] = meanArr < (totMean + outlier_std * stds[k])
+ totMean = np.nanmean(hvsrDF[k+'_CurveMean'])
+ stds[k] = np.nanstd(hvsrDF[k+'_CurveMean'])
+
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+ #meanArr = hvsrDF[k+'_CurveMean'].loc[hvsrDF['Use']]
+ threshVal = totMean + outlier_std * stds[k]
+ hvsrDF['Use'] = hvsrDF[k+'_CurveMean'][hvsrDF['Use']].lt(threshVal)
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
psds_to_rid = np.unique(psds_to_rid)
@@ -9470,8 +10424,14 @@ Raises
RuntimeError
If the data being processed is a single file, an error will be raised if generate_ppsds() does not work correctly. No errors are raised for remove_noise() errors (since that is an optional step) and the process_hvsr() step (since that is the last processing step) .
"""
+ if 'hvsr_band' not in kwargs.keys():
+ kwargs['hvsr_band'] = inspect.signature(input_params).parameters['hvsr_band'].default
+ if 'peak_freq_range' not in kwargs.keys():
+ kwargs['peak_freq_range'] = inspect.signature(input_params).parameters['peak_freq_range'].default
+
#Get the input parameters
- input_params_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in input_params.__code__.co_varnames}
+ input_params_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(input_params).parameters.keys())}
+
try:
params = input_params(datapath=datapath, verbose=verbose, **input_params_kwargs)
except:
@@ -9484,15 +10444,14 @@ Raises
#Fetch Data
try:
- fetch_data_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in fetch_data.__code__.co_varnames}
+ fetch_data_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(fetch_data).parameters.keys())}
dataIN = fetch_data(params=params, source=source, verbose=verbose, **fetch_data_kwargs)
except:
#Even if batch, this is reading in data for all sites so we want to raise error, not just warn
raise RuntimeError('Data not read correctly, see sprit.fetch_data() function and parameters for more details.')
-
#Remove Noise
try:
- remove_noise_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in remove_noise.__code__.co_varnames}
+ remove_noise_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(remove_noise).parameters.keys())}
data_noiseRemoved = remove_noise(hvsr_data=dataIN, verbose=verbose,**remove_noise_kwargs)
except:
data_noiseRemoved = dataIN
@@ -9516,8 +10475,8 @@ Raises
#Generate PPSDs
try:
- generate_ppsds_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in generate_ppsds.__code__.co_varnames}
- PPSDkwargs = {k: v for k, v in locals()['kwargs'].items() if k in PPSD.__init__.__code__.co_varnames}
+ generate_ppsds_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(generate_ppsds).parameters.keys())}
+ PPSDkwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(PPSD).parameters.keys())}
generate_ppsds_kwargs.update(PPSDkwargs)
ppsd_data = generate_ppsds(hvsr_data=data_noiseRemoved, verbose=verbose,**generate_ppsds_kwargs)
except Exception as e:
@@ -9543,7 +10502,7 @@ Raises
#Process HVSR Curves
try:
- process_hvsr_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in process_hvsr.__code__.co_varnames}
+ process_hvsr_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(process_hvsr).parameters.keys())}
hvsr_results = process_hvsr(hvsr_data=ppsd_data, verbose=verbose,**process_hvsr_kwargs)
except Exception as e:
traceback.print_exception(sys.exc_info()[1])
@@ -9573,10 +10532,10 @@ Raises
#Final post-processing/reporting
#Check peaks
- check_peaks_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in check_peaks.__code__.co_varnames}
+ check_peaks_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(check_peaks).parameters.keys())}
hvsr_results = check_peaks(hvsr_data=hvsr_results, verbose=verbose, **check_peaks_kwargs)
- get_report_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in get_report.__code__.co_varnames}
+ get_report_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(get_report).parameters.keys())}
get_report(hvsr_results=hvsr_results, verbose=verbose, **get_report_kwargs)
if verbose:
@@ -9595,8 +10554,9 @@ Raises
#We do not need to plot another report if already plotted
pass
else:
- hvplot_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in plot_hvsr.__code__.co_varnames}
- hvsr_results['HV_Plot'] = plot_hvsr(hvsr_results, **hvplot_kwargs)
+ #hvplot_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in plot_hvsr.__code__.co_varnames}
+ #hvsr_results['HV_Plot'] = plot_hvsr(hvsr_results, return_fig=True, show=False, close_figs=True)
+ pass
else:
pass
@@ -10050,7 +11010,9 @@ Returns
Parameters
----------
export_path : filepath, default=True
- Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes). By default True. If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
+ Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes).
+ By default True.
+ If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
ext : str, optional
The extension to use for the output, by default 'hvsr'. This is still a pickle file that can be read with pickle.load(), but will have .hvsr extension.
"""
@@ -10372,7 +11334,9 @@ Parameters
Parameters
export_path : filepath, default=True- Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes). By default True. If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
+- Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes).
+By default True.
+If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
ext : str, optional- The extension to use for the output, by default 'hvsr'. This is still a pickle file that can be read with pickle.load(), but will have .hvsr extension.
@@ -10386,7 +11350,9 @@ Parameters
Parameters
----------
export_path : filepath, default=True
- Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes). By default True. If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
+ Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes).
+ By default True.
+ If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
ext : str, optional
The extension to use for the output, by default 'hvsr'. This is still a pickle file that can be read with pickle.load(), but will have .hvsr extension.
"""
@@ -10538,12 +11504,14 @@ Index
check_instancecheck_peaksexport_dataexport_settingsfetch_datagenerate_ppsdsget_metadataget_reportguiimport_dataimport_settingsinput_paramsplot_hvsrplot_streamModule sprit.sprit_utils
channel_order = {'Z': 0, '1': 1, 'N': 1, '2': 2, 'E': 2}
def check_gui_requirements():
- print("Checking requirements for gui")
+ #First, check requirements
# Define a command that tries to open a window
command = "python -c \"import tkinter; tkinter.Tk()\""
@@ -60,7 +60,7 @@ Module sprit.sprit_utils
oktoproceed=True
else:
oktoproceed=False
- print("GUI window could not be created.")
+ print("GUI window cannot be created.")
return oktoproceed
@@ -339,6 +339,16 @@ Module sprit.sprit_utils
#Make input data (dict) into sprit_hvsr class
def make_it_classy(input_data, verbose=False):
if isinstance(input_data, (sprit_hvsr.HVSRData, sprit_hvsr.HVSRBatch)):
+ for k, v in input_data.items():
+ if k=='input_params':
+ for kin in input_data['input_params'].keys():
+ if kin not in input_data.keys():
+ input_data[kin] = input_data['input_params'][kin]
+ if k=='params':
+ for kin in input_data['params'].keys():
+ print(kin)
+ if kin not in input_data.keys():
+ input_data[kin] = input_data['params'][kin]
output_class = input_data
else:
output_class = sprit_hvsr.HVSRData(input_data)
@@ -464,7 +474,7 @@ Functions
Expand source code
def check_gui_requirements():
- print("Checking requirements for gui")
+ #First, check requirements
# Define a command that tries to open a window
command = "python -c \"import tkinter; tkinter.Tk()\""
@@ -477,7 +487,7 @@ Functions
oktoproceed=True
else:
oktoproceed=False
- print("GUI window could not be created.")
+ print("GUI window cannot be created.")
return oktoproceed
@@ -855,6 +865,16 @@ Returns
def make_it_classy(input_data, verbose=False):
if isinstance(input_data, (sprit_hvsr.HVSRData, sprit_hvsr.HVSRBatch)):
+ for k, v in input_data.items():
+ if k=='input_params':
+ for kin in input_data['input_params'].keys():
+ if kin not in input_data.keys():
+ input_data[kin] = input_data['input_params'][kin]
+ if k=='params':
+ for kin in input_data['params'].keys():
+ print(kin)
+ if kin not in input_data.keys():
+ input_data[kin] = input_data['params'][kin]
output_class = input_data
else:
output_class = sprit_hvsr.HVSRData(input_data)
diff --git a/setup.py b/setup.py
index 8750d49..3b9f9de 100644
--- a/setup.py
+++ b/setup.py
@@ -8,7 +8,8 @@
author= "Riley Balikian",
author_email = "balikian@illinois.edu",
version="0.1.50",
- package_data={'sprit': ['resources/*', 'resources/icon/*', 'resources/themes/*', 'resources/themes/forest-dark/*', 'resources/themes/forest-light/*', 'resources/sample_data/*',]},
+ package_data={'sprit': ['resources/*', 'resources/icon/*', 'resources/themes/*', 'resources/themes/forest-dark/*',
+ 'resources/themes/forest-light/*', 'resources/sample_data/*','resources/settings/*']},
long_description_content_type="text/markdown",
long_description=long_description,
packages=find_packages(),
diff --git a/sprit/__init__.py b/sprit/__init__.py
index 8ba1844..6b7c8e0 100644
--- a/sprit/__init__.py
+++ b/sprit/__init__.py
@@ -10,7 +10,9 @@
from sprit.sprit_hvsr import(
run,
export_data,
+ export_settings,
import_data,
+ import_settings,
input_params,
gui,
get_metadata,
@@ -51,8 +53,10 @@
'get_char',
'time_it',
'checkifpath',
- 'export_data',
+ 'export_data',
+ 'export_settings',
'import_data',
+ 'import_settings',
'input_params',
'gui',
'get_metadata',
diff --git a/sprit/__pycache__/__init__.cpython-310.pyc b/sprit/__pycache__/__init__.cpython-310.pyc
index bda4ac5..99f85f0 100644
Binary files a/sprit/__pycache__/__init__.cpython-310.pyc and b/sprit/__pycache__/__init__.cpython-310.pyc differ
diff --git a/sprit/__pycache__/__init__.cpython-311.pyc b/sprit/__pycache__/__init__.cpython-311.pyc
index 8ee45c4..94e8612 100644
Binary files a/sprit/__pycache__/__init__.cpython-311.pyc and b/sprit/__pycache__/__init__.cpython-311.pyc differ
diff --git a/sprit/__pycache__/sprit_gui.cpython-310.pyc b/sprit/__pycache__/sprit_gui.cpython-310.pyc
index 81c0f72..f4ec6f4 100644
Binary files a/sprit/__pycache__/sprit_gui.cpython-310.pyc and b/sprit/__pycache__/sprit_gui.cpython-310.pyc differ
diff --git a/sprit/__pycache__/sprit_gui.cpython-311.pyc b/sprit/__pycache__/sprit_gui.cpython-311.pyc
index 888dee2..83ae436 100644
Binary files a/sprit/__pycache__/sprit_gui.cpython-311.pyc and b/sprit/__pycache__/sprit_gui.cpython-311.pyc differ
diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc
index d484c29..662c49a 100644
Binary files a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc and b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc differ
diff --git a/sprit/__pycache__/sprit_hvsr.cpython-311.pyc b/sprit/__pycache__/sprit_hvsr.cpython-311.pyc
index ae0ffa1..4417451 100644
Binary files a/sprit/__pycache__/sprit_hvsr.cpython-311.pyc and b/sprit/__pycache__/sprit_hvsr.cpython-311.pyc differ
diff --git a/sprit/__pycache__/sprit_utils.cpython-310.pyc b/sprit/__pycache__/sprit_utils.cpython-310.pyc
index 95d7864..b75eeea 100644
Binary files a/sprit/__pycache__/sprit_utils.cpython-310.pyc and b/sprit/__pycache__/sprit_utils.cpython-310.pyc differ
diff --git a/sprit/__pycache__/sprit_utils.cpython-311.pyc b/sprit/__pycache__/sprit_utils.cpython-311.pyc
index eabf970..e3e5ecb 100644
Binary files a/sprit/__pycache__/sprit_utils.cpython-311.pyc and b/sprit/__pycache__/sprit_utils.cpython-311.pyc differ
diff --git a/sprit/resources/settings/gui_theme.json b/sprit/resources/settings/gui_theme.json
new file mode 100644
index 0000000..2582acb
--- /dev/null
+++ b/sprit/resources/settings/gui_theme.json
@@ -0,0 +1 @@
+{"theme_name": "alt"}
\ No newline at end of file
diff --git a/sprit/resources/settings/instrument_settings.inst b/sprit/resources/settings/instrument_settings.inst
new file mode 100644
index 0000000..7c786fb
--- /dev/null
+++ b/sprit/resources/settings/instrument_settings.inst
@@ -0,0 +1,10 @@
+{
+ "instrument": "Raspberry Shake",
+ "net": "AM",
+ "sta": "RAC84",
+ "loc": "00",
+ "cha": ["EHZ", "EHN", "EHE"],
+ "depth": 0,
+ "metapath": "c:/Users/balikian/LocalData/CodesScripts/Github/SPRIT-HVSR/sprit/resources/rs3dv5plus_metadata.inv",
+ "hvsr_band": [0.4, 40]
+}
\ No newline at end of file
diff --git a/sprit/resources/settings/processing_settings.proc b/sprit/resources/settings/processing_settings.proc
new file mode 100644
index 0000000..edfdf9a
--- /dev/null
+++ b/sprit/resources/settings/processing_settings.proc
@@ -0,0 +1,69 @@
+{
+ "fetch_data":
+ {
+ "source": "file",
+ "trim_dir": null,
+ "export_format": "mseed",
+ "detrend": "spline",
+ "detrend_order": 2,
+ "update_metadata": true,
+ "plot_input_stream": false,
+ "verbose": false,
+ "kwargs":
+ {}
+ },
+
+ "remove_noise":
+ {
+ "remove_method": "auto",
+ "sat_percent": 0.995,
+ "noise_percent": 0.8,
+ "sta": 2,
+ "lta": 30,
+ "stalta_thresh": [ 0.5, 5 ],
+ "warmup_time": 0,
+ "cooldown_time": 0,
+ "min_win_size": 1,
+ "remove_raw_noise": false,
+ "verbose": false
+ },
+
+ "generate_ppsds":
+ {
+ "remove_outliers": true,
+ "outlier_std": 3,
+ "verbose": false,
+ "ppsd_kwargs":
+ { "skip_on_gaps": true, "db_bins": [ -200, -50, 1.0 ], "ppsd_length": 30.0, "overlap": 0.5, "special_handling": null, "period_smoothing_width_octaves": 1.0, "period_step_octaves": 0.03125, "period_limits": [ 0.025, 2.5 ] }
+ },
+
+ "process_hvsr":
+ {
+ "method": 3,
+ "smooth": true,
+ "freq_smooth": "konno ohmachi",
+ "f_smooth_width": 40,
+ "resample": true,
+ "outlier_curve_std": 1.75,
+ "verbose": false
+ },
+
+ "check_peaks":
+ {
+ "hvsr_band": [ 0.4, 40 ],
+ "peak_selection": "max",
+ "peak_freq_range": [ 0.4, 40 ],
+ "verbose": false
+ },
+
+ "get_report":
+ {
+ "report_format": "print",
+ "plot_type": "HVSR p ann C+ p ann Spec",
+ "export_path": null,
+ "return_results": false,
+ "csv_overwrite_opt": "append",
+ "no_output": false,
+ "verbose": false
+ }
+}
\ No newline at end of file
diff --git a/sprit/sprit_gui.py b/sprit/sprit_gui.py
index 6e2c700..3f6135d 100644
--- a/sprit/sprit_gui.py
+++ b/sprit/sprit_gui.py
@@ -3,6 +3,7 @@
import datetime
import functools
import linecache
+import json
import os
import pathlib
import pkg_resources
@@ -34,6 +35,14 @@
pass
global spritApp
+global current_theme_name
+
+resource_dir = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/'))
+settings_dir = resource_dir.joinpath('settings')
+gui_theme_file = settings_dir.joinpath('gui_theme.json')
+with open(gui_theme_file, 'r') as f:
+ curr_gui_dict = json.load(f)
+current_theme_name = curr_gui_dict['theme_name']
#Decorator that catches errors and warnings (to be modified later for gui)
def catch_errors(func):
@@ -151,10 +160,11 @@ def __init__(self, master):
self.darkthemepath = pathlib.Path(pkg_resources.resource_filename(__name__, "resources/themes/forest-dark.tcl"))
self.lightthemepath = pathlib.Path(pkg_resources.resource_filename(__name__, "resources/themes/forest-light.tcl"))
+
+
# Create the style object
self.style = ttk.Style(master)
- self.master.tk.call('source', self.lightthemepath)
- #self.style.theme_use('default')
+ #
#self.style.theme_use('forest-light')
self.create_menubar()
@@ -164,6 +174,13 @@ def __init__(self, master):
self.master.columnconfigure(0, weight=1)
+ if 'forest' in current_theme_name:
+ if 'light' in current_theme_name:
+ self.master.tk.call('source', self.lightthemepath)
+ else:
+ self.master.tk.call('source', self.darkthemepath)
+ else:
+ self.style.theme_use(current_theme_name)
# Create the dark theme
#self.style.theme_create("dark", parent="alt", settings={
# "TLabel": {"configure": {"background": "black", "foreground": "white"}},
@@ -211,7 +228,33 @@ def create_menubar(self):
def on_theme_select():
# Set the theme based on the selected value
self.style = ttk.Style()
-
+
+ #Update the theme file so the new theme opens on reboot
+ prev_theme = curr_gui_dict['theme_name']
+ curr_gui_dict['theme_name'] = self.theme_var.get()
+ with open(gui_theme_file, 'w') as f:
+ json.dump(curr_gui_dict, f)
+
+ def apply_theme():
+ if 'forest' in self.theme_var.get():
+ if self.theme_var.get()=='forest-dark' and 'forest-dark' not in self.style.theme_names():
+ self.master.tk.call('source', self.darkthemepath)
+ elif self.theme_var.get()=='forest-light' and 'forest-light' not in self.style.theme_names():
+ self.master.tk.call('source', self.lightthemepath)
+ self.master.tk.call("ttk::style", "theme", "use", self.theme_var.get())
+
+ if curr_gui_dict['theme_name']=='forest-light' or curr_gui_dict['theme_name'] == 'forest-dark':
+ do_reboot = messagebox.askyesno('App Restart Required',
+ f"It is recommended to restart the SpRIT GUI at this time to apply this theme. If not, you may continue but theme errors may occur. Click No to retain current theme ({prev_theme}) \nReboot now?",
+ )
+ print(do_reboot)
+ if do_reboot:
+ reboot_app()
+ else:
+ self.theme_var.set(prev_theme)
+ else:
+ apply_theme()
+
"""An attempt to get the backgrounds right
def apply_to_all_children(widget, func):
Recursively apply a function to all child widgets of a given widget
@@ -229,12 +272,7 @@ def change_background_color(widget):
apply_to_all_children(self.master, change_background_color)
"""
- if 'forest' in self.theme_var.get():
- if self.theme_var.get()=='forest-dark' and 'forest-dark' not in self.style.theme_names():
- self.master.tk.call('source', self.darkthemepath)
- elif self.theme_var.get()=='forest-light' and 'forest-light' not in self.style.theme_names():
- self.master.tk.call('source', self.lightthemepath)
- self.master.tk.call("ttk::style", "theme", "use", self.theme_var.get())
+
#self.master.tk.call("ttk::setTheme", self.theme_var.get())
#self.style.theme_use(self.theme_var.get())
@@ -250,7 +288,7 @@ def export_parameters(self):
filepath = filedialog.asksaveasfilename()
self.theme_menu = tk.Menu(self.menubar, tearoff=0)
- self.theme_var = tk.StringVar(value="Default")
+ self.theme_var = tk.StringVar(value=current_theme_name)
self.theme_menu.add_radiobutton(label="Default", variable=self.theme_var, value="default", command=on_theme_select)
self.theme_menu.add_radiobutton(label="Clam", variable=self.theme_var, value="clam", command=on_theme_select)
self.theme_menu.add_radiobutton(label="Alt", variable=self.theme_var, value="alt", command=on_theme_select)
@@ -495,8 +533,8 @@ def report_results(hvsr_results):
self.peakTest6ResultText.configure(text=hvsr_results['BestPeak']['Report']['Sa'][:-1])
self.peakTest6Result.configure(text=hvsr_results['BestPeak']['Report']['Sa'][-1])
- peakPass = (hvsr_results['BestPeak']['PassList']['PeakFreqClarityBelow'] +
- hvsr_results['BestPeak']['PassList']['PeakFreqClarityAbove']+
+ peakPass = (hvsr_results['BestPeak']['PassList']['PeakProminenceBelow'] +
+ hvsr_results['BestPeak']['PassList']['PeakProminenceAbove']+
hvsr_results['BestPeak']['PassList']['PeakAmpClarity']+
hvsr_results['BestPeak']['PassList']['FreqStability']+
hvsr_results['BestPeak']['PassList']['PeakStability_FreqStD']+
@@ -3036,6 +3074,13 @@ def on_closing():
root.destroy()
exit()
+def reboot_app():
+ """Restarts the current program.
