From 4d2e4a9be1258a1f1fb969fa57172e8f82769230 Mon Sep 17 00:00:00 2001 From: RJbalikian <46536937+RJbalikian@users.noreply.github.com> Date: Sat, 21 Oct 2023 02:30:16 -0500 Subject: [PATCH 01/17] update saving and some reading of settings --- setup.py | 3 +- sprit/__init__.py | 2 + sprit/__pycache__/sprit_gui.cpython-310.pyc | Bin 78404 -> 79169 bytes sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 166588 -> 167670 bytes sprit/resources/settings/gui_theme.json | 1 + .../settings/instrument_settings.json | 12 ++ .../settings/processing_settings.json | 15 ++ sprit/sprit_gui.py | 65 ++++++-- sprit/sprit_hvsr.py | 142 +++++++++++++++++- sprit/sprit_utils.py | 4 +- 10 files changed, 223 insertions(+), 21 deletions(-) create mode 100644 sprit/resources/settings/gui_theme.json create mode 100644 sprit/resources/settings/instrument_settings.json create mode 100644 sprit/resources/settings/processing_settings.json 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..3ebff50 100644 --- a/sprit/__init__.py +++ b/sprit/__init__.py @@ -20,6 +20,7 @@ process_hvsr, plot_hvsr, remove_noise, + save_settings, check_peaks, get_report, HVSRData, @@ -63,6 +64,7 @@ 'process_hvsr', 'plot_hvsr', 'remove_noise', + 'save_settings', 'check_peaks', 'get_report', 'HVSRData', diff --git 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zHzt+k1bLAtJy$SMnK+jA1D&1%ZxDEBOH2!Ca zVRg3=ES2`K+caqF&5YYZa2G&I|1X29`FSM49=;YcG=Sh!f~y%PoWle7T(ZQA%i-$-1au2BEZwbz|~#9C0A|9(~~|2+KVLG_m!yWCA4vD*D- JW~B;C{~yqC`DFkA 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.json b/sprit/resources/settings/instrument_settings.json new file mode 100644 index 0000000..fe9c14f --- /dev/null +++ b/sprit/resources/settings/instrument_settings.json @@ -0,0 +1,12 @@ +{ + "instrument":"Raspberry Shake", + "network":"AM", + "station":"RAC84", + "loc":"00", + "channels":["EHZ", "EHE", "EHN"], + "depth":0, + "metapath":"", + "hvsr_band":[0.4, 40], + "instrument_info":{"version":9, + "manufacturer":"Raspberry Shake, S.A."} +} \ No newline at end of file diff --git a/sprit/resources/settings/processing_settings.json b/sprit/resources/settings/processing_settings.json new file mode 100644 index 0000000..6d80c14 --- /dev/null +++ b/sprit/resources/settings/processing_settings.json @@ -0,0 +1,15 @@ +{ + "input_params":{}, + "export_data":{}, + "import_data":{}, + "gui":{}, + "fetch_data":{}, + "batch_data_read":{}, + "generate_ppsds":{}, + "process_hvsr":{}, + "plot_hvsr":{}, + "remove_noise":{}, + "save_settings":{}, + "check_peaks":{}, + "get_report":{} +} \ No newline at end of file diff --git a/sprit/sprit_gui.py b/sprit/sprit_gui.py index 6e2c700..8458ebc 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) @@ -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..2673584 100644 --- a/sprit/sprit_hvsr.py +++ b/sprit/sprit_hvsr.py @@ -48,7 +48,11 @@ 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'), @@ -282,7 +286,9 @@ 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. """ @@ -711,6 +717,10 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_freq_range=[1, 20], verbose """ orig_args = locals().copy() #Get the initial arguments + 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 @@ -994,6 +1004,12 @@ 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'] + #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: @@ -1012,28 +1028,27 @@ 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) @@ -1080,6 +1095,7 @@ 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': @@ -1191,6 +1207,7 @@ 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) @@ -1205,6 +1222,7 @@ 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: @@ -1492,6 +1510,11 @@ def convert_to_mpl_dates(obspyUTCDateTime): hvsr_data = sprit_utils.make_it_classy(hvsr_data) + 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 @@ -1587,6 +1610,11 @@ def get_report(hvsr_results, report_format='print', plot_type='HVSR p ann C+ p a #Curve pass? orig_args = locals().copy() #Get the initial arguments + 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 @@ -2106,6 +2134,7 @@ def input_params(datapath, metapath = '', hvsr_band = [0.4, 40], peak_freq_range=[0.4, 40], + instrument_settings=None, verbose=False ): """Function for designating input parameters for reading in and processing data @@ -2156,6 +2185,10 @@ def input_params(datapath, 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. + instrument_settings : None, str, default=None + The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most. + If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap). + The default settings can be reset using the save_settings() function with the parameter settings_path='default'. verbose : bool, default=False Whether to print output and results to terminal @@ -2299,6 +2332,24 @@ def input_params(datapath, 'ProcessingStatus':{'InputStatus':True, 'OverallStatus':True} } + #Replace any default parameter settings with those from json file of interest, potentially + if instrument_settings is None: + instrument_settings_dict = {} + elif instrument_settings == "default" or instrument_settings is True: + # Update inputParamDict with default file + default_settings_json = settings_dir.joinpath('instrument_settings.json') + with open(default_settings_json.as_posix(), 'r') as f: + instrument_settings_dict = json.load(f) + else: + # Update inputParamDict with file + with open(instrument_settings, 'r') as f: + instrument_settings_dict = json.load(f) + + for settingName in instrument_settings_dict.keys(): + if settingName in inputParamDict.keys(): + inputParamDict[settingName] = instrument_settings_dict[settingName] + + #Format everything nicely params = sprit_utils.make_it_classy(inputParamDict) params['ProcessingStatus']['InputParams'] = True params = _check_processing_status(params) @@ -2760,6 +2811,10 @@ def process_hvsr(hvsr_data, method=3, smooth=True, freq_smooth='konno ohmachi', 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 @@ -3042,6 +3097,77 @@ def remove_outlier_curves(params, outlier_std=3, ppsd_length=30): params['ppsds'][k]['current_times_used'] = np.delete(params['ppsds'][k]['current_times_used'], index, axis=0) return params +###WORKING ON THIS +#Save default instrument and processing settings to json file(s) +def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False): + """Save settings to json file + + Parameters + ---------- + 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. + 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' + 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 settings_path == 'default' or settings_path is True: + settingsPath = resource_dir + else: + settings_path = pathlib.Path(settings_path) + if not settings_path.exists(): + if not settings_path.parent.exists(): + print(f'The provided value for settings_path ({settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}') + settingsPath = pathlib.Path.home() + else: + settingsPath = settings_path.parent + + if settings_path.is_dir(): + settingsPath = settings_path + elif settings_path.is_file(): + settingsPath = settings_path.parent + fnameDict['instrument'] = settings_path.name+"_instrumentSettings.json" + fnameDict['processing'] = settings_path.name+"_processingSettings.json" + + #Get final filepaths + instSetFPath = settingsPath.joinpath(fnameDict['instrument']) + procSetFPath = settingsPath.joinpath(fnameDict['processing']) + + #Get settings values + instKeys = ["instrument", "network", "station", "loc", "channels", "depth", "metapath", "hvsr_band"] + procFuncs = [generate_ppsds, process_hvsr, check_peaks, get_report] + + instrument_settings_dict = {} + processing_settings_dict = {} + + for k in instKeys: + instrument_settings_dict[k] = hvsr_data[k] + + for func in procFuncs: + funcName = func.__name__ + processing_settings_dict[funcName] = {} + for arg in inspect.getfullargspec(func)[0]: + processing_settings_dict[funcName][arg] = hvsr_data['processing_parameters'][funcName][arg] + + #Save settings files + if settings_type.lower()=='instrument' or settings_type.lower()=='all': + with open(instSetFPath.as_posix(), 'w') as instSetF: + json.dump(instrument_settings_dict, instSetF) + if settings_type.lower()=='processing' or settings_type.lower()=='all': + with open(procSetFPath.as_posix(), 'w') as procSetF: + json.dump(processing_settings_dict, procSetF) + #Read data as batch def batch_data_read(input_data, batch_type='table', param_col=None, batch_params=None, verbose=False, **readcsv_getMeta_fetch_kwargs): """Function to read data in data as a batch of multiple data files. This is best used through sprit.fetch_data(*args, source='batch', **other_kwargs). diff --git a/sprit/sprit_utils.py b/sprit/sprit_utils.py index 892006a..99bfe6f 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 From 517c82abfa438515e0163b7f6cf46ccad3593fae Mon Sep 17 00:00:00 2001 From: RJbalikian <46536937+RJbalikian@users.noreply.github.com> Date: Sat, 21 Oct 2023 14:33:53 -0500 Subject: [PATCH 02/17] save_settings working now --- sprit/__pycache__/__init__.cpython-310.pyc | Bin 1300 -> 1329 bytes sprit/__pycache__/sprit_gui.cpython-310.pyc | Bin 79169 -> 79800 bytes sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 167670 -> 170332 bytes sprit/__pycache__/sprit_utils.cpython-310.pyc | Bin 8897 -> 8978 bytes .../settings/processing_settings.json | 7 ++--- sprit/sprit_hvsr.py | 26 +++++++++--------- sprit/sprit_utils.py | 5 ++++ 7 files changed, 20 insertions(+), 18 deletions(-) diff --git a/sprit/__pycache__/__init__.cpython-310.pyc b/sprit/__pycache__/__init__.cpython-310.pyc index bda4ac573cb4712333f019831ad3998ceeb74c80..672c9e7f6e6a52a4292503e648cbe8fb3bb9e019 100644 GIT binary patch delta 288 zcmbQjwULWApO=@50SK51j8nHvqlew54E34e%DlRC>EQ!x3D=sQx0-9MQ0wN?pgbauf zo-DvD%P2WnkJ*&>7B@t3X-Q^I@#Gv9bER9H5RvrK%pzH!@+!Wd%$(Fp1*gQE% zJWbWf=a^-U<$+RB{9vVe2n`A#Q2}I8uq)(%!bMCVYbRT?lCbnk9zI OgULjghmVDoLk$4dq)3MV diff --git a/sprit/__pycache__/sprit_gui.cpython-310.pyc b/sprit/__pycache__/sprit_gui.cpython-310.pyc index 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21:41:54 -0500 Subject: [PATCH 03/17] update filelist support --- sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 170332 -> 170510 bytes sprit/sprit_hvsr.py | 29 ++++++++++++------- 2 files changed, 18 insertions(+), 11 deletions(-) diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc index 5e28c6c566cf9ad40998acd9c5d12c50ac11b4ce..92642aff0f8e0b46f674d25bceaf04f76ece7795 100644 GIT binary patch delta 16744 zcmbVz34B!5_5a+LnaREZAq!jB0+W!Cu!C$NVUsOL1Q~@eWjdtF=EBr6|^=>d;!XREoPIR

