From 2cd4fac68fda3ece430e5884b0e95c2afec1b424 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 4 Dec 2021 22:01:47 +0100 Subject: [PATCH 01/24] small fixes --- pyEvalData/evaluation.py | 15 ++++++++++----- pyEvalData/io/source.py | 2 +- 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index da22dc5..19f6af3 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -2,7 +2,7 @@ # -*- coding: utf-8 -*- # The MIT License (MIT) -# Copyright (c) 2015-2020 Daniel Schick +# Copyright (c) 2015-2021 Daniel Schick # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal @@ -58,9 +58,13 @@ class Evaluation(object): t0 (float): approx. time zero for delay scans to determine the unpumped region of the data for normalization. custom_counters (list[str]): list of custom counters - default is [] - math_keys (list[str]): list of keywords which are evaluated as numpy functions - statistic_type (str): 'gauss' for normal averaging, 'poisson' for counting statistics - propagate_errors (bool): propagate errors for dpendent counters. + math_keys (list[str]): list of keywords which are evaluated as numpy + functions. + ignore_keys (list[str]): list of keywords which should not be + evaluated. + statistic_type (str): 'gauss' for normal averaging, 'poisson' for + counting statistics. + propagate_errors (bool): propagate errors for dependent counters. """ @@ -76,6 +80,7 @@ def __init__(self, source): self.math_keys = ['mean', 'sum', 'diff', 'max', 'min', 'round', 'abs', 'sin', 'cos', 'tan', 'arcsin', 'arccos', 'arctan', 'pi', 'exp', 'log', 'log10', 'sqrt'] + self.ignore_keys = [] self.statistic_type = 'gauss' self.propagate_errors = True self.apply_data_filter = False @@ -85,7 +90,7 @@ def get_clist(self): """get_clist Returns a list of counters as defined by the user. - If the counters where defined in a ``dict`` it will be converted + If the counters were defined in a ``dict`` it will be converted to a ``list`` for backwards compatibility. Returns: diff --git a/pyEvalData/io/source.py b/pyEvalData/io/source.py index fec7933..3478cb2 100644 --- a/pyEvalData/io/source.py +++ b/pyEvalData/io/source.py @@ -65,7 +65,7 @@ class Source(object): Attributes: log (logging.logger): logger instance from logging. name (str): name of the source - scan_dict (dict(scan)): dict of scan objects with + scan_dict (dict{uint:Scan}): dict of scan objects with key being the scan number. start_scan_number (uint): start of scan numbers to parse. stop_scan_number (uint): stop of scan numbers to parse. From 793dd33c685c975f62e5e86972d5db60a98975f2 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 4 Dec 2021 22:13:50 +0100 Subject: [PATCH 02/24] remove get_clist and use getter/setter instead --- pyEvalData/evaluation.py | 80 +++++++++++++++++++--------------------- 1 file changed, 38 insertions(+), 42 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index da22dc5..2a1d61a 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -81,26 +81,6 @@ def __init__(self, source): self.apply_data_filter = False self.data_filters = ['evaluatable statement'] - def get_clist(self): - """get_clist - - Returns a list of counters as defined by the user. - If the counters where defined in a ``dict`` it will be converted - to a ``list`` for backwards compatibility. - - Returns: - clist (list[str]): list of counter names to evaluate. - - """ - - if isinstance(self.clist, dict): - # the clist property is a dict, so retrun its keys as list - clist = list(self.clist.keys()) - else: - clist = list(self.clist) - - return clist - def traverse_counters(self, clist, source_cols=''): """traverse_counters @@ -317,16 +297,15 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): name = self.source.name + " #{0:04d}".format(scan_list[0]) # get the counters which should be evaluated - clist = self.get_clist() - if not clist: + if not self.clist: raise Exception('No clist is defined. Do not know what to plot!') return # process also the xcol as counter in order to allow for newly defined xcols if not self.xcol: raise Exception('No xcol is defined. Do not know what to plot!') return - if self.xcol not in clist: - clist.append(self.xcol) + if self.xcol not in self.clist: + self.clist.append(self.xcol) source_cols = [] concat_data = np.array([]) @@ -351,7 +330,7 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): # resolve the clist and retrieve the resolves counters and the # necessary base spec counters for error propagation resolved_counters, source_counters = self.traverse_counters( - clist, source_cols) + self.clist, source_cols) # counter names and resolved strings for further calculations if self.statistic_type == 'poisson' or self.propagate_errors: @@ -361,15 +340,15 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): col_strings = source_counters[:] # add the xcol to both lists col_names.append(self.xcol) - col_strings.append(resolved_counters[clist.index(self.xcol)]) + col_strings.append(resolved_counters[self.clist.index(self.xcol)]) else: # we need to average the resolved counters - col_names = clist[:] + col_names = self.clist[:] col_strings = resolved_counters[:] # create the dtype of the return array dtypes = [] - for col_name in clist: + for col_name in self.clist: dtypes.append((col_name, ' 1: + if len(self.clist) > 1: # for multiple counters add the counter name to the label lt = label_text + ' | ' + col else: @@ -662,12 +640,10 @@ def plot_mesh_scan(self, scan_num, skip_plot=False, grid_on=False, ytext='', xte xx = np.sort(np.unique(X)) yy = np.sort(np.unique(Y)) - clist = self.get_clist() - - if len(clist) > 1: + if len(self.clist) > 1: print('WARNING: Only the first counter of the clist is plotted.') - Z = spec_data[clist[0]] + Z = spec_data[self.clist[0]] zz = griddata(X, Y, Z, xx, yy, interp='linear') @@ -1005,7 +981,7 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_si # initialization of returns res = {} # initialize the results dict - for i, counter in enumerate(self.get_clist()): + for i, counter in enumerate(self.clist): # traverse all counters in the counter list to initialize the returns # results for this counter is again a Dict @@ -1063,7 +1039,7 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_si skip_plot=True) # this is the number of different counters - num_sub_plots = len(self.get_clist()) + num_sub_plots = len(self.clist) # fitting and plotting the data l_plot = 1 # counter for single plots @@ -1333,3 +1309,23 @@ def get_next_fig_number(self): """ return self.get_last_fig_number() + 1 + + @property + def clist(self): + return self._clist + + @clist.setter + def clist(self, clist): + """clist + + In order to keep backwards compatibility and to catch some wrong user + inputs, the given ``clist`` is converted to a ``list`` when a ``dict`` + or number is given. + + """ + if isinstance(clist, dict): + # the clist property is a dict, so retrun its keys as list + clist = list(clist.keys()) + else: + clist = list(clist) + self._clist = clist From e3913c18c6aec700be5e5943eaf57e7e24c54211 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 4 Dec 2021 22:30:54 +0100 Subject: [PATCH 03/24] simplify traverse_counters() --- pyEvalData/evaluation.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 2a1d61a..3c3f95a 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -81,14 +81,13 @@ def __init__(self, source): self.apply_data_filter = False self.data_filters = ['evaluatable statement'] - def traverse_counters(self, clist, source_cols=''): + def traverse_counters(self, source_cols=''): """traverse_counters Traverse all counters and replace all predefined counter definitions. Returns also a list of the included source counters for error propagation. Args: - clist (list[str]): Initial counter list. source_cols (list[str], optional): counters in the raw source data. Returns: @@ -100,7 +99,7 @@ def traverse_counters(self, clist, source_cols=''): resolved_counters = [] source_counters = [] - for counter_name in clist: + for counter_name in self.clist: # resolve each counter in the clist counter_string, res_source_counters = \ self.resolve_counter_name(counter_name, source_cols) @@ -329,8 +328,7 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): # resolve the clist and retrieve the resolves counters and the # necessary base spec counters for error propagation - resolved_counters, source_counters = self.traverse_counters( - self.clist, source_cols) + resolved_counters, source_counters = self.traverse_counters(source_cols) # counter names and resolved strings for further calculations if self.statistic_type == 'poisson' or self.propagate_errors: From c80891c4b9d709552766ffb4ae90b23261e722f9 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 4 Dec 2021 22:38:05 +0100 Subject: [PATCH 04/24] rename spec_data to source_data --- pyEvalData/evaluation.py | 38 +++++++++++++++----------------------- 1 file changed, 15 insertions(+), 23 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 3c3f95a..ffc8428 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -156,7 +156,7 @@ def resolve_counter_name(self, col_name, source_cols=''): return col_string, source_counters - def col_string_to_eval_string(self, col_string, array_name='spec_data'): + def col_string_to_eval_string(self, col_string, array_name='source_data'): """Use regular expressions in order to generate an evaluateable string from the counter string in order to append the new counter to the spec data. @@ -198,21 +198,21 @@ def col_string_to_eval_string(self, col_string, array_name='spec_data'): (col_string, _) = re.subn(r'\b' + mk + r'\b', 'np.' + mk, col_string) return col_string - def add_custom_counters(self, spec_data, scan_num, source_counters): + def add_custom_counters(self, source_data, scan_num, source_counters): """Add custom counters to the spec data array. This is a stub for child classes. Args: - spec_data (ndarray) : Data array from the spec scan. + source_data (ndarray) : Data array from the spec scan. scan_num (int) : Scan number of the spec scan. source_counters list(str) : List of the source counters and custom counters from the clist and xcol. Returns: - spec_data (ndarray): Updated data array from the spec scan. + source_data (ndarray): Updated data array from the spec scan. """ - return spec_data + return source_data def filter_data(self, data): """filter_data @@ -311,20 +311,12 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): data_list = self.get_scan_list_data(scan_list) - for i, (spec_data, scan_num) in enumerate(zip(data_list, scan_list)): - # traverse the scan list and read data - # try: - # # try to read the motors and data of this scan - # spec_data = self.get_scan_data(scan_num) - # except Exception: - # raise - # print('Scan #' + scan_num + ' not found, skipping') - + for i, (source_data, scan_num) in enumerate(zip(data_list, scan_list)): if i == 0 or len(source_cols) == 0: # we need to evaluate this only once # these are the base spec counters which are present in the data # file plus custom counters source_cols = list( - set(list(spec_data.dtype.names) + self.custom_counters)) + set(list(source_data.dtype.names) + self.custom_counters)) # resolve the clist and retrieve the resolves counters and the # necessary base spec counters for error propagation @@ -350,7 +342,7 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): dtypes.append((col_name, ' 1: print('WARNING: Only the first counter of the clist is plotted.') - Z = spec_data[self.clist[0]] + Z = source_data[self.clist[0]] zz = griddata(X, Y, Z, xx, yy, interp='linear') From 1fe89c3d3427932be96fffa1d044e32421d60dc7 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 4 Dec 2021 22:43:12 +0100 Subject: [PATCH 05/24] replace spec with source --- pyEvalData/evaluation.py | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index ffc8428..cb18c6f 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -159,7 +159,7 @@ def resolve_counter_name(self, col_name, source_cols=''): def col_string_to_eval_string(self, col_string, array_name='source_data'): """Use regular expressions in order to generate an evaluateable string from the counter string in order to append the new counter to the - spec data. + source data. Args: col_string (str) : Definition of the counter. @@ -167,7 +167,7 @@ def col_string_to_eval_string(self, col_string, array_name='source_data'): Returns: eval_string (str): Evaluateable string to add the new counter - to the spec data. + to the source data. """ @@ -199,17 +199,17 @@ def col_string_to_eval_string(self, col_string, array_name='source_data'): return col_string def add_custom_counters(self, source_data, scan_num, source_counters): - """Add custom counters to the spec data array. + """Add custom counters to the source data array. This is a stub for child classes. Args: - source_data (ndarray) : Data array from the spec scan. - scan_num (int) : Scan number of the spec scan. + source_data (ndarray) : Data array from the source scan. + scan_num (int) : Scan number of the source scan. source_counters list(str) : List of the source counters and custom counters from the clist and xcol. Returns: - source_data (ndarray): Updated data array from the spec scan. + source_data (ndarray): Updated data array from the source scan. """ return source_data @@ -292,7 +292,7 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): """ - # generate the name of the data set from the spec file name and scan_list + # generate the name of the data set from the source file name and scan_list name = self.source.name + " #{0:04d}".format(scan_list[0]) # get the counters which should be evaluated @@ -313,18 +313,18 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): for i, (source_data, scan_num) in enumerate(zip(data_list, scan_list)): if i == 0 or len(source_cols) == 0: # we need to evaluate this only once - # these are the base spec counters which are present in the data + # these are the base source counters which are present in the data # file plus custom counters source_cols = list( set(list(source_data.dtype.names) + self.custom_counters)) # resolve the clist and retrieve the resolves counters and the - # necessary base spec counters for error propagation + # necessary base source counters for error propagation resolved_counters, source_counters = self.traverse_counters(source_cols) # counter names and resolved strings for further calculations if self.statistic_type == 'poisson' or self.propagate_errors: - # for error propagation we just need the base spec counters + # for error propagation we just need the base source counters # and the xcol col_names = source_counters[:] col_strings = source_counters[:] @@ -377,8 +377,8 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): try: # bin the concatenated data to the xgrid # if a custom counter was calculated it might have a different length - # than the spec counters which will result in an error while binning data - # from a default spec counter and a custom counter. + # than the source counters which will result in an error while binning data + # from a default source counter and a custom counter. if binning: xgrid_reduced, _, _, _, _, _, _, _, _ = bin_data( concat_data[self.xcol], concat_data[self.xcol], xgrid) @@ -410,7 +410,7 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): concat_data[self.xcol], xgrid_reduced, statistic=bin_stat) - # add spec base counters to uncData arrays + # add source base counters to uncData arrays # the uncertainty package cannot handle masked arrays # e.g. for divisions in the clist # --> convert all base counter results to np.array() @@ -457,7 +457,7 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): except Exception: raise print('xcol and ycol must have the same length --> probably you try plotting a custom' - ' counter together with a spec counter') + ' counter together with a source counter') return avg_data, std_data, err_data, name @@ -465,7 +465,7 @@ def plot_scans(self, scan_list, ylims=[], xlims=[], fig_size=[], xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, label_text='', title_text='', skip_plot=False, grid_on=True, ytext='', xtext='', fmt='-o'): - """Plot a list of scans from the spec file. + """Plot a list of scans from the source file. Various plot parameters are provided. The plotted data are returned. @@ -589,12 +589,12 @@ def plot_scans(self, scan_list, ylims=[], xlims=[], fig_size=[], xgrid=[], def plot_mesh_scan(self, scan_num, skip_plot=False, grid_on=False, ytext='', xtext='', levels=20, cbar=True): - """Plot a single mesh scan from the spec file. + """Plot a single mesh scan from the source file. Various plot parameters are provided. The plotted data are returned. Args: - scan_num (int) : Scan number of the spec scan. + scan_num (int) : Scan number of the source scan. skip_plot (Optional[bool]) : Skip plotting, just return data default is False. grid_on (Optional[bool]) : Add grid to plot - default is False. @@ -611,7 +611,7 @@ def plot_mesh_scan(self, scan_num, skip_plot=False, grid_on=False, ytext='', xte from matplotlib.mlab import griddata from matplotlib import gridspec - # read data from spec file + # read data from source file try: # try to read data of this scan source_data = self.get_scan_data(scan_num) @@ -703,7 +703,7 @@ def plot_scan_sequence(self, scan_sequence, ylims=[], xlims=[], fig_size=[], binning=True, sequence_type='', label_text='', title_text='', skip_plot=False, grid_on=True, ytext='', xtext='', fmt='-o'): - """Plot a list of scans from the spec file. + """Plot a list of scans from the source file. Various plot parameters are provided. The plotted data are returned. @@ -834,7 +834,7 @@ def plot_scan_sequence(self, scan_sequence, ylims=[], xlims=[], fig_size=[], def export_scan_sequence(self, scan_sequence, path, fileName, yerr='std', xerr='std', xgrid=[], norm2one=False, binning=True): - """Exports spec data for each scan list in the sequence as individual file. + """Exports source data for each scan list in the sequence as individual file. Args: scan_sequence (List[ From 9f363f240854d1185a62dee1442f192dce763199 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 4 Dec 2021 22:46:50 +0100 Subject: [PATCH 06/24] add ignore_keys --- pyEvalData/evaluation.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index cb18c6f..c770ba1 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -194,7 +194,7 @@ def col_string_to_eval_string(self, col_string, array_name='source_data'): # add 'np.' prefix to numpy functions/math keys for mk in math_keys: - if mk != '0x0001FFFF': + if mk not in self.ignore_keys: (col_string, _) = re.subn(r'\b' + mk + r'\b', 'np.' + mk, col_string) return col_string @@ -461,6 +461,8 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): return avg_data, std_data, err_data, name + def + def plot_scans(self, scan_list, ylims=[], xlims=[], fig_size=[], xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, label_text='', title_text='', skip_plot=False, grid_on=True, From bd7a1bc50279f603595f5a2d491148fd2623a278 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 4 Dec 2021 23:04:51 +0100 Subject: [PATCH 07/24] change mx line width from 99 to 100 --- .flake8 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.flake8 b/.flake8 index d497808..95ff2a6 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,4 @@ [flake8] -max-line-length=99 +max-line-length=100 ignore = E121, E123, E126, E133, E226, E241, E242, E402, E704, W503, W504, W505 and W605 exclude = docs,build,dist \ No newline at end of file From 5ad1d3e393bccaf8436d3de5c1525c738d23ba7e Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 4 Dec 2021 23:05:04 +0100 Subject: [PATCH 08/24] add eval_scans --- pyEvalData/evaluation.py | 126 +++++++++++++++++++-------------------- 1 file changed, 60 insertions(+), 66 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index c770ba1..1683c49 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -461,48 +461,17 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): return avg_data, std_data, err_data, name - def - - def plot_scans(self, scan_list, ylims=[], xlims=[], fig_size=[], xgrid=[], - yerr='std', xerr='std', norm2one=False, binning=True, - label_text='', title_text='', skip_plot=False, grid_on=True, - ytext='', xtext='', fmt='-o'): - """Plot a list of scans from the source file. - Various plot parameters are provided. - The plotted data are returned. + def eval_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True): + """eval_scans [summary] Args: - scan_list (List[int]) : List of scan numbers. - ylims (Optional[ndarray]) : ylim for the plot. - xlims (Optional[ndarray]) : xlim for the plot. - fig_size (Optional[ndarray]) : Figure size of the figure. - xgrid (Optional[ndarray]) : Grid to bin the data to - - default in empty so use the - x-axis of the first scan. - yerr (Optional[ndarray]) : Type of the errors in y: [err, std, none] - default is 'std'. - xerr (Optional[ndarray]) : Type of the errors in x: [err, std, none] - default is 'std'. - norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 - default is False. - label_text (Optional[str]) : Label of the plot - default is none. - title_text (Optional[str]) : Title of the figure - default is none. - skip_plot (Optional[bool]) : Skip plotting, just return data - default is False. - grid_on (Optional[bool]) : Add grid to plot - default is True. - ytext (Optional[str]) : y-Label of the plot - defaults is none. - xtext (Optional[str]) : x-Label of the plot - defaults is none. - fmt (Optional[str]) : format string of the plot - defaults is -o. - - Returns: - y2plot (OrderedDict) : y-data which was plotted. - x2plot (ndarray) : x-data which was plotted. - yerr2plot (OrderedDict) : y-error which was plotted. - xerr2plot (ndarray) : x-error which was plotted. - name (str) : Name of the data set. - + scan_list ([type]): [description] + xgrid (list, optional): [description]. Defaults to []. + yerr (str, optional): [description]. Defaults to 'std'. + xerr (str, optional): [description]. Defaults to 'std'. + norm2one (bool, optional): [description]. Defaults to False. + binning (bool, optional): [description]. Defaults to True. """ - # initialize the y-data as ordered dict in order to allow for multiple # counters at the same time y2plot = collections.OrderedDict() @@ -546,18 +515,59 @@ def plot_scans(self, scan_list, ylims=[], xlims=[], fig_size=[], xgrid=[], y2plot[col] = y2plot[col]/np.mean(before_zero) yerr2plot[col] = yerr2plot[col]/np.mean(before_zero) - if len(label_text) == 0: - # if no label_text is given use the counter name - lt = col - else: - if len(self.clist) > 1: - # for multiple counters add the counter name to the label - lt = label_text + ' | ' + col + return y2plot, x2plot, yerr2plot, xerr2plot, name + + def plot_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, + label_text='', fmt='-o', skip_plot=False): + """plot_scans + + Plot a list of scans from the source file. + Various plot parameters are provided. + The plotted data are returned. + + Args: + scan_list (List[int]): List of scan numbers. + xgrid (Optional[ndarray]): Grid to bin the data to - + default in empty so use the x-axis of the first scan. + yerr (Optional[ndarray]): Type of the errors in y: [err, std, none] + default is 'std'. + xerr (Optional[ndarray]): Type of the errors in x: [err, std, none] + default is 'std'. + norm2one (Optional[bool]): Norm transient data to 1 for t < t0 + default is False. + label_text (Optional[str]): Label of the plot - default is none. + fmt (Optional[str]): format string of the plot - defaults is -o. + skip_plot (Optional[bool]): Skip plotting, just return data default + is False. + + Returns: + y2plot (OrderedDict): y-data which was plotted. + x2plot (ndarray): x-data which was plotted. + yerr2plot (OrderedDict): y-error which was plotted. + xerr2plot (ndarray): x-error which was plotted. + name (str): Name of the data set. + + """ + + y2plot, x2plot, yerr2plot, xerr2plot, name = \ + self.eval_scans(scan_list, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, + binning=binning) + + if not skip_plot: + # plot all keys in the clist + for col in self.clist: + # traverse the counter list + if len(label_text) == 0: + # if no label_text is given use the counter name + lt = col else: - # for a single counter just use the label_text - lt = label_text + if len(self.clist) > 1: + # for multiple counters add the counter name to the label + lt = label_text + ' | ' + col + else: + # for a single counter just use the label_text + lt = label_text - if not skip_plot: # plot the errorbar for each counter if (xerr == 'none') & (yerr == 'none'): plt.plot(x2plot, y2plot[col], fmt, label=lt) @@ -566,26 +576,10 @@ def plot_scans(self, scan_list, ylims=[], xlims=[], fig_size=[], xgrid=[], x2plot, y2plot[col], fmt=fmt, label=lt, xerr=xerr2plot, yerr=yerr2plot[col]) - if not skip_plot: # add a legend, labels, title and set the limits and grid plt.legend(frameon=True, loc=0, numpoints=1) plt.xlabel(self.xcol) - if xlims: - plt.xlim(xlims) - if ylims: - plt.ylim(ylims) - if len(title_text) > 0: - plt.title(title_text) - else: - plt.title(name) - if len(xtext) > 0: - plt.xlabel(xtext) - - if len(ytext) > 0: - plt.ylabel(ytext) - - if grid_on: - plt.grid(True) + plt.title(name) return y2plot, x2plot, yerr2plot, xerr2plot, name From 955ac369c4379b4da16c9c1fef431bbe33800282 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 4 Dec 2021 23:12:09 +0100 Subject: [PATCH 09/24] remove uneccessary plotting arguments --- pyEvalData/evaluation.py | 17 +---------------- 1 file changed, 1 insertion(+), 16 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 7d06120..e5b7dbb 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -790,21 +790,14 @@ def plot_scan_sequence(self, scan_sequence, ylims=[], xlims=[], fig_size=[], # get the plot data for the scan list y2plot, x2plot, yerr2plot, xerr2plot, name = self.plot_scans( scan_list, - ylims=ylims, - xlims=xlims, - fig_size=fig_size, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, binning=binning, label_text=lt, - title_text=title_text, + fmt=fmt, skip_plot=skip_plot, - grid_on=grid_on, - ytext=ytext, - xtext=xtext, - fmt=fmt ) if self.xcol not in sequence_data.keys(): @@ -1000,9 +993,6 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_si # get only the parameters _, parameters, names, label_texts = self.plot_scan_sequence( scan_sequence, - ylims=ylims, - xlims=xlims, - fig_size=fig_size, xgrid=xgrid, yerr=yerr, xerr=xerr, @@ -1010,15 +1000,11 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_si binning=True, sequence_type=sequence_type, label_text=label_text, - title_text=title_text, skip_plot=True) else: # get the sequence data and parameters sequence_data, parameters, names, label_texts = self.plot_scan_sequence( scan_sequence, - ylims=ylims, - xlims=xlims, - fig_size=fig_size, xgrid=xgrid, yerr=yerr, xerr=xerr, @@ -1026,7 +1012,6 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_si binning=True, sequence_type=sequence_type, label_text=label_text, - title_text=title_text, skip_plot=True) # this is the number of different counters From 1d8fe0694e9278e1837dd70de300fa4880e1c73e Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Mon, 6 Dec 2021 14:27:14 +0100 Subject: [PATCH 10/24] wip - restructure --- pyEvalData/evaluation.py | 526 ++++++++++++++++++++------------------- 1 file changed, 271 insertions(+), 255 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index e5b7dbb..3291233 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -492,14 +492,14 @@ def eval_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False elif xerr == 'err': xerr_data = err_data else: - xerr_data = np.zeros_like(std_data) + xerr_data = None if yerr == 'std': yerr_data = std_data elif yerr == 'err': yerr_data = err_data else: - yerr_data = np.zeros_like(std_data) + yerr_data = None # set x-data and errors x2plot = avg_data[self.xcol] @@ -515,15 +515,100 @@ def eval_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False if norm2one: # normalize the y-data to 1 for t < t0 - # just makes sense for delay scans + # e.g. for delay scans before_zero = y2plot[col][x2plot <= self.t0] y2plot[col] = y2plot[col]/np.mean(before_zero) yerr2plot[col] = yerr2plot[col]/np.mean(before_zero) return y2plot, x2plot, yerr2plot, xerr2plot, name - def plot_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, - label_text='', fmt='-o', skip_plot=False): + def eval_scan_sequence(self, scan_sequence, xgrid=[], yerr='std', xerr='std', norm2one=False, + binning=True): + """eval_scan_sequence + + Evaluate a sequence of scans for a given set of external parameters. + + Args: + scan_sequence (List[ + List/Tuple[List[int], + int/str]]) : Sequence of scan lists and parameters. + xgrid (Optional[ndarray]) : Grid to bin the data to - + default in empty so use the + x-axis of the first scan. + yerr (Optional[ndarray]) : Type of the errors in y: [err, std, none] + default is 'std'. + xerr (Optional[ndarray]) : Type of the errors in x: [err, std, none] + default is 'std'. + norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 + default is False. + Returns: + sequence_data (OrderedDict) : Dictionary of the averaged scan data. + parameters (List[str, float]) : Parameters of the sequence. + names (List[str]) : List of names of each data set. + + """ + + # initialize the return data + sequence_data = collections.OrderedDict() + names = [] + parameters = [] + + for i, (scan_list, parameter) in enumerate(scan_sequence): + # traverse the scan sequence + + parameters.append(parameter) + # get the data for the current scan list + y2plot, x2plot, yerr2plot, xerr2plot, name = self.eval_scans( + scan_list, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, + binning=binning, + ) + # create a list of all counters from the scan and append the xcol + sequence_counters = list(y2plot.keys()).append(self.xcol) + for counter in sequence_counters: + # traverse all counters in the data set + if counter not in sequence_data.keys(): + # if the counter is not in the return data dict - add the key + sequence_data[counter] = [] + sequence_data[counter + 'Err'] = [] + + # add the counter data to the return data dict + sequence_data[counter].append(y2plot[counter]) + sequence_data[counter + 'Err'].append(yerr2plot[counter]) + + names.append(name) + + return sequence_data, parameters, names + + def _plot_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, label_text='', fmt='-o'): + # plot all keys in the clist + for col in self.clist: + # iterate the counter list + if len(label_text) == 0: + # if no label_text is given use the counter name + lt = col + else: + if len(self.clist) > 1: + # for multiple counters add the counter name to the label + lt = label_text + ' | ' + col + else: + # for a single counter just use the label_text + lt = label_text + + # plot the errorbar for each counter + if (xerr2plot is None) & (yerr2plot is None): + plt.plot(x2plot, y2plot[col], fmt, label=lt) + else: + plt.errorbar( + x2plot, y2plot[col], fmt=fmt, label=lt, + xerr=xerr2plot, yerr=yerr2plot[col]) + + # add a legend, labels, title and set the limits and grid + plt.legend(frameon=True, loc=0, numpoints=1) + plt.xlabel(self.xcol) + plt.title(name) + + def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm2one=False, + binning=True, label_text='', fmt='-o'): """plot_scans Plot a list of scans from the source file. @@ -542,8 +627,6 @@ def plot_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False default is False. label_text (Optional[str]): Label of the plot - default is none. fmt (Optional[str]): format string of the plot - defaults is -o. - skip_plot (Optional[bool]): Skip plotting, just return data default - is False. Returns: y2plot (OrderedDict): y-data which was plotted. @@ -558,152 +641,13 @@ def plot_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False self.eval_scans(scan_list, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, binning=binning) - if not skip_plot: - # plot all keys in the clist - for col in self.clist: - # traverse the counter list - if len(label_text) == 0: - # if no label_text is given use the counter name - lt = col - else: - if len(self.clist) > 1: - # for multiple counters add the counter name to the label - lt = label_text + ' | ' + col - else: - # for a single counter just use the label_text - lt = label_text - - # plot the errorbar for each counter - if (xerr == 'none') & (yerr == 'none'): - plt.plot(x2plot, y2plot[col], fmt, label=lt) - else: - plt.errorbar( - x2plot, y2plot[col], fmt=fmt, label=lt, - xerr=xerr2plot, yerr=yerr2plot[col]) - - # add a legend, labels, title and set the limits and grid - plt.legend(frameon=True, loc=0, numpoints=1) - plt.xlabel(self.xcol) - plt.title(name) + self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, fmt=fmt) return y2plot, x2plot, yerr2plot, xerr2plot, name - def plot_mesh_scan(self, scan_num, skip_plot=False, grid_on=False, ytext='', xtext='', - levels=20, cbar=True): - """Plot a single mesh scan from the source file. - Various plot parameters are provided. - The plotted data are returned. - - Args: - scan_num (int) : Scan number of the source scan. - skip_plot (Optional[bool]) : Skip plotting, just return data - default is False. - grid_on (Optional[bool]) : Add grid to plot - default is False. - ytext (Optional[str]) : y-Label of the plot - defaults is none. - xtext (Optional[str]) : x-Label of the plot - defaults is none. - levels (Optional[int]) : levels of contour plot - defaults is 20. - cbar (Optional[bool]) : Add colorbar to plot - default is True. - - Returns: - xx, yy, zz : x,y,z data which was plotted - - """ - - from matplotlib.mlab import griddata - from matplotlib import gridspec - - # read data from source file - try: - # try to read data of this scan - source_data = self.get_scan_data(scan_num) - except Exception: - print('Scan #' + int(scan_num) + ' not found, skipping') - - dt = source_data.dtype - dt = dt.descr - - xmotor = dt[0][0] - ymotor = dt[1][0] - - X = source_data[xmotor] - Y = source_data[ymotor] - - xx = np.sort(np.unique(X)) - yy = np.sort(np.unique(Y)) - - if len(self.clist) > 1: - print('WARNING: Only the first counter of the clist is plotted.') - - Z = source_data[self.clist[0]] - - zz = griddata(X, Y, Z, xx, yy, interp='linear') - - if not skip_plot: - - if cbar: - gs = gridspec.GridSpec(4, 2, - width_ratios=[3, 1], - height_ratios=[0.2, 0.1, 1, 3] - ) - k = 4 - else: - gs = gridspec.GridSpec(2, 2, - width_ratios=[3, 1], - height_ratios=[1, 3] - ) - k = 0 - - ax1 = plt.subplot(gs[0+k]) - - plt.plot(xx, np.mean(zz, 0), label='mean') - - plt.plot(xx, zz[np.argmax(np.mean(zz, 1)), :], label='peak') - - plt.xlim([min(xx), max(xx)]) - plt.legend(loc=0) - ax1.xaxis.tick_top() - if grid_on: - plt.grid(True) - - plt.subplot(gs[2+k]) - - plt.contourf(xx, yy, zz, levels, cmap='viridis') - - plt.xlabel(xmotor) - plt.ylabel(ymotor) - - if len(xtext) > 0: - plt.xlabel(xtext) - - if len(ytext) > 0: - plt.ylabel(ytext) - - if grid_on: - plt.grid(True) - - if cbar: - cb = plt.colorbar(cax=plt.subplot( - gs[0]), orientation='horizontal') - cb.ax.xaxis.set_ticks_position('top') - cb.ax.xaxis.set_label_position('top') - - ax4 = plt.subplot(gs[3+k]) - - plt.plot(np.mean(zz, 1), yy) - plt.plot(zz[:, np.argmax(np.mean(zz, 0))], yy) - plt.ylim([np.min(yy), np.max(yy)]) - - ax4.yaxis.tick_right() - if grid_on: - plt.grid(True) - - return xx, yy, zz - - def plot_scan_sequence(self, scan_sequence, ylims=[], xlims=[], fig_size=[], - xgrid=[], yerr='std', xerr='std', norm2one=False, - binning=True, sequence_type='', label_text='', - title_text='', skip_plot=False, grid_on=True, ytext='', - xtext='', fmt='-o'): + def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr='std', + norm2one=False, binning=True, sequence_type='', label_texts=[], + fmt='-o'): """Plot a list of scans from the source file. Various plot parameters are provided. The plotted data are returned. @@ -712,9 +656,6 @@ def plot_scan_sequence(self, scan_sequence, ylims=[], xlims=[], fig_size=[], scan_sequence (List[ List/Tuple[List[int], int/str]]) : Sequence of scan lists and parameters. - ylims (Optional[ndarray]) : ylim for the plot. - xlims (Optional[ndarray]) : xlim for the plot. - fig_size (Optional[ndarray]) : Figure size of the figure. xgrid (Optional[ndarray]) : Grid to bin the data to - default in empty so use the x-axis of the first scan. @@ -727,13 +668,7 @@ def plot_scan_sequence(self, scan_sequence, ylims=[], xlims=[], fig_size=[], sequence_type (Optional[str]): Type of the sequence: [fluence, delay, energy, theta, position, voltage, none, text] - default is enumeration. - label_text (Optional[str]) : Label of the plot - default is none. - title_text (Optional[str]) : Title of the figure - default is none. - skip_plot (Optional[bool]) : Skip plotting, just return data - default is False. - grid_on (Optional[bool]) : Add grid to plot - default is True. - ytext (Optional[str]) : y-Label of the plot - defaults is none. - xtext (Optional[str]) : x-Label of the plot - defaults is none. + label_texts (Optional[str]) : lift of labels of the plot - default is none. fmt (Optional[str]) : format string of the plot - defaults is -o. Returns: @@ -744,19 +679,16 @@ def plot_scan_sequence(self, scan_sequence, ylims=[], xlims=[], fig_size=[], """ - # initialize the return data - sequence_data = collections.OrderedDict() - names = [] - label_texts = [] - parameters = [] + sequence_data, parameters, names = self.eval_scan_sequence( + scan_sequence, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, binning=binning) + ret_label_texts = [] for i, (scan_list, parameter) in enumerate(scan_sequence): - # traverse the scan sequence + # iterate the scan sequence - parameters.append(parameter) # format the parameter as label text of this plot if no label text # is given - if len(label_text) == 0: + if len(label_texts) == 0: if sequence_type == 'fluence': lt = str.format('{:.2f} mJ/cm²', parameter) elif sequence_type == 'delay': @@ -785,95 +717,19 @@ def plot_scan_sequence(self, scan_sequence, ylims=[], xlims=[], fig_size=[], # no sequence type is given --> enumerate lt = str.format('#{}', i+1) else: - lt = label_text[i] - - # get the plot data for the scan list - y2plot, x2plot, yerr2plot, xerr2plot, name = self.plot_scans( - scan_list, - xgrid=xgrid, - yerr=yerr, - xerr=xerr, - norm2one=norm2one, - binning=binning, - label_text=lt, - fmt=fmt, - skip_plot=skip_plot, - ) + lt = label_texts[i] - if self.xcol not in sequence_data.keys(): - # if the xcol is not in the return data dict - add the key - sequence_data[self.xcol] = [] - sequence_data[self.xcol + 'Err'] = [] + ret_label_texts.append(lt) + # extract clist und xcol from sequence_data + self._plot_scans(sequence_data['y2plot'], + sequence_data['x2plot'], + sequence_data['yerr2plot'], + sequence_data['xerr2plot'], + names[i], + label_text=ret_label_texts[i], + fmt=fmt) - # add the x-axis data to the return data dict - sequence_data[self.xcol].append(x2plot) - sequence_data[self.xcol + 'Err'].append(xerr2plot) - - for counter in y2plot: - # traverse all counters in the data set - if counter not in sequence_data.keys(): - # if the counter is not in the return data dict - add the key - sequence_data[counter] = [] - sequence_data[counter + 'Err'] = [] - - # add the counter data to the return data dict - sequence_data[counter].append(y2plot[counter]) - sequence_data[counter + 'Err'].append(yerr2plot[counter]) - - # append names and labels to their lists - names.append(name) - label_texts.append(lt) - - return sequence_data, parameters, names, label_texts - - def export_scan_sequence(self, scan_sequence, path, fileName, yerr='std', - xerr='std', xgrid=[], norm2one=False, binning=True): - """Exports source data for each scan list in the sequence as individual file. - - Args: - scan_sequence (List[ - List/Tuple[List[int], - int/str]]) : Sequence of scan lists and parameters. - path (str) : Path of the file to export to. - fileName (str) : Name of the file to export to. - yerr (Optional[ndarray]) : Type of the errors in y: [err, std, none] - default is 'std'. - xerr (Optional[ndarray]) : Type of the errors in x: [err, std, none] - default is 'std'. - xgrid (Optional[ndarray]) : Grid to bin the data to - - default in empty so use the - x-axis of the first scan. - norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 - default is False. - - """ - # get scan_sequence data without plotting - sequence_data, parameters, names, label_texts = self.plot_scan_sequence( - scan_sequence, - xgrid=xgrid, - yerr=yerr, - xerr=xerr, - norm2one=norm2one, - binning=binning, - skip_plot=True) - - for i, label_text in enumerate(label_texts): - # travserse the sequence - - header = '' - saveData = [] - for counter in sequence_data: - # travserse all counters in the data - - # build the file header - header = header + counter + '\t ' - # build the data matrix - saveData.append(sequence_data[counter][i]) - - # save data with header to text file - np.savetxt('{:s}/{:s}_{:s}.dat'.format(path, fileName, - "".join(x for x in label_text if x.isalnum())), - np.r_[saveData].T, delimiter='\t', header=header) + return sequence_data, parameters, names, ret_label_texts def fit_scans(self, scans, mod, pars, ylims=[], xlims=[], fig_size=[], xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, @@ -1286,6 +1142,166 @@ def get_next_fig_number(self): """ return self.get_last_fig_number() + 1 + # def plot_mesh_scan(self, scan_num, skip_plot=False, grid_on=False, ytext='', xtext='', + # levels=20, cbar=True): + # """Plot a single mesh scan from the source file. + # Various plot parameters are provided. + # The plotted data are returned. + + # Args: + # scan_num (int) : Scan number of the source scan. + # skip_plot (Optional[bool]) : Skip plotting, just return data + # default is False. + # grid_on (Optional[bool]) : Add grid to plot - default is False. + # ytext (Optional[str]) : y-Label of the plot - defaults is none. + # xtext (Optional[str]) : x-Label of the plot - defaults is none. + # levels (Optional[int]) : levels of contour plot - defaults is 20. + # cbar (Optional[bool]) : Add colorbar to plot - default is True. + + # Returns: + # xx, yy, zz : x,y,z data which was plotted + + # """ + + # from matplotlib.mlab import griddata + # from matplotlib import gridspec + + # # read data from source file + # try: + # # try to read data of this scan + # source_data = self.get_scan_data(scan_num) + # except Exception: + # print('Scan #' + int(scan_num) + ' not found, skipping') + + # dt = source_data.dtype + # dt = dt.descr + + # xmotor = dt[0][0] + # ymotor = dt[1][0] + + # X = source_data[xmotor] + # Y = source_data[ymotor] + + # xx = np.sort(np.unique(X)) + # yy = np.sort(np.unique(Y)) + + # if len(self.clist) > 1: + # print('WARNING: Only the first counter of the clist is plotted.') + + # Z = source_data[self.clist[0]] + + # zz = griddata(X, Y, Z, xx, yy, interp='linear') + + # if not skip_plot: + + # if cbar: + # gs = gridspec.GridSpec(4, 2, + # width_ratios=[3, 1], + # height_ratios=[0.2, 0.1, 1, 3] + # ) + # k = 4 + # else: + # gs = gridspec.GridSpec(2, 2, + # width_ratios=[3, 1], + # height_ratios=[1, 3] + # ) + # k = 0 + + # ax1 = plt.subplot(gs[0+k]) + + # plt.plot(xx, np.mean(zz, 0), label='mean') + + # plt.plot(xx, zz[np.argmax(np.mean(zz, 1)), :], label='peak') + + # plt.xlim([min(xx), max(xx)]) + # plt.legend(loc=0) + # ax1.xaxis.tick_top() + # if grid_on: + # plt.grid(True) + + # plt.subplot(gs[2+k]) + + # plt.contourf(xx, yy, zz, levels, cmap='viridis') + + # plt.xlabel(xmotor) + # plt.ylabel(ymotor) + + # if len(xtext) > 0: + # plt.xlabel(xtext) + + # if len(ytext) > 0: + # plt.ylabel(ytext) + + # if grid_on: + # plt.grid(True) + + # if cbar: + # cb = plt.colorbar(cax=plt.subplot( + # gs[0]), orientation='horizontal') + # cb.ax.xaxis.set_ticks_position('top') + # cb.ax.xaxis.set_label_position('top') + + # ax4 = plt.subplot(gs[3+k]) + + # plt.plot(np.mean(zz, 1), yy) + # plt.plot(zz[:, np.argmax(np.mean(zz, 0))], yy) + # plt.ylim([np.min(yy), np.max(yy)]) + + # ax4.yaxis.tick_right() + # if grid_on: + # plt.grid(True) + + # return xx, yy, zz + + # def export_scan_sequence(self, scan_sequence, path, fileName, yerr='std', + # xerr='std', xgrid=[], norm2one=False, binning=True): + # """Exports source data for each scan list in the sequence as individual file. + + # Args: + # scan_sequence (List[ + # List/Tuple[List[int], + # int/str]]) : Sequence of scan lists and parameters. + # path (str) : Path of the file to export to. + # fileName (str) : Name of the file to export to. + # yerr (Optional[ndarray]) : Type of the errors in y: [err, std, none] + # default is 'std'. + # xerr (Optional[ndarray]) : Type of the errors in x: [err, std, none] + # default is 'std'. + # xgrid (Optional[ndarray]) : Grid to bin the data to - + # default in empty so use the + # x-axis of the first scan. + # norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 + # default is False. + + # """ + # # get scan_sequence data without plotting + # sequence_data, parameters, names, label_texts = self.plot_scan_sequence( + # scan_sequence, + # xgrid=xgrid, + # yerr=yerr, + # xerr=xerr, + # norm2one=norm2one, + # binning=binning, + # skip_plot=True) + + # for i, label_text in enumerate(label_texts): + # # travserse the sequence + + # header = '' + # saveData = [] + # for counter in sequence_data: + # # travserse all counters in the data + + # # build the file header + # header = header + counter + '\t ' + # # build the data matrix + # saveData.append(sequence_data[counter][i]) + + # # save data with header to text file + # np.savetxt('{:s}/{:s}_{:s}.dat'.format(path, fileName, + # "".join(x for x in label_text if x.isalnum())), + # np.r_[saveData].T, delimiter='\t', header=header) + @property def clist(self): return self._clist From 22c942ec81eb3d930aefcb62be5a13409f694798 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Wed, 8 Dec 2021 16:52:25 +0100 Subject: [PATCH 11/24] wip restructure allow for label_format in sequence --- pyEvalData/evaluation.py | 218 +++++++++++++++++++++++++-------------- 1 file changed, 143 insertions(+), 75 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 3291233..ca55da3 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -563,7 +563,8 @@ def eval_scan_sequence(self, scan_sequence, xgrid=[], yerr='std', xerr='std', no binning=binning, ) # create a list of all counters from the scan and append the xcol - sequence_counters = list(y2plot.keys()).append(self.xcol) + sequence_counters = list(y2plot.keys()) + sequence_counters.append(self.xcol) for counter in sequence_counters: # traverse all counters in the data set if counter not in sequence_data.keys(): @@ -572,43 +573,49 @@ def eval_scan_sequence(self, scan_sequence, xgrid=[], yerr='std', xerr='std', no sequence_data[counter + 'Err'] = [] # add the counter data to the return data dict - sequence_data[counter].append(y2plot[counter]) - sequence_data[counter + 'Err'].append(yerr2plot[counter]) + try: + sequence_data[counter].append(y2plot[counter]) + sequence_data[counter + 'Err'].append(yerr2plot[counter]) + except KeyError: + sequence_data[counter].append(x2plot) + sequence_data[counter + 'Err'].append(xerr2plot) names.append(name) return sequence_data, parameters, names - def _plot_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, label_text='', fmt='-o'): + def _plot_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, label_text='', fmt='-o', + **kwargs): # plot all keys in the clist - for col in self.clist: + for counter in self.clist: # iterate the counter list if len(label_text) == 0: # if no label_text is given use the counter name - lt = col + lt = counter else: if len(self.clist) > 1: # for multiple counters add the counter name to the label - lt = label_text + ' | ' + col + lt = label_text + ' | ' + counter else: # for a single counter just use the label_text lt = label_text # plot the errorbar for each counter if (xerr2plot is None) & (yerr2plot is None): - plt.plot(x2plot, y2plot[col], fmt, label=lt) + plot = plt.plot(x2plot, y2plot[counter], fmt, label=lt, **kwargs) else: - plt.errorbar( - x2plot, y2plot[col], fmt=fmt, label=lt, - xerr=xerr2plot, yerr=yerr2plot[col]) + plot = plt.errorbar(x2plot, y2plot[counter], fmt=fmt, label=lt, xerr=xerr2plot, + yerr=yerr2plot[counter], **kwargs) # add a legend, labels, title and set the limits and grid plt.legend(frameon=True, loc=0, numpoints=1) plt.xlabel(self.xcol) plt.title(name) + return plot + def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm2one=False, - binning=True, label_text='', fmt='-o'): + binning=True, label_text='', fmt='-o', **kwargs): """plot_scans Plot a list of scans from the source file. @@ -641,13 +648,14 @@ def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm self.eval_scans(scan_list, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, binning=binning) - self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, fmt=fmt) + _ = self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, + fmt=fmt, **kwargs) return y2plot, x2plot, yerr2plot, xerr2plot, name def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr='std', - norm2one=False, binning=True, sequence_type='', label_texts=[], - fmt='-o'): + norm2one=False, binning=True, label_format='', label_texts=[], + fmt='-o', **kwargs): """Plot a list of scans from the source file. Various plot parameters are provided. The plotted data are returned. @@ -665,10 +673,7 @@ def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr default is 'std'. norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 default is False. - sequence_type (Optional[str]): Type of the sequence: [fluence, delay, - energy, theta, position, voltage, none, - text] - default is enumeration. - label_texts (Optional[str]) : lift of labels of the plot - default is none. + label_format (Optional[str]): fprintf style format for labels fmt (Optional[str]) : format string of the plot - defaults is -o. Returns: @@ -686,72 +691,135 @@ def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr for i, (scan_list, parameter) in enumerate(scan_sequence): # iterate the scan sequence - # format the parameter as label text of this plot if no label text - # is given - if len(label_texts) == 0: - if sequence_type == 'fluence': - lt = str.format('{:.2f} mJ/cm²', parameter) - elif sequence_type == 'delay': - lt = str.format('{:.2f} ps', parameter) - elif sequence_type == 'energy': - lt = str.format('{:.2f} eV', parameter) - elif sequence_type == 'theta': - lt = str.format('{:.2f} deg', parameter) - elif sequence_type == 'temperature': - lt = str.format('{:.2f} K', parameter) - elif sequence_type == 'position': - lt = str.format('{:.2f} mm', parameter) - elif sequence_type == 'voltage': - lt = str.format('{:.2f} V', parameter) - elif sequence_type == 'current': - lt = str.format('{:.2f} A', parameter) - elif sequence_type == 'scans': - lt = str(scan_list) - elif sequence_type == 'none': - # no parameter for single scans - lt = '' - elif sequence_type == 'text': - # parameter is a string - lt = parameter - else: - # no sequence type is given --> enumerate - lt = str.format('#{}', i+1) - else: - lt = label_texts[i] + lt = '#{:d}'.format(i+1) + if len(label_format) > 0: + try: + lt = label_format.format(parameter) + except ValueError: + self.log.warning('Could not apply \'label_format\' to parameter!') ret_label_texts.append(lt) # extract clist und xcol from sequence_data - self._plot_scans(sequence_data['y2plot'], - sequence_data['x2plot'], - sequence_data['yerr2plot'], - sequence_data['xerr2plot'], - names[i], - label_text=ret_label_texts[i], - fmt=fmt) + _ = self._plot_scans({c: sequence_data[c][i] for c in self.clist}, + sequence_data[self.xcol][i], + {c: sequence_data[c + 'Err'][i] for c in self.clist}, + sequence_data[self.xcol + 'Err'][i], + names[i], + label_text=ret_label_texts[i], + fmt=fmt, + **kwargs) return sequence_data, parameters, names, ret_label_texts - def fit_scans(self, scans, mod, pars, ylims=[], xlims=[], fig_size=[], xgrid=[], - yerr='std', xerr='std', norm2one=False, binning=True, - sequence_type='text', label_text='', title_text='', ytext='', - xtext='', select='', fit_report=0, show_single=False, - weights=False, fit_method='leastsq', offset_t0=False, - plot_separate=False, grid_on=True, fmt='o'): + def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, weights, + fit_method='leastsq', nan_policy='propagate'): + res = {} # initialize the results dict + report = {} + + for counter in y2plot: + res[counter] = {} + # get the fit models and fit parameters if they are lists/tupels + + # evaluate the select statement + if select == '': + # select all + sel = np.ones_like(y2plot[counter], dtype=bool) + else: + sel = eval(select) + + # execute the select statement + y2plot = y2plot[counter][sel] + x2plot = x2plot[sel] + yerr2plot = yerr2plot[counter][sel] + xerr2plot = xerr2plot[sel] + + # remove nans + y2plot = y2plot[~np.isnan(y2plot)] + x2plot = x2plot[~np.isnan(y2plot)] + yerr2plot = yerr2plot[~np.isnan(y2plot)] + xerr2plot = xerr2plot[~np.isnan(y2plot)] + + # do the fitting with or without weighting the data + if weights: + out = mod.fit(y2plot, pars, x=x2plot, weights=1/yerr2plot, method=fit_method, + nan_policy=nan_policy) + else: + out = mod.fit(y2plot, pars, x=x2plot, method=fit_method, nan_policy=nan_policy) + + report_1 = counter + ':' + '\n' + '_'*40 + '\n' + for key in out.best_values: + report_1 += '{:>12}: {:>10.4e}\n'.format(key, out.best_values[key]) + + report_2 = out.fit_report() + + report[counter] = [report_1, report_2] + + # add the fit results to the returns + for pname, par in pars.items(): + res[counter][pname] = out.best_values[pname] + res[counter][pname + 'Err'] = out.params[pname].stderr + + res[counter]['chisqr'] = out.chisqr + res[counter]['redchi'] = out.redchi + res[counter]['CoM'] = sum(y2plot*x2plot)/sum(y2plot) + res[counter]['int'] = sum(y2plot) + res[counter]['fit'] = out + + return res, report + + def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offset_t0=False, + label_text='', fmt='o'): + plot = self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, + fmt=fmt, alpha=0.25) + + # set the x-offset for delay scans - offset parameter in + # the fit must be called 't0' + offsetX = 0 + if offset_t0: + try: + offsetX = res['t0'] + except KeyError: + self.log.warning('No parameter \'t0\' present in model!') + else: + offsetX = 0 + + for counter in y2plot: + # plot the fit and the data as errorbars + x2plotFit = np.linspace( + np.min(x2plot), np.max(x2plot), 10000) + plt.plot(x2plotFit-offsetX, res[counter]['fit'].eval(x=x2plotFit), '-', lw=2, alpha=1, + color=plot[0].get_color()) + + def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm2one=False, + binning=True, label_text='', select='', fit_report=0, weights=False, + fit_method='leastsq', nan_policy='propagate', offset_t0=False, fmt='o'): """Fit, plot, and return the data of scans. This is just a wrapper for the fit_scan_sequence method """ - scan_sequence = [[scans, '']] - return self.fit_scan_sequence(scan_sequence, mod, pars, ylims, xlims, fig_size, - xgrid, yerr, xerr, norm2one, binning, - 'none', label_text, title_text, ytext, - xtext, select, fit_report, show_single, - weights, fit_method, offset_t0, plot_separate, - grid_on, fmt=fmt) + # get the data for the scan list + y2plot, x2plot, yerr2plot, xerr2plot, name = \ + self.eval_scans(scan_list, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, + binning=binning) + + # fit the model and parameters to the data + res, report = self._fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, + weights, fit_method='leastsq', nan_policy='propagate') + + # plot the data and fit + self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, res, offset_t0=False, + fmt='o') + + # print the fit report + for counter in y2plot: + if fit_report > 0: + print(report[counter][0]) + if fit_report > 1: + print(report[counter][1]) def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_size=[], xgrid=[], yerr='std', xerr='std', norm2one=False, - binning=True, sequence_type='', label_text='', + binning=True, sequence_type='', label_texts='', title_text='', ytext='', xtext='', select='', fit_report=0, show_single=False, weights=False, fit_method='leastsq', offset_t0=False, @@ -779,7 +847,7 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_si default is False. sequence_type (Optional[str]): Type of the sequence: [fluence, delay, energy, theta] - default is fluence. - label_text (Optional[str]) : Label of the plot - default is none. + label_texts (Optional[str]) : list of Labels of the plot - default is none. title_text (Optional[str]) : Title of the figure - default is none. ytext (Optional[str]) : y-Label of the plot - defaults is none. xtext (Optional[str]) : x-Label of the plot - defaults is none. @@ -855,7 +923,7 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_si norm2one=norm2one, binning=True, sequence_type=sequence_type, - label_text=label_text, + label_texts=label_texts, skip_plot=True) else: # get the sequence data and parameters @@ -867,7 +935,7 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_si norm2one=norm2one, binning=True, sequence_type=sequence_type, - label_text=label_text, + label_texts=label_texts, skip_plot=True) # this is the number of different counters From d96bd37f0619aa6c7b05adf80d8a78be44970dff Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Fri, 10 Dec 2021 00:47:30 +0100 Subject: [PATCH 12/24] print fit results using tabulate --- pyEvalData/evaluation.py | 67 ++++++++++++++++++++++------------------ setup.py | 4 ++- 2 files changed, 40 insertions(+), 31 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index ca55da3..25037d5 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -31,6 +31,7 @@ import matplotlib as mpl import re from uncertainties import unumpy +from tabulate import tabulate from .helpers import bin_data __all__ = ['Evaluation'] @@ -586,6 +587,7 @@ def eval_scan_sequence(self, scan_sequence, xgrid=[], yerr='std', xerr='std', no def _plot_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, label_text='', fmt='-o', **kwargs): + plots = [] # plot all keys in the clist for counter in self.clist: # iterate the counter list @@ -606,13 +608,14 @@ def _plot_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, label_text='', else: plot = plt.errorbar(x2plot, y2plot[counter], fmt=fmt, label=lt, xerr=xerr2plot, yerr=yerr2plot[counter], **kwargs) + plots.append(plot) # add a legend, labels, title and set the limits and grid plt.legend(frameon=True, loc=0, numpoints=1) plt.xlabel(self.xcol) plt.title(name) - return plot + return plots def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm2one=False, binning=True, label_text='', fmt='-o', **kwargs): @@ -714,7 +717,11 @@ def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, weights, fit_method='leastsq', nan_policy='propagate'): res = {} # initialize the results dict - report = {} + report = [] + param_names = mod.param_names.copy() + param_names.insert(0, 'counter') + report_1 = [param_names] + report_2 = {} for counter in y2plot: res[counter] = {} @@ -728,32 +735,29 @@ def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, we sel = eval(select) # execute the select statement - y2plot = y2plot[counter][sel] - x2plot = x2plot[sel] - yerr2plot = yerr2plot[counter][sel] - xerr2plot = xerr2plot[sel] + _y2plot = y2plot[counter][sel] + _x2plot = x2plot[sel] + _yerr2plot = yerr2plot[counter][sel] + _xerr2plot = xerr2plot[sel] # remove nans - y2plot = y2plot[~np.isnan(y2plot)] - x2plot = x2plot[~np.isnan(y2plot)] - yerr2plot = yerr2plot[~np.isnan(y2plot)] - xerr2plot = xerr2plot[~np.isnan(y2plot)] + _y2plot = _y2plot[~np.isnan(_y2plot)] + _x2plot = _x2plot[~np.isnan(_y2plot)] + _yerr2plot = _yerr2plot[~np.isnan(_y2plot)] + _xerr2plot = _xerr2plot[~np.isnan(_y2plot)] # do the fitting with or without weighting the data if weights: - out = mod.fit(y2plot, pars, x=x2plot, weights=1/yerr2plot, method=fit_method, + out = mod.fit(_y2plot, pars, x=_x2plot, weights=1/_yerr2plot, method=fit_method, nan_policy=nan_policy) else: - out = mod.fit(y2plot, pars, x=x2plot, method=fit_method, nan_policy=nan_policy) + out = mod.fit(_y2plot, pars, x=_x2plot, method=fit_method, nan_policy=nan_policy) - report_1 = counter + ':' + '\n' + '_'*40 + '\n' - for key in out.best_values: - report_1 += '{:>12}: {:>10.4e}\n'.format(key, out.best_values[key]) - - report_2 = out.fit_report() - - report[counter] = [report_1, report_2] + best_values = list(out.best_values.values()) + best_values.insert(0, counter) + report_1.append(best_values) + report_2[counter] = out.fit_report() # add the fit results to the returns for pname, par in pars.items(): res[counter][pname] = out.best_values[pname] @@ -761,16 +765,18 @@ def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, we res[counter]['chisqr'] = out.chisqr res[counter]['redchi'] = out.redchi - res[counter]['CoM'] = sum(y2plot*x2plot)/sum(y2plot) - res[counter]['int'] = sum(y2plot) + res[counter]['CoM'] = sum(_y2plot*_x2plot)/sum(_y2plot) + res[counter]['int'] = sum(_y2plot) res[counter]['fit'] = out + report = [report_1, report_2] + return res, report def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offset_t0=False, label_text='', fmt='o'): - plot = self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, - fmt=fmt, alpha=0.25) + plots = self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, + fmt=fmt, alpha=0.25) # set the x-offset for delay scans - offset parameter in # the fit must be called 't0' @@ -783,12 +789,12 @@ def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offse else: offsetX = 0 - for counter in y2plot: + for i, counter in enumerate(y2plot): # plot the fit and the data as errorbars x2plotFit = np.linspace( np.min(x2plot), np.max(x2plot), 10000) plt.plot(x2plotFit-offsetX, res[counter]['fit'].eval(x=x2plotFit), '-', lw=2, alpha=1, - color=plot[0].get_color()) + color=plots[i][0].get_color()) def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, label_text='', select='', fit_report=0, weights=False, @@ -811,11 +817,12 @@ def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm fmt='o') # print the fit report - for counter in y2plot: - if fit_report > 0: - print(report[counter][0]) - if fit_report > 1: - print(report[counter][1]) + if fit_report > 0: + print(tabulate(report[0][1:], headers=report[0][0], tablefmt="fancy_grid")) + if fit_report > 1: + for counter in y2plot: + print('='*10 + counter + '='*10) + print(report[1][counter]) def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_size=[], xgrid=[], yerr='std', xerr='std', norm2one=False, diff --git a/setup.py b/setup.py index d147624..62b6bec 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,9 @@ 'uncertainties', 'xrayutilities', 'h5py>=3.0', - 'nexusformat'], + 'nexusformat', + 'tabulate', + ], extras_require={ 'testing': ['flake8', 'pytest'], 'documentation': ['sphinx', 'nbsphinx', 'sphinxcontrib-napoleon'], From 6013875d926f1befda56a93de42f75dd6043696c Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Fri, 10 Dec 2021 22:16:35 +0100 Subject: [PATCH 13/24] initial version of fit_scan_sequece --- pyEvalData/evaluation.py | 765 +++++++++++++++++++++------------------ 1 file changed, 407 insertions(+), 358 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 25037d5..c40f091 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -657,8 +657,7 @@ def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm return y2plot, x2plot, yerr2plot, xerr2plot, name def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr='std', - norm2one=False, binning=True, label_format='', label_texts=[], - fmt='-o', **kwargs): + norm2one=False, binning=True, label_format='', fmt='-o', **kwargs): """Plot a list of scans from the source file. Various plot parameters are provided. The plotted data are returned. @@ -690,7 +689,7 @@ def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr sequence_data, parameters, names = self.eval_scan_sequence( scan_sequence, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, binning=binning) - ret_label_texts = [] + label_texts = [] for i, (scan_list, parameter) in enumerate(scan_sequence): # iterate the scan sequence @@ -701,18 +700,18 @@ def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr except ValueError: self.log.warning('Could not apply \'label_format\' to parameter!') - ret_label_texts.append(lt) + label_texts.append(lt) # extract clist und xcol from sequence_data _ = self._plot_scans({c: sequence_data[c][i] for c in self.clist}, sequence_data[self.xcol][i], {c: sequence_data[c + 'Err'][i] for c in self.clist}, sequence_data[self.xcol + 'Err'][i], names[i], - label_text=ret_label_texts[i], + label_text=lt, fmt=fmt, **kwargs) - return sequence_data, parameters, names, ret_label_texts + return sequence_data, parameters, names, label_texts def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, weights, fit_method='leastsq', nan_policy='propagate'): @@ -766,7 +765,7 @@ def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, we res[counter]['chisqr'] = out.chisqr res[counter]['redchi'] = out.redchi res[counter]['CoM'] = sum(_y2plot*_x2plot)/sum(_y2plot) - res[counter]['int'] = sum(_y2plot) + res[counter]['int'] = np.trapz(_y2plot, x=_x2plot) res[counter]['fit'] = out report = [report_1, report_2] @@ -810,380 +809,430 @@ def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm # fit the model and parameters to the data res, report = self._fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, - weights, fit_method='leastsq', nan_policy='propagate') + weights, fit_method=fit_method, nan_policy=nan_policy) # plot