+ Note: this function does not return. Any cleanup action (like
+ saving data) must be done before calling this function."""
+ python = sys.executable
+ os.execl(python, python, * sys.argv)
+
if __name__ == "__main__":
can_gui = sprit_utils.check_gui_requirements()
diff --git a/sprit/sprit_hvsr.py b/sprit/sprit_hvsr.py
index 5e616fd..709af57 100644
--- a/sprit/sprit_hvsr.py
+++ b/sprit/sprit_hvsr.py
@@ -14,11 +14,12 @@
import pathlib
import pickle
import pkg_resources
+import struct
+import sys
import tempfile
import traceback
import warnings
import xml.etree.ElementTree as ET
-import sys
import matplotlib
from matplotlib.backend_bases import MouseButton
@@ -48,7 +49,11 @@
max_rank = 0
plotRows = 4
-sample_data_dir = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/sample_data/'))
+#Get the main resources directory path, and the other paths as well
+resource_dir = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/'))
+sample_data_dir = resource_dir.joinpath('sample_data')
+settings_dir = resource_dir.joinpath('settings')
+
sampleFileKeyMap = {'1':sample_data_dir.joinpath('SampleHVSRSite1_AM.RAC84.00.2023.046_2023-02-15_1704-1734.MSEED'),
'2':sample_data_dir.joinpath('SampleHVSRSite2_AM.RAC84.00.2023-02-15_2132-2200.MSEED'),
'3':sample_data_dir.joinpath('SampleHVSRSite3_AM.RAC84.00.2023.199_2023-07-18_1432-1455.MSEED'),
@@ -218,6 +223,29 @@ def report(self, **kwargs):
"""Wrapper of get_report()"""
return self.get_report(**kwargs)
+ def export_settings(self, site_name=None, export_settings_path='default', export_settings_type='all', include_location=False, verbose=True):
+ """Method to export settings from HVSRData object in HVSRBatch object. Simply calls sprit.export_settings() from specified HVSRData object in the HVSRBatch object. See sprit.export_settings() for more details.
+
+ Parameters
+ ----------
+ site_name : str, default=None
+ The name of the site whose settings should be exported. If None, will default to the first site, by default None.
+ export_settings_path : str, optional
+ Filepath to output file. If left as 'default', will save as the default value in the resources directory. If that is not possible, will save to home directory, by default 'default'
+ export_settings_type : str, {'all', 'instrument', 'processing'}, optional
+ They type of settings to save, by default 'all'
+ include_location : bool, optional
+ Whether to include the location information in the instrument settings, if that settings type is selected, by default False
+ verbose : bool, optional
+ Whether to print output (filepath and settings) to terminal, by default True
+ """
+ #If no site name selected, use first site
+ if site_name is None:
+ site_name = self.sites[0]
+
+ export_settings(hvsr_data=self[site_name],
+ export_settings_path=export_settings_path, export_settings_type=export_settings_type, include_location=include_location, verbose=verbose)
+
def __iter__(self):
return iter(self._batch_dict.keys())
@@ -282,7 +310,9 @@ def export(self, export_path=None, ext='hvsr'):
Parameters
----------
export_path : filepath, default=True
- Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes). By default True. If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
+ Filepath to save file. Can be either directory (which will assign a filename based on the HVSRData attributes).
+ By default True.
+ If True, it will first try to save each file to the same directory as datapath, then if that does not work, to the current working directory, then to the user's home directory, by default True
ext : str, optional
The extension to use for the output, by default 'hvsr'. This is still a pickle file that can be read with pickle.load(), but will have .hvsr extension.
"""
@@ -355,6 +385,23 @@ def report(self, **kwargs):
"""Wrapper of get_report()"""
report_return = get_report(self, **kwargs)
return report_return
+
+ def export_settings(self, export_settings_path='default', export_settings_type='all', include_location=False, verbose=True):
+ """Method to export settings from HVSRData object. Simply calls sprit.export_settings() from the HVSRData object. See sprit.export_settings() for more details.
+
+ Parameters
+ ----------
+ export_settings_path : str, optional
+ Filepath to output file. If left as 'default', will save as the default value in the resources directory. If that is not possible, will save to home directory, by default 'default'
+ export_settings_type : str, {'all', 'instrument', 'processing'}, optional
+ They type of settings to save, by default 'all'
+ include_location : bool, optional
+ Whether to include the location information in the instrument settings, if that settings type is selected, by default False
+ verbose : bool, optional
+ Whether to print output (filepath and settings) to terminal, by default True
+ """
+ export_settings(hvsr_data=self,
+ export_settings_path=export_settings_path, export_settings_type=export_settings_type, include_location=include_location, verbose=verbose)
#ATTRIBUTES
#params
@@ -545,8 +592,14 @@ def run(datapath, source='file', verbose=False, **kwargs):
RuntimeError
If the data being processed is a single file, an error will be raised if generate_ppsds() does not work correctly. No errors are raised for remove_noise() errors (since that is an optional step) and the process_hvsr() step (since that is the last processing step) .
"""
+ if 'hvsr_band' not in kwargs.keys():
+ kwargs['hvsr_band'] = inspect.signature(input_params).parameters['hvsr_band'].default
+ if 'peak_freq_range' not in kwargs.keys():
+ kwargs['peak_freq_range'] = inspect.signature(input_params).parameters['peak_freq_range'].default
+
#Get the input parameters
- input_params_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in input_params.__code__.co_varnames}
+ input_params_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(input_params).parameters.keys())}
+
try:
params = input_params(datapath=datapath, verbose=verbose, **input_params_kwargs)
except:
@@ -559,15 +612,14 @@ def run(datapath, source='file', verbose=False, **kwargs):
#Fetch Data
try:
- fetch_data_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in fetch_data.__code__.co_varnames}
+ fetch_data_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(fetch_data).parameters.keys())}
dataIN = fetch_data(params=params, source=source, verbose=verbose, **fetch_data_kwargs)
except:
#Even if batch, this is reading in data for all sites so we want to raise error, not just warn
raise RuntimeError('Data not read correctly, see sprit.fetch_data() function and parameters for more details.')
-
#Remove Noise
try:
- remove_noise_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in remove_noise.__code__.co_varnames}
+ remove_noise_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(remove_noise).parameters.keys())}
data_noiseRemoved = remove_noise(hvsr_data=dataIN, verbose=verbose,**remove_noise_kwargs)
except:
data_noiseRemoved = dataIN
@@ -591,8 +643,8 @@ def run(datapath, source='file', verbose=False, **kwargs):
#Generate PPSDs
try:
- generate_ppsds_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in generate_ppsds.__code__.co_varnames}
- PPSDkwargs = {k: v for k, v in locals()['kwargs'].items() if k in PPSD.__init__.__code__.co_varnames}
+ generate_ppsds_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(generate_ppsds).parameters.keys())}
+ PPSDkwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(PPSD).parameters.keys())}
generate_ppsds_kwargs.update(PPSDkwargs)
ppsd_data = generate_ppsds(hvsr_data=data_noiseRemoved, verbose=verbose,**generate_ppsds_kwargs)
except Exception as e:
@@ -618,7 +670,7 @@ def run(datapath, source='file', verbose=False, **kwargs):
#Process HVSR Curves
try:
- process_hvsr_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in process_hvsr.__code__.co_varnames}
+ process_hvsr_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(process_hvsr).parameters.keys())}
hvsr_results = process_hvsr(hvsr_data=ppsd_data, verbose=verbose,**process_hvsr_kwargs)
except Exception as e:
traceback.print_exception(sys.exc_info()[1])
@@ -648,10 +700,10 @@ def run(datapath, source='file', verbose=False, **kwargs):
#Final post-processing/reporting
#Check peaks
- check_peaks_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in check_peaks.__code__.co_varnames}
+ check_peaks_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(check_peaks).parameters.keys())}
hvsr_results = check_peaks(hvsr_data=hvsr_results, verbose=verbose, **check_peaks_kwargs)
- get_report_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in get_report.__code__.co_varnames}
+ get_report_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in tuple(inspect.signature(get_report).parameters.keys())}
get_report(hvsr_results=hvsr_results, verbose=verbose, **get_report_kwargs)
if verbose:
@@ -670,8 +722,9 @@ def run(datapath, source='file', verbose=False, **kwargs):
#We do not need to plot another report if already plotted
pass
else:
- hvplot_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in plot_hvsr.__code__.co_varnames}
- hvsr_results['HV_Plot'] = plot_hvsr(hvsr_results, **hvplot_kwargs)
+ #hvplot_kwargs = {k: v for k, v in locals()['kwargs'].items() if k in plot_hvsr.__code__.co_varnames}
+ #hvsr_results['HV_Plot'] = plot_hvsr(hvsr_results, return_fig=True, show=False, close_figs=True)
+ pass
else:
pass
@@ -690,7 +743,7 @@ def run(datapath, source='file', verbose=False, **kwargs):
#Quality checks, stability tests, clarity tests
#def check_peaks(hvsr, x, y, index_list, peak, peakm, peakp, hvsr_peaks, stdf, hvsr_log_std, rank, hvsr_band=[0.4, 40], do_rank=False):
-def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose=False):
+def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_selection='max', peak_freq_range=[1, 20], verbose=False):
"""Function to run tests on HVSR peaks to find best one and see if it passes quality checks
Parameters
@@ -699,6 +752,10 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose
Dictionary containing all the calculated information about the HVSR data (i.e., hvsr_out returned from process_hvsr)
hvsr_band : tuple or list, default=[0.4, 40]
2-item tuple or list with lower and upper limit of frequencies to analyze
+ peak_selection : str or numeric, default='max'
+ How to select the "best" peak used in the analysis. For peak_selection="max" (default value), the highest peak within peak_freq_range is used.
+ For peak_selection='scored', an algorithm is used to select the peak based in part on which peak passes the most SESAME criteria.