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+ dPath = params['datapath'] else: dPath = sprit_utils.checkifpath(params['datapath'], sample_list=sampleList) @@ -1102,6 +1103,8 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr #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 @@ -1138,11 +1141,13 @@ 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 @@ -1157,17 +1162,19 @@ 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 From d812ec8554685d9efe88ee257d1fc831b05d098c Mon Sep 17 00:00:00 2001 From: RJbalikian <46536937+RJbalikian@users.noreply.github.com> Date: Wed, 25 Oct 2023 00:33:32 -0500 Subject: [PATCH 04/17] fix specgram chart, add tromino (exp.) --- sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 170510 -> 175092 bytes sprit/sprit_hvsr.py | 153 ++++++++++++++++++- 2 files changed, 145 insertions(+), 8 deletions(-) diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc index 92642aff0f8e0b46f674d25bceaf04f76ece7795..4d4161f52112ef1110097c961c34098356a4a08d 100644 GIT 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in str(params['datapath']): params['datapath'] = params['datapath'].as_posix().replace('{','') params['datapath'] = params['datapath'].split('}') @@ -953,6 +956,8 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr for i, d in enumerate(params['datapath']): 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) @@ -1004,6 +1009,7 @@ 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 = {} @@ -1013,12 +1019,16 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr #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) @@ -1548,14 +1558,57 @@ 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, SAME AS RASPBERRY SHAKE! + 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 @@ -4033,6 +4086,88 @@ 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): @@ -5588,7 +5723,9 @@ def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix 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) + psdArr = np.flip(psdArr, axis=0) + print(extList) + im = axy.imshow(psdArr, origin='lower', extent=extList, aspect='auto', interpolation='nearest', **kwargs) ax.tick_params(left=False, right=False) #plt.sca(ax) if peak_plot: From 24138d7df5aa31a425c17c839639a478f6b55b72 Mon Sep 17 00:00:00 2001 From: RJbalikian <46536937+RJbalikian@users.noreply.github.com> Date: Wed, 25 Oct 2023 15:50:50 -0500 Subject: [PATCH 05/17] updates to noise removal (works now) --- sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 175092 -> 175327 bytes sprit/__pycache__/sprit_utils.cpython-310.pyc | Bin 8978 -> 9060 bytes sprit/sprit_hvsr.py | 83 +++++++++++------- sprit/sprit_utils.py | 5 ++ 4 files changed, 54 insertions(+), 34 deletions(-) diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc index 4d4161f52112ef1110097c961c34098356a4a08d..461c8dd5ab025d361c6805793380ba5802e9faa0 100644 GIT binary patch delta 17828 zcma)jd0^DV`S(7P&E^i_$O%a{fdHFugxf$M1Tb<2kchGZamj8%Am{9cK->_NdbVhA z(eXAM74Iw7)oR6CEA_ymt&3KzKSf%v*4ui#&*%Au1a1F#(S7pl%rnnC^UO2PJoC)# z7k`O=`;GYI=A@)}8~;1AtHgKoo_mvTIBjSSe?{36{`eoW+iYICVqt2Gy(2N8yv3o& 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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 @@ -1374,7 +1374,7 @@ 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 @@ -1509,10 +1509,18 @@ 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) @@ -1568,14 +1576,14 @@ def get_metadata(params, write_path='', update_metadata=True, source=None, **rea params = _read_RS_Metadata(params, source=source) elif params['instrument'].lower() in trominoNameList: params['paz'] = {'Z':{}, 'E':{}, 'N':{}} - #ALL THESE VALUES ARE PLACEHOLDERS, SAME AS RASPBERRY SHAKE! + #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, @@ -1674,7 +1682,6 @@ def get_report(hvsr_results, report_format='print', plot_type='HVSR p ann C+ p a 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 @@ -2806,7 +2813,6 @@ 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) @@ -3034,12 +3040,15 @@ 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'] 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() @@ -3053,8 +3062,10 @@ 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 @@ -3065,8 +3076,10 @@ def remove_noise(hvsr_data, remove_method='auto', sat_percent=0.995, noise_perce 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 @@ -3131,16 +3144,19 @@ 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) @@ -4701,13 +4717,13 @@ 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 @@ -4731,10 +4747,10 @@ 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): @@ -5724,7 +5740,6 @@ def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix ax.set_facecolor('#ffffff00') #Create transparent background for front axis #plt.sca(axy) psdArr = np.flip(psdArr, axis=0) - print(extList) im = axy.imshow(psdArr, origin='lower', extent=extList, aspect='auto', interpolation='nearest', **kwargs) ax.tick_params(left=False, right=False) #plt.sca(ax) diff --git a/sprit/sprit_utils.py b/sprit/sprit_utils.py index dcb675e..3875a04 100644 --- a/sprit/sprit_utils.py +++ b/sprit/sprit_utils.py @@ -316,6 +316,11 @@ def make_it_classy(input_data, verbose=False): 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) From 02b13aa28bb16c650e2f7f5a2258683de538b081 Mon Sep 17 00:00:00 2001 From: RJbalikian <46536937+RJbalikian@users.noreply.github.com> Date: Fri, 27 Oct 2023 01:52:18 -0500 Subject: [PATCH 06/17] updates to finding the peak --- sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 175327 -> 176424 bytes sprit/sprit_hvsr.py | 62 +++++++++++++++---- 2 files changed, 49 insertions(+), 13 deletions(-) diff --git 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N<7LMTTT@c%{{alJN>cy; diff --git a/sprit/sprit_hvsr.py b/sprit/sprit_hvsr.py index 99a3ceb..c3f267e 100644 --- a/sprit/sprit_hvsr.py +++ b/sprit/sprit_hvsr.py @@ -552,8 +552,14 @@ 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: @@ -566,14 +572,14 @@ 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 @@ -597,8 +603,8 @@ 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: @@ -624,7 +630,7 @@ 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]) @@ -654,10 +660,10 @@ 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: @@ -697,7 +703,7 @@ 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 @@ -706,6 +712,10 @@ 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 @@ -757,13 +767,38 @@ 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 @@ -789,7 +824,7 @@ 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) @@ -799,6 +834,7 @@ 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) @@ -1228,7 +1264,8 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr 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) @@ -6302,7 +6339,6 @@ def __check_freq_stability(_peak, _peakm, _peakp): return _peak - # Check stability def __check_stability(_stdf, _peak, _hvsr_log_std, rank): """Test peaks for satisfying stability conditions as outlined by SESAME 2004 From b30e4b07628b256ee11ba69d4eb65aadf77e354e Mon Sep 17 00:00:00 2001 From: RJbalikian <46536937+RJbalikian@users.noreply.github.com> Date: Fri, 27 Oct 2023 22:29:51 -0500 Subject: [PATCH 07/17] fixed print report --- sprit/__pycache__/sprit_gui.cpython-310.pyc | Bin 79800 -> 79798 bytes sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 176424 -> 176966 bytes sprit/sprit_gui.py | 4 +- sprit/sprit_hvsr.py | 158 +++++++++---------- 4 files changed, 75 insertions(+), 87 deletions(-) diff --git a/sprit/__pycache__/sprit_gui.cpython-310.pyc b/sprit/__pycache__/sprit_gui.cpython-310.pyc index adb14eb334c41ec6569465362fa5451c9affe0f3..f4ec6f43b22261c06a39922e53b6f4ea3519cb22 100644 GIT binary patch delta 66 zcmdn-o@Lv67T$bbUM>b82sN@v{j-sGL#v8#Kx$%kKv8~fW?pJua;j5mPJa0;M1& delta 68 zcmdn?o@K{-7T$bbUM>b8XvjBC{kM^KL#vudKx$&PTTyDEb53GWW=W+}YEFLnEp*Y% J4_mKp1OQEW8v6hM diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc index 3cda950bc5a03b2d10cf66bc52f74c1c0cc9e316..197cac973b775763f798abe332dfd7b32b30e2b3 100644 GIT binary patch delta 15311 zcmc(G33yaRws4>7HJyd*dqNXJl7;{Y1O$x88bBayfk0Sd$nAuXy}H@j8yk!WgWxbW 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(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']+ diff --git a/sprit/sprit_hvsr.py b/sprit/sprit_hvsr.py index c3f267e..fdf19e6 100644 --- a/sprit/sprit_hvsr.py +++ b/sprit/sprit_hvsr.py @@ -794,7 +794,7 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_selection='max', peak_freq_ 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] @@ -846,7 +846,7 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_selection='max', peak_freq_ # 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']: @@ -880,6 +880,10 @@ def check_peaks(hvsr_data, hvsr_band=[0.4, 40], peak_selection='max', peak_freq_ 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 @@ -1788,8 +1792,8 @@ 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']+ @@ -1924,24 +1928,25 @@ 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) @@ -1965,7 +1970,7 @@ 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"]: @@ -6122,6 +6127,7 @@ 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 @@ -6131,38 +6137,37 @@ 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'σ_A for all freqs {round(peakFreq*0.5, 3)}-{round(peakFreq*2, 3)} < {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'σ_A for all freqs {round(peakFreq*0.5, 3)}-{round(peakFreq*2, 3)} < {compVal} {sprit_utils.x_mark()}' _peak[_i]['PassList']['WindowLengthFreq.'] = test1 _peak[_i]['PassList']['SignificantCycles'] = test2 @@ -6208,16 +6213,16 @@ 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} (peak amp. {_peak[_i]['A0']:0.3f}) {sprit_utils.check_mark()}" + _peak[_i]['PassList']['PeakProminenceBelow'] = True break else: pass @@ -6226,25 +6231,21 @@ 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 @@ -6254,11 +6255,11 @@ 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 @@ -6295,19 +6296,17 @@ 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() @@ -6321,21 +6320,18 @@ 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 @@ -6379,104 +6375,96 @@ 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 From f9094d1e3b2bf9ad3e3c5451d228bf95623ea53d Mon Sep 17 00:00:00 2001 From: RJbalikian <46536937+RJbalikian@users.noreply.github.com> Date: Sat, 28 Oct 2023 03:28:44 -0500 Subject: [PATCH 08/17] plot update --- sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 176966 -> 177177 bytes sprit/sprit_hvsr.py | 100 +++++++++++-------- 2 files changed, 60 insertions(+), 40 deletions(-) diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc index 197cac973b775763f798abe332dfd7b32b30e2b3..a750cdb6432e5eb5878fc1600ad5fe0ad04a68ef 100644 GIT binary patch delta 14282 zcma)i34D~r75Ki{Bb$@lVRHvU!jhYt5UvCw_ZdJIC2aC7VZ&xO%q~a5w=Nj*sG#U7 zQ@PwIqGCBT=(dVhX{9ROM=R@HtMzQD{#)zW|9kW0wEh2n|G>U)=Dj!Xn0wyL`Hy4n z_#`I56CWR=!@rT|Ya9!ncrf8_hhc2+$HSKeRKr%_lN7V6Q=%<8#ca`KA?(e?P_Bik zCMBvyr^G68*XaCFYLt(4f#0ASLVDHI4WLsEEub2e_!^z6=SzJCRp&EyG4CqO?LtWj z7M*vO#G1MoU$0^KdUt^@N{JrUg#s{<7z`cBuqJI{e9LAR=95Fws#%ThMpd9AOqUM% zdPVcsgYO?H2qd9Gor*>(DfmmZ==f8evA$Sku2ZMR9)+8Ls5QFPy3M};EDbs(Z4iGt zx->YSTBBQooI`N`gJ<(ERNV^Ht7Mc~bpCiu499PRn$Q}jSd`4Y;2)+AYmHaXtkwj7 zqK~PGYP@0z{_4)06?n2QY~7W!{Y8U4#da(nD$_r@H=h-B-<1`h z0fE0R@C`@fE$V2sXcOy}Jzxml`g44XzE}4otoLlRNzXg_D@2dp+&$Eep;q+S5TJ&{ z?yOhyw8GeogBl8lMJAasw6KOV==3_OdCxO_!ZcYLbrW+lhNIX&Mjf+H@9pqq{ZF}p zAp~U|r|<%e1^&HdwX4TrF2ihU@$lYK2;!rSn&+m1^lG z1J+QATInyv_WGtzPCyE(`rm@o5?N@$P$kMJrD!+ns}oIiU#$%_L@Vzt*{9!}rIsqi zQktUXpdpGcJCvi0_RYyff;CmAjOk(rbxO%$*{bdoRgX;DB{N$)LP?3RS4QKgrp$u%$2{=3|{|i&qiy4nSI8U9R^+0tjH8c$} zEImNMH3lgN|BEt4QV?nsbW*6f0Tel2c1-=|fG@wbK+RRhDP#9C|3r17QjSFo3RrX* zI?Ro$(JAAV3Ajv6QYI>s@H<)K35`%E;e;_!nT+*{(4zB=Y%Nq`w6e;Sy-b#`mQ<99 z{F5dB)S3VlnA)dCsman*O+!@%q)eAZrel#&z9LzKF#R1~nPH)RW${nZq=bsqDdc~l&g!4k!4HJSBaC5qKRFJY!`kacZ+)(se^PHY{cOjZoati24|dm@@w+g~>& z-O{tZ!H;_iol=KXKu6vg;?_Gp9FZ#PZJ616^#T9j>YURjf&@@E!+F0fRIZ}ser@Ti z?_IR&k$M@bFow)>M8;pi{APc&8jU3-4V%;~tNK6KklG#s*Lz=uN(&>W;oIWp)#my9dS0p_Kmf-JCo&GZ`wJ+4Glk^$g|&E1xc4d{J=m)9J80=y>{bL@hf%CwTDu=vW&u z+6k=fdG`ETfN8-|&zyjXJ+D2pAtjw7p@Dx&*82&x5u>|j$=eAGvV-g1-3Oa`{{C*8 zIcFXcI$3ExpG%3j=s7OOR=dyXaaZT_`8^$feU`-rsO%>6f$t11`s`oe3eNugJ}Bw= z@^b}XQgHc~E1)ZQ?8_Rk^?dl{B`7*Z{Pz;LufOE|l=_S;sY75j@=d{S|GuQKgwQ() z{Stwf3H+HfZ6m^V0v$our9AKl_gtEsb&wDWGT9Qi!?%UI8@a=~#f9y3b@07QWl$B2 z|3@|S1n2&vaO~B@dy2qm0$l`tB>4|g>LCKsp4o)E3c)QsNB%Jk;Ip2SUt!&!3;y%} zd>*);7M~5E8q`wNs-$D5GAbE|Olmys-*Ekg%>`g<=^&Y_vunHm8?U0HC}ELgA4wZmGp&bXw3`dcjLOKno!x-J=^{jh%ucYHzwcy z1JoAo#^iTU!oNXRta`ql+?q_}M+sn^>i9LH31BuH6!!w;nlz%g0PqlO7FRL2I+g0z z7PrOgX!LGrYIk|CV$X?GJ&b|hi5Ysp3Kng8SZz8>B2S3t^l%!!7ds4aGWHo`x}u432&4}}v(!?76nMm#(g zYDRpGn(k-(P5juRUG^r2%j@iP@E$67kgC}%QpUk(3!wP#VpcZPEUL|lZ`I^ViP>BNs&XWA!i@%JA8L{Ju zV=RGjqI?1@gUMp&1gL;}#OVpp4Rzt=6XAsnxL@Qi#wK}F)GdZ`C=h%xbR-v0u?iBF zk14B(&lW!}hWT(p%v}Np7B*1L6cPDm0$YiP*`k~5Ze^QO@okJmyFI+k?n0Lr^LCQ8 z21{5WlDNs~RvI^O$L25-*0wl^I?ITxSC&^>Pi_1`Lk+3X> zHE^ZyE{7@D4^J$I%qd4n?n>gK$tx1I!R4|0#*O8bWZa#EFGUVpRFlKE&EarIObWK5 z&%v$xSHMp&Mf_(4oJEmm8{j3AnaXSyx2%NqP$s@w2~Isr1+i`wSOLPjEvIw^fimIe zP+ooqX}g`kVKQ?+rXulPpWWrNH)54Iyc_ueq8=n5#6^xh;RccEg^{Kpp>~KmFI*No zg+%_FBpepEdtoe8i&q+1it!Rwhco=X7a~!4%ZWdU7*7x*wWc=AW#cXs7k9vz%Zmu> zASCONKa#Ib9jcJd>n8x(bfvXS_|iXyWLM8#}Ig!W8^pK4r=jIgM~RVI@)Z zy-RG#lzNX+GBc9f<7ZErqVh=%1+5?pS6#|>tXK!*j7CPKG zDNZ{CFTF@N&+1&`jn{D?wY*7xkLJ8i21U`qrMq)89#J!epi{jux z%toT>xC6Nqew#SwRz6-=HJx~gdiYI!Y&e(*ej2EUe5P+ue zwP&EwG)nf=bJW<&kjQ^WDLKI%Bn90F>iN?m<}p|a`^5UkAOkLl?T^7&lk9R&h|`b3 zKGQ5JS}W?$!ld*%Om`;O9Ne>Zrh7|UlY>ty#+4{|7OYAu@yYSsL*6KqbrN`<)V@IA z0s&bA=PC6E0?!aQH>5)NLdw!=f@=saPj;7!&n1hW75R_D^kO*+yh`O>BJeT++EUp} z?VSsqULPMr%&&;ukK@|(sQB&U5HvO;{eW2g1ng$bOw150JN(@f@Dbn?{r;1Xjcdw} zPeOYPgoXP8_TUTRwhK@KzZ6eifU96sc*gVKWXTsv6Rqv_4ZNLnEE12ti2Ztv`0Pc< z!%b4mOK=blg^#@i9~xnI_!n=$9{@fQ%l`x?;N$&o!sN7p>G3Ez8BJ2cV$qv$0RALC zc@sOz3DJ5HYSFF}7hwTBAO7|ttTU#|Eu%cvX;o&&VK~4-r0uz-DY%zG(3heuYSSAEw4$NhSYE#CowB5e2mHM3UO< zKH5gQoK5BJ9h8cgv@fp`J@3PxVWQao0W7jqlk7TDEP>pUUqoqR!#{igZ?WvRkZz5U zEjh3+&=#`i#n*p#ph3g6 z?OFdy&woh;o}dD8qU8%{UHd*FA~Bjdjkp!;PcK@!kS`^9WVg1`Ux(#z73I#B6&sx! 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Date: Sat, 28 Oct 2023 14:04:37 -0500 Subject: [PATCH 09/17] update plot and print reports --- conda/meta.yaml | 4 +- docs/generate_docs.py | 2 +- docs/main.html | 527 +++++-- docs/sprit_gui.html | 183 ++- docs/sprit_hvsr.html | 1323 +++++++++++++----- docs/sprit_utils.html | 28 +- pyproject.toml | 2 +- setup.py | 2 +- sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 177177 -> 177566 bytes sprit/sprit_hvsr.py | 122 +- 10 files changed, 1597 insertions(+), 596 deletions(-) diff --git a/conda/meta.yaml b/conda/meta.yaml index 3cb9414..af2ce6b 100644 --- a/conda/meta.yaml +++ b/conda/meta.yaml @@ -1,10 +1,10 @@ package: name: sprit - version: 0.1.50 + version: 0.1.51 source: git_url: https://github.com/RJbalikian/SPRIT-HVSR - git_tag: v0.1.50 + git_tag: v0.1.51 build: number: 0 diff --git a/docs/generate_docs.py b/docs/generate_docs.py index 7383dae..5a34648 100644 --- a/docs/generate_docs.py +++ b/docs/generate_docs.py @@ -8,7 +8,7 @@ #Whether to convert_md using markdown library (True), or let github do it (False) convert_md=True rtd_theme=False #Not currently working -release_version= '0.1.50' +release_version= '0.1.51' run_tests=True lint_it=True diff --git a/docs/main.html b/docs/main.html index 677390b..5690b38 100644 --- a/docs/main.html +++ b/docs/main.html @@ -49,6 +49,7 @@