the data and fit - self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, res, offset_t0=False, - fmt='o') + self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, res, offset_t0=offset_t0, + fmt=fmt) # print the fit report if fit_report > 0: print(tabulate(report[0][1:], headers=report[0][0], tablefmt="fancy_grid")) if fit_report > 1: for counter in y2plot: - print('='*10 + counter + '='*10) + head_len = int(len(counter)/2) + if np.mod(len(counter), 2) != 0: + fix = 1 + else: + fix = 0 + + print("\n" + "="*(39-head_len-fix) + " {:} ".format(counter) + "="*(39-head_len)) print(report[1][counter]) - def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_size=[], - xgrid=[], yerr='std', xerr='std', norm2one=False, - binning=True, sequence_type='', label_texts='', - title_text='', ytext='', xtext='', select='', - fit_report=0, show_single=False, weights=False, - fit_method='leastsq', offset_t0=False, - plot_separate=False, grid_on=True, - last_res_as_par=False, sequence_data=[], fmt='o'): - """Fit, plot, and return the data of a scan sequence. + def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr='std', + norm2one=False, binning=True, label_format='', select='', fit_report=0, + weights=False, fit_method='leastsq', nan_policy='propagate', + offset_t0=False, fmt='o'): + # load data + sequence_data, parameters, names = self.eval_scan_sequence( + scan_sequence, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, binning=binning) - Args: - scan_sequence (List[ - List/Tuple[List[int], - int/str]]) : Sequence of scan lists and parameters. - mod (Model[lmfit]) : lmfit model for fitting the data. - pars (Parameters[lmfit]) : lmfit parameters for fitting the data. - ylims (Optional[ndarray]) : ylim for the plot. - xlims (Optional[ndarray]) : xlim for the plot. - fig_size (Optional[ndarray]) : Figure size of the figure. - xgrid (Optional[ndarray]) : Grid to bin the data to - - default in empty so use the - x-axis of the first scan. - yerr (Optional[ndarray]) : Type of the errors in y: [err, std, none] - default is 'std'. - xerr (Optional[ndarray]) : Type of the errors in x: [err, std, none] - default is 'std'. - norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 - default is False. - sequence_type (Optional[str]): Type of the sequence: [fluence, delay, - energy, theta] - default is fluence. - label_texts (Optional[str]) : list of Labels of the plot - default is none. - title_text (Optional[str]) : Title of the figure - default is none. - ytext (Optional[str]) : y-Label of the plot - defaults is none. - xtext (Optional[str]) : x-Label of the plot - defaults is none. - select (Optional[str]) : String to evaluate as select statement - for the fit region - default is none - fit_report (Optional[int]) : Set the fit reporting level: - [0: none, 1: basic, 2: full] - default 0. - show_single (Optional[bool]) : Plot each fit seperately - default False. - weights (Optional[bool]) : Use weights for fitting - default False. - fit_method (Optional[str]) : Method to use for fitting; refer to - lmfit - default is 'leastsq'. - offset_t0 (Optional[bool]) : Offset time scans by the fitted - t0 parameter - default False. - plot_separate (Optional[bool]):A single plot for each counter - default False. - grid_on (Optional[bool]) : Add grid to plot - default is True. - last_res_as_par (Optional[bool]): Use the last fit result as start - values for next fit - default is False. - sequence_data (Optional[ndarray]): actual exp. data are externally given. - default is empty - fmt (Optional[str]) : format string of the plot - defaults is -o. + res = {} + for counter in self.clist: + res[counter] = {} + for i, ((scan_list, parameter), name) in enumerate(zip(scan_sequence, names)): + # iterate the scan sequence - Returns: - res (Dict[ndarray]) : Fit results. - parameters (ndarray) : Parameters of the sequence. - sequence_data (OrderedDict) : Dictionary of the averaged scan data.equenceData + lt = '#{:d}'.format(i+1) + if len(label_format) > 0: + try: + lt = label_format.format(parameter) + except ValueError: + self.log.warning('Could not apply \'label_format\' to parameter!') - """ + # extract clist und xcol from sequence_data + y2plot = {c: sequence_data[c][i] for c in self.clist} + yerr2plot = {c: sequence_data[c + 'Err'][i] for c in self.clist} + x2plot = sequence_data[self.xcol][i] + xerr2plot = sequence_data[self.xcol + 'Err'][i] + # fit the model and parameters to the data + _res, report = self._fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, + weights, fit_method=fit_method, nan_policy='propagate') - # get the last open figure number - main_fig_num = self.get_last_fig_number() + # plot the data and fit + self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, _res, label_text=lt, + offset_t0=offset_t0, fmt=fmt) - if not fig_size: - # use default figure size if none is given - fig_size = mpl.rcParams['figure.figsize'] + for counter in self.clist: + for key in _res[counter].keys(): + try: + res[counter][key].append(_res[counter][key]) + except KeyError: + res[counter][key] = [_res[counter][key]] - # initialization of returns - res = {} # initialize the results dict + return res, parameters, sequence_data - for i, counter in enumerate(self.clist): - # traverse all counters in the counter list to initialize the returns + # def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_size=[], + # xgrid=[], yerr='std', xerr='std', norm2one=False, + # binning=True, sequence_type='', label_texts='', + # title_text='', ytext='', xtext='', select='', + # fit_report=0, show_single=False, weights=False, + # fit_method='leastsq', offset_t0=False, + # plot_separate=False, grid_on=True, + # last_res_as_par=False, sequence_data=[], fmt='o'): + # """Fit, plot, and return the data of a scan sequence. - # results for this counter is again a Dict - res[counter] = {} + # Args: + # scan_sequence (List[ + # List/Tuple[List[int], + # int/str]]) : Sequence of scan lists and parameters. + # mod (Model[lmfit]) : lmfit model for fitting the data. + # pars (Parameters[lmfit]) : lmfit parameters for fitting the data. + # ylims (Optional[ndarray]) : ylim for the plot. + # xlims (Optional[ndarray]) : xlim for the plot. + # fig_size (Optional[ndarray]) : Figure size of the figure. + # xgrid (Optional[ndarray]) : Grid to bin the data to - + # default in empty so use the + # x-axis of the first scan. + # yerr (Optional[ndarray]) : Type of the errors in y: [err, std, none] + # default is 'std'. + # xerr (Optional[ndarray]) : Type of the errors in x: [err, std, none] + # default is 'std'. + # norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 + # default is False. + # sequence_type (Optional[str]): Type of the sequence: [fluence, delay, + # energy, theta] - default is fluence. + # label_texts (Optional[str]) : list of Labels of the plot - default is none. + # title_text (Optional[str]) : Title of the figure - default is none. + # ytext (Optional[str]) : y-Label of the plot - defaults is none. + # xtext (Optional[str]) : x-Label of the plot - defaults is none. + # select (Optional[str]) : String to evaluate as select statement + # for the fit region - default is none + # fit_report (Optional[int]) : Set the fit reporting level: + # [0: none, 1: basic, 2: full] + # default 0. + # show_single (Optional[bool]) : Plot each fit seperately - default False. + # weights (Optional[bool]) : Use weights for fitting - default False. + # fit_method (Optional[str]) : Method to use for fitting; refer to + # lmfit - default is 'leastsq'. + # offset_t0 (Optional[bool]) : Offset time scans by the fitted + # t0 parameter - default False. + # plot_separate (Optional[bool]):A single plot for each counter + # default False. + # grid_on (Optional[bool]) : Add grid to plot - default is True. + # last_res_as_par (Optional[bool]): Use the last fit result as start + # values for next fit - default is False. + # sequence_data (Optional[ndarray]): actual exp. data are externally given. + # default is empty + # fmt (Optional[str]) : format string of the plot - defaults is -o. - if isinstance(pars, (list, tuple)): - # the fit paramters might individual for each counter - _pars = pars[i] - else: - _pars = pars - - for pname, par in _pars.items(): - # add a dict key for each fit parameter in the result dict - res[counter][pname] = [] - res[counter][pname + 'Err'] = [] - - # add some more results - res[counter]['chisqr'] = [] - res[counter]['redchi'] = [] - res[counter]['CoM'] = [] - res[counter]['int'] = [] - res[counter]['fit'] = [] - - if len(sequence_data) > 0: - # get only the parameters - _, parameters, names, label_texts = self.plot_scan_sequence( - scan_sequence, - xgrid=xgrid, - yerr=yerr, - xerr=xerr, - norm2one=norm2one, - binning=True, - sequence_type=sequence_type, - label_texts=label_texts, - skip_plot=True) - else: - # get the sequence data and parameters - sequence_data, parameters, names, label_texts = self.plot_scan_sequence( - scan_sequence, - xgrid=xgrid, - yerr=yerr, - xerr=xerr, - norm2one=norm2one, - binning=True, - sequence_type=sequence_type, - label_texts=label_texts, - skip_plot=True) - - # this is the number of different counters - num_sub_plots = len(self.clist) - - # fitting and plotting the data - l_plot = 1 # counter for single plots - - for i, parameter in enumerate(parameters): - # traverse all parameters of the sequence - lt = label_texts[i] - name = names[i] - x2plot = sequence_data[self.xcol][i] - xerr2plot = sequence_data[self.xcol + 'Err'][i] + # Returns: + # res (Dict[ndarray]) : Fit results. + # parameters (ndarray) : Parameters of the sequence. + # sequence_data (OrderedDict) : Dictionary of the averaged scan data.equenceData + + # """ - if fit_report > 0: - # plot for basics and full fit reporting - print('') - print('='*10 + ' Parameter: ' + lt + ' ' + '='*15) - - j = 0 # counter for counters ;) - k = 1 # counter for subplots - for counter in sequence_data: - # traverse all counters in the sequence - - # plot only y counters - next is the coresp. error - if j >= 2 and j % 2 == 0: - - # add the counter name to the label for not seperate plots - if sequence_type == 'none': - _lt = counter - else: - if plot_separate or num_sub_plots == 1: - _lt = lt - else: - _lt = lt + ' | ' + counter - - # get the fit models and fit parameters if they are lists/tupels - if isinstance(mod, (list, tuple)): - _mod = mod[k-1] - else: - _mod = mod - - if last_res_as_par and i > 0: - # use last results as start values for pars - _pars = pars - for pname, par in pars.items(): - _pars[pname].value = res[counter][pname][i-1] - else: - if isinstance(pars, (list, tuple)): - _pars = pars[k-1] - else: - _pars = pars - - # get the actual y-data and -errors for plotting and fitting - y2plot = sequence_data[counter][i] - yerr2plot = sequence_data[counter + 'Err'][i] - - # evaluate the select statement - if select == '': - # select all - sel = np.ones_like(y2plot, dtype=bool) - else: - sel = eval(select) - - # execute the select statement - y2plot = y2plot[sel] - x2plot = x2plot[sel] - yerr2plot = yerr2plot[sel] - xerr2plot = xerr2plot[sel] - - # remove nans - y2plot = y2plot[~np.isnan(y2plot)] - x2plot = x2plot[~np.isnan(y2plot)] - yerr2plot = yerr2plot[~np.isnan(y2plot)] - xerr2plot = xerr2plot[~np.isnan(y2plot)] - - # do the fitting with or without weighting the data - if weights: - out = _mod.fit(y2plot, _pars, x=x2plot, - weights=1/yerr2plot, method=fit_method, - nan_policy='propagate') - else: - out = _mod.fit(y2plot, _pars, x=x2plot, - method=fit_method, nan_policy='propagate') - - if fit_report > 0: - # for basic and full fit reporting - print('') - print('-'*10 + ' ' + counter + ': ' + '-'*15) - for key in out.best_values: - print('{:>12}: {:>10.4e} '.format( - key, out.best_values[key])) - - # set the x-offset for delay scans - offset parameter in - # the fit must be called 't0' - if offset_t0: - offsetX = out.best_values['t0'] - else: - offsetX = 0 - - plt.figure(main_fig_num) # select the main figure - - if plot_separate: - # use subplot for separate plotting - plt.subplot((num_sub_plots+num_sub_plots % 2)/2, 2, k) - - # plot the fit and the data as errorbars - x2plotFit = np.linspace( - np.min(x2plot), np.max(x2plot), 10000) - plot = plt.plot(x2plotFit-offsetX, - out.eval(x=x2plotFit), '-', lw=2, alpha=1) - plt.errorbar(x2plot-offsetX, y2plot, fmt=fmt, xerr=xerr2plot, - yerr=yerr2plot, label=_lt, alpha=0.25, color=plot[0].get_color()) - - if len(parameters) > 5: - # move the legend outside the plot for more than - # 5 sequence parameters - plt.legend(bbox_to_anchor=(0., 1.08, 1, .102), frameon=True, - loc=3, numpoints=1, ncol=3, mode="expand", - borderaxespad=0.) - else: - plt.legend(frameon=True, loc=0, numpoints=1) - - # set the axis limits, title, labels and gird - if xlims: - plt.xlim(xlims) - if ylims: - plt.ylim(ylims) - if len(title_text) > 0: - if isinstance(title_text, (list, tuple)): - plt.title(title_text[k-1]) - else: - plt.title(title_text) - else: - plt.title(name) - - if len(xtext) > 0: - plt.xlabel(xtext) - - if len(ytext) > 0: - if isinstance(ytext, (list, tuple)): - plt.ylabel(ytext[k-1]) - else: - plt.ylabel(ytext) - - if grid_on: - plt.grid(True) - - # show the single fits and residuals - if show_single: - plt.figure(main_fig_num+l_plot, figsize=fig_size) - gs = mpl.gridspec.GridSpec( - 2, 1, height_ratios=[1, 3], hspace=0.1) - ax1 = plt.subplot(gs[0]) - markerline, stemlines, baseline = plt.stem( - x2plot-offsetX, out.residual, markerfmt=' ', - use_line_collection=True) - plt.setp(stemlines, 'color', - plot[0].get_color(), 'linewidth', 2, alpha=0.5) - plt.setp(baseline, 'color', 'k', 'linewidth', 0) - - ax1.xaxis.tick_top() - ax1.yaxis.set_major_locator(plt.MaxNLocator(3)) - plt.ylabel('Residuals') - if xlims: - plt.xlim(xlims) - if ylims: - plt.ylim(ylims) - - if len(xtext) > 0: - plt.xlabel(xtext) - - if grid_on: - plt.grid(True) - - if len(title_text) > 0: - if isinstance(title_text, (list, tuple)): - plt.title(title_text[k-1]) - else: - plt.title(title_text) - else: - plt.title(name) - ax2 = plt.subplot(gs[1]) - x2plotFit = np.linspace( - np.min(x2plot), np.max(x2plot), 1000) - ax2.plot(x2plotFit-offsetX, out.eval(x=x2plotFit), - '-', lw=2, alpha=1, color=plot[0].get_color()) - ax2.errorbar(x2plot-offsetX, y2plot, fmt=fmt, xerr=xerr2plot, - yerr=yerr2plot, label=_lt, alpha=0.25, - color=plot[0].get_color()) - plt.legend(frameon=True, loc=0, numpoints=1) - - if xlims: - plt.xlim(xlims) - if ylims: - plt.ylim(ylims) - - if len(xtext) > 0: - plt.xlabel(xtext) - - if len(ytext) > 0: - if isinstance(ytext, (list, tuple)): - plt.ylabel(ytext[k-1]) - else: - plt.ylabel(ytext) - - if grid_on: - plt.grid(True) - - l_plot += 1 - if fit_report > 1: - # for full fit reporting - print('_'*40) - print(out.fit_report()) - - # add the fit results to the returns - for pname, par in _pars.items(): - res[counter][pname] = np.append( - res[counter][pname], out.best_values[pname]) - res[counter][pname + 'Err'] = np.append( - res[counter][pname + 'Err'], out.params[pname].stderr) - - res[counter]['chisqr'] = np.append( - res[counter]['chisqr'], out.chisqr) - res[counter]['redchi'] = np.append( - res[counter]['redchi'], out.redchi) - res[counter]['CoM'] = np.append( - res[counter]['CoM'], sum(y2plot*x2plot)/sum(y2plot)) - res[counter]['int'] = np.append( - res[counter]['int'], sum(y2plot)) - res[counter]['fit'] = np.append(res[counter]['fit'], out) - - k += 1 - - j += 1 - - plt.figure(main_fig_num) # set as active figure + # # get the last open figure number + # main_fig_num = self.get_last_fig_number() - return res, parameters, sequence_data + # if not fig_size: + # # use default figure size if none is given + # fig_size = mpl.rcParams['figure.figsize'] + + # # initialization of returns + # res = {} # initialize the results dict + + # for i, counter in enumerate(self.clist): + # # traverse all counters in the counter list to initialize the returns + + # # results for this counter is again a Dict + # res[counter] = {} + + # if isinstance(pars, (list, tuple)): + # # the fit paramters might individual for each counter + # _pars = pars[i] + # else: + # _pars = pars + + # for pname, par in _pars.items(): + # # add a dict key for each fit parameter in the result dict + # res[counter][pname] = [] + # res[counter][pname + 'Err'] = [] + + # # add some more results + # res[counter]['chisqr'] = [] + # res[counter]['redchi'] = [] + # res[counter]['CoM'] = [] + # res[counter]['int'] = [] + # res[counter]['fit'] = [] + + # if len(sequence_data) > 0: + # # get only the parameters + # _, parameters, names, label_texts = self.plot_scan_sequence( + # scan_sequence, + # xgrid=xgrid, + # yerr=yerr, + # xerr=xerr, + # norm2one=norm2one, + # binning=True, + # sequence_type=sequence_type, + # label_texts=label_texts, + # skip_plot=True) + # else: + # # get the sequence data and parameters + # sequence_data, parameters, names, label_texts = self.plot_scan_sequence( + # scan_sequence, + # xgrid=xgrid, + # yerr=yerr, + # xerr=xerr, + # norm2one=norm2one, + # binning=True, + # sequence_type=sequence_type, + # label_texts=label_texts, + # skip_plot=True) + + # # this is the number of different counters + # num_sub_plots = len(self.clist) + + # # fitting and plotting the data + # l_plot = 1 # counter for single plots + + # for i, parameter in enumerate(parameters): + # # traverse all parameters of the sequence + # lt = label_texts[i] + # name = names[i] + + # x2plot = sequence_data[self.xcol][i] + # xerr2plot = sequence_data[self.xcol + 'Err'][i] + + # if fit_report > 0: + # # plot for basics and full fit reporting + # print('') + # print('='*10 + ' Parameter: ' + lt + ' ' + '='*15) + + # j = 0 # counter for counters ;) + # k = 1 # counter for subplots + # for counter in sequence_data: + # # traverse all counters in the sequence + + # # plot only y counters - next is the coresp. error + # if j >= 2 and j % 2 == 0: + + # # add the counter name to the label for not seperate plots + # if sequence_type == 'none': + # _lt = counter + # else: + # if plot_separate or num_sub_plots == 1: + # _lt = lt + # else: + # _lt = lt + ' | ' + counter + + # # get the fit models and fit parameters if they are lists/tupels + # if isinstance(mod, (list, tuple)): + # _mod = mod[k-1] + # else: + # _mod = mod + + # if last_res_as_par and i > 0: + # # use last results as start values for pars + # _pars = pars + # for pname, par in pars.items(): + # _pars[pname].value = res[counter][pname][i-1] + # else: + # if isinstance(pars, (list, tuple)): + # _pars = pars[k-1] + # else: + # _pars = pars + + # # get the actual y-data and -errors for plotting and fitting + # y2plot = sequence_data[counter][i] + # yerr2plot = sequence_data[counter + 'Err'][i] + + # # evaluate the select statement + # if select == '': + # # select all + # sel = np.ones_like(y2plot, dtype=bool) + # else: + # sel = eval(select) + + # # execute the select statement + # y2plot = y2plot[sel] + # x2plot = x2plot[sel] + # yerr2plot = yerr2plot[sel] + # xerr2plot = xerr2plot[sel] + + # # remove nans + # y2plot = y2plot[~np.isnan(y2plot)] + # x2plot = x2plot[~np.isnan(y2plot)] + # yerr2plot = yerr2plot[~np.isnan(y2plot)] + # xerr2plot = xerr2plot[~np.isnan(y2plot)] + + # # do the fitting with or without weighting the data + # if weights: + # out = _mod.fit(y2plot, _pars, x=x2plot, + # weights=1/yerr2plot, method=fit_method, + # nan_policy='propagate') + # else: + # out = _mod.fit(y2plot, _pars, x=x2plot, + # method=fit_method, nan_policy='propagate') + + # if fit_report > 0: + # # for basic and full fit reporting + # print('') + # print('-'*10 + ' ' + counter + ': ' + '-'*15) + # for key in out.best_values: + # print('{:>12}: {:>10.4e} '.format( + # key, out.best_values[key])) + + # # set the x-offset for delay scans - offset parameter in + # # the fit must be called 't0' + # if offset_t0: + # offsetX = out.best_values['t0'] + # else: + # offsetX = 0 + + # plt.figure(main_fig_num) # select the main figure + + # if plot_separate: + # # use subplot for separate plotting + # plt.subplot((num_sub_plots+num_sub_plots % 2)/2, 2, k) + + # # plot the fit and the data as errorbars + # x2plotFit = np.linspace( + # np.min(x2plot), np.max(x2plot), 10000) + # plot = plt.plot(x2plotFit-offsetX, + # out.eval(x=x2plotFit), '-', lw=2, alpha=1) + # plt.errorbar(x2plot-offsetX, y2plot, fmt=fmt, xerr=xerr2plot, + # yerr=yerr2plot, label=_lt, alpha=0.25, color=plot[0].get_color()) + + # if len(parameters) > 5: + # # move the legend outside the plot for more than + # # 5 sequence parameters + # plt.legend(bbox_to_anchor=(0., 1.08, 1, .102), frameon=True, + # loc=3, numpoints=1, ncol=3, mode="expand", + # borderaxespad=0.) + # else: + # plt.legend(frameon=True, loc=0, numpoints=1) + + # # set the axis limits, title, labels and gird + # if xlims: + # plt.xlim(xlims) + # if ylims: + # plt.ylim(ylims) + # if len(title_text) > 0: + # if isinstance(title_text, (list, tuple)): + # plt.title(title_text[k-1]) + # else: + # plt.title(title_text) + # else: + # plt.title(name) + + # if len(xtext) > 0: + # plt.xlabel(xtext) + + # if len(ytext) > 0: + # if isinstance(ytext, (list, tuple)): + # plt.ylabel(ytext[k-1]) + # else: + # plt.ylabel(ytext) + + # if grid_on: + # plt.grid(True) + + # # show the single fits and residuals + # if show_single: + # plt.figure(main_fig_num+l_plot, figsize=fig_size) + # gs = mpl.gridspec.GridSpec( + # 2, 1, height_ratios=[1, 3], hspace=0.1) + # ax1 = plt.subplot(gs[0]) + # markerline, stemlines, baseline = plt.stem( + # x2plot-offsetX, out.residual, markerfmt=' ', + # use_line_collection=True) + # plt.setp(stemlines, 'color', + # plot[0].get_color(), 'linewidth', 2, alpha=0.5) + # plt.setp(baseline, 'color', 'k', 'linewidth', 0) + + # ax1.xaxis.tick_top() + # ax1.yaxis.set_major_locator(plt.MaxNLocator(3)) + # plt.ylabel('Residuals') + # if