+ If a numeric value is used (e.g., int or float), this should be a frequency value to manually select as the peak of interest.
peak_freq_range : tuple or list, default=[1, 20];
The frequency range within which to check for peaks. If there is an HVSR curve with multiple peaks, this allows the full range of data to be processed while limiting peak picks to likely range.
verbose : bool, default=False
@@ -711,6 +768,25 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose
"""
orig_args = locals().copy() #Get the initial arguments
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'check_peaks' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['check_peaks'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(check_peaks).args[1:],
+ inspect.getfullargspec(check_peaks).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ hvsr_band = orig_args['hvsr_band']
+ peak_selection = orig_args['peak_selection']
+ peak_freq_range = orig_args['peak_freq_range']
+ verbose = orig_args['verbose']
+
+ hvsr_data['processing_parameters']['check_peaks'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['check_peaks'][key] = value
+
if (verbose and 'input_params' not in hvsr_data.keys()) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
pass
@@ -746,13 +822,38 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose
if hvsr_data['ProcessingStatus']['OverallStatus']:
if not hvsr_band:
hvsr_band = [0.4,40]
+
hvsr_data['hvsr_band'] = hvsr_band
anyK = list(hvsr_data['x_freqs'].keys())[0]
x = hvsr_data['x_freqs'][anyK] #Consistent for all curves
y = hvsr_data['hvsr_curve'] #Calculated based on "Use" column
- index_list = hvsr_data['hvsr_peak_indices'] #Calculated based on hvsr_curve
+
+ scorelist = ['score', 'scored', 'best', 's']
+ maxlist = ['max', 'highest', 'm']
+ # Convert peak_selection to numeric, get index of nearest value as list item for __init_peaks()
+ try:
+ peak_val = float(peak_selection)
+ index_list = [np.argmin(np.abs(x - peak_val))]
+ except:
+ # If score method is being used, get index list for __init_peaks()
+ if peak_selection in scorelist:
+ index_list = hvsr_data['hvsr_peak_indices'] #Calculated based on hvsr_curve
+ elif peak_selection in maxlist:
+ #Get max index as item in list for __init_peaks()
+ startInd = np.argmin(np.abs(x - peak_freq_range[0]))
+ endInd = np.argmin(np.abs(x - peak_freq_range[1]))
+ if startInd > endInd:
+ holder = startInd
+ startInd = endInd
+ endInd = holder
+ subArrayMax = np.argmax(y[startInd:endInd])
+
+ # If max val is in subarray, this will be the same as the max of curve
+ # Otherwise, it will be the index of the value that is max within peak_freq_range
+ index_list = [subArrayMax+startInd]
+
hvsrp = hvsr_data['hvsrp'] #Calculated based on "Use" column
hvsrm = hvsr_data['hvsrm'] #Calculated based on "Use" column
@@ -778,7 +879,7 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose
peakp = __init_peaks(x, hvsrp, index_p, hvsr_band, peak_freq_range)
peakp = __check_clarity(x, hvsrp, peakp, do_rank=True)
- #Do for hvsrm
+ # Do for hvsrm
# Find the relative extrema of hvsrm (hvsr - 1 standard deviation)
if not np.isnan(np.sum(hvsrm)):
index_m = __find_peaks(hvsrm)
@@ -788,6 +889,7 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose
peakm = __init_peaks(x, hvsrm, index_m, hvsr_band, peak_freq_range)
peakm = __check_clarity(x, hvsrm, peakm, do_rank=True)
+ # Get standard deviation of time peaks
stdf = __get_stdf(x, index_list, hvsrPeaks)
peak = __check_freq_stability(peak, peakm, peakp)
@@ -799,7 +901,7 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose
# Get the BestPeak based on the peak score
# Calculate whether each peak passes enough tests
curveTests = ['WindowLengthFreq.','SignificantCycles', 'LowCurveStDevOverTime']
- peakTests = ['PeakFreqClarityBelow', 'PeakFreqClarityAbove', 'PeakAmpClarity', 'FreqStability', 'PeakStability_FreqStD', 'PeakStability_AmpStD']
+ peakTests = ['PeakProminenceBelow', 'PeakProminenceAbove', 'PeakAmpClarity', 'FreqStability', 'PeakStability_FreqStD', 'PeakStability_AmpStD']
bestPeakScore = 0
for p in hvsr_data['PeakReport']:
@@ -833,6 +935,10 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose
else:
hvsr_data['BestPeak'] = {}
print(f"Processing Errors: No Best Peak identified for {hvsr_data['site']}")
+ try:
+ hvsr_data.plot()
+ except:
+ pass
return hvsr_data
@@ -877,9 +983,178 @@ def _do_export(_hvsr_data=hvsr_data, _export_path=export_path, _ext=ext):
print("Error in data export. Data must be either of type sprit.HVSRData or sprit.HVSRBatch")
return
+###WORKING ON THIS
+#Save default instrument and processing settings to json file(s)
+def export_settings(hvsr_data, export_settings_path='default', export_settings_type='all', include_location=False, verbose=True):
+ """Save settings to json file
+
+ Parameters
+ ----------
+ export_settings_path : str, default="default"
+ Where to save the json file(s) containing the settings, by default 'default'.
+ If "default," will save to sprit package resources. Otherwise, set a filepath location you would like for it to be saved to.
+ If 'all' is selected, a directory should be supplied.
+ Otherwise, it will save in the directory of the provided file, if it exists. Otherwise, defaults to the home directory.
+ export_settings_type : str, {'all', 'instrument', 'processing'}
+ What kind of settings to save.
+ If 'all', saves all possible types in their respective json files.
+ If 'instrument', save the instrument settings to their respective file.
+ If 'processing', saves the processing settings to their respective file. By default 'all'
+ include_location : bool, default=False, input CRS
+ Whether to include the location parametersin the exported settings document.This includes xcoord, ycoord, elevation, elev_unit, and input_crs
+ verbose : bool, default=True
+ Whether to print outputs and information to the terminal
+
+ """
+ fnameDict = {}
+ fnameDict['instrument'] = "instrument_settings.json"
+ fnameDict['processing'] = "processing_settings.json"
+
+ if export_settings_path == 'default' or export_settings_path is True:
+ settingsPath = resource_dir.joinpath('settings')
+ else:
+ export_settings_path = pathlib.Path(export_settings_path)
+ if not export_settings_path.exists():
+ if not export_settings_path.parent.exists():
+ print(f'The provided value for export_settings_path ({export_settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}')
+ settingsPath = pathlib.Path.home()
+ else:
+ settingsPath = export_settings_path.parent
+
+ if export_settings_path.is_dir():
+ settingsPath = export_settings_path
+ elif export_settings_path.is_file():
+ settingsPath = export_settings_path.parent
+ fnameDict['instrument'] = export_settings_path.name+"_instrumentSettings.json"
+ fnameDict['processing'] = export_settings_path.name+"_processingSettings.json"
+
+ #Get final filepaths
+ instSetFPath = settingsPath.joinpath(fnameDict['instrument'])
+ procSetFPath = settingsPath.joinpath(fnameDict['processing'])
+
+ #Get settings values
+ instKeys = ["instrument", "net", "sta", "loc", "cha", "depth", "metapath", "hvsr_band"]
+ inst_location_keys = ['xcoord', 'ycoord', 'elevation', 'elev_unit', 'input_crs']
+ procFuncs = [fetch_data, remove_noise, generate_ppsds, process_hvsr, check_peaks, get_report]
+
+ instrument_settings_dict = {}
+ processing_settings_dict = {}
+
+ for k in instKeys:
+ if isinstance(hvsr_data[k], pathlib.PurePath):
+ #For those that are paths and cannot be serialized
+ instrument_settings_dict[k] = hvsr_data[k].as_posix()
+ else:
+ instrument_settings_dict[k] = hvsr_data[k]
+
+ if include_location:
+ for k in inst_location_keys:
+ if isinstance(hvsr_data[k], pathlib.PurePath):
+ #For those that are paths and cannot be serialized
+ instrument_settings_dict[k] = hvsr_data[k].as_posix()
+ else:
+ instrument_settings_dict[k] = hvsr_data[k]
+
+
+ for func in procFuncs:
+ funcName = func.__name__
+ processing_settings_dict[funcName] = {}
+ for arg in hvsr_data['processing_parameters'][funcName]:
+ if isinstance(hvsr_data['processing_parameters'][funcName][arg], (HVSRBatch, HVSRData)):
+ pass
+ else:
+ processing_settings_dict[funcName][arg] = hvsr_data['processing_parameters'][funcName][arg]
+
+ if verbose:
+ print("Exporting Settings")
+ #Save settings files
+ if export_settings_type.lower()=='instrument' or export_settings_type.lower()=='all':
+ try:
+ with open(instSetFPath.with_suffix('.inst').as_posix(), 'w') as instSetF:
+ jsonString = json.dumps(instrument_settings_dict, indent=2)
+ #Format output for readability
+ jsonString = jsonString.replace('\n ', ' ')
+ jsonString = jsonString.replace('[ ', '[')
+ jsonString = jsonString.replace('\n ]', ']')
+ #Export
+ instSetF.write(jsonString)
+ except:
+ instSetFPath = pathlib.Path.home().joinpath(instSetFPath.name)
+ with open(instSetFPath.with_suffix('.inst').as_posix(), 'w') as instSetF:
+ jsonString = json.dumps(instrument_settings_dict, indent=2)
+ #Format output for readability
+ jsonString = jsonString.replace('\n ', ' ')
+ jsonString = jsonString.replace('[ ', '[')
+ jsonString = jsonString.replace('\n ]', ']')
+ #Export
+ instSetF.write(jsonString)
+
+ if verbose:
+ print(f"Instrument settings exported to {instSetFPath}")
+ print(f"{jsonString}")
+ print()
+ if export_settings_type.lower()=='processing' or export_settings_type.lower()=='all':
+ try:
+ with open(procSetFPath.with_suffix('.proc').as_posix(), 'w') as procSetF:
+ jsonString = json.dumps(processing_settings_dict, indent=2)
+ #Format output for readability
+ jsonString = jsonString.replace('\n ', ' ')
+ jsonString = jsonString.replace('[ ', '[')
+ jsonString = jsonString.replace('\n ]', ']')
+ jsonString = jsonString.replace('\n },','\n\t\t},\n')
+ jsonString = jsonString.replace('{ "', '\n\t\t{\n\t\t"')
+ jsonString = jsonString.replace(', "', ',\n\t\t"')
+ jsonString = jsonString.replace('\n }', '\n\t\t}')
+ jsonString = jsonString.replace(': {', ':\n\t\t\t{')
+
+ #Export
+ procSetF.write(jsonString)
+ except:
+ procSetFPath = pathlib.Path.home().joinpath(procSetFPath.name)
+ with open(procSetFPath.with_suffix('.proc').as_posix(), 'w') as procSetF:
+ jsonString = json.dumps(processing_settings_dict, indent=2)
+ #Format output for readability
+ jsonString = jsonString.replace('\n ', ' ')
+ jsonString = jsonString.replace('[ ', '[')
+ jsonString = jsonString.replace('\n ]', ']')
+ jsonString = jsonString.replace('\n },','\n\t\t},\n')
+ jsonString = jsonString.replace('{ "', '\n\t\t{\n\t\t"')
+ jsonString = jsonString.replace(', "', ',\n\t\t"')
+ jsonString = jsonString.replace('\n }', '\n\t\t}')
+ jsonString = jsonString.replace(': {', ':\n\t\t\t{')
+
+ #Export
+ procSetF.write(jsonString)
+ if verbose:
+ print(f"Processing settings exported to {procSetFPath}")
+ print(f"{jsonString}")
+ print()
#Reads in traces to obspy stream
def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detrend='spline', detrend_order=2, update_metadata=True, plot_input_stream=False, verbose=False, **kwargs):
- import warnings
+ #Get intput paramaters
+ orig_args = locals().copy()
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in params.keys():
+ if 'fetch_data' in params['processing_parameters'].keys():
+ defaultVDict = dict(zip(inspect.getfullargspec(fetch_data).args[1:],
+ inspect.getfullargspec(fetch_data).defaults))
+ defaultVDict['kwargs'] = kwargs
+ for k, v in params['processing_parameters']['fetch_data'].items():
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ #Update local variables, in case of previously-specified parameters
+ source=orig_args['source']
+ trim_dir=orig_args['trim_dir']
+ export_format=orig_args['export_format']
+ detrend=orig_args['detrend']
+ detrend_order=orig_args['detrend_order']
+ update_metadata=orig_args['update_metadata']
+ plot_input_stream=orig_args['plot_input_stream']
+ verbose=orig_args['verbose']
+ kwargs=orig_args['kwargs']
"""Fetch ambient seismic data from a source to read into obspy stream
@@ -928,7 +1203,9 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
date=params['acq_date']
#Cleanup for gui input
- if '}' in str(params['datapath']):
+ if isinstance(params['datapath'], (obspy.Stream, obspy.Trace)):
+ pass
+ elif '}' in str(params['datapath']):
params['datapath'] = params['datapath'].as_posix().replace('{','')
params['datapath'] = params['datapath'].split('}')
@@ -941,7 +1218,10 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
#Make sure datapath is pointing to an actual file
if isinstance(params['datapath'],list):
for i, d in enumerate(params['datapath']):
- params['datapath'][i] = sprit_utils.checkifpath(str(d).strip())
+ params['datapath'][i] = sprit_utils.checkifpath(str(d).strip(), sample_list=sampleList)
+ dPath = params['datapath']
+ elif isinstance(params['datapath'], (obspy.Stream, obspy.Trace)):
+ pass
else:
dPath = sprit_utils.checkifpath(params['datapath'], sample_list=sampleList)
@@ -993,15 +1273,26 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
#Select which instrument we are reading from (requires different processes for each instrument)
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
+ trominoNameList = ['tromino', 'trom', 'tromino 3g', 'tromino 3g+', 'tr', 't']
+
+ #Get any kwargs that are included in obspy.read
+ obspyReadKwargs = {}
+ for argName in inspect.getfullargspec(obspy.read)[0]:
+ if argName in kwargs.keys():
+ obspyReadKwargs[argName] = kwargs[argName]
#Select how reading will be done
if source=='raw':
- if inst.lower() in raspShakeInstNameList:
- try:
+ try:
+ if inst.lower() in raspShakeInstNameList:
rawDataIN = __read_RS_file_struct(dPath, source, year, doy, inv, params, verbose=verbose)
- except:
- raise RuntimeError(f"Data not fetched for {params['site']}. Check input parameters or the data file.")
- return params
+
+ elif inst.lower() in trominoNameList:
+ rawDataIN = __read_tromino_files(dPath, params, verbose=verbose)
+ except:
+ raise RuntimeError(f"Data not fetched for {params['site']}. Check input parameters or the data file.")