Package sprit

process_hvsr, plot_hvsr, remove_noise, + save_settings, check_peaks, get_report, HVSRData, @@ -92,6 +93,7 @@

Package sprit

'process_hvsr', 'plot_hvsr', 'remove_noise', + 'save_settings', 'check_peaks', 'get_report', 'HVSRData', @@ -516,7 +518,7 @@

Raises

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()\""
 
@@ -529,7 +531,7 @@ 
Raises

         oktoproceed=True
     else:
         oktoproceed=False
-        print("GUI window could not be created.")
+        print("GUI window cannot be created.")
 
     return oktoproceed
 
@@ -571,7 +573,7 @@ 
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 +583,10 @@ 
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=False

@@ -596,7 +602,7 @@ 
Returns

 
 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 +611,10 @@ 
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 +627,10 @@ 
Returns

     """
     orig_args = locals().copy() #Get the initial arguments
 
+    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 +666,38 @@ 
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 +723,7 @@ 
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 +733,7 @@ 
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 +745,7 @@ 
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 +779,10 @@ 
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

 
@@ -962,7 +1006,9 @@ 
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 +1021,10 @@ 
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 +1076,26 @@ 
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 +1106,27 @@ 
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 +1173,15 @@ 
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 +1218,13 @@ 
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 +1239,19 @@ 
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 +1291,12 @@ 
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 +1307,7 @@ 
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:
@@ -1546,6 +1615,8 @@ 
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
@@ -1559,7 +1630,6 @@ 
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]
 
     if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
@@ -1594,7 +1664,7 @@ 
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 +1799,18 @@ 
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 +1825,11 @@ 
Returns

         
         hvsr_data = sprit_utils.make_it_classy(hvsr_data)
     
+    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 +1901,57 @@ 
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
@@ -1931,6 +2057,10 @@ 
Returns

     #Curve pass?
     orig_args = locals().copy() #Get the initial arguments
 
+    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 +2130,8 @@ 
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 +2266,25 @@ 
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 +2308,7 @@ 
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"]:
@@ -2365,7 +2496,7 @@ 
Returns

 
 
 

-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='2023-10-28', 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], instrument_settings=None, verbose=False)
 

 

 
Function for designating input parameters for reading in and processing data

@@ -2415,6 +2546,10 @@ 
Parameters

 
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.

+
instrument_settings : None, str, default=None

+
The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most.
+If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap).
+The default settings can be reset using the save_settings() function with the parameter settings_path='default'.

 
verbose : bool, default=False

 
Whether to print output and results to terminal

 
@@ -2448,6 +2583,7 @@ 
Returns

                 metapath = '',
                 hvsr_band = [0.4, 40],
                 peak_freq_range=[0.4, 40],
+                instrument_settings=None,
                 verbose=False
                 ):
     """Function for designating input parameters for reading in and processing data
@@ -2498,6 +2634,10 @@ 
Returns

         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.
+    instrument_settings : None, str, default=None
+        The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most. 
+        If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap).
+        The default settings can be reset using the save_settings() function with the parameter settings_path='default'.
     verbose : bool, default=False
         Whether to print output and results to terminal
 
@@ -2641,6 +2781,24 @@ 
Returns

                     'ProcessingStatus':{'InputStatus':True, 'OverallStatus':True}
                     }
     
+    #Replace any default parameter settings with those from json file of interest, potentially
+    if instrument_settings is None:
+        instrument_settings_dict = {}
+    elif instrument_settings == "default" or instrument_settings is True:
+        # Update inputParamDict with default file
+        default_settings_json = settings_dir.joinpath('instrument_settings.json')
+        with open(default_settings_json.as_posix(), 'r') as f:
+            instrument_settings_dict = json.load(f)
+    else:
+        # Update inputParamDict with file
+        with open(instrument_settings, 'r') as f:
+            instrument_settings_dict = json.load(f)        
+
+    for settingName in instrument_settings_dict.keys():
+        if settingName in inputParamDict.keys():
+            inputParamDict[settingName] = instrument_settings_dict[settingName]
+
+    #Format everything nicely
     params = sprit_utils.make_it_classy(inputParamDict)
     params['ProcessingStatus']['InputParams'] = True
     params = _check_processing_status(params)
@@ -2658,6 +2816,16 @@ 
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 +2835,7 @@ 
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 +2844,25 @@ 
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 = 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

@@ -2729,32 +2893,32 @@ 
Returns

 
 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 +3037,17 @@ 
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:
+                    print(c)
+                    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)   
 
@@ -3062,10 +3231,8 @@ 
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 +3260,6 @@ 
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 +3277,8 @@ 
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 +3295,7 @@ 
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 +3369,6 @@ 
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 +3422,15 @@ 
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

 
 
@@ -3566,12 +3729,15 @@ 
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']
         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()
@@ -3585,8 +3751,10 @@ 
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
             
@@ -3597,8 +3765,10 @@ 
Returns

     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 +3867,14 @@ 
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 +3887,14 @@ 
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 +3918,8 @@ 
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 +3945,7 @@ 
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 +3975,10 @@ 
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 +3997,9 @@ 
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
     
@@ -3841,6 +4017,106 @@ 
Raises

     return hvsr_results

 
 
+

+def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False)
+

+

+
Save settings to json file

+
Parameters

+

+
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.