xlims: + # plt.xlim(xlims) + # if ylims: + # plt.ylim(ylims) + + # if len(xtext) > 0: + # plt.xlabel(xtext) + + # if grid_on: + # plt.grid(True) + + # if len(title_text) > 0: + # if isinstance(title_text, (list, tuple)): + # plt.title(title_text[k-1]) + # else: + # plt.title(title_text) + # else: + # plt.title(name) + # ax2 = plt.subplot(gs[1]) + # x2plotFit = np.linspace( + # np.min(x2plot), np.max(x2plot), 1000) + # ax2.plot(x2plotFit-offsetX, out.eval(x=x2plotFit), + # '-', lw=2, alpha=1, color=plot[0].get_color()) + # ax2.errorbar(x2plot-offsetX, y2plot, fmt=fmt, xerr=xerr2plot, + # yerr=yerr2plot, label=_lt, alpha=0.25, + # color=plot[0].get_color()) + # plt.legend(frameon=True, loc=0, numpoints=1) + + # if xlims: + # plt.xlim(xlims) + # if ylims: + # plt.ylim(ylims) + + # if len(xtext) > 0: + # plt.xlabel(xtext) + + # if len(ytext) > 0: + # if isinstance(ytext, (list, tuple)): + # plt.ylabel(ytext[k-1]) + # else: + # plt.ylabel(ytext) + + # if grid_on: + # plt.grid(True) + + # l_plot += 1 + # if fit_report > 1: + # # for full fit reporting + # print('_'*40) + # print(out.fit_report()) + + # # add the fit results to the returns + # for pname, par in _pars.items(): + # res[counter][pname] = np.append( + # res[counter][pname], out.best_values[pname]) + # res[counter][pname + 'Err'] = np.append( + # res[counter][pname + 'Err'], out.params[pname].stderr) + + # res[counter]['chisqr'] = np.append( + # res[counter]['chisqr'], out.chisqr) + # res[counter]['redchi'] = np.append( + # res[counter]['redchi'], out.redchi) + # res[counter]['CoM'] = np.append( + # res[counter]['CoM'], sum(y2plot*x2plot)/sum(y2plot)) + # res[counter]['int'] = np.append( + # res[counter]['int'], sum(y2plot)) + # res[counter]['fit'] = np.append(res[counter]['fit'], out) + + # k += 1 + + # j += 1 + + # plt.figure(main_fig_num) # set as active figure + + # return res, parameters, sequence_data # move to the end for plotting From 132f42541c96af08fff329cb37675344f1ecdc46 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Fri, 10 Dec 2021 23:43:41 +0100 Subject: [PATCH 14/24] [WIP] plotting of fits --- pyEvalData/evaluation.py | 304 +++++++++++---------------------------- 1 file changed, 85 insertions(+), 219 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index c40f091..7cb6df6 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -586,11 +586,16 @@ def eval_scan_sequence(self, scan_sequence, xgrid=[], yerr='std', xerr='std', no return sequence_data, parameters, names def _plot_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, label_text='', fmt='-o', - **kwargs): + plot_separate=False, **kwargs): plots = [] # plot all keys in the clist - for counter in self.clist: + for i, counter in enumerate(self.clist): # iterate the counter list + + if plot_separate: + # use subplot for separate plotting + plt.subplot(1, len(self.clist), i+1) + if len(label_text) == 0: # if no label_text is given use the counter name lt = counter @@ -618,7 +623,7 @@ def _plot_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, label_text='', return plots def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm2one=False, - binning=True, label_text='', fmt='-o', **kwargs): + binning=True, label_text='', fmt='-o', plot_separate=False, **kwargs): """plot_scans Plot a list of scans from the source file. @@ -652,12 +657,13 @@ def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm binning=binning) _ = self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, - fmt=fmt, **kwargs) + fmt=fmt, plot_separate=plot_separate, **kwargs) return y2plot, x2plot, yerr2plot, xerr2plot, name def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr='std', - norm2one=False, binning=True, label_format='', fmt='-o', **kwargs): + norm2one=False, binning=True, label_format='', fmt='-o', + plot_separate=False, **kwargs): """Plot a list of scans from the source file. Various plot parameters are provided. The plotted data are returned. @@ -709,6 +715,7 @@ def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr names[i], label_text=lt, fmt=fmt, + plot_separate=plot_separate, **kwargs) return sequence_data, parameters, names, label_texts @@ -717,9 +724,7 @@ def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, we fit_method='leastsq', nan_policy='propagate'): res = {} # initialize the results dict report = [] - param_names = mod.param_names.copy() - param_names.insert(0, 'counter') - report_1 = [param_names] + report_1 = [] report_2 = {} for counter in y2plot: @@ -773,9 +778,9 @@ def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, we return res, report def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offset_t0=False, - label_text='', fmt='o'): + label_text='', fmt='o', plot_separate=False): plots = self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, - fmt=fmt, alpha=0.25) + fmt=fmt, alpha=0.25, plot_separate=plot_separate) # set the x-offset for delay scans - offset parameter in # the fit must be called 't0' @@ -789,12 +794,25 @@ def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offse offsetX = 0 for i, counter in enumerate(y2plot): + if plot_separate: + # use subplot for separate plotting + plt.subplot(1, len(self.clist), i+1) # plot the fit and the data as errorbars x2plotFit = np.linspace( np.min(x2plot), np.max(x2plot), 10000) plt.plot(x2plotFit-offsetX, res[counter]['fit'].eval(x=x2plotFit), '-', lw=2, alpha=1, color=plots[i][0].get_color()) + # figure formatting + if True: # len(parameters)*len(self.clist) > 6: + # move the legend outside the plot for more than + # 5 sequence parameters + plt.legend(bbox_to_anchor=(0., 1.08, 1, .102), frameon=True, + loc=3, numpoints=1, ncol=3, mode="expand", + borderaxespad=0.) + else: + plt.legend(frameon=True, loc=0, numpoints=1) + def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, label_text='', select='', fit_report=0, weights=False, fit_method='leastsq', nan_policy='propagate', offset_t0=False, fmt='o'): @@ -817,7 +835,8 @@ def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm # print the fit report if fit_report > 0: - print(tabulate(report[0][1:], headers=report[0][0], tablefmt="fancy_grid")) + print(tabulate(report[0], headers=['counter', *mod.param_names], + tablefmt="fancy_grid")) if fit_report > 1: for counter in y2plot: head_len = int(len(counter)/2) @@ -826,23 +845,41 @@ def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm else: fix = 0 - print("\n" + "="*(39-head_len-fix) + " {:} ".format(counter) + "="*(39-head_len)) + print('\n' + '='*(39-head_len-fix) + ' {:} '.format(counter) + '='*(39-head_len)) print(report[1][counter]) def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, label_format='', select='', fit_report=0, weights=False, fit_method='leastsq', nan_policy='propagate', - offset_t0=False, fmt='o'): + last_res_as_par=False, offset_t0=False, plot_separate=False, fmt='o'): # load data sequence_data, parameters, names = self.eval_scan_sequence( scan_sequence, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, binning=binning) res = {} + report_1 = [] + report_2 = [] + label_texts = [] for counter in self.clist: res[counter] = {} for i, ((scan_list, parameter), name) in enumerate(zip(scan_sequence, names)): - # iterate the scan sequence + # get the fit models and fit parameters if they are lists/tupels + if isinstance(mod, (list, tuple)): + _mod = mod[i] + else: + _mod = mod + + if last_res_as_par and i > 0: + # use last results as start values for pars + _pars = pars + for pname, par in pars.items(): + _pars[pname].value = res[counter][pname][i-1] + else: + if isinstance(pars, (list, tuple)): + _pars = pars[i] + else: + _pars = pars lt = '#{:d}'.format(i+1) if len(label_format) > 0: @@ -851,19 +888,22 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr except ValueError: self.log.warning('Could not apply \'label_format\' to parameter!') + label_texts.append(lt) # extract clist und xcol from sequence_data y2plot = {c: sequence_data[c][i] for c in self.clist} yerr2plot = {c: sequence_data[c + 'Err'][i] for c in self.clist} x2plot = sequence_data[self.xcol][i] xerr2plot = sequence_data[self.xcol + 'Err'][i] # fit the model and parameters to the data - _res, report = self._fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, - weights, fit_method=fit_method, nan_policy='propagate') + _res, _report = self._fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, _mod, _pars, + select, weights, fit_method=fit_method, + nan_policy='propagate') # plot the data and fit self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, _res, label_text=lt, - offset_t0=offset_t0, fmt=fmt) + offset_t0=offset_t0, fmt=fmt, plot_separate=plot_separate) + # store the results for counter in self.clist: for key in _res[counter].keys(): try: @@ -871,6 +911,30 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr except KeyError: res[counter][key] = [_res[counter][key]] + # store the the report + report_1.append(['>> ' + lt + ' <<']) + for rep in _report[0]: + report_1.append(rep) + report_2.append(_report[1]) + + # print the basic fit report + if fit_report > 0: + print(tabulate(report_1, headers=['counter', *mod.param_names], + tablefmt="fancy_grid")) + # print the advanced fit report + if fit_report > 1: + for i, lt in enumerate(label_texts): + lt_len = int(len(str(lt))/2) + fix = 1 if np.mod(len(lt), 2) != 0 else 0 + print('\n' + '_'*(39-lt_len-fix) + ' {:} '.format(lt) + '_'*(39-lt_len)) + for counter in self.clist: + head_len = int(len(counter)/2) + fix = 1 if np.mod(len(counter), 2) != 0 else 0 + + print('\n' + '='*(39-head_len-fix) + ' {:} '.format(counter) + + '='*(39-head_len)) + print(report_2[i][counter]) + return res, parameters, sequence_data # def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_size=[], @@ -938,62 +1002,7 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr # # get the last open figure number # main_fig_num = self.get_last_fig_number() - # if not fig_size: - # # use default figure size if none is given - # fig_size = mpl.rcParams['figure.figsize'] - - # # initialization of returns - # res = {} # initialize the results dict - - # for i, counter in enumerate(self.clist): - # # traverse all counters in the counter list to initialize the returns - - # # results for this counter is again a Dict - # res[counter] = {} - - # if isinstance(pars, (list, tuple)): - # # the fit paramters might individual for each counter - # _pars = pars[i] - # else: - # _pars = pars - - # for pname, par in _pars.items(): - # # add a dict key for each fit parameter in the result dict - # res[counter][pname] = [] - # res[counter][pname + 'Err'] = [] - - # # add some more results - # res[counter]['chisqr'] = [] - # res[counter]['redchi'] = [] - # res[counter]['CoM'] = [] - # res[counter]['int'] = [] - # res[counter]['fit'] = [] - - # if len(sequence_data) > 0: - # # get only the parameters - # _, parameters, names, label_texts = self.plot_scan_sequence( - # scan_sequence, - # xgrid=xgrid, - # yerr=yerr, - # xerr=xerr, - # norm2one=norm2one, - # binning=True, - # sequence_type=sequence_type, - # label_texts=label_texts, - # skip_plot=True) - # else: - # # get the sequence data and parameters - # sequence_data, parameters, names, label_texts = self.plot_scan_sequence( - # scan_sequence, - # xgrid=xgrid, - # yerr=yerr, - # xerr=xerr, - # norm2one=norm2one, - # binning=True, - # sequence_type=sequence_type, - # label_texts=label_texts, - # skip_plot=True) - + # # this is the number of different counters # num_sub_plots = len(self.clist) @@ -1001,17 +1010,6 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr # l_plot = 1 # counter for single plots # for i, parameter in enumerate(parameters): - # # traverse all parameters of the sequence - # lt = label_texts[i] - # name = names[i] - - # x2plot = sequence_data[self.xcol][i] - # xerr2plot = sequence_data[self.xcol + 'Err'][i] - - # if fit_report > 0: - # # plot for basics and full fit reporting - # print('') - # print('='*10 + ' Parameter: ' + lt + ' ' + '='*15) # j = 0 # counter for counters ;) # k = 1 # counter for subplots @@ -1021,79 +1019,6 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr # # plot only y counters - next is the coresp. error # if j >= 2 and j % 2 == 0: - # # add the counter name to the label for not seperate plots - # if sequence_type == 'none': - # _lt = counter - # else: - # if plot_separate or num_sub_plots == 1: - # _lt = lt - # else: - # _lt = lt + ' | ' + counter - - # # get the fit models and fit parameters if they are lists/tupels - # if isinstance(mod, (list, tuple)): - # _mod = mod[k-1] - # else: - # _mod = mod - - # if last_res_as_par and i > 0: - # # use last results as start values for pars - # _pars = pars - # for pname, par in pars.items(): - # _pars[pname].value = res[counter][pname][i-1] - # else: - # if isinstance(pars, (list, tuple)): - # _pars = pars[k-1] - # else: - # _pars = pars - - # # get the actual y-data and -errors for plotting and fitting - # y2plot = sequence_data[counter][i] - # yerr2plot = sequence_data[counter + 'Err'][i] - - # # evaluate the select statement - # if select == '': - # # select all - # sel = np.ones_like(y2plot, dtype=bool) - # else: - # sel = eval(select) - - # # execute the select statement - # y2plot = y2plot[sel] - # x2plot = x2plot[sel] - # yerr2plot = yerr2plot[sel] - # xerr2plot = xerr2plot[sel] - - # # remove nans - # y2plot = y2plot[~np.isnan(y2plot)] - # x2plot = x2plot[~np.isnan(y2plot)] - # yerr2plot = yerr2plot[~np.isnan(y2plot)] - # xerr2plot = xerr2plot[~np.isnan(y2plot)] - - # # do the fitting with or without weighting the data - # if weights: - # out = _mod.fit(y2plot, _pars, x=x2plot, - # weights=1/yerr2plot, method=fit_method, - # nan_policy='propagate') - # else: - # out = _mod.fit(y2plot, _pars, x=x2plot, - # method=fit_method, nan_policy='propagate') - - # if fit_report > 0: - # # for basic and full fit reporting - # print('') - # print('-'*10 + ' ' + counter + ': ' + '-'*15) - # for key in out.best_values: - # print('{:>12}: {:>10.4e} '.format( - # key, out.best_values[key])) - - # # set the x-offset for delay scans - offset parameter in - # # the fit must be called 't0' - # if offset_t0: - # offsetX = out.best_values['t0'] - # else: - # offsetX = 0 - # plt.figure(main_fig_num) # select the main figure # if plot_separate: @@ -1117,31 +1042,7 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr # else: # plt.legend(frameon=True, loc=0, numpoints=1) - # # set the axis limits, title, labels and gird - # if xlims: - # plt.xlim(xlims) - # if ylims: - # plt.ylim(ylims) - # if len(title_text) > 0: - # if isinstance(title_text, (list, tuple)): - # plt.title(title_text[k-1]) - # else: - # plt.title(title_text) - # else: - # plt.title(name) - - # if len(xtext) > 0: - # plt.xlabel(xtext) - - # if len(ytext) > 0: - # if isinstance(ytext, (list, tuple)): - # plt.ylabel(ytext[k-1]) - # else: - # plt.ylabel(ytext) - - # if grid_on: - # plt.grid(True) - + # # # show the single fits and residuals # if show_single: # plt.figure(main_fig_num+l_plot, figsize=fig_size) @@ -1186,45 +1087,10 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr # color=plot[0].get_color()) # plt.legend(frameon=True, loc=0, numpoints=1) - # if xlims: - # plt.xlim(xlims) - # if ylims: - # plt.ylim(ylims) - - # if len(xtext) > 0: - # plt.xlabel(xtext) - - # if len(ytext) > 0: - # if isinstance(ytext, (list, tuple)): - # plt.ylabel(ytext[k-1]) - # else: - # plt.ylabel(ytext) - - # if grid_on: - # plt.grid(True) + # # l_plot += 1 - # if fit_report > 1: - # # for full fit reporting - # print('_'*40) - # print(out.fit_report()) - - # # add the fit results to the returns - # for pname, par in _pars.items(): - # res[counter][pname] = np.append( - # res[counter][pname], out.best_values[pname]) - # res[counter][pname + 'Err'] = np.append( - # res[counter][pname + 'Err'], out.params[pname].stderr) - - # res[counter]['chisqr'] = np.append( - # res[counter]['chisqr'], out.chisqr) - # res[counter]['redchi'] = np.append( - # res[counter]['redchi'], out.redchi) - # res[counter]['CoM'] = np.append( - # res[counter]['CoM'], sum(y2plot*x2plot)/sum(y2plot)) - # res[counter]['int'] = np.append( - # res[counter]['int'], sum(y2plot)) - # res[counter]['fit'] = np.append(res[counter]['fit'], out) + # # k += 1 From a511c377b382105d45e45c803f6fd75631815109 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Tue, 21 Dec 2021 13:26:30 +0100 Subject: [PATCH 15/24] fix: error selection --- pyEvalData/evaluation.py | 29 ++++++++++++++--------------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 7cb6df6..530ea01 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -487,24 +487,18 @@ def eval_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False avg_data, std_data, err_data, name = self.avg_N_bin_scans( scan_list, xgrid=xgrid, binning=binning) - # set the error data - if xerr == 'std': - xerr_data = std_data - elif xerr == 'err': - xerr_data = err_data - else: - xerr_data = None + - if yerr == 'std': - yerr_data = std_data - elif yerr == 'err': - yerr_data = err_data - else: - yerr_data = None # set x-data and errors x2plot = avg_data[self.xcol] - xerr2plot = xerr_data[self.xcol] + # set the error data + if xerr == 'std': + xerr2plot = std_data[self.xcol] + elif xerr == 'err': + xerr2plot = err_data[self.xcol] + else: + xerr2plot = None # plot all keys in the clist for col in self.clist: @@ -512,7 +506,12 @@ def eval_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False # save the counter data and errors in the ordered dictionary y2plot[col] = avg_data[col] - yerr2plot[col] = yerr_data[col] + if yerr == 'std': + yerr2plot[col] = std_data[col] + elif yerr == 'err': + yerr2plot[col] = err_data[col] + else: + yerr2plot[col] = None if norm2one: # normalize the y-data to 1 for t < t0 From 3165a812955622ea056578dd175a96ee5c0a5b81 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Tue, 21 Dec 2021 14:02:38 +0100 Subject: [PATCH 16/24] fix norm2one for yerr --- pyEvalData/evaluation.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 530ea01..a8c1f54 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -487,9 +487,6 @@ def eval_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False avg_data, std_data, err_data, name = self.avg_N_bin_scans( scan_list, xgrid=xgrid, binning=binning) - - - # set x-data and errors x2plot = avg_data[self.xcol] # set the error data @@ -518,7 +515,8 @@ def eval_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False # e.g. for delay scans before_zero = y2plot[col][x2plot <= self.t0] y2plot[col] = y2plot[col]/np.mean(before_zero) - yerr2plot[col] = yerr2plot[col]/np.mean(before_zero) + if yerr2plot[col] is not None: + yerr2plot[col] = yerr2plot[col]/np.mean(before_zero) return y2plot, x2plot, yerr2plot, xerr2plot, name From e75edf6e693c6ab58f05acceab4007ae598cbde6 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Tue, 21 Dec 2021 14:28:19 +0100 Subject: [PATCH 17/24] return fit results as arrays instead of lists --- pyEvalData/evaluation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index a8c1f54..3e7b804 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -904,9 +904,9 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr for counter in self.clist: for key in _res[counter].keys(): try: - res[counter][key].append(_res[counter][key]) + res[counter][key] = np.append(res[counter][key], _res[counter][key]) except KeyError: - res[counter][key] = [_res[counter][key]] + res[counter][key] = np.array([_res[counter][key]]) # store the the report report_1.append(['>> ' + lt + ' <<']) From 550780a5592d3218f58a2fa7e6835c50c1b1a0ad Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Fri, 3 Feb 2023 22:29:37 +0100 Subject: [PATCH 18/24] add Pt_Nb to scan data !BUGGY! --- pyEvalData/io/scan.