+ elif source=='stream' or isinstance(params, (obspy.Stream, obspy.Trace)):
+ rawDataIN = params['datapath'].copy()
elif source=='dir':
if inst.lower() in raspShakeInstNameList:
rawDataIN = __read_RS_file_struct(dPath, source, year, doy, inv, params, verbose=verbose)
@@ -1012,28 +1303,27 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
for f in temp_file_glob:
currParams = params
currParams['datapath'] = f
+
curr_data = fetch_data(params, source='file', #all the same as input, except just reading the one file using the source='file'
- trim_dir=trim_dir, export_format=export_format, detrend=detrend, detrend_order=detrend_order, update_metadata=update_metadata, verbose=verbose, **kwargs),
+ trim_dir=trim_dir, export_format=export_format, detrend=detrend, detrend_order=detrend_order, update_metadata=update_metadata, verbose=verbose, **kwargs)
+ curr_data.merge()
obspyFiles[f.stem] = curr_data #Add path object to dict, with filepath's stem as the site name
return HVSRBatch(obspyFiles)
-
elif source=='file' and str(params['datapath']).lower() not in sampleList:
if isinstance(dPath, list) or isinstance(dPath, tuple):
rawStreams = []
for datafile in dPath:
- rawStream = obspy.read(datafile)
+ rawStream = obspy.read(datafile, **obspyReadKwargs)
rawStreams.append(rawStream) #These are actually streams, not traces
-
for i, stream in enumerate(rawStreams):
if i == 0:
rawDataIN = obspy.Stream(stream) #Just in case
else:
rawDataIN = rawDataIN + stream #This adds a stream/trace to the current stream object
-
elif str(dPath)[:6].lower()=='sample':
pass
else:
- rawDataIN = obspy.read(dPath)#, starttime=obspy.core.UTCDateTime(params['starttime']), endttime=obspy.core.UTCDateTime(params['endtime']), nearest_sample =True)
+ rawDataIN = obspy.read(dPath, **obspyReadKwargs)#, starttime=obspy.core.UTCDateTime(params['starttime']), endttime=obspy.core.UTCDateTime(params['endtime']), nearest_sample =True)
import warnings
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=UserWarning)
@@ -1080,12 +1370,15 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
except:
RuntimeError(f'source={source} not recognized, and datapath cannot be read using obspy.read()')
+ #Get metadata from the data itself, if not reading raw data
try:
dataIN = rawDataIN.copy()
if source!='raw':
#Use metadata from file for;
# site
if params['site'] == "HVSR Site":
+ if isinstance(dPath, (list, tuple)):
+ dPath = dPath[0]
params['site'] = dPath.stem
params['params']['site'] = dPath.stem
@@ -1122,11 +1415,13 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
today_Starttime = obspy.UTCDateTime(datetime.datetime(year=datetime.date.today().year, month=datetime.date.today().month,
day = datetime.date.today().day,
hour=0, minute=0, second=0, microsecond=0))
- maxStarttime = datetime.time(hour=0, minute=0, second=0, microsecond=0)
+ maxStarttime = datetime.datetime(year=params['acq_date'].year, month=params['acq_date'].month, day=params['acq_date'].day,
+ hour=0, minute=0, second=0, microsecond=0, tzinfo=datetime.timezone.utc)
if str(params['starttime']) == str(today_Starttime):
for tr in dataIN.merge():
- currTime = datetime.time(hour=tr.stats.starttime.hour, minute=tr.stats.starttime.minute,
- second=tr.stats.starttime.second, microsecond=tr.stats.starttime.microsecond)
+ currTime = datetime.datetime(year=tr.stats.starttime.year, month=tr.stats.starttime.month, day=tr.stats.starttime.day,
+ hour=tr.stats.starttime.hour, minute=tr.stats.starttime.minute,
+ second=tr.stats.starttime.second, microsecond=tr.stats.starttime.microsecond, tzinfo=datetime.timezone.utc)
if currTime > maxStarttime:
maxStarttime = currTime
@@ -1141,17 +1436,19 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
today_Endtime = obspy.UTCDateTime(datetime.datetime(year=datetime.date.today().year, month=datetime.date.today().month,
day = datetime.date.today().day,
hour=23, minute=59, second=59, microsecond=999999))
- minEndtime = datetime.time(hour=23, minute=59, second=59, microsecond=999999)
- if str(params['endtime']) == str(today_Endtime):
+ tomorrow_Endtime = today_Endtime + (60*60*24)
+ minEndtime = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc)#(hour=23, minute=59, second=59, microsecond=999999)
+ if str(params['endtime']) == str(today_Endtime) or str(params['endtime'])==tomorrow_Endtime:
for tr in dataIN.merge():
- currTime = datetime.time(hour=tr.stats.endtime.hour, minute=tr.stats.endtime.minute,
- second=tr.stats.endtime.second, microsecond=tr.stats.endtime.microsecond)
+ currTime = datetime.datetime(year=tr.stats.endtime.year, month=tr.stats.endtime.month, day=tr.stats.endtime.day,
+ hour=tr.stats.endtime.hour, minute=tr.stats.endtime.minute,
+ second=tr.stats.endtime.second, microsecond=tr.stats.endtime.microsecond, tzinfo=datetime.timezone.utc)
if currTime < minEndtime:
minEndtime = currTime
- newEndtime = obspy.UTCDateTime(datetime.datetime(year=params['acq_date'].year, month=params['acq_date'].month,
- day = params['acq_date'].day,
+ newEndtime = obspy.UTCDateTime(datetime.datetime(year=minEndtime.year, month=minEndtime.month,
+ day = minEndtime.day,
hour=minEndtime.hour, minute=minEndtime.minute,
- second=minEndtime.second, microsecond=minEndtime.microsecond))
+ second=minEndtime.second, microsecond=minEndtime.microsecond, tzinfo=datetime.timezone.utc))
params['endtime'] = newEndtime
params['params']['endtime'] = newEndtime
@@ -1191,10 +1488,12 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
#Remerge data
dataIN = dataIN.merge(method=1)
+ #Plot the input stream?
if plot_input_stream:
try:
params['InputPlot'] = _plot_specgram_stream(stream=dataIN, params=params, component='Z', stack_type='linear', detrend='mean', dbscale=True, fill_gaps=None, ylimstd=3, return_fig=True, fig=None, ax=None, show_plot=False)
- _get_removed_windows(input=dataIN, fig=params['InputPlot'][0], ax=params['InputPlot'][1], lineArtist =[], winArtist = [], existing_lineArtists=[], existing_xWindows=[], exist_win_format='matplotlib', keep_line_artists=True, time_type='matplotlib', show_plot=True)
+ #_get_removed_windows(input=dataIN, fig=params['InputPlot'][0], ax=params['InputPlot'][1], lineArtist =[], winArtist = [], existing_lineArtists=[], existing_xWindows=[], exist_win_format='matplotlib', keep_line_artists=True, time_type='matplotlib', show_plot=True)
+ plt.show()
except:
print('Error with default plotting method, falling back to internal obspy plotting method')
dataIN.plot(method='full', linewidth=0.25)
@@ -1205,6 +1504,7 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
else:
dataIN = _sort_channels(input=dataIN, source=source, verbose=verbose)
+ #Clean up the ends of the data unless explicitly specified to do otherwise (this is a kwarg, not a parameter)
if 'clean_ends' not in kwargs.keys():
clean_ends=True
else:
@@ -1240,6 +1540,14 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
params['batch'] = False #Set False by default, will get corrected later in batch mode
params['input_stream'] = dataIN.copy()
params['stream'] = dataIN.copy()
+
+ if 'processing_parameters' not in params.keys():
+ params['processing_parameters'] = {}
+ params['processing_parameters']['fetch_data'] = {}
+ for key, value in orig_args.items():
+ params['processing_parameters']['fetch_data'][key] = value
+
+
params['ProcessingStatus']['FetchDataStatus'] = True
if verbose and not isinstance(params, HVSRBatch):
dataINStr = dataIN.__str__().split('\n')
@@ -1291,6 +1599,8 @@ def generate_ppsds(hvsr_data, remove_outliers=True, outlier_std=3, verbose=False
ppsd_kwargs_sprit_defaults['skip_on_gaps'] = True
if 'period_step_octaves' not in ppsd_kwargs:
ppsd_kwargs_sprit_defaults['period_step_octaves'] = 0.03125
+ if 'period_limits' not in ppsd_kwargs:
+ ppsd_kwargs_sprit_defaults['period_limits'] = [1/hvsr_data['hvsr_band'][1], 1/hvsr_data['hvsr_band'][0]]
#Get Probablistic power spectral densities (PPSDs)
#Get default args for function
@@ -1304,8 +1614,24 @@ def get_default_args(func):
ppsd_kwargs = get_default_args(PPSD)
ppsd_kwargs.update(ppsd_kwargs_sprit_defaults)#Update with sprit defaults, or user input
+ orig_args['ppsd_kwargs'] = ppsd_kwargs
- orig_args['ppsd_kwargs'] = [ppsd_kwargs]
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'generate_ppsds' in hvsr_data['processing_parameters'].keys():
+ defaultVDict = dict(zip(inspect.getfullargspec(generate_ppsds).args[1:],
+ inspect.getfullargspec(generate_ppsds).defaults))
+ defaultVDict['ppsd_kwargs'] = ppsd_kwargs
+ for k, v in hvsr_data['processing_parameters']['generate_ppsds'].items():
+
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ remove_outliers = orig_args['remove_outliers']
+ outlier_std = orig_args['outlier_std']
+ verbose = orig_args['verbose']
+ ppsd_kwargs = orig_args['ppsd_kwargs']
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
@@ -1339,7 +1665,7 @@ def get_default_args(func):
hvsr_data[site_name]['ProcessingStatus']['OverallStatus'] = False
return hvsr_data
else:
- paz=hvsr_data['paz']
+ paz = hvsr_data['paz']
stream = hvsr_data['stream']
#Get ppsds of e component
@@ -1474,10 +1800,18 @@ def convert_to_mpl_dates(obspyUTCDateTime):
hvsrDF['TimesProcessed_MPLEnd'] = hvsrDF['TimesProcessed_MPL'] + (ppsd_kwargs['ppsd_length']/86400)
hvsrDF['Use'] = True
+ hvsrDF['Use']=hvsrDF['Use'].astype(bool)
for gap in hvsr_data['ppsds']['Z']['times_gaps']:
- hvsrDF['Use'] = (hvsrDF['TimesProcessed_Obspy'].gt(gap[0]) & hvsrDF['TimesProcessed_Obspy'].gt(gap[1]) )| \
- (hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[0]) & hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[1]))# | \
-
+ hvsrDF['Use'] = (hvsrDF['TimesProcessed_MPL'].gt(gap[1].matplotlib_date))| \
+ (hvsrDF['TimesProcessed_MPLEnd'].lt(gap[0].matplotlib_date))# | \
+
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+ if 'xwindows_out' in hvsr_data.keys():
+ for window in hvsr_data['xwindows_out']:
+ hvsrDF['Use'] = (hvsrDF['TimesProcessed_MPL'][hvsrDF['Use']].lt(window[0]) & hvsrDF['TimesProcessed_MPLEnd'][hvsrDF['Use']].lt(window[0]) )| \
+ (hvsrDF['TimesProcessed_MPL'][hvsrDF['Use']].gt(window[1]) & hvsrDF['TimesProcessed_MPLEnd'][hvsrDF['Use']].gt(window[1]))
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+
hvsrDF.set_index('TimesProcessed', inplace=True)
hvsr_data['hvsr_df'] = hvsrDF
#Create dict entry to keep track of how many outlier hvsr curves are removed (2-item list with [0]=current number, [1]=original number of curves)
@@ -1492,6 +1826,12 @@ def convert_to_mpl_dates(obspyUTCDateTime):
hvsr_data = sprit_utils.make_it_classy(hvsr_data)
+ if 'processing_parameters' not in hvsr_data.keys():
+ hvsr_data['processing_parameters'] = {}
+ hvsr_data['processing_parameters']['generate_ppsds'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['generate_ppsds'][key] = value
+
hvsr_data['ProcessingStatus']['PPSDStatus'] = True
hvsr_data = _check_processing_status(hvsr_data)
return hvsr_data
@@ -1518,14 +1858,57 @@ def get_metadata(params, write_path='', update_metadata=True, source=None, **rea
params : dict
Modified input dictionary with additional key:value pair containing paz dictionary (key = "paz")
"""
-
invPath = params['metapath']
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
+ trominoNameList = ['tromino', 'trom', 'trm', 't']
if params['instrument'].lower() in raspShakeInstNameList:
if update_metadata:
params = _update_shake_metadata(filepath=invPath, params=params, write_path=write_path)
params = _read_RS_Metadata(params, source=source)
+ elif params['instrument'].lower() in trominoNameList:
+ params['paz'] = {'Z':{}, 'E':{}, 'N':{}}
+ #ALL THESE VALUES ARE PLACEHOLDERS, taken from RASPBERRY SHAKE! (Needed for PPSDs)
+ params['paz']['Z'] = {'sensitivity': 360000000.0,
+ 'gain': 360000000.0,
+ 'poles': [(-1+0j), (-3.03+0j), (-3.03+0j), (-666.67+0j)],
+ 'zeros': [0j, 0j, 0j]}
+ params['paz']['E'] = params['paz']['Z']
+ params['paz']['N'] = params['paz']['Z']
+
+ channelObj_Z = obspy.core.inventory.channel.Channel(code='BHZ', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=0, dip=90, types=None, external_references=None,
+ sample_rate=None, sample_rate_ratio_number_samples=None, sample_rate_ratio_number_seconds=None,
+ storage_format=None, clock_drift_in_seconds_per_sample=None, calibration_units=None,
+ calibration_units_description=None, sensor=None, pre_amplifier=None, data_logger=None,
+ equipments=None, response=None, description=None, comments=None, start_date=None, end_date=None,
+ restricted_status=None, alternate_code=None, historical_code=None, data_availability=None,
+ identifiers=None, water_level=None, source_id=None)
+ channelObj_E = obspy.core.inventory.channel.Channel(code='BHE', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=90, dip=0)
+
+ channelObj_N = obspy.core.inventory.channel.Channel(code='BHN', location_code='00', latitude=params['params']['latitude'],
+ longitude=params['params']['longitude'], elevation=params['params']['elevation'], depth=params['params']['depth'],
+ azimuth=0, dip=0)
+
+ siteObj = obspy.core.inventory.util.Site(name=params['params']['site'], description=None, town=None, county=None, region=None, country=None)
+ stationObj = obspy.core.inventory.station.Station(code='TZ', latitude=params['params']['latitude'], longitude=params['params']['longitude'],
+ elevation=params['params']['elevation'], channels=[channelObj_Z, channelObj_E, channelObj_N], site=siteObj,
+ vault=None, geology=None, equipments=None, operators=None, creation_date=datetime.datetime.today(),
+ termination_date=None, total_number_of_channels=None,
+ selected_number_of_channels=None, description='Estimated data for Tromino, this is NOT from the manufacturer',
+ comments=None, start_date=None,
+ end_date=None, restricted_status=None, alternate_code=None, historical_code=None,
+ data_availability=None, identifiers=None, water_level=None, source_id=None)
+
+ network = [obspy.core.inventory.network.Network(code='TROM', stations=[stationObj], total_number_of_stations=None,
+ selected_number_of_stations=None, description=None, comments=None, start_date=None,
+ end_date=None, restricted_status=None, alternate_code=None, historical_code=None,
+ data_availability=None, identifiers=None, operators=None, source_id=None)]
+
+ params['inv'] = obspy.Inventory(networks=network)
else:
if not invPath:
pass #if invPath is None
@@ -1582,11 +1965,30 @@ def get_report(hvsr_results, report_format='print', plot_type='HVSR p ann C+ p a
list/tuple - a list or tuple of the above objects, in the same order they are in the report_format list
"""
- #print statement
- #Check if results are good
- #Curve pass?
orig_args = locals().copy() #Get the initial arguments
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_results.keys():
+ if 'get_report' in hvsr_results['processing_parameters'].keys():
+ for k, v in hvsr_results['processing_parameters']['get_report'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(get_report).args[1:],
+ inspect.getfullargspec(get_report).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ report_format = orig_args['report_format']
+ plot_type = orig_args['plot_type']
+ export_path = orig_args['export_path']
+ return_results = orig_args['return_results']
+ csv_overwrite_opt = orig_args['csv_overwrite_opt']
+ no_output = orig_args['no_output']
+ verbose = orig_args['verbose']
+
+ hvsr_results['processing_parameters']['get_report'] = {}
+ for key, value in orig_args.items():
+ hvsr_results['processing_parameters']['get_report'][key] = value
+
if (verbose and isinstance(hvsr_results, HVSRBatch)) or (verbose and not hvsr_results['batch']):
if isinstance(hvsr_results, HVSRData) and hvsr_results['batch']:
pass
@@ -1656,8 +2058,8 @@ def get_report(hvsr_results, report_format='print', plot_type='HVSR p ann C+ p a
curvePass = curvTestsPassed > 2
#Peak Pass?