+
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'

+
verbose : bool, default=True

+
Whether to print outputs and information to the terminal

+

+

+
+Expand source code
+
+
def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False):
+    """Save settings to json file
+
+    Parameters
+    ----------
+    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.
+    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'
+    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 settings_path == 'default' or settings_path is True:
+        settingsPath = resource_dir.joinpath('settings')
+    else:
+        settings_path = pathlib.Path(settings_path)
+        if not settings_path.exists():
+            if not settings_path.parent.exists():
+                print(f'The provided value for settings_path ({settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}')
+                settingsPath = pathlib.Path.home()
+            else:
+                settingsPath = settings_path.parent
+        
+        if settings_path.is_dir():
+            settingsPath = settings_path
+        elif settings_path.is_file():
+            settingsPath = settings_path.parent
+            fnameDict['instrument'] = settings_path.name+"_instrumentSettings.json"
+            fnameDict['processing'] = 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"]
+    procFuncs = [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):
+            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 inspect.getfullargspec(func)[0]:
+            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 settings_type.lower()=='instrument' or settings_type.lower()=='all':
+        with open(instSetFPath.as_posix(), 'w') as instSetF:
+            json.dump(instrument_settings_dict, instSetF)
+    if settings_type.lower()=='processing' or settings_type.lower()=='all':
+        with open(procSetFPath.as_posix(), 'w') as procSetF:
+            json.dump(processing_settings_dict, procSetF)        

+

+

 

 def time_it(_t, proc_name='', verbose=True)
 

@@ -4285,7 +4561,9 @@ 
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 +4885,9 @@ 
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 +4901,9 @@ 
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.
     """
@@ -4797,6 +5079,7 @@ 
Index

 
  • read_from_RS
    • 
 
  • remove_noise
    • 
 
  • run
    • 
+
  • save_settings
    • 
 
  • time_it
    • 
 
 
diff --git a/docs/sprit_gui.html b/docs/sprit_gui.html
index 7c43f10..9635e6d 100644
--- a/docs/sprit_gui.html
+++ b/docs/sprit_gui.html
@@ -32,6 +32,7 @@ 
    Module 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..c420748 100644 --- a/docs/sprit_hvsr.html +++ b/docs/sprit_hvsr.html @@ -45,6 +45,7 @@

    Module sprit.sprit_hvsr

    import pathlib import pickle import pkg_resources +import struct import tempfile import traceback import warnings @@ -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 +759,10 @@

    Module sprit.sprit_hvsr

    """ orig_args = locals().copy() #Get the initial arguments + 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 +798,38 @@

    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 +855,7 @@

    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 +865,7 @@

    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 +877,7 @@

    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 +911,10 @@

    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 @@ -959,7 +1010,9 @@

    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 +1025,10 @@

    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 +1080,26 @@

    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 +1110,27 @@

    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 +1177,15 @@

    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 +1222,13 @@

    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 +1243,19 @@

    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 +1295,12 @@

    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 +1311,7 @@

    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: @@ -1322,6 +1398,8 @@

    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 @@ -1335,7 +1413,6 @@

    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] if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']): @@ -1370,7 +1447,7 @@

    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 +1582,18 @@

    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 +1608,11 @@

    Module sprit.sprit_hvsr

    hvsr_data = sprit_utils.make_it_classy(hvsr_data) + 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 +1639,57 @@

    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 @@ -1618,6 +1751,10 @@

    Module sprit.sprit_hvsr

    #Curve pass? orig_args = locals().copy() #Get the initial arguments + 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 +1824,8 @@

    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 +1960,25 @@

    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 +2002,7 @@

    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"]: @@ -1933,32 +2071,32 @@

    Module sprit.sprit_hvsr

    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): +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 @@ -2077,12 +2215,17 @@

    Module sprit.sprit_hvsr

    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: + print(c) + 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) @@ -2137,6 +2280,7 @@

    Module sprit.sprit_hvsr

    metapath = '', hvsr_band = [0.4, 40], peak_freq_range=[0.4, 40], + instrument_settings=None, verbose=False ): """Function for designating input parameters for reading in and processing data @@ -2187,6 +2331,10 @@

    Module sprit.sprit_hvsr

    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. + instrument_settings : None, str, default=None + The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most. + If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap). + The default settings can be reset using the save_settings() function with the parameter settings_path='default'. verbose : bool, default=False Whether to print output and results to terminal @@ -2330,6 +2478,24 @@

    Module sprit.sprit_hvsr

    'ProcessingStatus':{'InputStatus':True, 'OverallStatus':True} } + #Replace any default parameter settings with those from json file of interest, potentially + if instrument_settings is None: + instrument_settings_dict = {} + elif instrument_settings == "default" or instrument_settings is True: + # Update inputParamDict with default file + default_settings_json = settings_dir.joinpath('instrument_settings.json') + with open(default_settings_json.as_posix(), 'r') as f: + instrument_settings_dict = json.load(f) + else: + # Update inputParamDict with file + with open(instrument_settings, 'r') as f: + instrument_settings_dict = json.load(f) + + for settingName in instrument_settings_dict.keys(): + if settingName in inputParamDict.keys(): + inputParamDict[settingName] = instrument_settings_dict[settingName] + + #Format everything nicely params = sprit_utils.make_it_classy(inputParamDict) params['ProcessingStatus']['InputParams'] = True params = _check_processing_status(params) @@ -2585,10 +2751,8 @@

    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 +2780,6 @@

    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 +2797,8 @@

    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 +2815,7 @@

    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 +2889,6 @@

    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 +2942,15 @@

    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 @@ -2956,12 +3116,15 @@

    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'] 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() @@ -2975,8 +3138,10 @@

    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 @@ -2987,8 +3152,10 @@

    Module sprit.sprit_hvsr

    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 +3220,19 @@

    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) - - 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) @@ -3073,6 +3243,83 @@

    Module sprit.sprit_hvsr

    params['ppsds'][k]['current_times_used'] = np.delete(params['ppsds'][k]['current_times_used'], index, axis=0) return params +###WORKING ON THIS +#Save default instrument and processing settings to json file(s) +def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False): + """Save settings to json file + + Parameters + ---------- + 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. + 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' + 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 settings_path == 'default' or settings_path is True: + settingsPath = resource_dir.joinpath('settings') + else: + settings_path = pathlib.Path(settings_path) + if not settings_path.exists(): + if not settings_path.parent.exists(): + print(f'The provided value for settings_path ({settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}') + settingsPath = pathlib.Path.home() + else: + settingsPath = settings_path.parent + + if settings_path.is_dir(): + settingsPath = settings_path + elif settings_path.is_file(): + settingsPath = settings_path.parent + fnameDict['instrument'] = settings_path.name+"_instrumentSettings.json" + fnameDict['processing'] = 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"] + procFuncs = [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): + 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 inspect.getfullargspec(func)[0]: + 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 settings_type.lower()=='instrument' or settings_type.lower()=='all': + with open(instSetFPath.as_posix(), 'w') as instSetF: + json.dump(instrument_settings_dict, instSetF) + if settings_type.lower()=='processing' or settings_type.lower()=='all': + with open(procSetFPath.as_posix(), 'w') as procSetF: + json.dump(processing_settings_dict, procSetF) + #Read data as batch def batch_data_read(input_data, batch_type='table', param_col=None, batch_params=None, verbose=False, **readcsv_getMeta_fetch_kwargs): """Function to read data in data as a batch of multiple data files. This is best used through sprit.fetch_data(*args, source='batch', **other_kwargs). @@ -3931,6 +4178,88 @@

    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 +4793,13 @@

    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 +4823,10 @@

    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 +5562,6 @@

    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 +5614,7 @@

    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 +5684,7 @@

    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 +5717,28 @@

    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() + print(kwargs.keys()) 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 +5771,22 @@

    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 +5796,60 @@

    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: + xLocation = float(xmin) + (float(xmax)-float(xmin))*0.98 + ann = ax.text(x=xLocation, y=float(hvsr_data['BestPeak']['f0'])+0.3, fontsize='small', s=f"{hvsr_data['BestPeak']['f0']:0.2f} Hz", ha='right', va='bottom', + 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 +6168,7 @@

    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 +6178,37 @@

    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'σ_A for all freqs {round(peakFreq*0.5, 3)}-{round(peakFreq*2, 3)} < {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'σ_A for all freqs {round(peakFreq*0.5, 3)}-{round(peakFreq*2, 3)} < {compVal} {sprit_utils.x_mark()}' _peak[_i]['PassList']['WindowLengthFreq.'] = test1 _peak[_i]['PassList']['SignificantCycles'] = test2 @@ -5917,16 +6254,16 @@

    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} (peak amp. {_peak[_i]['A0']:0.3f}) {sprit_utils.check_mark()}" + _peak[_i]['PassList']['PeakProminenceBelow'] = True break else: pass @@ -5935,25 +6272,21 @@

    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 +6296,11 @@

    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 +6337,17 @@

    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 +6361,21 @@

    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 +6416,96 @@

    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 +6817,7 @@

    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 +6827,10 @@

    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=False
    @@ -6523,7 +6846,7 @@

    Returns

    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 +6855,10 @@ 
    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 +6871,10 @@ 
    Returns
    
     """
     orig_args = locals().copy() #Get the initial arguments
 
+    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 +6910,38 @@ 
    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 +6967,7 @@ 
    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 +6977,7 @@ 
    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 +6989,7 @@ 
    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 +7023,10 @@ 
    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
    
 
@@ -6789,7 +7150,9 @@ 
    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 +7165,10 @@ 
    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 +7220,26 @@ 
    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 +7250,27 @@ 
    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 +7317,15 @@ 
    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 +7362,13 @@ 
    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 +7383,19 @@ 
    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 +7435,12 @@ 
    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 +7451,7 @@ 
    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:
@@ -7185,6 +7571,8 @@ 
    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
@@ -7198,7 +7586,6 @@ 
    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]
 
     if (verbose and isinstance(hvsr_data, HVSRBatch)) or (verbose and not hvsr_data['batch']):
@@ -7233,7 +7620,7 @@ 
    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 +7755,18 @@ 
    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 +7781,11 @@ 
    Returns
    
         
         hvsr_data = sprit_utils.make_it_classy(hvsr_data)
     
+    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 +7839,57 @@

    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 @@ -7552,6 +7995,10 @@

    Returns

    #Curve pass? orig_args = locals().copy() #Get the initial arguments + 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 +8068,8 @@

    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 +8204,25 @@

    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 +8246,7 @@

    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"]: @@ -7966,7 +8414,7 @@

    Returns

    -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='2023-10-28', 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], instrument_settings=None, verbose=False)

    Function for designating input parameters for reading in and processing data

    @@ -8016,6 +8464,10 @@

    Parameters

    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.
    +
    instrument_settings : None, str, default=None
    +
    The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most. +If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap). +The default settings can be reset using the save_settings() function with the parameter settings_path='default'.
    verbose : bool, default=False
    Whether to print output and results to terminal
    @@ -8049,6 +8501,7 @@

    Returns

    metapath = '', hvsr_band = [0.4, 40], peak_freq_range=[0.4, 40], + instrument_settings=None, verbose=False ): """Function for designating input parameters for reading in and processing data @@ -8099,6 +8552,10 @@

    Returns

    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. + instrument_settings : None, str, default=None + The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most. + If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap). + The default settings can be reset using the save_settings() function with the parameter settings_path='default'. verbose : bool, default=False Whether to print output and results to terminal @@ -8242,6 +8699,24 @@