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/pyEvalData/io/scan.py b/pyEvalData/io/scan.py index 95fe57d..61b060c 100644 --- a/pyEvalData/io/scan.py +++ b/pyEvalData/io/scan.py @@ -30,7 +30,7 @@ __docformat__ = 'restructuredtext' import numpy as np - +from numpy.core.records import fromarrays class Scan(object): """Scan @@ -176,6 +176,20 @@ def clear_data(self): @property def data(self): + if (self._data is not None) and ('Pt_No' not in self._data.dtype.names): + # iterate through data fields + data_list = [] + dtype_list = [] + for field in self._data.dtype.names: + data_list.append(self._data[field]) + dtype_list.append((field, self._data[field].dtype, self._data[field].shape)) + + data_list.append(np.arange(self._data[field].shape[0])+1) + dtype_list.append(('Pt_No', int, self._data[field].shape)) + + if len(data_list) > 0: + self._data = fromarrays(data_list, dtype=dtype_list) + return self._data @data.setter From 5f3bc0b305efe254aedf7362f8e692a6f63df3d5 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Fri, 29 Dec 2023 00:14:44 +0100 Subject: [PATCH 19/24] move counter string functions to helpers --- pyEvalData/evaluation.py | 503 ++------------------------------------- pyEvalData/helpers.py | 134 ++++++++++- 2 files changed, 150 insertions(+), 487 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index d617459..3a0f96f 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -28,11 +28,9 @@ import numpy as np import collections import matplotlib.pyplot as plt -import matplotlib as mpl -import re from uncertainties import unumpy from tabulate import tabulate -from .helpers import bin_data +from .helpers import bin_data, traverse_counters, resolve_counter_name, col_string_to_eval_string __all__ = ['Evaluation'] @@ -87,123 +85,6 @@ def __init__(self, source): self.apply_data_filter = False self.data_filters = ['evaluatable statement'] - def traverse_counters(self, source_cols=''): - """traverse_counters - - Traverse all counters and replace all predefined counter definitions. - Returns also a list of the included source counters for error propagation. - - Args: - source_cols (list[str], optional): counters in the raw source data. - - Returns: - (tuple): - - *resolved_counters (list[str])* - resolved counters. - - *source_counters (list[str])* - all source counters in the resolved counters. - - """ - resolved_counters = [] - source_counters = [] - - for counter_name in self.clist: - # resolve each counter in the clist - counter_string, res_source_counters = \ - self.resolve_counter_name(counter_name, source_cols) - - resolved_counters.append(counter_string) - source_counters.extend(res_source_counters) - - return resolved_counters, list(set(source_counters)) - - def resolve_counter_name(self, col_name, source_cols=''): - """resolve_counter_name - - Replace all predefined counter definitions in a given counter name. - The function works recursively. - - Args: - col_name (str): initial counter string. - source_cols (list[str], optional): columns in the source data. - - Returns: - (tuple): - - *col_string (str)* - resolved counter string. - - *source_counters (list[str])* - source counters in the col_string - - """ - recall = False # boolean to stop recursive calls - source_counters = [] - col_string = col_name - - for find_cdef in self.cdef.keys(): - # check for all predefined counters - search_pattern = r'\b' + find_cdef + r'\b' - if re.search(search_pattern, col_string) is not None: - if self.cdef[find_cdef] in source_cols: - # this counter definition is a base source counter - source_counters.append(self.cdef[find_cdef]) - # found a predefined counter - # recursive call if predefined counter must be resolved again - recall = True - # replace the counter definition in the string - (col_string, _) = re.subn(search_pattern, - '(' + self.cdef[find_cdef] + ')', col_string) - - if recall: - # do the recursive call - col_string, rec_source_counters = self.resolve_counter_name(col_string, source_cols) - source_counters.extend(rec_source_counters) - - for find_cdef in source_cols: - # check for all base source counters - search_pattern = r'\b' + find_cdef + r'\b' - if re.search(search_pattern, col_string) is not None: - source_counters.append(find_cdef) - - return col_string, source_counters - - def col_string_to_eval_string(self, col_string, array_name='source_data'): - """Use regular expressions in order to generate an evaluateable string - from the counter string in order to append the new counter to the - source data. - - Args: - col_string (str) : Definition of the counter. - mode (int) : Flag for different modes - - Returns: - eval_string (str): Evaluateable string to add the new counter - to the source data. - - """ - - # search for alphanumeric counter names in col_string - iterator = re.finditer( - '([0-9]*[a-zA-Z\_]+[0-9]*[a-zA-Z]*)*', col_string) - # these are keys which should not be replaced but evaluated - math_keys = list(self.math_keys) - keys = math_keys.copy() - - for key in iterator: - # traverse all found counter names - if len(key.group()) > 0: - # the match is > 0 - if not key.group() in keys: - # the counter name is not in the keys list - - # remember this counter name in the key list in order - # not to replace it again - keys.append(key.group()) - # the actual replacement - (col_string, _) = re.subn(r'\b'+key.group()+r'\b', - array_name + '[\'' + key.group() + '\']', col_string) - - # add 'np.' prefix to numpy functions/math keys - for mk in math_keys: - if mk not in self.ignore_keys: - (col_string, _) = re.subn(r'\b' + mk + r'\b', 'np.' + mk, col_string) - return col_string - def add_custom_counters(self, source_data, scan_num, source_counters): """Add custom counters to the source data array. This is a stub for child classes. @@ -232,8 +113,9 @@ def filter_data(self, data): """ res = [] for data_filter in self.data_filters: - name, _ = self.resolve_counter_name(data_filter) - idx = eval(self.col_string_to_eval_string(name, array_name='data')) + name, _ = resolve_counter_name(self.cdef, data_filter) + idx = eval(col_string_to_eval_string( + name, self.math_keys, self.ignore_keys, array_name='data')) if len(res) == 0: res = idx else: @@ -326,7 +208,9 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): # resolve the clist and retrieve the resolves counters and the # necessary base source counters for error propagation - resolved_counters, source_counters = self.traverse_counters(source_cols) + resolved_counters, source_counters = traverse_counters(self.clist, + self.cdef, + source_cols) # counter names and resolved strings for further calculations if self.statistic_type == 'poisson' or self.propagate_errors: @@ -355,8 +239,8 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): for col_string, col_name in zip(col_strings, col_names): # traverse the counters in the clist and append to data if not # already present - eval_string = self.col_string_to_eval_string( - col_string, array_name='source_data') + eval_string = col_string_to_eval_string( + col_string, self.math_keys, self.ignore_keys,array_name='source_data') if len(data) == 0: data = np.array(eval(eval_string), dtype=[(col_name, float)]) @@ -426,15 +310,17 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): np.array(yerr)) for col_name, col_string in zip(self.clist, resolved_counters): - eval_string = self.col_string_to_eval_string( - col_string, array_name='unc_data_err') + eval_string = col_string_to_eval_string( + col_string, self.math_keys, self.ignore_keys, array_name='unc_data_err' + ) temp = eval(eval_string) avg_data[col_name] = unumpy.nominal_values(temp) err_data[col_name] = unumpy.std_devs(temp) - eval_string = self.col_string_to_eval_string( - col_string, array_name='unc_data_std') + eval_string = col_string_to_eval_string( + col_string, self.math_keys, self.ignore_keys, array_name='unc_data_std' + ) temp = eval(eval_string) std_data[col_name] = unumpy.std_devs(temp) else: @@ -450,8 +336,8 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): statistic=bin_stat) else: for col_name, col_string in zip(self.clist, resolved_counters): - eval_string = self.col_string_to_eval_string( - col_string, array_name='source_data') + eval_string = col_string_to_eval_string( + col_string, self.math_keys, self.ignore_keys, array_name='source_data') temp = eval(eval_string) avg_data[col_name] = temp avg_data[self.xcol] = concat_data[self.xcol] @@ -934,361 +820,6 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr return res, parameters, sequence_data - # def fit_scan_sequence(self, scan_sequence, mod, pars, ylims=[], xlims=[], fig_size=[], - # xgrid=[], yerr='std', xerr='std', norm2one=False, - # binning=True, sequence_type='', label_texts='', - # title_text='', ytext='', xtext='', select='', - # fit_report=0, show_single=False, weights=False, - # fit_method='leastsq', offset_t0=False, - # plot_separate=False, grid_on=True, - # last_res_as_par=False, sequence_data=[], fmt='o'): - # """Fit, plot, and return the data of a scan sequence. - - # Args: - # scan_sequence (List[ - # List/Tuple[List[int], - # int/str]]) : Sequence of scan lists and parameters. - # mod (Model[lmfit]) : lmfit model for fitting the data. - # pars (Parameters[lmfit]) : lmfit parameters for fitting the data. - # ylims (Optional[ndarray]) : ylim for the plot. - # xlims (Optional[ndarray]) : xlim for the plot. - # fig_size (Optional[ndarray]) : Figure size of the figure. - # xgrid (Optional[ndarray]) : Grid to bin the data to - - # default in empty so use the - # x-axis of the first scan. - # yerr (Optional[ndarray]) : Type of the errors in y: [err, std, none] - # default is 'std'. - # xerr (Optional[ndarray]) : Type of the errors in x: [err, std, none] - # default is 'std'. - # norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 - # default is False. - # sequence_type (Optional[str]): Type of the sequence: [fluence, delay, - # energy, theta] - default is fluence. - # label_texts (Optional[str]) : list of Labels of the plot - default is none. - # title_text (Optional[str]) : Title of the figure - default is none. - # ytext (Optional[str]) : y-Label of the plot - defaults is none. - # xtext (Optional[str]) : x-Label of the plot - defaults is none. - # select (Optional[str]) : String to evaluate as select statement - # for the fit region - default is none - # fit_report (Optional[int]) : Set the fit reporting level: - # [0: none, 1: basic, 2: full] - # default 0. - # show_single (Optional[bool]) : Plot each fit seperately - default False. - # weights (Optional[bool]) : Use weights for fitting - default False. - # fit_method (Optional[str]) : Method to use for fitting; refer to - # lmfit - default is 'leastsq'. - # offset_t0 (Optional[bool]) : Offset time scans by the fitted - # t0 parameter - default False. - # plot_separate (Optional[bool]):A single plot for each counter - # default False. - # grid_on (Optional[bool]) : Add grid to plot - default is True. - # last_res_as_par (Optional[bool]): Use the last fit result as start - # values for next fit - default is False. - # sequence_data (Optional[ndarray]): actual exp. data are externally given. - # default is empty - # fmt (Optional[str]) : format string of the plot - defaults is -o. - - - # Returns: - # res (Dict[ndarray]) : Fit results. - # parameters (ndarray) : Parameters of the sequence. - # sequence_data (OrderedDict) : Dictionary of the averaged scan data.equenceData - - # """ - - # # get the last open figure number - # main_fig_num = self.get_last_fig_number() - - - # # this is the number of different counters - # num_sub_plots = len(self.clist) - - # # fitting and plotting the data - # l_plot = 1 # counter for single plots - - # for i, parameter in enumerate(parameters): - - # j = 0 # counter for counters ;) - # k = 1 # counter for subplots - # for counter in sequence_data: - # # traverse all counters in the sequence - - # # plot only y counters - next is the coresp. error - # if j >= 2 and j % 2 == 0: - - # plt.figure(main_fig_num) # select the main figure - - # if plot_separate: - # # use subplot for separate plotting - # plt.subplot((num_sub_plots+num_sub_plots % 2)/2, 2, k) - - # # plot the fit and the data as errorbars - # x2plotFit = np.linspace( - # np.min(x2plot), np.max(x2plot), 10000) - # plot = plt.plot(x2plotFit-offsetX, - # out.eval(x=x2plotFit), '-', lw=2, alpha=1) - # plt.errorbar(x2plot-offsetX, y2plot, fmt=fmt, xerr=xerr2plot, - # yerr=yerr2plot, label=_lt, alpha=0.25, color=plot[0].get_color()) - - # if len(parameters) > 5: - # # move the legend outside the plot for more than - # # 5 sequence parameters - # plt.legend(bbox_to_anchor=(0., 1.08, 1, .102), frameon=True, - # loc=3, numpoints=1, ncol=3, mode="expand", - # borderaxespad=0.) - # else: - # plt.legend(frameon=True, loc=0, numpoints=1) - - # - # # show the single fits and residuals - # if show_single: - # plt.figure(main_fig_num+l_plot, figsize=fig_size) - # gs = mpl.gridspec.GridSpec( - # 2, 1, height_ratios=[1, 3], hspace=0.1) - # ax1 = plt.subplot(gs[0]) - # markerline, stemlines, baseline = plt.stem( - # x2plot-offsetX, out.residual, markerfmt=' ', - # use_line_collection=True) - # plt.setp(stemlines, 'color', - # plot[0].get_color(), 'linewidth', 2, alpha=0.5) - # plt.setp(baseline, 'color', 'k', 'linewidth', 0) - - # ax1.xaxis.tick_top() - # ax1.yaxis.set_major_locator(plt.MaxNLocator(3)) - # plt.ylabel('Residuals') - # if xlims: - # plt.xlim(xlims) - # if ylims: - # plt.ylim(ylims) - - # if len(xtext) > 0: - # plt.xlabel(xtext) - - # if grid_on: - # plt.grid(True) - - # if len(title_text) > 0: - # if isinstance(title_text, (list, tuple)): - # plt.title(title_text[k-1]) - # else: - # plt.title(title_text) - # else: - # plt.title(name) - # ax2 = plt.subplot(gs[1]) - # x2plotFit = np.linspace( - # np.min(x2plot), np.max(x2plot), 1000) - # ax2.plot(x2plotFit-offsetX, out.eval(x=x2plotFit), - # '-', lw=2, alpha=1, color=plot[0].get_color()) - # ax2.errorbar(x2plot-offsetX, y2plot, fmt=fmt, xerr=xerr2plot, - # yerr=yerr2plot, label=_lt, alpha=0.25, - # color=plot[0].get_color()) - # plt.legend(frameon=True, loc=0, numpoints=1) - - # - - # l_plot += 1 - # - - # k += 1 - - # j += 1 - - # plt.figure(main_fig_num) # set as active figure - - # return res, parameters, sequence_data - -# move to the end for plotting - - def get_last_fig_number(self): - """get_last_fig_number - - Return the last figure number of all opened figures for plotting - data in the same figure during for-loops. - - Returns: - fig_number (int): last figure number of all opened figures. - - """ - try: - # get the number of all opened figures - fig_number = mpl._pylab_helpers.Gcf.get_active().num - except Exception: - # there are no figures open - fig_number = 1 - - return fig_number - - def get_next_fig_number(self): - """get_next_fig_number - - Return the number of the next available figure. - - Returns: - next_fig_number (int): next figure number of all opened figures. - - """ - return self.get_last_fig_number() + 1 - - # def plot_mesh_scan(self, scan_num, skip_plot=False, grid_on=False, ytext='', xtext='', - # levels=20, cbar=True): - # """Plot a single mesh scan from the source file. - # Various plot parameters are provided. - # The plotted data are returned. - - # Args: - # scan_num (int) : Scan number of the source scan. - # skip_plot (Optional[bool]) : Skip plotting, just return data - # default is False. - # grid_on (Optional[bool]) : Add grid to plot - default is False. - # ytext (Optional[str]) : y-Label of the plot - defaults is none. - # xtext (Optional[str]) : x-Label of the plot - defaults is none. - # levels (Optional[int]) : levels of contour plot - defaults is 20. - # cbar (Optional[bool]) : Add colorbar to plot - default is True. - - # Returns: - # xx, yy, zz : x,y,z data which was plotted - - # """ - - # from matplotlib.mlab import griddata - # from matplotlib import gridspec - - # # read data from source file - # try: - # # try to read data of this scan - # source_data = self.get_scan_data(scan_num) - # except Exception: - # print('Scan #' + int(scan_num) + ' not found, skipping') - - # dt = source_data.dtype - # dt = dt.descr - - # xmotor = dt[0][0] - # ymotor = dt[1][0] - - # X = source_data[xmotor] - # Y = source_data[ymotor] - - # xx = np.sort(np.unique(X)) - # yy = np.sort(np.unique(Y)) - - # if len(self.clist) > 1: - # print('WARNING: Only the first counter of the clist is plotted.') - - # Z = source_data[self.clist[0]] - - # zz = griddata(X, Y, Z, xx, yy, interp='linear') - - # if not skip_plot: - - # if cbar: - # gs = gridspec.GridSpec(4, 2, - # width_ratios=[3, 1], - # height_ratios=[0.2, 0.1, 1, 3] - # ) - # k = 4 - # else: - # gs = gridspec.GridSpec(2, 2, - # width_ratios=[3, 1], - # height_ratios=[1, 3] - # ) - # k = 0 - - # ax1 = plt.subplot(gs[0+k]) - - # plt.plot(xx, np.mean(zz, 0), label='mean') - - # plt.plot(xx, zz[np.argmax(np.mean(zz, 1)), :], label='peak') - - # plt.xlim([min(xx), max(xx)]) - # plt.legend(loc=0) - # ax1.xaxis.tick_top() - # if grid_on: - # plt.grid(True) - - # plt.subplot(gs[2+k]) - - # plt.contourf(xx, yy, zz, levels, cmap='viridis') - - # plt.xlabel(xmotor) - # plt.ylabel(ymotor) - - # if len(xtext) > 0: - # plt.xlabel(xtext) - - # if len(ytext) > 0: - # plt.ylabel(ytext) - - # if grid_on: - # plt.grid(True) - - # if cbar: - # cb = plt.colorbar(cax=plt.subplot( - # gs[0]), orientation='horizontal') - # cb.ax.xaxis.set_ticks_position('top') - # cb.ax.xaxis.set_label_position('top') - - # ax4 = plt.subplot(gs[3+k]) - - # plt.plot(np.mean(zz, 1), yy) - # plt.plot(zz[:, np.argmax(np.mean(zz, 0))], yy) - # plt.ylim([np.min(yy), np.max(yy)]) - - # ax4.yaxis.tick_right() - # if grid_on: - # plt.grid(True) - - # return xx, yy, zz - - # def export_scan_sequence(self, scan_sequence, path, fileName, yerr='std', - # xerr='std', xgrid=[], norm2one=False, binning=True): - # """Exports source data for each scan list in the sequence as individual file. - - # Args: - # scan_sequence (List[ - # List/Tuple[List[int], - # int/str]]) : Sequence of scan lists and parameters. - # path (str) : Path of the file to export to. - # fileName (str) : Name of the file to export to. - # yerr (Optional[ndarray]) : Type of the errors in y: [err, std, none] - # default is 'std'. - # xerr (Optional[ndarray]) : Type of the errors in x: [err, std, none] - # default is 'std'. - # xgrid (Optional[ndarray]) : Grid to bin the data to - - # default in empty so use the - # x-axis of the first scan. - # norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 - # default is False. - - # """ - # # get scan_sequence data without plotting - # sequence_data, parameters, names, label_texts = self.plot_scan_sequence( - # scan_sequence, - # xgrid=xgrid, - # yerr=yerr, - # xerr=xerr, - # norm2one=norm2one, - # binning=binning, - # skip_plot=True) - - # for i, label_text in enumerate(label_texts): - # # travserse the sequence - - # header = '' - # saveData = [] - # for counter in sequence_data: - # # travserse all counters in the data - - # # build the file header - # header = header + counter + '\t ' - # # build the data matrix - # saveData.append(sequence_data[counter][i]) - - # # save data with header to text file - # np.savetxt('{:s}/{:s}_{:s}.dat'.format(path, fileName, - # "".join(x for x in label_text if x.isalnum())), - # np.r_[saveData].T, delimiter='\t', header=header) - @property def clist(self): return self._clist diff --git a/pyEvalData/helpers.py b/pyEvalData/helpers.py index 09b096d..552597d 100644 --- a/pyEvalData/helpers.py +++ b/pyEvalData/helpers.py @@ -24,8 +24,10 @@ import numpy as np from scipy.stats import binned_statistic +import re -__all__ = ['edges4grid', 'bin_data'] +__all__ = ['edges4grid', 'bin_data', 'traverse_counters', 'resolve_counter_name', + 'col_string_to_eval_string'] __docformat__ = 'restructuredtext' @@ -159,3 +161,133 @@ def bin_data(y, x, X, statistic='mean'): Xstd = Xstd[n > 0] return Y, X, Yerr, Xerr, Ystd, Xstd, edges, bins, n + + +def traverse_counters(clist, cdef, source_cols=''): + """traverse_counters + + Traverse all counters and replace all predefined counter definitions. + Returns also a list of the included source counters for error propagation. + + Args: + clist (list[str]): list of counter names to evaluate. + cdef (dict{str:str}): dict of predefined counter names and + definitions. + source_cols (list[str], optional): counters in the raw source data. + + Returns: + (tuple): + - *resolved_counters (list[str])* - resolved counters. + - *source_counters (list[str])* - all source counters in the resolved counters. + + """ + resolved_counters = [] + source_counters = [] + + for counter_name in clist: + # resolve each counter in the clist + counter_string, res_source_counters = \ + resolve_counter_name(cdef, counter_name, source_cols) + + resolved_counters.append(counter_string) + source_counters.extend(res_source_counters) + + return resolved_counters, list(set(source_counters)) + + +def resolve_counter_name(cdef, col_name, source_cols=''): + """resolve_counter_name + + Replace all predefined counter definitions in a given counter name. + The function works recursively. + + Args: + cdef (dict{str:str}): dict of predefined counter names and + definitions. + col_name (str): initial counter string. + source_cols (list[str], optional): columns in the source data. + + Returns: + (tuple): + - *col_string (str)* - resolved counter string. + - *source_counters (list[str])* - source counters in the col_string + + """ + recall = False # boolean to stop recursive calls + source_counters = [] + col_string = col_name + + for find_cdef in cdef.keys(): + # check for all predefined counters + search_pattern = r'\b' + find_cdef + r'\b' + if re.search(search_pattern, col_string) is not None: + if cdef[find_cdef] in source_cols: + # this counter definition is a base source counter + source_counters.append(cdef[find_cdef]) + # found a predefined counter + # recursive call if predefined counter must be resolved again + recall = True + # replace the counter definition in the string + (col_string, _) = re.subn(search_pattern, + '(' + cdef[find_cdef] + ')', col_string) + + if recall: + # do the recursive call + col_string, rec_source_counters = resolve_counter_name(cdef, col_string, source_cols) + source_counters.extend(rec_source_counters) + + for find_cdef in source_cols: + # check for all base source counters + search_pattern = r'\b' + find_cdef + r'\b' + if re.search(search_pattern, col_string) is not None: + source_counters.append(find_cdef) + + return col_string, source_counters + + +def col_string_to_eval_string(col_string, math_keys, ignore_keys, array_name='source_data'): + """col_string_to_eval_string + + Use regular expressions in order to generate an evaluateable string + from the counter string in order to append the new counter to the + source data. + + Args: + col_string (str) : Definition of the counter. + math_keys (list[str]): list of keywords which are evaluated as numpy + functions. + ignore_keys (list[str]): list of keywords which should not be + evaluated. + array_name (str) : name of the data array. + + Returns: + eval_string (str): Evaluateable string to add the new counter + to the source data. + + """ + + # search for alphanumeric counter names in col_string + iterator = re.finditer( + '([0-9]*[a-zA-Z\_]+[0-9]*[a-zA-Z]*)*', col_string) + # these are keys which should not be replaced but evaluated + keys = list(math_keys).copy() + + for key in iterator: + # traverse all found counter names + if len(key.group()) > 0: + # the match is > 0 + if not key.group() in keys: + # the counter name is not in the keys list + + # remember this counter name in the key list in order + # not to replace it again + keys.append(key.group()) + # the actual replacement + (col_string, _) = re.subn(r'\b'+key.group()+r'\b', + array_name + '[\'' + key.group() + '\']', col_string) + + # add 'np.' prefix to numpy functions/math keys + for mk in math_keys: + if mk not in ignore_keys: + (col_string, _) = re.subn(r'\b' + mk + r'\b', 'np.' + mk, col_string) + return col_string From bca1b711edc35f049db934ef27dd698049743ca0 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Fri, 29 Dec 2023 01:07:37 +0100 Subject: [PATCH 20/24] fix flake8 and improve doc strings --- pyEvalData/evaluation.py | 279 +++++++++++++++++++++++++++------------ pyEvalData/helpers.py | 12 +- pyEvalData/io/scan.py | 1 + 3 files changed, 202 insertions(+), 90 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 3a0f96f..44cc591 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -90,10 +90,10 @@ def add_custom_counters(self, source_data, scan_num, source_counters): This is a stub for child classes. Args: - source_data (ndarray) : Data array from the source scan. - scan_num (int) : Scan number of the source scan. - source_counters list(str) : List of the source counters and custom counters - from the clist and xcol. + source_data (ndarray): data array from the source scan. + scan_num (int): scan number of the source scan. + source_counters list(str): List of the source counters and custom + counters from the clist and xcol. Returns: source_data (ndarray): Updated data array from the source scan. @@ -104,11 +104,13 @@ def add_custom_counters(self, source_data, scan_num, source_counters): def filter_data(self, data): """filter_data + Apply data filter to data. + Args: - data (TYPE): DESCRIPTION. + data (ndarray): input data. Returns: - TYPE: DESCRIPTION. + ndarray: output data. """ res = [] @@ -131,13 +133,16 @@ def filter_data(self, data): return np.core.records.fromarrays(data_list, dtype=dtype_list) def get_scan_data(self, scan_num): - """ + """get_scan_data + + Get the data for a scan from the source and applying data filters if + enabled. Args: - scan_num (TYPE): DESCRIPTION. + scan_num (uint): scan number. Returns: - TYPE: DESCRIPTION. + ndarray: scan data array. """ data, meta = self.source.get_scan_data(scan_num) @@ -149,10 +154,10 @@ def get_scan_list_data(self, scan_list): """ Args: - scan_num (TYPE): DESCRIPTION. + scan_list (list[uint]): list of scan numbers. Returns: - TYPE: DESCRIPTION. + list[ndarray]: list of scan data arrays. """ data_list, meta_list = self.source.get_scan_list_data(scan_list) @@ -162,21 +167,24 @@ def get_scan_list_data(self, scan_list): return data_list def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): - """Averages data defined by the counter list, clist, onto an optional + """avg_N_bin_scans + + Averages data defined by the counter list, clist, onto an optional xgrid. If no xgrid is given the x-axis data of the first scan in the list is used instead. Args: - scan_list (List[int]) : List of scan numbers. - xgrid (Optional[ndarray]) : Grid to bin the data to - - default in empty so use the - x-axis of the first scan. + scan_list (list[int]): list of scan numbers. + xgrid (ndarray, optional): grid to bin the data to - default is + empty so use the x-axis of the first scan. + binning (bool, optional): enable binning of data - default is True Returns: - avg_data (ndarray) : Averaged data for the scan list. - std_data (ndarray) : Standart derivation of the data for the scan list. - err_data (ndarray) : Error of the data for the scan list. - name (str) : Name of the data set. + (tuple): + - *avg_data (ndarray)* - averaged data for the scan list. + - *std_data (ndarray)* - standard derivation of the data for the scan list. + - *err_data (ndarray)* - error of the data for the scan list. + - *name (str)* - name of the data set. """ @@ -208,7 +216,7 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): # resolve the clist and retrieve the resolves counters and the # necessary base source counters for error propagation - resolved_counters, source_counters = traverse_counters(self.clist, + resolved_counters, source_counters = traverse_counters(self.clist, self.cdef, source_cols) @@ -240,7 +248,7 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): # traverse the counters in the clist and append to data if not # already present eval_string = col_string_to_eval_string( - col_string, self.math_keys, self.ignore_keys,array_name='source_data') + col_string, self.math_keys, self.ignore_keys, array_name='source_data') if len(data) == 0: data = np.array(eval(eval_string), dtype=[(col_name, float)]) @@ -335,6 +343,7 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): xgrid_reduced, statistic=bin_stat) else: + # no binning for col_name, col_string in zip(self.clist, resolved_counters): eval_string = col_string_to_eval_string( col_string, self.math_keys, self.ignore_keys, array_name='source_data') @@ -353,16 +362,31 @@ def avg_N_bin_scans(self, scan_list, xgrid=np.array([]), binning=True): return avg_data, std_data, err_data, name - def eval_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True): - """eval_scans [summary] + def eval_scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False, + binning=True): + """eval_scans + + Evaluate a list of scans for a given set of external parameters. Args: - scan_list ([type]): [description] - xgrid (list, optional): [description]. Defaults to []. - yerr (str, optional): [description]. Defaults to 'std'. - xerr (str, optional): [description]. Defaults to 'std'. - norm2one (bool, optional): [description]. Defaults to False. - binning (bool, optional): [description]. Defaults to True. + scan_list (list[int]): list of scan numbers. + xgrid (ndarray, optional): grid to bin the data to - default is + empty so use the x-axis of the first scan. + yerr (ndarray, optional): type of the errors in y: [err, std, none] + default is 'std'. + xerr (ndarray, optional): type of the errors in x: [err, std, none] + default is 'std'. + norm2one (bool, optional): normalize transient data to 1 for t < t0 + default is False. + binning (bool, optional): enable binning of data - default is True + Returns: + (tuple): + - *y2plot (OrderedDict)* - evaluated y-data. + - *x2plot (ndarray)* -evaluated x-data. + - *yerr2plot (OrderedDict)* - evaluated y-error. + - *xerr2plot (ndarray)* - evaluated x-error. + - *name (str)* - name of the data set. + """ # initialize the y-data as ordered dict in order to allow for multiple # counters at the same time @@ -413,25 +437,25 @@ def eval_scan_sequence(self, scan_sequence, xgrid=[], yerr='std', xerr='std', no Evaluate a sequence of scans for a given set of external parameters. Args: - scan_sequence (List[ - List/Tuple[List[int], - int/str]]) : Sequence of scan lists and parameters. - xgrid (Optional[ndarray]) : Grid to bin the data to - - default in empty so use the - x-axis of the first scan. - yerr (Optional[ndarray]) : Type of the errors in y: [err, std, none] - default is 'std'. - xerr (Optional[ndarray]) : Type of the errors in x: [err, std, none] - default is 'std'. - norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 - default is False. + scan_sequence (list[ + list/tuple[list[int], + int/str]]): sequence of scan lists and parameters. + xgrid (ndarray, optional): grid to bin the data to - default is + empty so use the x-axis of the first scan. + yerr (ndarray, optional): type of the errors in y: [err, std, none] + default is 'std'. + xerr (ndarray, optional): type of the errors in x: [err, std, none] + default is 'std'. + norm2one (bool, optional): normalize transient data to 1 for t < t0 + default is False. + binning (bool, optional): enable binning of data - default is True Returns: - sequence_data (OrderedDict) : Dictionary of the averaged scan data. - parameters (List[str, float]) : Parameters of the sequence. - names (List[str]) : List of names of each data set. + (tuple): + - *sequence_data (OrderedDict)* - dictionary of the averaged scan data. + - *parameters (list[str, float])* - parameters of the sequence. + - *names (list[str])* - list of names of each data set. """ - # initialize the return data sequence_data = collections.OrderedDict() names = [] @@ -470,6 +494,25 @@ def eval_scan_sequence(self, scan_sequence, xgrid=[], yerr='std', xerr='std', no def _plot_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, label_text='', fmt='-o', plot_separate=False, **kwargs): + """_plot_scans + + Internal plotting function for a given data set. + + Args: + y2plot (OrderedDict): y-data to plot. + x2plot (ndarray): x-data to plot. + yerr2plot (OrderedDict): y-error to plot. + xerr2plot (ndarray): x-error which was plot. + name (str): name of the data set. + label_text (str, optional): label of the plot - default is none. + fmt (str, optional): format string of the plot - defaults is -o. + plot_separate (bool, optional): use separate subplots - default + is False. + + Returns: + plots (list[PlotObjects]): list of matplotlib plot objects. + + """ plots = [] # plot all keys in the clist for i, counter in enumerate(self.clist): @@ -510,28 +553,30 @@ def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm """plot_scans Plot a list of scans from the source file. - Various plot parameters are provided. - The plotted data are returned. Args: - scan_list (List[int]): List of scan numbers. - xgrid (Optional[ndarray]): Grid to bin the data to - - default in empty so use the x-axis of the first scan. - yerr (Optional[ndarray]): Type of the errors in y: [err, std, none] + scan_list (list[int]): list of scan numbers. + xgrid (ndarray, optional): grid to bin the data to - default is + empty so use the x-axis of the first scan. + yerr (ndarray, optional): type of the errors in y: [err, std, none] default is 'std'. - xerr (Optional[ndarray]): Type of the errors in x: [err, std, none] + xerr (ndarray, optional): type of the errors in x: [err, std, none] default is 'std'. - norm2one (Optional[bool]): Norm transient data to 1 for t < t0 + norm2one (bool, optional): normalize transient data to 1 for t < t0 default is False. - label_text (Optional[str]): Label of the plot - default is none. - fmt (Optional[str]): format string of the plot - defaults is -o. + binning (bool, optional): enable binning of data - default is True + label_text (str, optional): Label of the plot - default is none. + fmt (str, optional): format string of the plot - defaults is -o. + plot_separate (bool, optional): use separate subplots - default + is False. Returns: - y2plot (OrderedDict): y-data which was plotted. - x2plot (ndarray): x-data which was plotted. - yerr2plot (OrderedDict): y-error which was plotted. - xerr2plot (ndarray): x-error which was plotted. - name (str): Name of the data set. + (tuple): + - *y2plot (OrderedDict)* - y-data which was plotted. + - *x2plot (ndarray)* - x-data which was plotted. + - *yerr2plot (OrderedDict)* - y-error which was plotted. + - *xerr2plot (ndarray)* - x-error which was plotted. + - *name (str)* - Name of the data set. """ @@ -547,31 +592,35 @@ def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr='std', norm2one=False, binning=True, label_format='', fmt='-o', plot_separate=False, **kwargs): - """Plot a list of scans from the source file. - Various plot parameters are provided. - The plotted data are returned. + """plot_scan_sequence + + Plot a scan sequence from the source file. Args: - scan_sequence (List[ - List/Tuple[List[int], - int/str]]) : Sequence of scan lists and parameters. - xgrid (Optional[ndarray]) : Grid to bin the data to - - default in empty so use the - x-axis of the first scan. - yerr (Optional[ndarray]) : Type of the errors in y: [err, std, none] - default is 'std'. - xerr (Optional[ndarray]) : Type of the errors in x: [err, std, none] - default is 'std'. - norm2one (Optional[bool]) : Norm transient data to 1 for t < t0 - default is False. - label_format (Optional[str]): fprintf style format for labels - fmt (Optional[str]) : format string of the plot - defaults is -o. + scan_sequence (list[ + list/tuple[list[int], + int/str]]): sequence of scan lists and parameters. + xgrid (ndarray, optional): grid to bin the data to - default is + empty so use the x-axis of the first scan. + yerr (ndarray, optional): type of the errors in y: [err, std, none] + default is 'std'. + xerr (ndarray, optional): type of the errors in x: [err, std, none] + default is 'std'. + norm2one (bool, optional): normalize transient data to 1 for t < t0 + default is False. + binning (bool, optional): enable binning of data - default is True + label_format (str, optional): format string for label text - default + is empty. + fmt (str, optional): format string of the plot - defaults is -o. + plot_separate (bool, optional): use separate subplots - default + is False. Returns: - sequence_data (OrderedDict) : Dictionary of the averaged scan data. - parameters (List[str, float]) : Parameters of the sequence. - names (List[str]) : List of names of each data set. - label_texts (List[str]) : List of labels for each data set. + (tuple): + - *sequence_data (OrderedDict)* - dictionary of the averaged scan data. + - *parameters (list[str, float])* - parameters of the sequence. + - *names (list[str])* - list of names of each data set. + - *label_texts (list[str])* - list of labels for each data set. """ @@ -605,6 +654,25 @@ def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, weights, fit_method='leastsq', nan_policy='propagate'): + """_fit_scans + + Internal method to fit a given data set. + + Args: + y2plot (_type_): _description_ + x2plot (_type_): _description_ + yerr2plot (_type_): _description_ + xerr2plot (_type_): _description_ + mod (_type_): _description_ + pars (_type_): _description_ + select (_type_): _description_ + weights (_type_): _description_ + fit_method (str, optional): _description_. Defaults to 'leastsq'. + nan_policy (str, optional): _description_. Defaults to 'propagate'. + + Returns: + _type_: _description_ + """ res = {} # initialize the results dict report = [] report_1 = [] @@ -662,6 +730,22 @@ def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, we def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offset_t0=False, label_text='', fmt='o', plot_separate=False): + """_plot_fit_scans + + Internal function to fit and plot a given data set. + + Args: + y2plot (_type_): _description_ + x2plot (_type_): _description_ + yerr2plot (_type_): _description_ + xerr2plot (_type_): _description_ + name (_type_): _description_ + res (_type_): _description_ + offset_t0 (bool, optional): _description_. Defaults to False. + label_text (str, optional): _description_. Defaults to ''. + fmt (str, optional): _description_. Defaults to 'o'. + plot_separate (bool, optional): _description_. Defaults to False. + """ plots = self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, fmt=fmt, alpha=0.25, plot_separate=plot_separate) @@ -687,12 +771,12 @@ def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offse color=plots[i][0].get_color()) # figure formatting - if True: # len(parameters)*len(self.clist) > 6: + if True: # len(parameters)*len(self.clist) > 6: # move the legend outside the plot for more than # 5 sequence parameters plt.legend(bbox_to_anchor=(0., 1.08, 1, .102), frameon=True, - loc=3, numpoints=1, ncol=3, mode="expand", - borderaxespad=0.) + loc=3, numpoints=1, ncol=3, mode="expand", + borderaxespad=0.) else: plt.legend(frameon=True, loc=0, numpoints=1) @@ -735,6 +819,31 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr norm2one=False, binning=True, label_format='', select='', fit_report=0, weights=False, fit_method='leastsq', nan_policy='propagate', last_res_as_par=False, offset_t0=False, plot_separate=False, fmt='o'): + """fit_scan_sequence _summary_ + + Args: + scan_sequence (_type_): _description_ + mod (_type_): _description_ + pars (_type_): _description_ + xgrid (list, optional): _description_. Defaults to []. + yerr (str, optional): _description_. Defaults to 'std'. + xerr (str, optional): _description_. Defaults to 'std'. + norm2one (bool, optional): _description_. Defaults to False. + binning (bool, optional): _description_. Defaults to True. + label_format (str, optional): _description_. Defaults to ''. + select (str, optional): _description_. Defaults to ''. + fit_report (int, optional): _description_. Defaults to 0. + weights (bool, optional): _description_. Defaults to False. + fit_method (str, optional): _description_. Defaults to 'leastsq'. + nan_policy (str, optional): _description_. Defaults to 'propagate'. + last_res_as_par (bool, optional): _description_. Defaults to False. + offset_t0 (bool, optional): _description_. Defaults to False. + plot_separate (bool, optional): _description_. Defaults to False. + fmt (str, optional): _description_. Defaults to 'o'. + + Returns: + _type_: _description_ + """ # load data sequence_data, parameters, names = self.eval_scan_sequence( scan_sequence, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, binning=binning) diff --git a/pyEvalData/helpers.py b/pyEvalData/helpers.py index 552597d..d306b9e 100644 --- a/pyEvalData/helpers.py +++ b/pyEvalData/helpers.py @@ -229,7 +229,8 @@ def resolve_counter_name(cdef, col_name, source_cols=''): recall = True # replace the counter definition in the string (col_string, _) = re.subn(search_pattern, - '(' + cdef[find_cdef] + ')', col_string) + '(' + cdef[find_cdef] + ')', col_string + ) if recall: # do the recursive call @@ -253,12 +254,12 @@ def col_string_to_eval_string(col_string, math_keys, ignore_keys, array_name='so source data. Args: - col_string (str) : Definition of the counter. - math_keys (list[str]): list of keywords which are evaluated as numpy + col_string (str): Definition of the counter. + math_keys (list[str]): list of keywords which are evaluated as numpy functions. ignore_keys (list[str]): list of keywords which should not be evaluated. - array_name (str) : name of the data array. + array_name (str): name of the data array. Returns: eval_string (str): Evaluateable string to add the new counter @@ -284,7 +285,8 @@ def col_string_to_eval_string(col_string, math_keys, ignore_keys, array_name='so keys.append(key.group()) # the actual replacement (col_string, _) = re.subn(r'\b'+key.group()+r'\b', - array_name + '[\'' + key.group() + '\']', col_string) + array_name + '[\'' + key.group() + '\']', col_string + ) # add 'np.' prefix to numpy functions/math keys for mk in math_keys: diff --git a/pyEvalData/io/scan.py b/pyEvalData/io/scan.py index 61b060c..5de93ed 100644 --- a/pyEvalData/io/scan.py +++ b/pyEvalData/io/scan.py @@ -32,6 +32,7 @@ import numpy as np from numpy.core.records import fromarrays + class Scan(object): """Scan From 5ae0ea968decd8f82d8820a365a6d687369fbc46 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 30 Dec 2023 00:26:25 +0100 Subject: [PATCH 21/24] work on doc_string and further cleaning --- pyEvalData/evaluation.py | 241 ++++++++++++++++++++++++++------------- 1 file changed, 162 insertions(+), 79 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 44cc591..3dd0c10 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -151,7 +151,9 @@ def get_scan_data(self, scan_num): return data def get_scan_list_data(self, scan_list): - """ + """get_scan_list_data + + Return a list of data sets for a given list of scan numbers. Args: scan_list (list[uint]): list of scan numbers. @@ -506,8 +508,8 @@ def _plot_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, label_text='', name (str): name of the data set. label_text (str, optional): label of the plot - default is none. fmt (str, optional): format string of the plot - defaults is -o. - plot_separate (bool, optional): use separate subplots - default - is False. + plot_separate (bool, optional): use separate subplots for different + counters. Defaults to False. Returns: plots (list[PlotObjects]): list of matplotlib plot objects. @@ -541,10 +543,8 @@ def _plot_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, label_text='', yerr=yerr2plot[counter], **kwargs) plots.append(plot) - # add a legend, labels, title and set the limits and grid - plt.legend(frameon=True, loc=0, numpoints=1) - plt.xlabel(self.xcol) - plt.title(name) + plt.xlabel(self.xcol) + plt.title(name) return plots @@ -567,8 +567,8 @@ def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm binning (bool, optional): enable binning of data - default is True label_text (str, optional): Label of the plot - default is none. fmt (str, optional): format string of the plot - defaults is -o. - plot_separate (bool, optional): use separate subplots - default - is False. + plot_separate (bool, optional): use separate subplots for different + counters. Defaults to False. Returns: (tuple): @@ -586,12 +586,13 @@ def plot_scans(self, scan_list, xgrid=np.array([]), yerr='std', xerr='std', norm _ = self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, fmt=fmt, plot_separate=plot_separate, **kwargs) + plt.legend(frameon=True, loc=0, numpoints=1) return y2plot, x2plot, yerr2plot, xerr2plot, name def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr='std', norm2one=False, binning=True, label_format='', fmt='-o', - plot_separate=False, **kwargs): + plot_separate=False, show_single=False, **kwargs): """plot_scan_sequence Plot a scan sequence from the source file. @@ -612,8 +613,10 @@ def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr label_format (str, optional): format string for label text - default is empty. fmt (str, optional): format string of the plot - defaults is -o. - plot_separate (bool, optional): use separate subplots - default - is False. + plot_separate (bool, optional): use separate subplots for different + counters. Defaults to False. + show_single (bool, optional): show single figure for each sequence + element. Returns: (tuple): @@ -630,7 +633,8 @@ def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr label_texts = [] for i, (scan_list, parameter) in enumerate(scan_sequence): # iterate the scan sequence - + if show_single: + plt.figure() lt = '#{:d}'.format(i+1) if len(label_format) > 0: try: @@ -649,29 +653,41 @@ def