- peakTestsPassed = ( hvsr_results['BestPeak']['PassList']['PeakFreqClarityBelow'] +
- hvsr_results['BestPeak']['PassList']['PeakFreqClarityAbove']+
+ peakTestsPassed = ( hvsr_results['BestPeak']['PassList']['PeakProminenceBelow'] +
+ hvsr_results['BestPeak']['PassList']['PeakProminenceAbove']+
hvsr_results['BestPeak']['PassList']['PeakAmpClarity']+
hvsr_results['BestPeak']['PassList']['FreqStability']+
hvsr_results['BestPeak']['PassList']['PeakStability_FreqStD']+
@@ -1792,24 +2194,25 @@ def report_output(_report_format, _plot_type='HVSR p ann C+ p ann Spec', _return
report_string_list.append(internalSeparator)
report_string_list.append('')
+ justSize=34
#Print individual results
report_string_list.append('\tCurve Tests: {}/3 passed (3/3 needed)'.format(curvTestsPassed))
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Lw'][-1]} Length of processing windows: {hvsr_results['BestPeak']['Report']['Lw']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Nc'][-1]} Number of significant cycles: {hvsr_results['BestPeak']['Report']['Nc']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['σ_A(f)'][-1]} Low StDev. of H/V Curve over time: {hvsr_results['BestPeak']['Report']['σ_A(f)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Lw'][-1]}"+" Length of processing windows".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Lw']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Nc'][-1]}"+" Number of significant cycles".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Nc']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['σ_A(f)'][-1]}"+" Small H/V StDev over time".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['σ_A(f)']}")
report_string_list.append('')
report_string_list.append("\tPeak Tests: {}/6 passed (5/6 needed)".format(peakTestsPassed))
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f-)'][-1]} Clarity Below Peak Frequency: {hvsr_results['BestPeak']['Report']['A(f-)']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f+)'][-1]} Clarity Above Peak Frequency: {hvsr_results['BestPeak']['Report']['A(f+)']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A0'][-1]} Clarity of Peak Amplitude: {hvsr_results['BestPeak']['Report']['A0']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f-)'][-1]}"+" Peak is prominent below".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A(f-)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A(f+)'][-1]}"+" Peak is prominent above".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A(f+)']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['A0'][-1]}"+" Peak is large".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['A0']}")
if hvsr_results['BestPeak']['PassList']['FreqStability']:
res = sprit_utils.check_mark()
else:
res = sprit_utils.x_mark()
- report_string_list.append(f"\t\t {res} Stability of Peak Freq. Over time: {hvsr_results['BestPeak']['Report']['P-'][:5]} and {hvsr_results['BestPeak']['Report']['P+'][:-1]} {res}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sf'][-1]} Stability of Peak (Freq. StDev): {hvsr_results['BestPeak']['Report']['Sf']}")
- report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sa'][-1]} Stability of Peak (Amp. StDev): {hvsr_results['BestPeak']['Report']['Sa']}")
+ report_string_list.append(f"\t\t {res}"+ " Peak freq. is stable over time".ljust(justSize)+ f"{hvsr_results['BestPeak']['Report']['P-'][:5]} and {hvsr_results['BestPeak']['Report']['P+'][:-1]} {res}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sf'][-1]}"+" Stability of peak (Freq. StDev)".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Sf']}")
+ report_string_list.append(f"\t\t {hvsr_results['BestPeak']['Report']['Sa'][-1]}"+" Stability of peak (Amp. StDev)".ljust(justSize)+f"{hvsr_results['BestPeak']['Report']['Sa']}")
report_string_list.append('')
report_string_list.append(f"Calculated using {hvsr_results['hvsr_df']['Use'].sum()}/{hvsr_results['hvsr_df']['Use'].count()} time windows".rjust(sepLen-1))
report_string_list.append(extSiteSeparator)
@@ -1833,7 +2236,7 @@ def report_output(_report_format, _plot_type='HVSR p ann C+ p ann Spec', _return
import pandas as pd
pdCols = ['Site Name', 'Acq_Date', 'Longitude', 'Latitide', 'Elevation', 'PeakFrequency',
'WindowLengthFreq.','SignificantCycles','LowCurveStDevOverTime',
- 'PeakFreqClarityBelow','PeakFreqClarityAbove','PeakAmpClarity','FreqStability', 'PeakStability_FreqStD','PeakStability_AmpStD', 'PeakPasses']
+ 'PeakProminenceBelow','PeakProminenceAbove','PeakAmpClarity','FreqStability', 'PeakStability_FreqStD','PeakStability_AmpStD', 'PeakPasses']
d = hvsr_results
criteriaList = []
for p in hvsr_results['BestPeak']["PassList"]:
@@ -1901,167 +2304,6 @@ def report_output(_report_format, _plot_type='HVSR p ann C+ p ann Spec', _return
hvsr_results = report_output(_report_format=rep_form, _plot_type=plot_type, _return_results=return_results, _export_path=exp_path, _no_output=no_output, verbose=verbose)
return hvsr_results
-#Main function for plotting results
-def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, xtype='freq', fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True,**kwargs):
- """Function to plot HVSR data
-
- Parameters
- ----------
- hvsr_data : dict
- Dictionary containing output from process_hvsr function
- plot_type : str='HVSR' or list
- The plot_type of plot(s) to plot. If list, will plot all plots listed
- 'HVSR' : Standard HVSR plot, including standard deviation
- - '[HVSR] p' : HVSR plot with BestPeaks shown
- - '[HVSR] p' : HVSR plot with best picked peak shown
- - '[HVSR] p* all' : HVSR plot with all picked peaks shown
- - '[HVSR] p* t' : HVSR plot with peaks from all time steps in background
- - '[HVSR p* ann] : Annotates plot with peaks
- - '[HVSR] -s' : HVSR plots don't show standard deviation
- - '[HVSR] t' : HVSR plot with individual hv curves for each time step shown
- - '[HVSR] c' : HVSR plot with each components' spectra. Recommended to do this last (or just before 'specgram'), since doing c+ can make the component chart its own chart
- 'Specgram' : Combined spectrogram of all components
- - '[spec]' : basic spectrogram plot of H/V curve
- use_subplots : bool, default = True
- Whether to output the plots as subplots (True) or as separate plots (False)
- xtype : str, default = 'freq'
- String for what to use, between frequency or period
- For frequency, the following are accepted (case does not matter): 'f', 'Hz', 'freq', 'frequency'
- For period, the following are accepted (case does not matter): 'p', 'T', 's', 'sec', 'second', 'per', 'period'
- fig : matplotlib.Figure, default = None
- If not None, matplotlib figure on which plot is plotted
- ax : matplotlib.Axis, default = None
- If not None, matplotlib axis on which plot is plotted
- return_fig : bool
- Whether to return figure and axis objects
- save_dir : str or None
- Directory in which to save figures
- save_suffix : str
- Suffix to add to end of figure filename(s), if save_dir is used
- show_legend : bool, default=False
- Whether to show legend in plot
- show : bool
- Whether to show plot
- close_figs : bool, default=False
- Whether to close figures before plotting
- clear_fig : bool, default=True
- Whether to clear figures before plotting
- **kwargs : keyword arguments
- Keyword arguments for matplotlib.pyplot
-
- Returns
- -------
- fig, ax : matplotlib figure and axis objects
- Returns figure and axis matplotlib.pyplot objects if return_fig=True, otherwise, simply plots the figures
- """
- orig_args = locals().copy() #Get the initial arguments
- if isinstance(hvsr_data, HVSRBatch):
- #If running batch, we'll loop through each site
- for site_name in hvsr_data.keys():
- args = orig_args.copy() #Make a copy so we don't accidentally overwrite
- individual_params = hvsr_data[site_name] #Get what would normally be the "params" variable for each site
- args['hvsr_results'] = individual_params #reset the params parameter we originally read in to an individual site params
- if hvsr_data[site_name]['ProcessingStatus']['OverallStatus']:
- try:
- _hvsr_plot_batch(**args) #Call another function, that lets us run this function again
- except:
- print(f"{site_name} not able to be plotted.")
- else:
- if clear_fig and fig is not None and ax is not None: #Intended use for tkinter
- #Clear everything
- for key in ax:
- ax[key].clear()
- fig.clear()
- if close_figs:
- plt.close('all')
-
- compList = ['c', 'comp', 'component', 'components']
- specgramList = ['spec', 'specgram', 'spectrogram']
- hvsrList = ['hvsr', 'hv', 'h']
-
- hvsrInd = np.nan
- compInd = np.nan
- specInd = np.nan
-
- kList = plot_type.split(' ')
- for i, k in enumerate(kList):
- kList[i] = k.lower()
-
- #Get the plots in the right order, no matter how they were input (and ensure the right options go with the right plot)
- #HVSR index
- if len(set(hvsrList).intersection(kList)):
- for i, hv in enumerate(hvsrList):
- if hv in kList:
- hvsrInd = kList.index(hv)
- break
- #Component index
- #if len(set(compList).intersection(kList)):
- for i, c in enumerate(kList):
- if '+' in c and c[:-1] in compList:
- compInd = kList.index(c)
- break
-
- #Specgram index
- if len(set(specgramList).intersection(kList)):
- for i, sp in enumerate(specgramList):
- if sp in kList:
- specInd = kList.index(sp)
- break
-
- indList = [hvsrInd, compInd, specInd]
- indListCopy = indList.copy()
- plotTypeList = ['hvsr', 'comp', 'spec']
-
- plotTypeOrder = []
- plotIndOrder = []
-
- lastVal = 0
- while lastVal != 99:
- firstInd = np.nanargmin(indListCopy)
- plotTypeOrder.append(plotTypeList[firstInd])
- plotIndOrder.append(indList[firstInd])
- lastVal = indListCopy[firstInd]
- indListCopy[firstInd] = 99 #just a high number
-
- plotTypeOrder.pop()
- plotIndOrder[-1]=len(kList)
-
- for i, p in enumerate(plotTypeOrder):
- pStartInd = plotIndOrder[i]
- pEndInd = plotIndOrder[i+1]
- plotComponents = kList[pStartInd:pEndInd]
-
- if use_subplots and i==0 and fig is None and ax is None:
- mosaicPlots = []
- for pto in plotTypeOrder:
- mosaicPlots.append([pto])
- fig, ax = plt.subplot_mosaic(mosaicPlots, gridspec_kw={'hspace':0.3})
- axis = ax[p]
- elif use_subplots:
- with warnings.catch_warnings():
- warnings.simplefilter("ignore") #Often warns about xlim when it is not an issue
- ax[p].clear()
- axis = ax[p]
- else:
- fig, axis = plt.subplots()
-
- if p == 'hvsr':
- _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax)
- elif p=='comp':
- plotComponents[0] = plotComponents[0][:-1]
- _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax)
- elif p=='spec':
- _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False)
- else:
- warnings.warn('Plot type {p} not recognized', UserWarning)
-
- if show:
- fig.canvas.draw()
-
- if return_fig:
- return fig, ax
- return
-
#Import data
def import_data(import_filepath, data_format='pickle'):
"""Function to import .hvsr (or other extension) data exported using export_data() function
@@ -2084,6 +2326,28 @@ def import_data(import_filepath, data_format='pickle'):
dataIN = import_filepath
return dataIN
+#Import settings
+def import_settings(settings_import_path, settings_import_type='instrument', verbose=False):
+
+ allList = ['all', ':', 'both', 'any']
+ if settings_import_type.lower() not in allList:
+ # if just a single settings dict is desired
+ with open(settings_import_path, 'r') as f:
+ settingsDict = json.load(f)
+ else:
+ # Either a directory or list
+ if isinstance(settings_import_path, (list, tuple)):
+ for setPath in settings_import_path:
+ pass
+ else:
+ settings_import_path = sprit_utils.checkifpath(settings_import_path)
+ if not settings_import_path.is_dir():
+ raise RuntimeError(f'settings_import_type={settings_import_type}, but settings_import_path is not list/tuple or filepath to directory')
+ else:
+ instFile = settings_import_path.glob('*.inst')
+ procFile = settings_import_path.glob('*.proc')
+ return settingsDict
+
#Define input parameters
def input_params(datapath,
site='HVSR Site',
@@ -2103,9 +2367,10 @@ def input_params(datapath,
elev_unit = 'feet',
depth = 0,
instrument = 'Raspberry Shake',
- metapath = '',
+ metapath = None,
hvsr_band = [0.4, 40],
peak_freq_range=[0.4, 40],
+ processing_parameters={},
verbose=False
):
"""Function for designating input parameters for reading in and processing data
@@ -2150,12 +2415,19 @@ def input_params(datapath,
Depth of seismometer. Not currently used, but will likely be used in the future.
instrument : str or list {'Raspberry Shake')
Instrument from which the data was acquired.
- metapath : str or pathlib.Path object, default=''
- Filepath of metadata, in format supported by obspy.read_inventory. If default value of '', will read from resources folder of repository (only supported for Raspberry Shake).
+ metapath : str or pathlib.Path object, default=None
+ Filepath of metadata, in format supported by obspy.read_inventory. If default value of None, will read from resources folder of repository (only supported for Raspberry Shake).
hvsr_band : list, default=[0.4, 40]
Two-element list containing low and high "corner" frequencies (in Hz) for processing. This can specified again later.
peak_freq_range : list or tuple, default=[0.4, 40]
Two-element list or tuple containing low and high frequencies (in Hz) that are used to check for HVSR Peaks. This can be a tigher range than hvsr_band, but if larger, it will still only use the hvsr_band range.
+ processing_parameters={} : dict or filepath, default={}
+ If filepath, should point to a .proc json file with processing parameters (i.e, an output from sprit.export_settings()).
+ Note that this only applies to parameters for the functions: 'fetch_data', 'remove_noise', 'generate_ppsds', 'process_hvsr', 'check_peaks', and 'get_report.'
+ If dictionary, dictionary containing nested dictionaries of function names as they key, and the parameter names/values as key/value pairs for each key.
+ If a function name is not present, or if a parameter name is not present, default values will be used.
+ For example:
+ `{ 'fetch_data' : {'source':'batch', 'trim_dir':"/path/to/trimmed/data", 'export_format':'mseed', 'detrend':'spline', 'plot_input_stream':True, 'verbose':False, kwargs:{'kwargskey':'kwargsvalue'}} }`
verbose : bool, default=False
Whether to print output and results to terminal
@@ -2167,30 +2439,6 @@ def input_params(datapath,
"""
orig_args = locals().copy() #Get the initial arguments
- #Declare obspy here instead of at top of file for (for example) colab, where obspy first needs to be installed on environment
- global obspy
- import obspy
- if verbose:
- print('Gathering input parameters (input_params())')
- for key, value in orig_args.items():
- print('\t {}={}'.format(key, value))
- print()
-
- #Make Sure metapath is all good
- if not pathlib.Path(metapath).exists() or metapath=='':
- if metapath == '':
- pass
- else:
- print('Specified metadata file cannot be read!')