    Returns

    'ProcessingStatus':{'InputStatus':True, 'OverallStatus':True} } + #Replace any default parameter settings with those from json file of interest, potentially + if instrument_settings is None: + instrument_settings_dict = {} + elif instrument_settings == "default" or instrument_settings is True: + # Update inputParamDict with default file + default_settings_json = settings_dir.joinpath('instrument_settings.json') + with open(default_settings_json.as_posix(), 'r') as f: + instrument_settings_dict = json.load(f) + else: + # Update inputParamDict with file + with open(instrument_settings, 'r') as f: + instrument_settings_dict = json.load(f) + + for settingName in instrument_settings_dict.keys(): + if settingName in inputParamDict.keys(): + inputParamDict[settingName] = instrument_settings_dict[settingName] + + #Format everything nicely params = sprit_utils.make_it_classy(inputParamDict) params['ProcessingStatus']['InputParams'] = True params = _check_processing_status(params) @@ -8249,7 +8724,7 @@

    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 +8733,25 @@

    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 = 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
    @@ -8311,32 +8782,32 @@

    Returns

    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 +8926,17 @@ 
    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:
+                    print(c)
+                    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)   
 
@@ -8810,10 +9286,8 @@ 
    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 +9315,6 @@ 
    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 +9332,8 @@ 
    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 +9350,7 @@ 
    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 +9424,6 @@ 
    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 +9477,15 @@ 
    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
    
 
 
@@ -9231,12 +9701,15 @@ 
    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']
         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()
@@ -9250,8 +9723,10 @@ 
    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
             
@@ -9262,8 +9737,10 @@ 
    Returns
    
     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 +9831,19 @@

    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) + 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) @@ -9470,8 +9950,14 @@

    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 +9970,14 @@

    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 +10001,8 @@

    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 +10028,7 @@

    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 +10058,10 @@

    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 +10080,9 @@

    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 @@ -9614,6 +10100,106 @@

    Raises

    return hvsr_results +
    +def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False) +
    +
    +

    Save settings to json file

    +

    Parameters

    +
    +
    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.
    +
    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'
    +
    verbose : bool, default=True
    +
    Whether to print outputs and information to the terminal
    +
    +
    + +Expand source code + +
    def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False):
+    """Save settings to json file
+
+    Parameters
+    ----------
+    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.
+    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'
+    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 settings_path == 'default' or settings_path is True:
+        settingsPath = resource_dir.joinpath('settings')
+    else:
+        settings_path = pathlib.Path(settings_path)
+        if not settings_path.exists():
+            if not settings_path.parent.exists():
+                print(f'The provided value for settings_path ({settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}')
+                settingsPath = pathlib.Path.home()
+            else:
+                settingsPath = settings_path.parent
+        
+        if settings_path.is_dir():
+            settingsPath = settings_path
+        elif settings_path.is_file():
+            settingsPath = settings_path.parent
+            fnameDict['instrument'] = settings_path.name+"_instrumentSettings.json"
+            fnameDict['processing'] = 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"]
+    procFuncs = [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):
+            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 inspect.getfullargspec(func)[0]:
+            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 settings_type.lower()=='instrument' or settings_type.lower()=='all':
+        with open(instSetFPath.as_posix(), 'w') as instSetF:
+            json.dump(instrument_settings_dict, instSetF)
+    if settings_type.lower()=='processing' or settings_type.lower()=='all':
+        with open(procSetFPath.as_posix(), 'w') as procSetF:
+            json.dump(processing_settings_dict, procSetF)
    +
    +
    def test_function()
    @@ -10050,7 +10636,9 @@

    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 +10960,9 @@

    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 +10976,9 @@

    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. """ @@ -10551,6 +11143,7 @@

    Index

  • remove_noise
  • remove_outlier_curves
  • run
    • +
  • save_settings
  • test_function
    • diff --git a/docs/sprit_utils.html b/docs/sprit_utils.html index d3baac2..5de3f03 100644 --- a/docs/sprit_utils.html +++ b/docs/sprit_utils.html @@ -47,7 +47,7 @@

    Module 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/pyproject.toml b/pyproject.toml
index 7d62bff..8fb79f3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ name = "sprit"
 authors = [{name="Riley Balikian"}, {name="Hongyu Xaio"}]
 dynamic = ["readme"]
 license = {file = "LICENSE"}
-version="0.1.50"
+version="0.1.51"
 description = "A package for processing and analyzing HVSR (Horizontal to Vertical Spectral Ratio) data"
 keywords = ["HVSR", "seismic", "horizontal to vertical spectral ratio", "obspy", 'geology', 'geophysics', 'geotechnical']
 requires-python = ">=3.9"
diff --git a/setup.py b/setup.py
index 3b9f9de..a2fe0ed 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@
     name="sprit",
     author= "Riley Balikian",
     author_email = "balikian@illinois.edu",
-    version="0.1.50",
+    version="0.1.51",
     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",
diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc
index a750cdb6432e5eb5878fc1600ad5fe0ad04a68ef..842fd6b7603953ae80a5a5ba1dd629670e928412 100644
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diff --git a/sprit/sprit_hvsr.py b/sprit/sprit_hvsr.py
index 740c209..a24912f 100644
--- a/sprit/sprit_hvsr.py
+++ b/sprit/sprit_hvsr.py
@@ -2040,32 +2040,32 @@ def report_output(_report_format, _plot_type='HVSR p ann C+ p ann Spec', _return
     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):
+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
@@ -2189,6 +2189,11 @@ def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True
                 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:
+                    print(c)
+                    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)   
@@ -5681,18 +5686,28 @@ 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()    
 
+    print(kwargs.keys())
     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)
@@ -5725,33 +5740,22 @@ 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))
+    # 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=0.3, a_max=1)
-    #psdArr = hvsr_data['ind_hvsr_curves'].T
-    #print(psdArr.shape)
+    useArr = np.clip(useArr, a_min=0.15, a_max=1)
 
+    # Get times
     xmin = hvsr_data['hvsr_df']['TimesProcessed_MPL'].min()
     xmax = hvsr_data['hvsr_df']['TimesProcessed_MPL'].max()
 
-    #xmin = min(hvsr_data['ppsds'][anyKey]['current_times_used'][:-1]).matplotlib_date
-    #xmax = max(hvsr_data['ppsds'][anyKey]['current_times_used'][:-1]).matplotlib_date
-  
+    #Format times
     tTicks = mdates.MinuteLocator(byminute=range(0,60,5))
     ax.xaxis.set_major_locator(tTicks)
     tTicks_minor = mdates.SecondLocator(bysecond=[0])
@@ -5761,15 +5765,16 @@ 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)
 
+    #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['hvsr_df'].index[0].date())
 
+    #Get extents
     ymin = hvsr_data['input_params']['hvsr_band'][0]
     ymax = hvsr_data['input_params']['hvsr_band'][1]
 
-
     freqticks = np.flip(hvsr_data['x_freqs'][anyKey])
     yminind = np.argmin(np.abs(ymin-freqticks))
     ymaxind = np.argmin(np.abs(ymax-freqticks))
@@ -5778,40 +5783,29 @@ def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix
 
     extList = [xmin, xmax, ymin, ymax]
 
-    #Set up axes, since data is already in semilog
-    #axy = ax.twinx()
-    #axy.set_yticks([])
-    #axy.zorder=1
-    #ax.zorder=0
-    ax.set_facecolor([0,0,0]) #Create transparent background for front axis
-    #ax.set_facecolor('#ffffff00') #Create transparent background for front axis
-    #plt.sca(axy)  
-    #psdArr = np.flip(psdArr, axis=0)
+    #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))
-    # use nupy.interp to interpolate your original array onto the new indices
     linList = []
     for row in psdArr:
         row = row.astype(np.float16)
         linList.append(np.interp(new_indices, freqticks, row))
     linear_arr = np.stack(linList)
 
-    #im = ax.imshow(psdArr.T, origin='lower', extent=extList, aspect='auto', interpolation=None, alpha=useArr, **kwargs)
-    im = ax.imshow(linear_arr.T, origin='lower', extent=extList, aspect='auto',alpha=useArr, **kwargs)
-    #x=hvsr_data['hvsr_df']['TimesProcessed_MPL']
-    #y=freqticks
-    #z=psdArr.T
-    #im = ax.pcolormesh(x, y, z, alpha=useArr, **kwargs)
-    ax.tick_params(left=True, right=True)
-    #plt.sca(ax)
-    peak_plot=True
+    # 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)
+
     if peak_plot:
         ax.axhline(hvsr_data['BestPeak']['f0'], c='k',  linestyle='dotted', zorder=1000)
 
-    #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)
+    if annotate:
+        xLocation = float(xmin) + (float(xmax)-float(xmin))*0.98
+        ann = ax.text(x=xLocation, y=float(hvsr_data['BestPeak']['f0'])+0.3, fontsize='small', s=f"{hvsr_data['BestPeak']['f0']:0.2f} Hz", ha='right', va='bottom', 
+                      bbox={'alpha':0.8, 'edgecolor':'w', 'fc':'w', 'pad':0.3})
+
     ax.set_xlabel('UTC Time \n'+day)
     if colorbar:
         cbar = plt.colorbar(mappable=im, orientation='horizontal')
@@ -5819,16 +5813,12 @@ def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix
 
     ax.set_ylabel(ylabel)
     ax.set_yscale('log')
-    #ax.semilogy()
-
-    #ax.set_ylim(hvsr_data['input_params']['hvsr_band'])
 
-    #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

From 890beb2a21444863d5ae2312b1df72b43065e5ad Mon Sep 17 00:00:00 2001
From: RJbalikian <46536937+RJbalikian@users.noreply.github.com>
Date: Sat, 28 Oct 2023 14:12:45 -0500
Subject: [PATCH 10/17] update print report format

---
 docs/sprit_hvsr.html                         |   6 +++---
 sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 177566 -> 177522 bytes
 sprit/sprit_hvsr.py                          |   6 +++---
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/sprit_hvsr.html b/docs/sprit_hvsr.html
index c420748..cff6f14 100644
--- a/docs/sprit_hvsr.html
+++ b/docs/sprit_hvsr.html
@@ -6206,9 +6206,9 @@ 
    Module sprit.sprit_hvsr
    
             _peak[_i]['Report']['Nc'] = f'{int(nc)} > 200  {sprit_utils.x_mark()}'
 
         if test3:
-            _peak[_i]['Report']['σ_A(f)'] = f'σ_A for all freqs {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)'] = f'σ_A for all freqs {round(peakFreq*0.5, 3)}-{round(peakFreq*2, 3)} < {compVal}  {sprit_utils.x_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.x_mark()}'
 
         _peak[_i]['PassList']['WindowLengthFreq.'] = test1
         _peak[_i]['PassList']['SignificantCycles'] = test2
@@ -6262,7 +6262,7 @@ 
    Module sprit.sprit_hvsr
    
             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-)'] = f"Amp. of H/V Curve @{_x[_j]:0.3f}Hz ({_y[_j]:0.3f}) < {_peak[_i]['A0']/2:0.3f} (peak amp. {_peak[_i]['A0']:0.3f}) {sprit_utils.check_mark()}"
+                _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:
diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc
index 842fd6b7603953ae80a5a5ba1dd629670e928412..b59230addf0401ad3221c13dbac1aa486c864d75 100644
GIT binary patch
delta 800
zcma)4&ubG=5S}-ipW9|NrKQxQ$!pUe7BsC?YKbc-)e^mkMC(Ng+LvC$)~suh@F0}Z
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O-N3wCzGq(JY2^>-iqePx

delta 804
zcmb7COK1~O6n*bbKFu_VjZM&6)4Zms_JgTfFc2fucGC(XQmvp1iA(~Lnl{r^G!7IA
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zPlDy58lPnS5yO59M(m;acAE2D@#1ljo>4_|dRnB_RBleF3!JeH$;jM=2`-KD5j2({
zBzuTGMl~0~^(8og4ISDrUIvvH@NpT2oy{z8mc4+v3hV*3iFy?RxLb#b0Sz=eeUg*P
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zcWw7iFq%_xu`#3eYdksPAozsds>G
zaXK}1kyZuI_|TLXn^&) 200  {sprit_utils.x_mark()}'
 
         if test3:
-            _peak[_i]['Report']['σ_A(f)'] = f'σ_A for all freqs {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)'] = f'σ_A for all freqs {round(peakFreq*0.5, 3)}-{round(peakFreq*2, 3)} < {compVal}  {sprit_utils.x_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.x_mark()}'
 