- repoDir = pathlib.Path(os.path.dirname(__file__))
- metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv5plus_metadata.inv'))
- #print('Using default metadata file for Raspberry Shake v.7 distributed with package')
- else:
- if isinstance(metapath, pathlib.PurePath):
- metapath = metapath.as_posix()
- else:
- metapath = pathlib.Path(metapath).as_posix()
-
#Reformat times
if type(acq_date) is datetime.datetime:
date = str(acq_date.date())
@@ -2273,12 +2521,6 @@ def input_params(datapath,
acq_date = datetime.date(year=int(date.split('-')[0]), month=int(date.split('-')[1]), day=int(date.split('-')[2]))
raspShakeInstNameList = ['raspberry shake', 'shake', 'raspberry', 'rs', 'rs3d', 'rasp. shake', 'raspshake']
- #Raspberry shake stationxml is in the resources folder, double check we have right path
- if instrument.lower() in raspShakeInstNameList:
- if metapath == r'resources/rs3dv7_metadata.inv':
- metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv7_metadata.inv'))
- #metapath = pathlib.Path(os.path.realpath(__file__)).parent.joinpath('/resources/rs3dv7_metadata.inv')
-
if output_crs is None:
output_crs='EPSG:4326'
@@ -2299,11 +2541,213 @@ def input_params(datapath,
'ProcessingStatus':{'InputStatus':True, 'OverallStatus':True}
}
+ #Replace any default parameter settings with those from json file of interest, potentially
+ instrument_settings_dict = {}
+ if pathlib.Path(instrument).exists():
+ instrument_settings = import_settings(settings_import_path=instrument, settings_import_type='instrument', verbose=verbose)
+ input_params_args = inspect.getfullargspec(input_params).args
+ input_params_args.append('net')
+ input_params_args.append('sta')
+ for k, settings_value in instrument_settings.items():
+ if k in input_params_args:
+ instrument_settings_dict[k] = settings_value
+ inputParamDict['instrument_settings'] = inputParamDict['instrument']
+ inputParamDict.update(instrument_settings_dict)
+
+ if instrument.lower() in raspShakeInstNameList:
+ if metapath is None:
+ metapath = pathlib.Path(pkg_resources.resource_filename(__name__, 'resources/rs3dv5plus_metadata.inv')).as_posix()
+ inputParamDict['metapath'] = metapath
+ #metapath = pathlib.Path(os.path.realpath(__file__)).parent.joinpath('/resources/rs3dv7_metadata.inv')
+
+ for settingName in instrument_settings_dict.keys():
+ if settingName in inputParamDict.keys():
+ inputParamDict[settingName] = instrument_settings_dict[settingName]
+
+ #Declare obspy here instead of at top of file for (for example) colab, where obspy first needs to be installed on environment
+ if verbose:
+ print('Gathering input parameters (input_params())')
+ for key, value in inputParamDict.items():
+ print('\t {}={}'.format(key, value))
+ print()
+
+ if isinstance(processing_parameters, dict):
+ inputParamDict['processing_parameters'] = processing_parameters
+ else:
+ processing_parameters = sprit_utils.checkifpath(processing_parameters)
+ inputParamDict['processing_parameters'] = import_settings(processing_parameters, settings_import_type='processing', verbose=verbose)
+
+ #Format everything nicely
params = sprit_utils.make_it_classy(inputParamDict)
params['ProcessingStatus']['InputParams'] = True
params = _check_processing_status(params)
return params
+#Main function for plotting results
+def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True, fig=None, ax=None, return_fig=False, save_dir=None, save_suffix='', show_legend=False, show=True, close_figs=False, clear_fig=True,**kwargs):
+ """Function to plot HVSR data
+
+ Parameters
+ ----------
+ hvsr_data : dict
+ Dictionary containing output from process_hvsr function
+ plot_type : str or list, default = 'HVSR ann p C+ ann p SPEC'
+ The plot_type of plot(s) to plot. If list, will plot all plots listed
+ - 'HVSR' - Standard HVSR plot, including standard deviation. Options are included below:
+ - 'p' shows a vertical dotted line at frequency of the "best" peak
+ - 'ann' annotates the frequency value of of the "best" peak
+ - 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+ - 't' shows the H/V curve for all time windows
+ -'tp' shows all the peaks from the H/V curves of all the time windows
+ - 'COMP' - plot of the PPSD curves for each individual component ("C" also works)
+ - '+' (as a suffix in 'C+' or 'COMP+') plots C on a plot separate from HVSR (C+ is default, but without + will plot on the same plot as HVSR)
+ - 'p' shows a vertical dotted line at frequency of the "best" peak
+ - 'ann' annotates the frequency value of of the "best" peak
+ - 'all' shows all the peaks identified in check_peaks() (by default, only the max is identified)
+ - 't' shows the H/V curve for all time windows
+ - 'SPEC' - spectrogram style plot of the H/V curve over time
+ - 'p' shows a horizontal dotted line at the frequency of the "best" peak
+ - 'ann' annotates the frequency value of the "best" peak
+ use_subplots : bool, default = True
+ Whether to output the plots as subplots (True) or as separate plots (False)
+ fig : matplotlib.Figure, default = None
+ If not None, matplotlib figure on which plot is plotted
+ ax : matplotlib.Axis, default = None
+ If not None, matplotlib axis on which plot is plotted
+ return_fig : bool
+ Whether to return figure and axis objects
+ save_dir : str or None
+ Directory in which to save figures
+ save_suffix : str
+ Suffix to add to end of figure filename(s), if save_dir is used
+ show_legend : bool, default=False
+ Whether to show legend in plot
+ show : bool
+ Whether to show plot
+ close_figs : bool, default=False
+ Whether to close figures before plotting
+ clear_fig : bool, default=True
+ Whether to clear figures before plotting
+ **kwargs : keyword arguments
+ Keyword arguments for matplotlib.pyplot
+
+ Returns
+ -------
+ fig, ax : matplotlib figure and axis objects
+ Returns figure and axis matplotlib.pyplot objects if return_fig=True, otherwise, simply plots the figures
+ """
+ orig_args = locals().copy() #Get the initial arguments
+ if isinstance(hvsr_data, HVSRBatch):
+ #If running batch, we'll loop through each site
+ for site_name in hvsr_data.keys():
+ args = orig_args.copy() #Make a copy so we don't accidentally overwrite
+ individual_params = hvsr_data[site_name] #Get what would normally be the "params" variable for each site
+ args['hvsr_results'] = individual_params #reset the params parameter we originally read in to an individual site params
+ if hvsr_data[site_name]['ProcessingStatus']['OverallStatus']:
+ try:
+ _hvsr_plot_batch(**args) #Call another function, that lets us run this function again
+ except:
+ print(f"{site_name} not able to be plotted.")
+ else:
+ if clear_fig and fig is not None and ax is not None: #Intended use for tkinter
+ #Clear everything
+ for key in ax:
+ ax[key].clear()
+ fig.clear()
+ if close_figs:
+ plt.close('all')
+
+ compList = ['c', 'comp', 'component', 'components']
+ specgramList = ['spec', 'specgram', 'spectrogram']
+ hvsrList = ['hvsr', 'hv', 'h']
+
+ hvsrInd = np.nan
+ compInd = np.nan
+ specInd = np.nan
+
+ kList = plot_type.split(' ')
+ for i, k in enumerate(kList):
+ kList[i] = k.lower()
+
+ #Get the plots in the right order, no matter how they were input (and ensure the right options go with the right plot)
+ #HVSR index
+ if len(set(hvsrList).intersection(kList)):
+ for i, hv in enumerate(hvsrList):
+ if hv in kList:
+ hvsrInd = kList.index(hv)
+ break
+ #Component index
+ #if len(set(compList).intersection(kList)):
+ for i, c in enumerate(kList):
+ if '+' in c and c[:-1] in compList:
+ compInd = kList.index(c)
+ break
+
+ #Specgram index
+ if len(set(specgramList).intersection(kList)):
+ for i, sp in enumerate(specgramList):
+ if sp in kList:
+ specInd = kList.index(sp)
+ break
+
+ indList = [hvsrInd, compInd, specInd]
+ indListCopy = indList.copy()
+ plotTypeList = ['hvsr', 'comp', 'spec']
+
+ plotTypeOrder = []
+ plotIndOrder = []
+
+ lastVal = 0
+ while lastVal != 99:
+ firstInd = np.nanargmin(indListCopy)
+ plotTypeOrder.append(plotTypeList[firstInd])
+ plotIndOrder.append(indList[firstInd])
+ lastVal = indListCopy[firstInd]
+ indListCopy[firstInd] = 99 #just a high number
+
+ plotTypeOrder.pop()
+ plotIndOrder[-1]=len(kList)
+
+ for i, p in enumerate(plotTypeOrder):
+ pStartInd = plotIndOrder[i]
+ pEndInd = plotIndOrder[i+1]
+ plotComponents = kList[pStartInd:pEndInd]
+
+ if use_subplots and i==0 and fig is None and ax is None:
+ mosaicPlots = []
+ for pto in plotTypeOrder:
+ mosaicPlots.append([pto])
+ fig, ax = plt.subplot_mosaic(mosaicPlots, gridspec_kw={'hspace':0.3})
+ axis = ax[p]
+ elif use_subplots:
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore") #Often warns about xlim when it is not an issue
+ ax[p].clear()
+ axis = ax[p]
+ else:
+ fig, axis = plt.subplots()
+
+ if p == 'hvsr':
+ _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax, **kwargs)
+ elif p=='comp':
+ plotComponents[0] = plotComponents[0][:-1]
+ _plot_hvsr(hvsr_data, fig=fig, ax=axis, plot_type=plotComponents, xtype='x_freqs', show_legend=show_legend, axes=ax, **kwargs)
+ elif p=='spec':
+ plottypeKwargs = {}
+ for c in plotComponents:
+ plottypeKwargs[c] = True
+ kwargs.update(plottypeKwargs)
+ _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False, **kwargs)
+ else:
+ warnings.warn('Plot type {p} not recognized', UserWarning)
+
+ if show:
+ fig.canvas.draw()
+
+ if return_fig:
+ return fig, ax
+ return
+
#Plot Obspy Trace in axis using matplotlib
def plot_stream(stream, params, fig=None, axes=None, show_plot=False, ylim_std=0.75, return_fig=True):
"""Function to plot a stream of data with Z, E, N components using matplotlib. Similar to obspy.Stream.Plot(), but will be formatted differently and eventually more customizable.
@@ -2490,6 +2934,27 @@ def process_hvsr(hvsr_data, method=3, smooth=True, freq_smooth='konno ohmachi',
"""
orig_args = locals().copy() #Get the initial arguments
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'process_hvsr' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['process_hvsr'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(process_hvsr).args[1:],
+ inspect.getfullargspec(process_hvsr).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ method = orig_args['method']
+ smooth = orig_args['smooth']
+ freq_smooth = orig_args['freq_smooth']
+ f_smooth_width = orig_args['f_smooth_width']
+ resample = orig_args['resample']
+ outlier_curve_std = orig_args['outlier_curve_std']
+ verbose = orig_args['verbose']
+
+
+
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
pass
@@ -2554,10 +3019,8 @@ def process_hvsr(hvsr_data, method=3, smooth=True, freq_smooth='konno ohmachi',
xValMin = min(ppsds[k]['period_bin_centers'])
xValMax = max(ppsds[k]['period_bin_centers'])
-
#Resample period bin values
x_periods[k] = np.logspace(np.log10(xValMin), np.log10(xValMax), num=resample)
-
if smooth or type(smooth) is int:
if smooth:
smooth = 51 #Default smoothing window
@@ -2585,7 +3048,6 @@ def process_hvsr(hvsr_data, method=3, smooth=True, freq_smooth='konno ohmachi',
#Get average psd value across time for each channel (used to calc main H/V curve)
psdValsTAvg[k] = np.nanmean(np.array(psdRaw[k]), axis=0)
x_freqs[k] = np.divide(np.ones_like(x_periods[k]), x_periods[k])
-
stDev[k] = np.std(psdRaw[k], axis=0)
stDevValsM[k] = np.array(psdValsTAvg[k] - stDev[k])
stDevValsP[k] = np.array(psdValsTAvg[k] + stDev[k])
@@ -2603,9 +3065,8 @@ def process_hvsr(hvsr_data, method=3, smooth=True, freq_smooth='konno ohmachi',
anyK = list(x_freqs.keys())[0]
hvsr_curve, _ = __get_hvsr_curve(x=x_freqs[anyK], psd=psdValsTAvg, method=methodInt, hvsr_data=hvsr_data, verbose=verbose)
origPPSD = hvsr_data['ppsds_obspy'].copy()
-
#Add some other variables to our output dictionary
- hvsr_data = {'input_params':hvsr_data,
+ hvsr_dataUpdate = {'input_params':hvsr_data,
'x_freqs':x_freqs,
'hvsr_curve':hvsr_curve,
'x_period':x_periods,
@@ -2622,7 +3083,7 @@ def process_hvsr(hvsr_data, method=3, smooth=True, freq_smooth='konno ohmachi',
'hvsr_df':hvsr_data['hvsr_df']
}
- hvsr_out = HVSRData(hvsr_data)
+ hvsr_out = HVSRData(hvsr_dataUpdate)
#This is if manual editing was used (should probably be updated at some point to just use masks)
if 'xwindows_out' in hvsr_data.keys():
@@ -2696,7 +3157,6 @@ def process_hvsr(hvsr_data, method=3, smooth=True, freq_smooth='konno ohmachi',
bool_col='Use'
eval_col='HV_Curves'
- testCol = hvsr_out['hvsr_df'].loc[hvsr_out['hvsr_df'][bool_col], eval_col].apply(np.nanstd).gt((avg_stdT + (std_stdT * outlier_curve_std)))
low_std_val = avg_stdT - (std_stdT * outlier_curve_std)
hi_std_val = avg_stdT + (std_stdT * outlier_curve_std)
@@ -2750,16 +3210,15 @@ def process_hvsr(hvsr_data, method=3, smooth=True, freq_smooth='konno ohmachi',
hvsr_out = __gethvsrparams(hvsr_out)
#Include the original obspy stream in the output
- #print(hvsr_data.keys())
- #print(type(hvsr_data))
- #print(hvsr_data['input_params'].keys())
- hvsr_out['input_stream'] = hvsr_data['input_params']['input_stream'] #input_stream
-
+ hvsr_out['input_stream'] = hvsr_dataUpdate['input_params']['input_stream'] #input_stream
hvsr_out = sprit_utils.make_it_classy(hvsr_out)
-
hvsr_out['ProcessingStatus']['HVStatus'] = True
hvsr_out = _check_processing_status(hvsr_out)
+ hvsr_data['processing_parameters']['process_hvsr'] = {}
+ for key, value in orig_args.items():
+ hvsr_data['processing_parameters']['process_hvsr'][key] = value
+
return hvsr_out
#Function to remove noise windows from data
@@ -2808,7 +3267,30 @@ def remove_noise(hvsr_data, remove_method='auto', sat_percent=0.995, noise_perce
output : dict
Dictionary similar to hvsr_data, but containing modified data with 'noise' removed
"""
- orig_args = locals().copy() #Get the initial arguments
+ #Get intput paramaters
+ orig_args = locals().copy()
+
+ # Update with processing parameters specified previously in input_params, if applicable
+ if 'processing_parameters' in hvsr_data.keys():
+ if 'remove_noise' in hvsr_data['processing_parameters'].keys():
+ for k, v in hvsr_data['processing_parameters']['remove_noise'].items():
+ defaultVDict = dict(zip(inspect.getfullargspec(remove_noise).args[1:],
+ inspect.getfullargspec(remove_noise).defaults))
+ # Manual input to function overrides the imported parameter values
+ if k in orig_args.keys() and orig_args[k]==defaultVDict[k]:
+ orig_args[k] = v
+
+ remove_method = orig_args['remove_method']
+ sat_percent = orig_args['sat_percent']
+ noise_percent = orig_args['noise_percent']
+ sta = orig_args['sta']
+ lta = orig_args['lta']
+ stalta_thresh = orig_args['stalta_thresh']
+ warmup_time = orig_args['warmup_time']
+ cooldown_time = orig_args['cooldown_time']
+ min_win_size = orig_args['min_win_size']
+ remove_raw_noise = orig_args['remove_raw_noise']
+ verbose = orig_args['verbose']
if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
if isinstance(hvsr_data, HVSRData) and hvsr_data['batch']:
@@ -2925,12 +3407,22 @@ def remove_noise(hvsr_data, remove_method='auto', sat_percent=0.995, noise_perce
#Add output
if isinstance(output, (HVSRData, dict)):
- output['stream'] = outStream
+ if isinstance(outStream, (obspy.Stream, obspy.Trace)):
+ output['stream'] = outStream
+ else:
+ output['stream'] = outStream['stream']
output['input_stream'] = hvsr_data['input_stream']
+
+ if 'processing_parameters' not in output.keys():
+ output['processing_parameters'] = {}
+ output['processing_parameters']['remove_noise'] = {}
+ for key, value in orig_args.items():
+ output['processing_parameters']['remove_noise'][key] = value
+
output['ProcessingStatus']['RemoveNoiseStatus'] = True
output = _check_processing_status(output)
- if 'hvsr_df' in output.keys():
+ if 'hvsr_df' in output.keys() or ('params' in output.keys() and 'hvsr_df' in output['params'].keys())or ('input_params' in output.keys() and 'hvsr_df' in output['input_params'].keys()):
hvsrDF = output['hvsr_df']
outStream = output['stream'].split()
@@ -2944,20 +3436,27 @@ def remove_noise(hvsr_data, remove_method='auto', sat_percent=0.995, noise_perce
if trEndTime < trStartTime and comp_end==comp_start:
gap = [trEndTime,trStartTime]
+
output['hvsr_df']['Use'] = (hvsrDF['TimesProcessed_Obspy'].gt(gap[0]) & hvsrDF['TimesProcessed_Obspy'].gt(gap[1]) )| \
(hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[0]) & hvsrDF['TimesProcessed_ObspyEnd'].lt(gap[1]))# | \
+ output['hvsr_df']['Use'] = output['hvsr_df']['Use'].astype(bool)
trEndTime = trace.stats.endtime
outStream.merge()
- output['stream'] = outStream
+ output['stream'] = outStream
+
elif isinstance(hvsr_data, obspy.core.stream.Stream) or isinstance(hvsr_data, obspy.core.trace.Trace):
output = outStream
else:
warnings.warn(f"Output of type {type(output)} for this function will likely result in errors in other processing steps. Returning hvsr_data data.")
return hvsr_data
+
-
+
+ output = sprit_utils.make_it_classy(output)
+ if 'xwindows_out' not in output.keys():
+ output['xwindows_out'] = []
return output
#Remove outlier ppsds
@@ -3022,16 +3521,19 @@ def remove_outlier_curves(params, outlier_std=3, ppsd_length=30):
psds_to_rid.append(i)
#Use dataframe
- hvsrDF = params['hvsr_df']
+ hvsrDF = params['hvsr_df'].copy()
psdVals = hvsrDF['psd_values_'+k]
- params['hvsr_df'][k+'_CurveMedian'] = psdVals.apply(np.nanmedian)
- params['hvsr_df'][k+'_CurveMean'] = psdVals.apply(np.nanmean)
+ hvsrDF[k+'_CurveMedian'] = psdVals.apply(np.nanmedian)
+ hvsrDF[k+'_CurveMean'] = psdVals.apply(np.nanmean)
- totMean = np.nanmean(params['hvsr_df'][k+'_CurveMean'])
- stds[k] = np.nanstd(params['hvsr_df'][k+'_CurveMean'])
-
- meanArr = params['hvsr_df'][k+'_CurveMean']
- params['hvsr_df']['Use'] = meanArr < (totMean + outlier_std * stds[k])
+ totMean = np.nanmean(hvsrDF[k+'_CurveMean'])
+ stds[k] = np.nanstd(hvsrDF[k+'_CurveMean'])
+
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
+ #meanArr = hvsrDF[k+'_CurveMean'].loc[hvsrDF['Use']]
+ threshVal = totMean + outlier_std * stds[k]
+ hvsrDF['Use'] = hvsrDF[k+'_CurveMean'][hvsrDF['Use']].lt(threshVal)
+ hvsrDF['Use'] = hvsrDF['Use'].astype(bool)
psds_to_rid = np.unique(psds_to_rid)
@@ -3900,6 +4402,88 @@ def __read_RS_file_struct(datapath, source, year, doy, inv, params, verbose=Fals
return rawDataIN
+#Read data from Tromino
+def __read_tromino_files(datapath, params, verbose=False):
+ """Function to read data from tromino. Specifically, this has been lightly tested on Tromino 3G+ machines
+
+ Parameters
+ ----------
+ datapath : str, pathlib.Path()
+ The input parameter _datapath_ from sprit.input_params()
+ params : HVSRData or HVSRBatch
+ The parameters as read in from input_params() and and fetch_data()
+ verbose : bool, optional
+ Whether to print results to terminal, by default False
+
+ Returns
+ -------
+ obspy.Stream
+ An obspy.Stream object containing the trace data from the Tromino instrument
+ """
+ dPath = datapath
+
+ strucSizes = {'c':1, 'b':1,'B':1, '?':1,
+ 'h':2,'H':2,'e':2,
+ 'i':4,'I':4,'l':4,'L':4,'f':4,
+ 'q':8,'Q':8,'d':8,
+ 'n':8,'N':8,'s':16,'p':16,'P':16,'x':16}
+
+ #H (pretty sure it's Q) I L or Q all seem to work (probably not Q?)