         _peak[_i]['PassList']['WindowLengthFreq.'] = test1
         _peak[_i]['PassList']['SignificantCycles'] = test2
@@ -6231,7 +6231,7 @@ def __check_clarity(_x, _y, _peak, do_rank=True):
             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-)'] = f"Amp. of H/V Curve @{_x[_j]:0.3f}Hz ({_y[_j]:0.3f}) < {_peak[_i]['A0']/2:0.3f} (peak amp. {_peak[_i]['A0']:0.3f}) {sprit_utils.check_mark()}"
+                _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:

From a7e9cc8bed6db206405b65941ea421e9d7719dc6 Mon Sep 17 00:00:00 2001
From: RJbalikian <46536937+RJbalikian@users.noreply.github.com>
Date: Sat, 28 Oct 2023 14:14:14 -0500
Subject: [PATCH 11/17] minor updates to stop print

---
 sprit/sprit_hvsr.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/sprit/sprit_hvsr.py b/sprit/sprit_hvsr.py
index e473d76..ec1eea0 100644
--- a/sprit/sprit_hvsr.py
+++ b/sprit/sprit_hvsr.py
@@ -2191,7 +2191,6 @@ def plot_hvsr(hvsr_data, plot_type='HVSR ann p C+ ann p SPEC', use_subplots=True
             elif p=='spec':
                 plottypeKwargs = {}
                 for c in plotComponents:
-                    print(c)
                     plottypeKwargs[c] = True
                 kwargs.update(plottypeKwargs)
                 _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False, **kwargs)
@@ -5690,7 +5689,6 @@ def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix
     if fig is None and ax is None:
         fig, ax = plt.subplots()    
 
-    print(kwargs.keys())
     if 'kwargs' in kwargs.keys():
         kwargs = kwargs['kwargs']
 

From b8537ff89fc08431402b62c8457aba62a6be3930 Mon Sep 17 00:00:00 2001
From: RJbalikian <46536937+RJbalikian@users.noreply.github.com>
Date: Sun, 29 Oct 2023 10:12:53 -0500
Subject: [PATCH 12/17] update export settings function

---
 sprit/__init__.py                            |   8 +-
 sprit/__pycache__/__init__.cpython-310.pyc   | Bin 1329 -> 1374 bytes
 sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 177522 -> 177752 bytes
 sprit/sprit_hvsr.py                          | 166 ++++++++++---------
 4 files changed, 93 insertions(+), 81 deletions(-)

diff --git a/sprit/__init__.py b/sprit/__init__.py
index 3ebff50..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,
@@ -20,7 +22,6 @@
     process_hvsr,
     plot_hvsr,
     remove_noise,
-    save_settings,
     check_peaks,
     get_report,
     HVSRData,
@@ -52,8 +53,10 @@
             'get_char',
             'time_it',
             'checkifpath',
-            'export_data', 
+            'export_data',
+            'export_settings',
             'import_data',
+            'import_settings',
             'input_params',
             'gui',
             'get_metadata',
@@ -64,7 +67,6 @@
             'process_hvsr',
             'plot_hvsr',
             'remove_noise',
-            'save_settings',
             'check_peaks',
             'get_report',
             'HVSRData',
diff --git a/sprit/__pycache__/__init__.cpython-310.pyc b/sprit/__pycache__/__init__.cpython-310.pyc
index 672c9e7f6e6a52a4292503e648cbe8fb3bb9e019..4268e36b8817de6b28959df01371602ac0ad6c02 100644
GIT binary patch
delta 390
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diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc
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GIT binary patch
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Date: Sun, 29 Oct 2023 14:56:02 -0500
Subject: [PATCH 13/17] import export settings updated

---
 sprit/__pycache__/__init__.cpython-310.pyc   | Bin 1374 -> 1362 bytes
 sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 177752 -> 177698 bytes
 sprit/sprit_hvsr.py                          | 143 +++++++++++--------
 3 files changed, 81 insertions(+), 62 deletions(-)

diff --git a/sprit/__pycache__/__init__.cpython-310.pyc b/sprit/__pycache__/__init__.cpython-310.pyc
index 4268e36b8817de6b28959df01371602ac0ad6c02..99f85f0f2f1390e2ca718b34284ee93c19c6da77 100644
GIT binary patch
delta 29
jcmcb|b%~2RpO=@50SHRV>^5@iF;9+Q5#1cc%)$f!YS9Lh

delta 43
xcmcb_b&rcXpO=@50SK}e*>2?4V-}3!&&(~zFDi*IPE9Sz%u6rcY{x9Z1ONen40-?n

diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc
index eda357434b3c5f9624d25eb21aa49745b8944b0e..0576e4ea207101cc24bf98dc59a8a761f5c0c249 100644
GIT binary patch
delta 22145
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diff --git a/sprit/sprit_hvsr.py b/sprit/sprit_hvsr.py
index b26ebc9..6da8dea 100644
--- a/sprit/sprit_hvsr.py
+++ b/sprit/sprit_hvsr.py
@@ -931,21 +931,23 @@ def _do_export(_hvsr_data=hvsr_data, _export_path=export_path, _ext=ext):
 
 ###WORKING ON THIS
 #Save default instrument and processing settings to json file(s)
-def export_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False):
+def export_settings(hvsr_data, export_settings_path='default', export_settings_type='all', include_location=False, verbose=False):
     """Save settings to json file
 
     Parameters
     ----------
-    settings_path : str, default="default"
+    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.
-    settings_type : str, {'all', 'instrument', 'processing'}
+    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
 
@@ -954,23 +956,23 @@ def export_settings(hvsr_data, settings_path='default', settings_type='all', ver
     fnameDict['instrument'] = "instrument_settings.json"
     fnameDict['processing'] = "processing_settings.json"
 
-    if settings_path == 'default' or settings_path is True:
+    if export_settings_path == 'default' or export_settings_path is True:
         settingsPath = resource_dir.joinpath('settings')
     else:
-        settings_path = pathlib.Path(settings_path)
-        if not settings_path.exists():
-            if not settings_path.parent.exists():
-                print(f'The provided value for settings_path ({settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}')
+        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 = settings_path.parent
+                settingsPath = export_settings_path.parent
         
-        if settings_path.is_dir():
-            settingsPath = settings_path
-        elif settings_path.is_file():
-            settingsPath = settings_path.parent
-            fnameDict['instrument'] = settings_path.name+"_instrumentSettings.json"
-            fnameDict['processing'] = settings_path.name+"_processingSettings.json"
+        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'])
@@ -978,6 +980,7 @@ def export_settings(hvsr_data, settings_path='default', settings_type='all', ver
 
     #Get settings values
     instKeys = ["instrument", "net", "sta", "loc", "cha", "depth", "metapath", "hvsr_band"]
+    inst_location_keys = ['xcoord', 'ycoord', 'elevation', 'elev_unit', 'input_crs']
     procFuncs = [generate_ppsds, process_hvsr, check_peaks, get_report]
 
     instrument_settings_dict = {}
@@ -989,6 +992,15 @@ def export_settings(hvsr_data, settings_path='default', settings_type='all', ver
             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__
@@ -1000,16 +1012,27 @@ def export_settings(hvsr_data, settings_path='default', settings_type='all', ver
                 processing_settings_dict[funcName][arg] = hvsr_data['processing_parameters'][funcName][arg]
     
     #Save settings files
-    if settings_type.lower()=='instrument' or settings_type.lower()=='all':
-        with open(instSetFPath.as_posix(), 'w') as instSetF:
+    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 settings_type.lower()=='processing' or settings_type.lower()=='all':
-        with open(procSetFPath.as_posix(), 'w') as procSetF:
-            json.dump(processing_settings_dict, procSetF)        
+    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},')
+            jsonString = jsonString.replace('{ "', '\n\t\t{\n\t\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):
@@ -1057,6 +1080,7 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
         print('\nFetching data (fetch_data())')
         print()
 
+    print(params.items())
     params = get_metadata(params, update_metadata=update_metadata, source=source)
     inv = params['inv']
     date=params['acq_date']
@@ -2310,8 +2334,26 @@ def import_data(import_filepath, data_format='pickle'):
     return dataIN
 
 #Import settings
-def import_settings():
-    return
+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,
@@ -2332,10 +2374,9 @@ 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],
-                instrument_settings=None,
                 verbose=False
                 ):
     """Function for designating input parameters for reading in and processing data
@@ -2380,16 +2421,12 @@ 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.
-    instrument_settings : None, str, default=None
-        The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most. 
-        If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap).
-        The default settings can be reset using the save_settings() function with the parameter settings_path='default'.
     verbose : bool, default=False
         Whether to print output and results to terminal
 
@@ -2410,21 +2447,6 @@ def input_params(datapath,
             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())
@@ -2507,12 +2529,6 @@ 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'
 
@@ -2534,17 +2550,20 @@ def input_params(datapath,
                     }
     
     #Replace any default parameter settings with those from json file of interest, potentially
-    if instrument_settings is None:
-        instrument_settings_dict = {}
-    elif instrument_settings == "default" or instrument_settings is True:
-        # Update inputParamDict with default file
-        default_settings_json = settings_dir.joinpath('instrument_settings.json')
-        with open(default_settings_json.as_posix(), 'r') as f:
-            instrument_settings_dict = json.load(f)
-    else:
-        # Update inputParamDict with file
-        with open(instrument_settings, 'r') as f:
-            instrument_settings_dict = json.load(f)        
+    instrument_settings_dict = {}
+    if pathlib.Path(instrument).exists():
+        instrument_settings = import_settings(settings_import_path=instrument, export_settings_type='instrument', verbose=verbose)
+        for k, settings_value in instrument_settings.items():
+            if k in inspect.getfullargspec(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():

From 0883acb4c34593ebc8e334ae95c19d824ef7566d Mon Sep 17 00:00:00 2001
From: RJbalikian <46536937+RJbalikian@users.noreply.github.com>
Date: Sun, 29 Oct 2023 17:37:50 -0500
Subject: [PATCH 14/17] minor updates to settings read and plot

---
 sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 177698 -> 178113 bytes
 sprit/sprit_hvsr.py                          | 337 ++++++++++---------
 2 files changed, 169 insertions(+), 168 deletions(-)

diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc
index 0576e4ea207101cc24bf98dc59a8a761f5c0c249..147277d8159f9fcffdc09b9bf589d7c401561e3a 100644
GIT binary patch
delta 12479
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diff --git a/sprit/sprit_hvsr.py b/sprit/sprit_hvsr.py
index 6da8dea..431f553 100644
--- a/sprit/sprit_hvsr.py
+++ b/sprit/sprit_hvsr.py
@@ -2146,171 +2146,6 @@ 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, 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
-
 #Import data
 def import_data(import_filepath, data_format='pickle'):
     """Function to import .hvsr (or other extension) data exported using export_data() function
@@ -2552,7 +2387,7 @@ def input_params(datapath,
     #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, export_settings_type='instrument', verbose=verbose)
+        instrument_settings = import_settings(settings_import_path=instrument, settings_import_type='instrument', verbose=verbose)
         for k, settings_value in instrument_settings.items():
             if k in inspect.getfullargspec(input_params).args:
                 instrument_settings_dict[k] = settings_value
@@ -2575,6 +2410,171 @@ def input_params(datapath,
     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.
@@ -5829,8 +5829,9 @@ def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix
         ax.axhline(hvsr_data['BestPeak']['f0'], c='k',  linestyle='dotted', zorder=1000)
 
     if annotate:
-        xLocation = float(xmin) + (float(xmax)-float(xmin))*0.98
-        ann = ax.text(x=xLocation, y=float(hvsr_data['BestPeak']['f0'])+0.3, fontsize='small', s=f"{hvsr_data['BestPeak']['f0']:0.2f} Hz", ha='right', va='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])*(0.002)
+        ann = ax.text(x=xLocation, y=yLocation, fontsize='small', s=f"Peak at {hvsr_data['BestPeak']['f0']:0.2f} Hz", ha='right', va='bottom', 
                       bbox={'alpha':0.8, 'edgecolor':'w', 'fc':'w', 'pad':0.3})
 
     ax.set_xlabel('UTC Time \n'+day)