+ structFormat = 'H'
+ structSize = strucSizes[structFormat]
+
+ dataList = []
+ with open(dPath, 'rb') as f:
+ while True:
+ data = f.read(structSize) # Read 4 bytes
+ if not data: # End of file
+ break
+ value = struct.unpack(structFormat, data)[0] # Interpret as a float
+ dataList.append(value)
+
+ import numpy as np
+ dataArr = np.array(dataList)
+ import matplotlib.pyplot as plt
+
+ medVal = np.nanmedian(dataArr[50000:100000])
+
+ startByte=24576
+ comp1 = dataArr[startByte::3] - medVal
+ comp2 = dataArr[startByte+1::3] - medVal
+ comp3 = dataArr[startByte+2::3] - medVal
+ headerBytes = dataArr[:startByte]
+
+ #fig, ax = plt.subplots(3, sharex=True, sharey=True)
+ #ax[0].plot(comp1, linewidth=0.1, c='k')
+ #ax[1].plot(comp2, linewidth=0.1, c='k')
+ #ax[2].plot(comp3, linewidth=0.1, c='k')
+
+ sTime = obspy.UTCDateTime(params['acq_date'].year, params['acq_date'].month, params['acq_date'].day,
+ params['starttime'].hour, params['starttime'].minute,
+ params['starttime'].second,params['starttime'].microsecond)
+ eTime = sTime + (((len(comp1))/128)/60)*60
+
+ traceHeader1 = {'sampling_rate':128,
+ 'calib' : 1,
+ 'npts':len(comp1),
+ 'network':'AM',
+ 'location':'00',
+ 'station' : 'TRMNO',
+ 'channel':'BHE',
+ 'starttime':sTime}
+
+ traceHeader2=traceHeader1.copy()
+ traceHeader3=traceHeader1.copy()
+ traceHeader2['channel'] = 'BHN'
+ traceHeader3['channel'] = 'BHZ'
+
+ trace1 = obspy.Trace(data=comp1, header=traceHeader1)
+ trace2 = obspy.Trace(data=comp2, header=traceHeader2)
+ trace3 = obspy.Trace(data=comp3, header=traceHeader3)
+
+ st = obspy.Stream([trace1, trace2, trace3])
+ return st
+
##Helper functions for remove_noise()
#Helper function for removing gaps
def __remove_gaps(stream, window_gaps_obspy):
@@ -4433,13 +5017,13 @@ def _select_windows(input):
if 'hvsr_curve' in input.keys():
fig, ax = plot_hvsr(hvsr_data=input, plot_type='spec', returnfig=True, cmap='turbo')
else:
- params = input.copy()
- input = input['stream']
+ hvsr_data = input#.copy()
+ input_stream = hvsr_data['stream']
- if isinstance(input, obspy.core.stream.Stream):
- fig, ax = _plot_specgram_stream(input, component=['Z'])
- elif isinstance(input, obspy.core.trace.Trace):
- fig, ax = _plot_specgram_stream(input)
+ if isinstance(input_stream, obspy.core.stream.Stream):
+ fig, ax = _plot_specgram_stream(input_stream, component=['Z'])
+ elif isinstance(input_stream, obspy.core.trace.Trace):
+ fig, ax = _plot_specgram_stream(input_stream)
global lineArtist
global winArtist
@@ -4463,10 +5047,10 @@ def _select_windows(input):
fig.canvas.mpl_connect('close_event', _on_fig_close)#(clickNo, xWindows, pathList, windowDrawn, winArtist, lineArtist, x0, fig, ax))
plt.pause(1)
- params['xwindows_out'] = xWindows
- params['fig'] = fig
- params['ax'] = ax
- return params
+ hvsr_data['xwindows_out'] = xWindows
+ hvsr_data['fig_noise'] = fig
+ hvsr_data['ax_noise'] = ax
+ return hvsr_data
#Support function to help select_windows run properly
def _on_fig_close(event):
@@ -5202,7 +5786,6 @@ def _plot_hvsr(hvsr_data, plot_type, xtype='frequency', fig=None, ax=None, save_
ax.set_ylabel('H/V Ratio'+'\n['+hvsr_data['method']+']')
ax.set_title(hvsr_data['input_params']['site'])
-
#print("label='comp'" in str(ax.__dict__['_axes']))
for k in plot_type:
if k=='p' and 'all' not in plot_type:
@@ -5255,7 +5838,7 @@ def _plot_hvsr(hvsr_data, plot_type, xtype='frequency', fig=None, ax=None, save_
plotSuff = plotSuff+'IndComponents_'
if 'c' not in plot_type[0]:#This is part of the hvsr axis
- fig.tight_layout()
+ #fig.tight_layout()
axis2 = ax.twinx()
compAxis = axis2
#axis2 = plt.gca()
@@ -5325,7 +5908,7 @@ def _plot_hvsr(hvsr_data, plot_type, xtype='frequency', fig=None, ax=None, save_
axisbox.append(float(i))
if kwargs['show_legend']:
- ax.legend(loc=legendLoc)
+ ax.legend(loc=legendLoc,bbox_to_anchor=(1.05, 1))
__plot_current_fig(save_dir=save_dir,
filename=filename,
@@ -5358,18 +5941,27 @@ def __plot_current_fig(save_dir, filename, fig, ax, plot_suffix, user_suffix, sh
def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix='',**kwargs):
"""Private function for plotting average spectrogram of all three channels from ppsds
"""
+ # Get all input parameters
if fig is None and ax is None:
fig, ax = plt.subplots()
if 'kwargs' in kwargs.keys():
kwargs = kwargs['kwargs']
- if 'peak_plot' in kwargs.keys():
+ if 'spec' in kwargs.keys():
+ del kwargs['spec']
+
+ if 'p' in kwargs.keys():
peak_plot=True
- del kwargs['peak_plot']
+ del kwargs['p']
else:
peak_plot=False
-
+
+ if 'ann' in kwargs.keys():
+ annotate=True
+ del kwargs['ann']
+ else:
+ annotate=False
if 'grid' in kwargs.keys():
ax.grid(which=kwargs['grid'], alpha=0.25)
@@ -5402,25 +5994,22 @@ def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix
else:
kwargs['cmap'] = 'turbo'
+ # Setup
ppsds = hvsr_data['ppsds']#[k]['current_times_used']
import matplotlib.dates as mdates
anyKey = list(ppsds.keys())[0]
-
- psdHList =[]
- psdZList =[]
- for k in hvsr_data['psd_raw']:
- if 'z' in k.lower():
- psdZList.append(hvsr_data['psd_raw'][k])
- else:
- psdHList.append(hvsr_data['psd_raw'][k])
- #if detrend:
- # psdArr = np.subtract(psdArr, np.median(psdArr, axis=0))
- psdArr = hvsr_data['ind_hvsr_curves'].T
+ # Get data
+ psdArr = np.stack(hvsr_data['hvsr_df']['HV_Curves'].apply(np.flip))
+ useArr = np.array(hvsr_data['hvsr_df']['Use'])
+ useArr = np.tile(useArr, (psdArr.shape[1], 1)).astype(int)
+ useArr = np.clip(useArr, a_min=0.15, a_max=1)
- xmin = min(hvsr_data['ppsds'][anyKey]['current_times_used'][:-1]).matplotlib_date
- xmax = max(hvsr_data['ppsds'][anyKey]['current_times_used'][:-1]).matplotlib_date
-
+ # Get times
+ xmin = hvsr_data['hvsr_df']['TimesProcessed_MPL'].min()
+ xmax = hvsr_data['hvsr_df']['TimesProcessed_MPL'].max()
+
+ #Format times
tTicks = mdates.MinuteLocator(byminute=range(0,60,5))
ax.xaxis.set_major_locator(tTicks)
tTicks_minor = mdates.SecondLocator(bysecond=[0])
@@ -5430,58 +6019,67 @@ def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix
ax.xaxis.set_major_formatter(tLabels)
ax.tick_params(axis='x', labelsize=8)
- if hvsr_data['ppsds'][anyKey]['current_times_used'][0].date != hvsr_data['ppsds'][anyKey]['current_times_used'][-1].date:
- day = str(hvsr_data['ppsds'][anyKey]['current_times_used'][0].date)+' - '+str(hvsr_data['ppsds'][anyKey]['current_times_used'][1].date)
+ #Get day label for bottom of chart
+ if hvsr_data['hvsr_df'].index[0].date() != hvsr_data['hvsr_df'].index[-1].date():
+ day = str(hvsr_data['hvsr_df'].index[0].date())+' - '+str(hvsr_data['hvsr_df'].index[-1].date())
else:
- day = str(hvsr_data['ppsds'][anyKey]['current_times_used'][0].date)
+ day = str(hvsr_data['hvsr_df'].index[0].date())
+ #Get extents
ymin = hvsr_data['input_params']['hvsr_band'][0]
ymax = hvsr_data['input_params']['hvsr_band'][1]
- extList = [xmin, xmax, ymin, ymax]
-
- #ax = plt.gca()
- #fig = plt.gcf()
-
freqticks = np.flip(hvsr_data['x_freqs'][anyKey])
yminind = np.argmin(np.abs(ymin-freqticks))
ymaxind = np.argmin(np.abs(ymax-freqticks))
freqticks = freqticks[yminind:ymaxind]
+ freqticks = np.logspace(np.log10(freqticks[0]), np.log10(freqticks[-1]), num=999)
+
+ extList = [xmin, xmax, ymin, ymax]
+
+ #Set up axes
+ ax.set_facecolor([0,0,0]) #Create black background for transparency to look darker
+
+ # Interpolate into linear
+ new_indices = np.linspace(freqticks[0], freqticks[-1], len(freqticks))
+ linList = []
+ for row in psdArr:
+ row = row.astype(np.float16)
+ linList.append(np.interp(new_indices, freqticks, row))
+ linear_arr = np.stack(linList)
+
+ # Create chart
+ im = ax.imshow(linear_arr.T, origin='lower', extent=extList, aspect='auto', alpha=useArr, **kwargs)
+ ax.tick_params(left=True, right=True, top=True)
- #Set up axes, since data is already in semilog
- axy = ax.twinx()
- axy.set_yticks([])
- axy.zorder=0
- ax.zorder=1
- ax.set_facecolor('#ffffff00') #Create transparent background for front axis
- #plt.sca(axy)
- im = ax.imshow(psdArr, origin='lower', extent=extList, aspect='auto', interpolation='nearest', **kwargs)
- ax.tick_params(left=False, right=False)
- #plt.sca(ax)
if peak_plot:
- ax.hlines(hvsr_data['BestPeak']['f0'], xmin, xmax, colors='k', linestyles='dashed', alpha=0.5)
+ ax.axhline(hvsr_data['BestPeak']['f0'], c='k', linestyle='dotted', zorder=1000)
+
+ if annotate:
+ if float(hvsr_data['BestPeak']['f0']) < 1:
+ boxYPerc = 0.998
+ vertAlign = 'top'
+ else:
+ boxYPerc = 0.002
+ vertAlign = 'bottom'
+ xLocation = float(xmin) + (float(xmax)-float(xmin))*0.99
+ yLocation = hvsr_data['input_params']['hvsr_band'][0] + (hvsr_data['input_params']['hvsr_band'][1]-hvsr_data['input_params']['hvsr_band'][0])*(boxYPerc)
+ ann = ax.text(x=xLocation, y=yLocation, fontsize='small', s=f"Peak at {hvsr_data['BestPeak']['f0']:0.2f} Hz", ha='right', va=vertAlign,
+ bbox={'alpha':0.8, 'edgecolor':'w', 'fc':'w', 'pad':0.3})
- #FreqTicks =np.arange(1,np.round(max(hvsr_data['x_freqs'][anyKey]),0), 10)
- specTitle = ax.set_title(hvsr_data['input_params']['site']+': Spectrogram')
- bgClr = (fig.get_facecolor()[0], fig.get_facecolor()[1], fig.get_facecolor()[2], 0.1)
- specTitle.set_color(bgClr)
ax.set_xlabel('UTC Time \n'+day)
-
if colorbar:
- cbar = plt.colorbar(mappable=im)
+ cbar = plt.colorbar(mappable=im, orientation='horizontal')
cbar.set_label('H/V Ratio')
ax.set_ylabel(ylabel)
- ax.set_yticks(freqticks)
- ax.semilogy()
- ax.set_ylim(hvsr_data['input_params']['hvsr_band'])
+ ax.set_yscale('log')
- #fig.tight_layout()
+ #plt.sca(ax)
#plt.rcParams['figure.dpi'] = 500
#plt.rcParams['figure.figsize'] = (12,4)
fig.canvas.draw()
- #fig.tight_layout()
- #plt.show()
+
return fig, ax
#Plot spectrogram from stream
@@ -5800,6 +6398,7 @@ def __check_curve_reliability(hvsr_data, _peak):
window_num = np.array(hvsr_data['psd_raw'][anyKey]).shape[0]
for _i in range(len(_peak)):
+ # Test 1
peakFreq= _peak[_i]['f0']
test1 = peakFreq > 10/window_len
@@ -5809,38 +6408,37 @@ def __check_curve_reliability(hvsr_data, _peak):
halfF0 = peakFreq/2
doublef0 = peakFreq*2
+
test3 = True
failCount = 0
for i, freq in enumerate(hvsr_data['x_freqs'][anyKey][:-1]):
- ###IS THIS RIGHT???