From 2f346e13a7b51f8278a17bb018e30c7dd8a1418f Mon Sep 17 00:00:00 2001
From: RJbalikian <46536937+RJbalikian@users.noreply.github.com>
Date: Mon, 30 Oct 2023 11:43:07 -0500
Subject: [PATCH 15/17] instrument settings read in great now

---
 sprit/__pycache__/sprit_hvsr.cpython-310.pyc | Bin 178113 -> 178224 bytes
 sprit/sprit_hvsr.py                          |  32 +++++++++++--------
 2 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/sprit/__pycache__/sprit_hvsr.cpython-310.pyc b/sprit/__pycache__/sprit_hvsr.cpython-310.pyc
index 147277d8159f9fcffdc09b9bf589d7c401561e3a..662c49ae3919cef70d510fa52ed2d533778ed388 100644
GIT binary patch
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diff --git a/sprit/sprit_hvsr.py b/sprit/sprit_hvsr.py
index 431f553..442cb68 100644
--- a/sprit/sprit_hvsr.py
+++ b/sprit/sprit_hvsr.py
@@ -1080,7 +1080,6 @@ def fetch_data(params, source='file', trim_dir=None, export_format='mseed', detr
         print('\nFetching data (fetch_data())')
         print()
 
-    print(params.items())
     params = get_metadata(params, update_metadata=update_metadata, source=source)
     inv = params['inv']
     date=params['acq_date']
@@ -2273,15 +2272,6 @@ 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()
-
     #Reformat times
     if type(acq_date) is datetime.datetime:
         date = str(acq_date.date())
@@ -2388,8 +2378,11 @@ def input_params(datapath,
     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 inspect.getfullargspec(input_params).args:
+            if k in input_params_args:
                 instrument_settings_dict[k] = settings_value
         inputParamDict['instrument_settings'] = inputParamDict['instrument']
         inputParamDict.update(instrument_settings_dict)
@@ -2404,6 +2397,13 @@ def input_params(datapath,
         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()
+
     #Format everything nicely
     params = sprit_utils.make_it_classy(inputParamDict)
     params['ProcessingStatus']['InputParams'] = True
@@ -5829,9 +5829,15 @@ def _plot_specgram_hvsr(hvsr_data, fig=None, ax=None, save_dir=None, save_suffix
         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])*(0.002)
-        ann = ax.text(x=xLocation, y=yLocation, fontsize='small', s=f"Peak at {hvsr_data['BestPeak']['f0']:0.2f} Hz", ha='right', va='bottom', 
+        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})
 
     ax.set_xlabel('UTC Time \n'+day)

From d82ca80e1c26ac405e88d13c41763eff2192ab2d Mon Sep 17 00:00:00 2001
From: RJbalikian <46536937+RJbalikian@users.noreply.github.com>
Date: Mon, 30 Oct 2023 16:31:03 -0500
Subject: [PATCH 16/17] settings read in works, udpate vernum and docs

---
 conda/meta.yaml                               |    4 +-
 docs/generate_docs.py                         |    2 +-
 docs/main.html                                |  532 +++++--
 docs/sprit_hvsr.html                          | 1359 +++++++++++------
 pyproject.toml                                |    2 +-
 setup.py                                      |    2 +-
 sprit/__pycache__/__init__.cpython-311.pyc    |  Bin 1731 -> 1813 bytes
 sprit/__pycache__/sprit_gui.cpython-311.pyc   |  Bin 215193 -> 217949 bytes
 sprit/__pycache__/sprit_hvsr.cpython-311.pyc  |  Bin 328277 -> 362863 bytes
 sprit/__pycache__/sprit_utils.cpython-311.pyc |  Bin 18084 -> 18652 bytes
 .../settings/instrument_settings.inst         |   10 +
 .../settings/instrument_settings.json         |   12 -
 .../settings/processing_settings.json         |   12 -
 .../settings/processing_settings.proc         |   69 +
 sprit/sprit_hvsr.py                           |  177 ++-
 15 files changed, 1477 insertions(+), 704 deletions(-)
 create mode 100644 sprit/resources/settings/instrument_settings.inst
 delete mode 100644 sprit/resources/settings/instrument_settings.json
 delete mode 100644 sprit/resources/settings/processing_settings.json
 create mode 100644 sprit/resources/settings/processing_settings.proc

diff --git a/conda/meta.yaml b/conda/meta.yaml
index af2ce6b..3cb9414 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -1,10 +1,10 @@
 package:
   name: sprit
-  version: 0.1.51
+  version: 0.1.50
 
 source:
   git_url: https://github.com/RJbalikian/SPRIT-HVSR
-  git_tag: v0.1.51
+  git_tag: v0.1.50
 
 build:
   number: 0
diff --git a/docs/generate_docs.py b/docs/generate_docs.py
index 5a34648..7383dae 100644
--- a/docs/generate_docs.py
+++ b/docs/generate_docs.py
@@ -8,7 +8,7 @@
 #Whether to convert_md using markdown library (True), or let github do it (False)
 convert_md=True
 rtd_theme=False #Not currently working
-release_version= '0.1.51'
+release_version= '0.1.50'
 run_tests=True
 lint_it=True
 
diff --git a/docs/main.html b/docs/main.html
index 5690b38..322d7d7 100644
--- a/docs/main.html
+++ b/docs/main.html
@@ -39,7 +39,9 @@ 
    Package sprit
    
 from sprit.sprit_hvsr import(
     run,
     export_data,
+    export_settings,
     import_data,
+    import_settings,
     input_params,
     gui,
     get_metadata,
@@ -49,7 +51,6 @@ 
    Package sprit
    
     process_hvsr,
     plot_hvsr,
     remove_noise,
-    save_settings,
     check_peaks,
     get_report,
     HVSRData,
@@ -81,8 +82,10 @@ 
    Package sprit
    
             'get_char',
             'time_it',
             'checkifpath',
-            'export_data', 
+            'export_data',
+            'export_settings',
             'import_data',
+            'import_settings',
             'input_params',
             'gui',
             'get_metadata',
@@ -93,7 +96,6 @@ 
    Package sprit
    
             'process_hvsr',
             'plot_hvsr',
             'remove_noise',
-            'save_settings',
             'check_peaks',
             'get_report',
             'HVSRData',
@@ -627,6 +629,21 @@ 
    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
@@ -947,6 +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)
 
    
@@ -957,7 +1107,30 @@ 
    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
         
@@ -1343,6 +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')
@@ -1630,7 +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]
+    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']:
@@ -1825,7 +2023,8 @@ 
    Returns
    
         
         hvsr_data = sprit_utils.make_it_classy(hvsr_data)
     
-    hvsr_data['processing_parameters'] = {}
+    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
@@ -2052,11 +2251,26 @@ 
    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
@@ -2495,8 +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='2023-10-28', 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], instrument_settings=None, 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
    
@@ -2540,16 +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=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.
    
-
    instrument_settings : None, str, default=None
    
-
    The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most.
-If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap).
-The default settings can be reset using the save_settings() function with the parameter settings_path='default'.
    
+
    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
    
 
@@ -2580,10 +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],
-                instrument_settings=None,
+                processing_parameters={},
                 verbose=False
                 ):
     """Function for designating input parameters for reading in and processing data
@@ -2628,16 +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.
-    instrument_settings : None, str, default=None
-        The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most. 
-        If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap).
-        The default settings can be reset using the save_settings() function with the parameter settings_path='default'.
+    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
 
@@ -2649,30 +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())
@@ -2755,12 +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'
 
@@ -2782,22 +3003,41 @@ 
    Returns
    
                     }
     
     #Replace any default parameter settings with those from json file of interest, potentially
-    if instrument_settings is None:
-        instrument_settings_dict = {}
-    elif instrument_settings == "default" or instrument_settings is True:
-        # Update inputParamDict with default file
-        default_settings_json = settings_dir.joinpath('instrument_settings.json')
-        with open(default_settings_json.as_posix(), 'r') as f:
-            instrument_settings_dict = json.load(f)
-    else:
-        # Update inputParamDict with file
-        with open(instrument_settings, 'r') as f:
-            instrument_settings_dict = json.load(f)        
+    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
@@ -3044,7 +3284,6 @@ 
    Returns
    
             elif p=='spec':
                 plottypeKwargs = {}
                 for c in plotComponents:
-                    print(c)
                     plottypeKwargs[c] = True
                 kwargs.update(plottypeKwargs)
                 _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False, **kwargs)
@@ -3167,6 +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
@@ -3612,7 +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']:
@@ -3734,6 +4017,13 @@ 
    Returns
    
         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)
 
@@ -3759,13 +4049,16 @@ 
    Returns
    
                 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'] = []
@@ -4017,106 +4310,6 @@ 
    Raises
    
     return hvsr_results
    -
    -def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False) -
    -
    -

    Save settings to json file

    -

    Parameters

    -
    -
    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.
    -
    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'
    -
    verbose : bool, default=True
    -
    Whether to print outputs and information to the terminal
    -
    -
    - -Expand source code - -
    def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False):
-    """Save settings to json file
-
-    Parameters
-    ----------
-    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.
-    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'
-    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 settings_path == 'default' or settings_path is True:
-        settingsPath = resource_dir.joinpath('settings')
-    else:
-        settings_path = pathlib.Path(settings_path)
-        if not settings_path.exists():
-            if not settings_path.parent.exists():
-                print(f'The provided value for settings_path ({settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}')
-                settingsPath = pathlib.Path.home()
-            else:
-                settingsPath = settings_path.parent
-        
-        if settings_path.is_dir():
-            settingsPath = settings_path
-        elif settings_path.is_file():
-            settingsPath = settings_path.parent
-            fnameDict['instrument'] = settings_path.name+"_instrumentSettings.json"
-            fnameDict['processing'] = 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"]
-    procFuncs = [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):
-            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 inspect.getfullargspec(func)[0]:
-            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 settings_type.lower()=='instrument' or settings_type.lower()=='all':
-        with open(instSetFPath.as_posix(), 'w') as instSetF:
-            json.dump(instrument_settings_dict, instSetF)
-    if settings_type.lower()=='processing' or settings_type.lower()=='all':
-        with open(procSetFPath.as_posix(), 'w') as procSetF:
-            json.dump(processing_settings_dict, procSetF)
    -
    -
    def time_it(_t, proc_name='', verbose=True)
    @@ -5063,6 +5256,7 @@

    Index

  • check_xvalues
  • checkifpath
  • export_data
    • +
  • export_settings
  • fetch_data
  • format_time
  • generate_ppsds
    • @@ -5072,6 +5266,7 @@

    Index

  • gui
  • has_required_channels
  • import_data
    • +
  • import_settings
  • input_params
  • make_it_classy
  • plot_hvsr
    • @@ -5079,7 +5274,6 @@

    Index

  • read_from_RS
  • remove_noise
  • run
    • -
  • save_settings
  • time_it
    • diff --git a/docs/sprit_hvsr.html b/docs/sprit_hvsr.html index cff6f14..85f70b8 100644 --- a/docs/sprit_hvsr.html +++ b/docs/sprit_hvsr.html @@ -46,11 +46,11 @@

    Module sprit.sprit_hvsr

    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 @@ -759,6 +759,21 @@

    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 @@ -959,9 +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 @@ -1347,6 +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') @@ -1413,7 +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] + 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']: @@ -1608,7 +1779,8 @@