if freq >= halfF0 and freq = 0.5:
- if hvsr_data['hvsr_log_std'][i] >= 2:
+ if hvsr_data['hvsr_log_std'][i] >= compVal:
test3=False
failCount +=1
+
else: #if peak freq is less than 0.5
- if hvsr_data['hvsr_log_std'][i] >= 3:
+ compVal = 3
+ if hvsr_data['hvsr_log_std'][i] >= compVal:
test3=False
failCount +=1
if test1:
- _peak[_i]['Report']['Lw'] = '{} > 10 / {} {}'.format(round(peakFreq,3), int(window_len), sprit_utils.check_mark())
+ _peak[_i]['Report']['Lw'] = f'{round(peakFreq,3)} > {10/int(window_len):0.3} (10 / {int(window_len)}) {sprit_utils.check_mark()}'
else:
- _peak[_i]['Report']['Lw'] = '{} > 10 / {} {}'.format(round(peakFreq,3), int(window_len), '✘')
+ _peak[_i]['Report']['Lw'] = f'{round(peakFreq,3)} > {10/int(window_len):0.3} (10 / {int(window_len)}) {sprit_utils.x_mark()}'
if test2:
- _peak[_i]['Report']['Nc'] = '{} > 200 {}'.format(round(nc,0), sprit_utils.check_mark())
+ _peak[_i]['Report']['Nc'] = f'{int(nc)} > 200 {sprit_utils.check_mark()}'
else:
- _peak[_i]['Report']['Nc'] = '{} > 200 {}'.format(round(nc,0), '✘')
+ _peak[_i]['Report']['Nc'] = f'{int(nc)} > 200 {sprit_utils.x_mark()}'
if test3:
- if peakFreq >= 0.5:
- compVal = 2
- else:
- compVal = 3
- _peak[_i]['Report']['σ_A(f)'] = 'σ_A for all freqs {}-{} < {} {}'.format(round(peakFreq*0.5, 3), round(peakFreq*2, 3), compVal, sprit_utils.check_mark())
+ _peak[_i]['Report']['σ_A(f)'] = f'H/V Amp. St.Dev. for {peakFreq*0.5:0.3f}-{peakFreq*2:0.3f}Hz < {compVal} {sprit_utils.check_mark()}'
else:
- _peak[_i]['Report']['σ_A(f)'] = 'σ_A for all freqs {}-{} < {} {}'.format(round(peakFreq*0.5, 3), round(peakFreq*2, 3), compVal, '✘')
+ _peak[_i]['Report']['σ_A(f)'] = f'H/V Amp. St.Dev. for {peakFreq*0.5:0.3f}-{peakFreq*2:0.3f}Hz < {compVal} {sprit_utils.x_mark()}'
_peak[_i]['PassList']['WindowLengthFreq.'] = test1
_peak[_i]['PassList']['SignificantCycles'] = test2
@@ -5886,16 +6484,16 @@ def __check_clarity(_x, _y, _peak, do_rank=True):
for _i in range(len(_peak)):
#Initialize as False
- _peak[_i]['f-'] = '✘'
- _peak[_i]['Report']['A(f-)'] = 'No A_h/v in freqs {}-{} < {} {}'.format(round(_peak[_i]['A0']/4, 3), round(_peak[_i]['A0'], 3), round(_peak[_i]['A0']/2, 3), '✘')
- _peak[_i]['PassList']['PeakFreqClarityBelow'] = False #Start with assumption that it is False until we find an instance where it is True
+ _peak[_i]['f-'] = sprit_utils.x_mark()
+ _peak[_i]['Report']['A(f-)'] = f"H/V curve > {_peak[_i]['A0']/2:0.2f} for all {_peak[_i]['A0']/4:0.2f} Hz-{_peak[_i]['A0']:0.3f} Hz {sprit_utils.x_mark()}"
+ _peak[_i]['PassList']['PeakProminenceBelow'] = False #Start with assumption that it is False until we find an instance where it is True
for _j in range(jstart, -1, -1):
# There exist one frequency f-, lying between f0/4 and f0, such that A0 / A(f-) > 2.
if (float(_peak[_i]['f0']) / 4.0 <= _x[_j] < float(_peak[_i]['f0'])) and float(_peak[_i]['A0']) / _y[_j] > 2.0:
_peak[_i]['Score'] += 1
_peak[_i]['f-'] = '%10.3f %1s' % (_x[_j], sprit_utils.check_mark())
- _peak[_i]['Report']['A(f-)'] = 'A({}): {} < {} {}'.format(round(_x[_j], 3), round(_y[_j], 3), round(_peak[_i]['A0']/2,3), sprit_utils.check_mark())
- _peak[_i]['PassList']['PeakFreqClarityBelow'] = True
+ _peak[_i]['Report']['A(f-)'] = f"Amp. of H/V Curve @{_x[_j]:0.3f}Hz ({_y[_j]:0.3f}) < {_peak[_i]['A0']/2:0.3f} {sprit_utils.check_mark()}"
+ _peak[_i]['PassList']['PeakProminenceBelow'] = True
break
else:
pass
@@ -5904,25 +6502,21 @@ def __check_clarity(_x, _y, _peak, do_rank=True):
max_rank += 1
for _i in range(len(_peak)):
#Initialize as False
- _peak[_i]['f+'] = '✘'
- _peak[_i]['Report']['A(f+)'] = 'No A_h/v in freqs {}-{} < {} {}'.format(round(_peak[_i]['A0'], 3), round(_peak[_i]['A0']*4, 3), round(_peak[_i]['A0']/2, 3), '✘')
- _peak[_i]['PassList']['PeakFreqClarityAbove'] = False
+ _peak[_i]['f+'] = sprit_utils.x_mark()
+ _peak[_i]['Report']['A(f+)'] = f"H/V curve > {_peak[_i]['A0']/2:0.2f} for all {_peak[_i]['A0']:0.2f} Hz-{_peak[_i]['A0']*4:0.3f} Hz {sprit_utils.x_mark()}"
+ _peak[_i]['PassList']['PeakProminenceAbove'] = False
for _j in range(len(_x) - 1):
# There exist one frequency f+, lying between f0 and 4*f0, such that A0 / A(f+) > 2.
if float(_peak[_i]['f0']) * 4.0 >= _x[_j] > float(_peak[_i]['f0']) and \
float(_peak[_i]['A0']) / _y[_j] > 2.0:
_peak[_i]['Score'] += 1
- _peak[_i]['f+'] = '%10.3f %1s' % (_x[_j], sprit_utils.check_mark())
- _peak[_i]['Report']['A(f+)'] = 'A({}): {} < {} {}'.format(round(_x[_j], 3), round(_y[_j], 3), round(_peak[_i]['A0']/2,3), sprit_utils.check_mark())
- _peak[_i]['PassList']['PeakFreqClarityAbove'] = True
+ _peak[_i]['f+'] = f"{_x[_j]:0.3f} {sprit_utils.check_mark()}"
+ _peak[_i]['Report']['A(f+)'] = f"H/V Curve at {_x[_j]:0.2f} Hz: {_y[_j]:0.2f} < {_peak[_i]['A0']/2:0.2f} (f0/2) {sprit_utils.check_mark()}"
+ _peak[_i]['PassList']['PeakProminenceAbove'] = True
break
else:
pass
-# if False in clarityPass:
-# _peak[_i]['PassList']['PeakFreqClarityBelow'] = False
-# else:
-# _peak[_i]['PassList']['PeakFreqClarityAbove'] = True
#Amplitude Clarity test
# Only peaks with A0 > 2 pass
@@ -5932,11 +6526,11 @@ def __check_clarity(_x, _y, _peak, do_rank=True):
for _i in range(len(_peak)):
if float(_peak[_i]['A0']) > _a0:
- _peak[_i]['Report']['A0'] = '%10.2f > %0.1f %1s' % (_peak[_i]['A0'], _a0, sprit_utils.check_mark())
+ _peak[_i]['Report']['A0'] = f"Amplitude of peak ({_peak[_i]['A0']:0.2f}) > {int(_a0)} {sprit_utils.check_mark()}"
_peak[_i]['Score'] += 1
_peak[_i]['PassList']['PeakAmpClarity'] = True
else:
- _peak[_i]['Report']['A0'] = '%10.2f > %0.1f %1s' % (_peak[_i]['A0'], _a0, '✘')
+ _peak[_i]['Report']['A0'] = '%0.2f > %0.1f %1s' % (_peak[_i]['A0'], _a0, sprit_utils.x_mark())
_peak[_i]['PassList']['PeakAmpClarity'] = False
return _peak
@@ -5973,19 +6567,17 @@ def __check_freq_stability(_peak, _peakm, _peakp):
for _i in range(len(_peak)):
_dx = 1000000.
_found_m.append(False)
- _peak[_i]['Report']['P-'] = '✘'
+ _peak[_i]['Report']['P-'] = sprit_utils.x_mark()
for _j in range(len(_peakm)):
if abs(_peakm[_j]['f0'] - _peak[_i]['f0']) < _dx:
_index = _j
_dx = abs(_peakm[_j]['f0'] - _peak[_i]['f0'])
if _peak[_i]['f0'] * 0.95 <= _peakm[_j]['f0'] <= _peak[_i]['f0'] * 1.05:
- _peak[_i]['Report']['P-'] = '%0.3f within ±5%s of %0.3f %1s' % (_peakm[_j]['f0'], '%',
- _peak[_i]['f0'], sprit_utils.check_mark())
+ _peak[_i]['Report']['P-'] = f"{_peakm[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.check_mark()}"
_found_m[_i] = True
break
- if _peak[_i]['Report']['P-'] == '✘':
- _peak[_i]['Report']['P-'] = '%0.3f within ±5%s of %0.3f %1s' % (_peakm[_j]['f0'], '%', ##changed i to j
- _peak[_i]['f0'], '✘')
+ if _peak[_i]['Report']['P-'] == sprit_utils.x_mark():
+ _peak[_i]['Report']['P-'] = f"{_peakm[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.x_mark()}"
# Then Check above
_found_p = list()
@@ -5999,25 +6591,21 @@ def __check_freq_stability(_peak, _peakm, _peakp):
_dx = abs(_peakp[_j]['f0'] - _peak[_i]['f0'])
if _peak[_i]['f0'] * 0.95 <= _peakp[_j]['f0'] <= _peak[_i]['f0'] * 1.05:
if _found_m[_i]:
- _peak[_i]['Report']['P+'] = '%0.3f within ±5%s of %0.3f %1s' % (
- _peakp[_j]['f0'], '%', _peak[_i]['f0'], sprit_utils.check_mark())
+ _peak[_i]['Report']['P+'] = f"{_peakp[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.check_mark()}"
_peak[_i]['Score'] += 1
_peak[_i]['PassList']['FreqStability'] = True
else:
- _peak[_i]['Report']['P+'] = '%0.3f within ±5%s of %0.3f %1s' % (
- _peakp[_j]['f0'], '%', _peak[_i]['f0'], '✘')
+ _peak[_i]['Report']['P+'] = f"{_peakp[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.x_mark()}"
_peak[_i]['PassList']['FreqStability'] = False
break
else:
- _peak[_i]['Report']['P+'] = '%0.3f within ±5%s of %0.3f %1s' % (_peakp[_j]['f0'], '%', _peak[_i]['f0'], '✘')
+ _peak[_i]['Report']['P+'] = f"{_peakp[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.x_mark()}"
_peak[_i]['PassList']['FreqStability'] = False
if _peak[_i]['Report']['P+'] == sprit_utils.x_mark() and len(_peakp) > 0:
- _peak[_i]['Report']['P+'] = '%0.3f within ±5%s of %0.3f %1s' % (
- _peakp[_j]['f0'], '%', _peak[_i]['f0'], sprit_utils.x_mark()) #changed i to j
+ _peak[_i]['Report']['P+'] = f"{_peakp[_j]['f0']:0.2f} Hz within ±5% of {_peak[_i]['f0']:0.2f} Hz {sprit_utils.x_mark()}"
return _peak
-
# Check stability
def __check_stability(_stdf, _peak, _hvsr_log_std, rank):
"""Test peaks for satisfying stability conditions as outlined by SESAME 2004
@@ -6058,104 +6646,96 @@ def __check_stability(_stdf, _peak, _hvsr_log_std, rank):
if _this_peak['f0'] < 0.2:
_e = 0.25
if _stdf[_i] < _e * _this_peak['f0']:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'],
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_FreqStD'] = True
-
else:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'], '✘')
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.x_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
_t = 0.48
if _hvsr_log_std[_i] < _t:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t,
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_AmpStD'] = True
else:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, '✘')
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['PassList']['PeakStability_AmpStD'] = False
elif 0.2 <= _this_peak['f0'] < 0.5:
_e = 0.2
if _stdf[_i] < _e * _this_peak['f0']:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'],
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_FreqStD'] = True
else:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'], '✘')
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.x_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
_t = 0.40
if _hvsr_log_std[_i] < _t:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t,
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_AmpStD'] = True
else:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, '✘')
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['PassList']['PeakStability_AmpStD'] = False
elif 0.5 <= _this_peak['f0'] < 1.0:
_e = 0.15
if _stdf[_i] < _e * _this_peak['f0']:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'],
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_FreqStD'] = True
else:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'], '✘')
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.x_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
_t = 0.3
if _hvsr_log_std[_i] < _t:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, sprit_utils.check_mark())
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_AmpStD'] = True
else:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, '✘')
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['PassList']['PeakStability_AmpStD'] = False
elif 1.0 <= _this_peak['f0'] <= 2.0:
_e = 0.1
if _stdf[_i] < _e * _this_peak['f0']:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'],
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_FreqStD'] = True
else:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s ' % (_stdf[_i], _e, _this_peak['f0'], '✘')
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.x_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
_t = 0.25
if _hvsr_log_std[_i] < _t:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, sprit_utils.check_mark())
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_AmpStD'] = True
else:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, '✘')
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['PassList']['PeakStability_AmpStD'] = False
elif _this_peak['f0'] > 0.2:
_e = 0.05
if _stdf[_i] < _e * _this_peak['f0']:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'],
- sprit_utils.check_mark())
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_FreqStD'] = True
else:
- _peak[_i]['Report']['Sf'] = '%10.4f < %0.2f * %0.3f %1s' % (_stdf[_i], _e, _this_peak['f0'], '✘')
+ _peak[_i]['Report']['Sf'] = f"St.Dev. of Peak Freq. ({_stdf[_i]:0.2f}) < {(_e * _this_peak['f0']):0.3f} {sprit_utils.x_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
_t = 0.2
if _hvsr_log_std[_i] < _t:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, sprit_utils.check_mark())
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['Score'] += 1
_this_peak['PassList']['PeakStability_AmpStD'] = True
else:
- _peak[_i]['Report']['Sa'] = '%10.4f < %0.2f %1s' % (_hvsr_log_std[_i], _t, '✘')
+ _peak[_i]['Report']['Sa'] = f"St.Dev. of Peak Amp. ({_hvsr_log_std[_i]:0.3f}) < {_t:0.2f} {sprit_utils.check_mark()}"
_this_peak['PassList']['PeakStability_FreqStD'] = False
return _peak
diff --git a/sprit/sprit_utils.py b/sprit/sprit_utils.py
index 892006a..3875a04 100644
--- a/sprit/sprit_utils.py
+++ b/sprit/sprit_utils.py
@@ -19,7 +19,7 @@
channel_order = {'Z': 0, '1': 1, 'N': 1, '2': 2, 'E': 2}
def check_gui_requirements():
- print("Checking requirements for gui")
+ #First, check requirements
# Define a command that tries to open a window
command = "python -c \"import tkinter; tkinter.Tk()\""
@@ -32,7 +32,7 @@ def check_gui_requirements():
oktoproceed=True
else:
oktoproceed=False
- print("GUI window could not be created.")
+ print("GUI window cannot be created.")
return oktoproceed
@@ -311,6 +311,16 @@ def has_required_channels(stream):
#Make input data (dict) into sprit_hvsr class
def make_it_classy(input_data, verbose=False):
if isinstance(input_data, (sprit_hvsr.HVSRData, sprit_hvsr.HVSRBatch)):
+ for k, v in input_data.items():
+ if k=='input_params':
+ for kin in input_data['input_params'].keys():
+ if kin not in input_data.keys():
+ input_data[kin] = input_data['input_params'][kin]
+ if k=='params':
+ for kin in input_data['params'].keys():
+ print(kin)
+ if kin not in input_data.keys():
+ input_data[kin] = input_data['params'][kin]
output_class = input_data
else:
output_class = sprit_hvsr.HVSRData(input_data)