    Module sprit.sprit_hvsr

    hvsr_data = sprit_utils.make_it_classy(hvsr_data) - hvsr_data['processing_parameters'] = {} + 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 @@ -1746,11 +1918,26 @@

    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 @@ -2070,172 +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, 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: - print(c) - 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 - #Import data def import_data(import_filepath, data_format='pickle'): """Function to import .hvsr (or other extension) data exported using export_data() function @@ -2258,6 +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', @@ -2277,10 +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], - instrument_settings=None, + processing_parameters={}, verbose=False ): """Function for designating input parameters for reading in and processing data @@ -2325,16 +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. - instrument_settings : None, str, default=None - The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most. - If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap). - The default settings can be reset using the save_settings() function with the parameter settings_path='default'. + 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 @@ -2346,30 +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()) @@ -2452,12 +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' @@ -2479,28 +2495,212 @@

    Module sprit.sprit_hvsr

    } #Replace any default parameter settings with those from json file of interest, potentially - if instrument_settings is None: - instrument_settings_dict = {} - elif instrument_settings == "default" or instrument_settings is True: - # Update inputParamDict with default file - default_settings_json = settings_dir.joinpath('instrument_settings.json') - with open(default_settings_json.as_posix(), 'r') as f: - instrument_settings_dict = json.load(f) - else: - # Update inputParamDict with file - with open(instrument_settings, 'r') as f: - instrument_settings_dict = json.load(f) + 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. @@ -2687,6 +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 @@ -2999,7 +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']: @@ -3121,6 +3365,13 @@

    Module sprit.sprit_hvsr

    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) @@ -3146,13 +3397,16 @@

    Module sprit.sprit_hvsr

    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'] = [] @@ -3217,108 +3471,31 @@

    Module sprit.sprit_hvsr

    if t > win[0] and t < win[1]: psds_to_rid.append(i) elif nextT > win[0] and nextT < win[1]: - psds_to_rid.append(i) - - #Use dataframe - hvsrDF = params['hvsr_df'].copy() - psdVals = hvsrDF['psd_values_'+k] - hvsrDF[k+'_CurveMedian'] = psdVals.apply(np.nanmedian) - hvsrDF[k+'_CurveMean'] = psdVals.apply(np.nanmean) - - 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) - - for k in params['ppsds']: - for i, r in enumerate(psds_to_rid): - index = int(r-i) - params['ppsds'][k]['psd_values'] = np.delete(params['ppsds'][k]['psd_values'], index, axis=0) - params['ppsds'][k]['current_times_used'] = np.delete(params['ppsds'][k]['current_times_used'], index, axis=0) - return params - -###WORKING ON THIS -#Save default instrument and processing settings to json file(s) -def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False): - """Save settings to json file - - Parameters - ---------- - 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. - 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' - 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 settings_path == 'default' or settings_path is True: - settingsPath = resource_dir.joinpath('settings') - else: - settings_path = pathlib.Path(settings_path) - if not settings_path.exists(): - if not settings_path.parent.exists(): - print(f'The provided value for settings_path ({settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}') - settingsPath = pathlib.Path.home() - else: - settingsPath = settings_path.parent - - if settings_path.is_dir(): - settingsPath = settings_path - elif settings_path.is_file(): - settingsPath = settings_path.parent - fnameDict['instrument'] = settings_path.name+"_instrumentSettings.json" - fnameDict['processing'] = settings_path.name+"_processingSettings.json" - - #Get final filepaths - instSetFPath = settingsPath.joinpath(fnameDict['instrument']) - procSetFPath = settingsPath.joinpath(fnameDict['processing']) + psds_to_rid.append(i) + + #Use dataframe + hvsrDF = params['hvsr_df'].copy() + psdVals = hvsrDF['psd_values_'+k] + hvsrDF[k+'_CurveMedian'] = psdVals.apply(np.nanmedian) + hvsrDF[k+'_CurveMean'] = psdVals.apply(np.nanmean) - #Get settings values - instKeys = ["instrument", "net", "sta", "loc", "cha", "depth", "metapath", "hvsr_band"] - procFuncs = [generate_ppsds, process_hvsr, check_peaks, get_report] + 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) - instrument_settings_dict = {} - processing_settings_dict = {} + psds_to_rid = np.unique(psds_to_rid) - for k in instKeys: - if isinstance(hvsr_data[k], pathlib.PurePath): - 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 inspect.getfullargspec(func)[0]: - 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 settings_type.lower()=='instrument' or settings_type.lower()=='all': - with open(instSetFPath.as_posix(), 'w') as instSetF: - json.dump(instrument_settings_dict, instSetF) - if settings_type.lower()=='processing' or settings_type.lower()=='all': - with open(procSetFPath.as_posix(), 'w') as procSetF: - json.dump(processing_settings_dict, procSetF) + for k in params['ppsds']: + for i, r in enumerate(psds_to_rid): + index = int(r-i) + params['ppsds'][k]['psd_values'] = np.delete(params['ppsds'][k]['psd_values'], index, axis=0) + params['ppsds'][k]['current_times_used'] = np.delete(params['ppsds'][k]['current_times_used'], index, axis=0) + return params #Read data as batch def batch_data_read(input_data, batch_type='table', param_col=None, batch_params=None, verbose=False, **readcsv_getMeta_fetch_kwargs): @@ -5721,7 +5898,6 @@

    Module sprit.sprit_hvsr

    if fig is None and ax is None: fig, ax = plt.subplots() - print(kwargs.keys()) if 'kwargs' in kwargs.keys(): kwargs = kwargs['kwargs'] @@ -5833,8 +6009,15 @@

    Module sprit.sprit_hvsr

    ax.axhline(hvsr_data['BestPeak']['f0'], c='k', linestyle='dotted', zorder=1000) if annotate: - xLocation = float(xmin) + (float(xmax)-float(xmin))*0.98 - ann = ax.text(x=xLocation, y=float(hvsr_data['BestPeak']['f0'])+0.3, fontsize='small', s=f"{hvsr_data['BestPeak']['f0']:0.2f} Hz", ha='right', va='bottom', + 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}) ax.set_xlabel('UTC Time \n'+day) @@ -6871,6 +7054,21 @@

    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 @@ -7091,6 +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)
    @@ -7101,7 +7432,30 @@

    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
         
@@ -7487,6 +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')
@@ -7586,7 +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]
+    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']:
@@ -7781,7 +8160,8 @@ 
    Returns
    
         
         hvsr_data = sprit_utils.make_it_classy(hvsr_data)
     
-    hvsr_data['processing_parameters'] = {}
+    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
@@ -7990,11 +8370,26 @@ 
    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
@@ -8413,8 +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='2023-10-28', 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], instrument_settings=None, 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

    @@ -8458,16 +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=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.
    -
    instrument_settings : None, str, default=None
    -
    The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most. -If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap). -The default settings can be reset using the save_settings() function with the parameter settings_path='default'.
    +
    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
    @@ -8498,10 +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], - instrument_settings=None, + processing_parameters={}, verbose=False ): """Function for designating input parameters for reading in and processing data @@ -8546,16 +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. - instrument_settings : None, str, default=None - The instrument_settings parameter is intended to enable rapid reading in of settings pertaining to the instrument you use most. - If set to "default" or True, will read in the default instrument settings (note, these are different than the default parameters of input_params(), even where names overlap). - The default settings can be reset using the save_settings() function with the parameter settings_path='default'. + 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 @@ -8567,30 +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()) @@ -8673,12 +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' @@ -8700,22 +9102,41 @@

    Returns

    } #Replace any default parameter settings with those from json file of interest, potentially - if instrument_settings is None: - instrument_settings_dict = {} - elif instrument_settings == "default" or instrument_settings is True: - # Update inputParamDict with default file - default_settings_json = settings_dir.joinpath('instrument_settings.json') - with open(default_settings_json.as_posix(), 'r') as f: - instrument_settings_dict = json.load(f) - else: - # Update inputParamDict with file - with open(instrument_settings, 'r') as f: - instrument_settings_dict = json.load(f) + 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 @@ -8933,7 +9354,6 @@

    Returns

    elif p=='spec': plottypeKwargs = {} for c in plotComponents: - print(c) plottypeKwargs[c] = True kwargs.update(plottypeKwargs) _plot_specgram_hvsr(hvsr_data, fig=fig, ax=axis, colorbar=False, **kwargs) @@ -9222,6 +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 @@ -9584,7 +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']: @@ -9706,6 +10170,13 @@

    Returns

    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) @@ -9731,13 +10202,16 @@

    Returns

    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'] = [] @@ -10100,106 +10574,6 @@

    Raises

    return hvsr_results -
    -def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False) -
    -
    -

    Save settings to json file

    -

    Parameters

    -
    -
    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.
    -
    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'
    -
    verbose : bool, default=True
    -
    Whether to print outputs and information to the terminal
    -
    -
    - -Expand source code - -
    def save_settings(hvsr_data, settings_path='default', settings_type='all', verbose=False):
-    """Save settings to json file
-
-    Parameters
-    ----------
-    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.
-    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'
-    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 settings_path == 'default' or settings_path is True:
-        settingsPath = resource_dir.joinpath('settings')
-    else:
-        settings_path = pathlib.Path(settings_path)
-        if not settings_path.exists():
-            if not settings_path.parent.exists():
-                print(f'The provided value for settings_path ({settings_path}) does not exist. Saving settings to the home directory: {pathlib.Path.home()}')
-                settingsPath = pathlib.Path.home()
-            else:
-                settingsPath = settings_path.parent
-        
-        if settings_path.is_dir():
-            settingsPath = settings_path
-        elif settings_path.is_file():
-            settingsPath = settings_path.parent
-            fnameDict['instrument'] = settings_path.name+"_instrumentSettings.json"
-            fnameDict['processing'] = 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"]
-    procFuncs = [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):
-            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 inspect.getfullargspec(func)[0]:
-            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 settings_type.lower()=='instrument' or settings_type.lower()=='all':
-        with open(instSetFPath.as_posix(), 'w') as instSetF:
-            json.dump(instrument_settings_dict, instSetF)
-    if settings_type.lower()=='processing' or settings_type.lower()=='all':
-        with open(procSetFPath.as_posix(), 'w') as procSetF:
-            json.dump(processing_settings_dict, procSetF)
    -
    -
    def test_function()
    @@ -11130,12 +11504,14 @@

    Index

  • check_instance
  • check_peaks
  • export_data
    • +
  • export_settings
  • fetch_data
  • generate_ppsds
  • get_metadata
  • get_report
  • gui
  • import_data
    • +
  • import_settings
  • input_params
  • plot_hvsr
  • plot_stream
    • @@ -11143,7 +11519,6 @@

    Index

  • remove_noise
  • remove_outlier_curves
  • run
    • -
  • save_settings
  • test_function
    • diff --git a/pyproject.toml b/pyproject.toml index 8fb79f3..7d62bff 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ name = "sprit" authors = [{name="Riley Balikian"}, {name="Hongyu Xaio"}] dynamic = ["readme"] license = {file = "LICENSE"} -version="0.1.51" +version="0.1.50" description = "A package for processing and analyzing HVSR (Horizontal to Vertical Spectral Ratio) data" keywords = ["HVSR", "seismic", "horizontal to vertical spectral ratio", "obspy", 'geology', 'geophysics', 'geotechnical'] requires-python = ">=3.9" diff --git a/setup.py b/setup.py index a2fe0ed..3b9f9de 100644 --- a/setup.py +++ b/setup.py @@ -7,7 +7,7 @@ name="sprit", author= "Riley Balikian", author_email = "balikian@illinois.edu", - version="0.1.51", + version="0.1.50", package_data={'sprit': ['resources/*', 'resources/icon/*', 'resources/themes/*', 'resources/themes/forest-dark/*', 'resources/themes/forest-light/*', 'resources/sample_data/*','resources/settings/*']}, 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