plot_scan_sequence(self, scan_sequence, xgrid=np.array([]), yerr='std', xerr fmt=fmt, plot_separate=plot_separate, **kwargs) + if show_single: + plt.legend(frameon=True, loc=0, numpoints=1) + plt.show() + else: + plt.legend(bbox_to_anchor=(0., 1.08, 1, .102), frameon=True, + loc=3, numpoints=1, ncol=3, mode="expand", + borderaxespad=0.) return sequence_data, parameters, names, label_texts - def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, weights, + def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select='', weights=False, fit_method='leastsq', nan_policy='propagate'): """_fit_scans Internal method to fit a given data set. Args: - y2plot (_type_): _description_ - x2plot (_type_): _description_ - yerr2plot (_type_): _description_ - xerr2plot (_type_): _description_ - mod (_type_): _description_ - pars (_type_): _description_ - select (_type_): _description_ - weights (_type_): _description_ - fit_method (str, optional): _description_. Defaults to 'leastsq'. - nan_policy (str, optional): _description_. Defaults to 'propagate'. + y2plot (OrderedDict): y-data to plot. + x2plot (ndarray): x-data to plot. + yerr2plot (OrderedDict): y-error to plot. + xerr2plot (ndarray): x-error which was plot. + mod (lmfit.Model): fit model. + pars (lmfit.parameters): fit parameters. + select (str, optional): evaluatable string to select x-range. + Defaults to empty string. + weights (bool, optional): enable weighting by inverse of errors. + Defaults to False. + fit_method (str, optional): lmfit's fit method. Defaults to 'leastsq'. + nan_policy (str, optional): lmfit's NaN policy. Defaults to 'propagate'. Returns: - _type_: _description_ + (tuple): + - *res (dict)* - fit result dictionary. + - *report (list[dict, report])* - list of lmfit's best value + dictionary and fit report object """ res = {} # initialize the results dict report = [] @@ -680,7 +696,7 @@ def _fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, we for counter in y2plot: res[counter] = {} - # get the fit models and fit parameters if they are lists/tupels + # get the fit models and fit parameters if they are lists/tuples # evaluate the select statement if select == '': @@ -732,19 +748,22 @@ def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offse label_text='', fmt='o', plot_separate=False): """_plot_fit_scans - Internal function to fit and plot a given data set. + Internal function plot scans and fits of a given data set and fit results. Args: - y2plot (_type_): _description_ - x2plot (_type_): _description_ - yerr2plot (_type_): _description_ - xerr2plot (_type_): _description_ - name (_type_): _description_ - res (_type_): _description_ - offset_t0 (bool, optional): _description_. Defaults to False. - label_text (str, optional): _description_. Defaults to ''. - fmt (str, optional): _description_. Defaults to 'o'. - plot_separate (bool, optional): _description_. Defaults to False. + y2plot (OrderedDict): y-data to plot. + x2plot (ndarray): x-data to plot. + yerr2plot (OrderedDict): y-error to plot. + xerr2plot (ndarray): x-error which was plot. + name (str): name of the data set. + res (dict): fit results. + offset_t0 (bool, optional): offset plot by t0 parameter of the fit + results. Defaults to False. + label_text (str, optional): label of the plot - default is none. + fmt (str, optional): format string of the plot - defaults is -o. + plot_separate (bool, optional): use separate subplots for different + counters. Defaults to False. + """ plots = self._plot_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, label_text=label_text, fmt=fmt, alpha=0.25, plot_separate=plot_separate) @@ -770,22 +789,52 @@ def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offse plt.plot(x2plotFit-offsetX, res[counter]['fit'].eval(x=x2plotFit), '-', lw=2, alpha=1, color=plots[i][0].get_color()) - # figure formatting - if True: # len(parameters)*len(self.clist) > 6: - # move the legend outside the plot for more than - # 5 sequence parameters - plt.legend(bbox_to_anchor=(0., 1.08, 1, .102), frameon=True, - loc=3, numpoints=1, ncol=3, mode="expand", - borderaxespad=0.) - else: - plt.legend(frameon=True, loc=0, numpoints=1) - def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm2one=False, - binning=True, label_text='', select='', fit_report=0, weights=False, - fit_method='leastsq', nan_policy='propagate', offset_t0=False, fmt='o'): - """Fit, plot, and return the data of scans. + binning=True, label_text='', fmt='o', select='', fit_report=0, weights=False, + fit_method='leastsq', nan_policy='propagate', offset_t0=False, + plot_separate=False): + """fit_scans + + Evaluate, fit, and plot the results of a given list of scans from the + source file. + + Args: + scan_list (list[int]): list of scan numbers. + mod (lmfit.Model): fit model. + pars (lmfit.parameters): fit parameters. + xgrid (ndarray, optional): grid to bin the data to - default is + empty so use the x-axis of the first scan. + yerr (ndarray, optional): type of the errors in y: [err, std, none] + default is 'std'. + xerr (ndarray, optional): type of the errors in x: [err, std, none] + default is 'std'. + norm2one (bool, optional): normalize transient data to 1 for t < t0 + default is False. + binning (bool, optional): enable binning of data - default is True + label_text (str, optional): label of the plot - default is none. + fmt (str, optional): format string of the plot - defaults is -o. + select (str, optional): evaluatable string to select x-range. + Defaults to empty string. + fit_report (uint, optional): Default is 0 - no report. 1 - fit + results. 2 - fit results and correlations. + weights (bool, optional): enable weighting by inverse of errors. + Defaults to False. + fit_method (str, optional): lmfit's fit method. Defaults to 'leastsq'. + nan_policy (str, optional): lmfit's NaN policy. Defaults to 'propagate'. + offset_t0 (bool, optional): offset plot by t0 parameter of the fit + results. Defaults to False. + plot_separate (bool, optional): use separate subplots for different + counters. Defaults to False. + + Returns: + (tuple): + - *res (dict)* - fit result dictionary. + - *y2plot (OrderedDict)* - y-data which was fitted and plotted. + - *x2plot (ndarray)* - x-data which was fitted and plotted. + - *yerr2plot (OrderedDict)* - y-error which was fitted and plotted. + - *xerr2plot (ndarray)* - x-error which was fitted and plotted. + - *name (str)* - Name of the data set. - This is just a wrapper for the fit_scan_sequence method """ # get the data for the scan list y2plot, x2plot, yerr2plot, xerr2plot, name = \ @@ -798,7 +847,9 @@ def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm # plot the data and fit self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, res, offset_t0=offset_t0, - fmt=fmt) + label_text=label_text, fmt=fmt, plot_separate=plot_separate) + + plt.legend(frameon=True, loc=0, numpoints=1) # print the fit report if fit_report > 0: @@ -815,34 +866,55 @@ def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm print('\n' + '='*(39-head_len-fix) + ' {:} '.format(counter) + '='*(39-head_len)) print(report[1][counter]) + return res, y2plot, x2plot, yerr2plot, xerr2plot, name + def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr='std', - norm2one=False, binning=True, label_format='', select='', fit_report=0, - weights=False, fit_method='leastsq', nan_policy='propagate', - last_res_as_par=False, offset_t0=False, plot_separate=False, fmt='o'): - """fit_scan_sequence _summary_ + norm2one=False, binning=True, label_format='', fmt='o', select='', + fit_report=0, weights=False, fit_method='leastsq', + nan_policy='propagate', last_res_as_par=False, offset_t0=False, + plot_separate=False, show_single=False): + """fit_scan_sequence Args: - scan_sequence (_type_): _description_ - mod (_type_): _description_ - pars (_type_): _description_ - xgrid (list, optional): _description_. Defaults to []. - yerr (str, optional): _description_. Defaults to 'std'. - xerr (str, optional): _description_. Defaults to 'std'. - norm2one (bool, optional): _description_. Defaults to False. - binning (bool, optional): _description_. Defaults to True. - label_format (str, optional): _description_. Defaults to ''. - select (str, optional): _description_. Defaults to ''. - fit_report (int, optional): _description_. Defaults to 0. - weights (bool, optional): _description_. Defaults to False. - fit_method (str, optional): _description_. Defaults to 'leastsq'. - nan_policy (str, optional): _description_. Defaults to 'propagate'. - last_res_as_par (bool, optional): _description_. Defaults to False. - offset_t0 (bool, optional): _description_. Defaults to False. - plot_separate (bool, optional): _description_. Defaults to False. - fmt (str, optional): _description_. Defaults to 'o'. - + scan_sequence (list[ + list/tuple[list[int], + int/str]]): sequence of scan lists and parameters. + mod (lmfit.Model): fit model. + pars (lmfit.parameters): fit parameters. + xgrid (ndarray, optional): grid to bin the data to - default is + empty so use the x-axis of the first scan. + yerr (ndarray, optional): type of the errors in y: [err, std, none] + default is 'std'. + xerr (ndarray, optional): type of the errors in x: [err, std, none] + default is 'std'. + norm2one (bool, optional): normalize transient data to 1 for t < t0 + default is False. + binning (bool, optional): enable binning of data - default is True + label_format (str, optional): format string for label text - default + is empty. + fmt (str, optional): format string of the plot - defaults is -o. + select (str, optional): evaluatable string to select x-range. + Defaults to empty string. + fit_report (uint, optional): Default is 0 - no report. 1 - fit + results. 2 - fit results and correlations. + weights (bool, optional): enable weighting by inverse of errors. + Defaults to False. + fit_method (str, optional): lmfit's fit method. Defaults to 'leastsq'. + nan_policy (str, optional): lmfit's NaN policy. Defaults to 'propagate'. + last_res_as_par (bool, optional): use last fit result as start value + for next fit. Defaults to False. + offset_t0 (bool, optional): offset plot by t0 parameter of the fit + results. Defaults to False. + plot_separate (bool, optional): use separate subplots for different + counters. Defaults to False. + show_single (bool, optional): show single figure for each sequence + element. Returns: - _type_: _description_ + (tuple): + - *res (dict)* - fit result dictionary. + - *sequence_data (OrderedDict)* - dictionary of the averaged scan data. + - *parameters (list[str, float])* - parameters of the sequence. + """ # load data sequence_data, parameters, names = self.eval_scan_sequence( @@ -856,6 +928,8 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr res[counter] = {} for i, ((scan_list, parameter), name) in enumerate(zip(scan_sequence, names)): + if show_single: + plt.figure() # get the fit models and fit parameters if they are lists/tupels if isinstance(mod, (list, tuple)): _mod = mod[i] @@ -889,11 +963,20 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr # fit the model and parameters to the data _res, _report = self._fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, _mod, _pars, select, weights, fit_method=fit_method, - nan_policy='propagate') + nan_policy=nan_policy) # plot the data and fit - self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, _res, label_text=lt, - offset_t0=offset_t0, fmt=fmt, plot_separate=plot_separate) + self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, _res, + offset_t0=offset_t0, label_text=lt, fmt=fmt, + plot_separate=plot_separate) + + if show_single: + plt.legend(frameon=True, loc=0, numpoints=1) + plt.show() + else: + plt.legend(bbox_to_anchor=(0., 1.08, 1, .102), frameon=True, + loc=3, numpoints=1, ncol=3, mode="expand", + borderaxespad=0.) # store the results for counter in self.clist: From abfe62adf0c8cfdfa6aac2d2275b2e17896bd66a Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 30 Dec 2023 00:38:33 +0100 Subject: [PATCH 22/24] add skip_plot parameter to fitting methods --- pyEvalData/evaluation.py | 43 ++++++++++++++++++++++------------------ 1 file changed, 24 insertions(+), 19 deletions(-) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 3dd0c10..61df6a4 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -791,7 +791,7 @@ def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offse def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, label_text='', fmt='o', select='', fit_report=0, weights=False, - fit_method='leastsq', nan_policy='propagate', offset_t0=False, + fit_method='leastsq', nan_policy='propagate', skip_plot=False, offset_t0=False, plot_separate=False): """fit_scans @@ -821,6 +821,7 @@ def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm Defaults to False. fit_method (str, optional): lmfit's fit method. Defaults to 'leastsq'. nan_policy (str, optional): lmfit's NaN policy. Defaults to 'propagate'. + skip_plot (bool, optional): Skip plotting. Defaults to False. offset_t0 (bool, optional): offset plot by t0 parameter of the fit results. Defaults to False. plot_separate (bool, optional): use separate subplots for different @@ -845,11 +846,13 @@ def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm res, report = self._fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, mod, pars, select, weights, fit_method=fit_method, nan_policy=nan_policy) - # plot the data and fit - self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, res, offset_t0=offset_t0, - label_text=label_text, fmt=fmt, plot_separate=plot_separate) + if not skip_plot: + # plot the data and fit + self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, res, + offset_t0=offset_t0, label_text=label_text, fmt=fmt, + plot_separate=plot_separate) - plt.legend(frameon=True, loc=0, numpoints=1) + plt.legend(frameon=True, loc=0, numpoints=1) # print the fit report if fit_report > 0: @@ -871,8 +874,8 @@ def fit_scans(self, scan_list, mod, pars, xgrid=[], yerr='std', xerr='std', norm def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, label_format='', fmt='o', select='', fit_report=0, weights=False, fit_method='leastsq', - nan_policy='propagate', last_res_as_par=False, offset_t0=False, - plot_separate=False, show_single=False): + nan_policy='propagate', last_res_as_par=False, skip_plot=False, + offset_t0=False, plot_separate=False, show_single=False): """fit_scan_sequence Args: @@ -903,6 +906,7 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr nan_policy (str, optional): lmfit's NaN policy. Defaults to 'propagate'. last_res_as_par (bool, optional): use last fit result as start value for next fit. Defaults to False. + skip_plot (bool, optional): Skip plotting. Defaults to False. offset_t0 (bool, optional): offset plot by t0 parameter of the fit results. Defaults to False. plot_separate (bool, optional): use separate subplots for different @@ -928,7 +932,7 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr res[counter] = {} for i, ((scan_list, parameter), name) in enumerate(zip(scan_sequence, names)): - if show_single: + if show_single and not skip_plot: plt.figure() # get the fit models and fit parameters if they are lists/tupels if isinstance(mod, (list, tuple)): @@ -965,18 +969,19 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr select, weights, fit_method=fit_method, nan_policy=nan_policy) - # plot the data and fit - self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, _res, - offset_t0=offset_t0, label_text=lt, fmt=fmt, - plot_separate=plot_separate) + if not skip_plot: + # plot the data and fit + self._plot_fit_scans(y2plot, x2plot, yerr2plot, xerr2plot, name, _res, + offset_t0=offset_t0, label_text=lt, fmt=fmt, + plot_separate=plot_separate) - if show_single: - plt.legend(frameon=True, loc=0, numpoints=1) - plt.show() - else: - plt.legend(bbox_to_anchor=(0., 1.08, 1, .102), frameon=True, - loc=3, numpoints=1, ncol=3, mode="expand", - borderaxespad=0.) + if show_single: + plt.legend(frameon=True, loc=0, numpoints=1) + plt.show() + else: + plt.legend(bbox_to_anchor=(0., 1.08, 1, .102), frameon=True, + loc=3, numpoints=1, ncol=3, mode="expand", + borderaxespad=0.) # store the results for counter in self.clist: From 4b3eae005d3151f8f4f4f56d83e9de1608bebaa6 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 30 Dec 2023 00:59:48 +0100 Subject: [PATCH 23/24] add description to fit_scan_sequence --- pyEvalData/evaluation.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 61df6a4..87d1dd7 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -878,6 +878,9 @@ def fit_scan_sequence(self, scan_sequence, mod, pars, xgrid=[], yerr='std', xerr offset_t0=False, plot_separate=False, show_single=False): """fit_scan_sequence + Evaluate, fit, and plot the results of a given scan sequence from the + source file. + Args: scan_sequence (list[ list/tuple[list[int], From 0fac6147dd04a7f7375c11e1a79ea31fe12e1dc7 Mon Sep 17 00:00:00 2001 From: Daniel Schick Date: Sat, 30 Dec 2023 22:52:39 +0100 Subject: [PATCH 24/24] hack with new syntax --- docs/source/examples/evaluation.ipynb | 865 +++++++++++++++++++++++--- pyEvalData/evaluation.py | 164 ++++- 2 files changed, 954 insertions(+), 75 deletions(-) diff --git a/docs/source/examples/evaluation.ipynb b/docs/source/examples/evaluation.ipynb index b69bfec..6424903 100644 --- a/docs/source/examples/evaluation.ipynb +++ b/docs/source/examples/evaluation.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "ab69a1d7-6d05-42fd-906a-ed4f4bc6c0f4", "metadata": {}, "outputs": [], @@ -78,10 +78,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "20aa88a2-fb7a-422a-94f5-332a881147d6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pyEvalData.io.source - INFO: Update source\n", + "pyEvalData.io.source - INFO: parse_nexus\n" + ] + } + ], "source": [ "spec = ped.io.Spec(file_name='sardana_spec.spec',\n", " file_path=example_data_path,\n", @@ -103,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "97dd6950-308e-483d-9917-cffa63a8b060", "metadata": {}, "outputs": [], @@ -122,10 +131,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "cd562c04-b48a-4a2a-8e70-4cf96ef28065", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluation\n", + "\n", + " Main class for evaluating data.\n", + " The raw data is accessed via a ``Source`` object.\n", + " The evaluation allows to bin data, calculate errors and propagate them.\n", + " There is also an interface to ``lmfit`` for easy batch-fitting.\n", + "\n", + " Args:\n", + " source (Source): raw data source.\n", + "\n", + " Attributes:\n", + " log (logging.logger): logger instance from logging.\n", + " clist (list[str]): list of counter names to evaluate.\n", + " cdef (dict{str:str}): dict of predefined counter names and\n", + " definitions.\n", + " xcol (str): counter or motor for x-axis.\n", + " t0 (float): approx. time zero for delay scans to determine the\n", + " unpumped region of the data for normalization.\n", + " custom_counters (list[str]): list of custom counters - default is []\n", + " math_keys (list[str]): list of keywords which are evaluated as numpy\n", + " functions.\n", + " ignore_keys (list[str]): list of keywords which should not be\n", + " evaluated.\n", + " statistic_type (str): 'gauss' for normal averaging, 'poisson' for\n", + " counting statistics.\n", + " propagate_errors (bool): propagate errors for dependent counters.\n", + "\n", + " \n" + ] + } + ], "source": [ "print(ev.__doc__)" ] @@ -163,10 +207,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "1988d314-5ffd-4501-82cc-c022a3469f94", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "spec.get_all_scan_numbers()" ] @@ -181,10 +236,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "71d4f135-50d5-474d-bca4-67cbf5f420ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "('Diff',\n", + " 'DiffM',\n", + " 'Pt_No',\n", + " 'Pumped',\n", + " 'PumpedErr',\n", + " 'PumpedErrM',\n", + " 'PumpedM',\n", + " 'Rel',\n", + " 'RelM',\n", + " 'Unpumped',\n", + " 'UnpumpedErr',\n", + " 'UnpumpedErrM',\n", + " 'UnpumpedM',\n", + " 'chirp',\n", + " 'delay',\n", + " 'dt',\n", + " 'duration',\n", + " 'durationM',\n", + " 'envHumid',\n", + " 'envTemp',\n", + " 'freqTriggers',\n", + " 'magneticfield',\n", + " 'numTriggers',\n", + " 'numTriggersM',\n", + " 'thorlabsPM',\n", + " 'thorlabsPPM',\n", + " 'thorlabsPPMonitor')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "spec.scan1.data.dtype.names" ] @@ -199,10 +291,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "4cf70da2-5d10-4cf2-9237-17fff61068b9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ev.xcol = 'delay'\n", "ev.clist = ['Pumped', 'Unpumped']\n", @@ -243,10 +348,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "58b27432-5eaf-4776-8c72-db3f135531b7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ev.xcol = 'delay'\n", "ev.clist = ['Pumped-PumpedM', 'Unpumped-UnpumpedM']\n", @@ -269,10 +387,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "0197a30d-6441-4ceb-83c0-dbb7173dc538", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ev.xcol = 'delay'\n", "ev.clist = ['(Pumped-PumpedM)/(Unpumped-UnpumpedM)']\n", @@ -296,10 +427,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "e76299c1-27e2-4936-95c6-9550c136c7dc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ev.xcol = 'delay'\n", "ev.clist = ['(Pumped-PumpedM)/(Unpumped-UnpumpedM)*mean(Unpumped-UnpumpedM)']\n", @@ -323,10 +467,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "c8e744cb-d882-4864-a00c-8e66447a5cd6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['mean',\n", + " 'sum',\n", + " 'diff',\n", + " 'max',\n", + " 'min',\n", + " 'round',\n", + " 'abs',\n", + " 'sin',\n", + " 'cos',\n", + " 'tan',\n", + " 'arcsin',\n", + " 'arccos',\n", + " 'arctan',\n", + " 'pi',\n", + " 'exp',\n", + " 'log',\n", + " 'log10',\n", + " 'sqrt',\n", + " 'sign']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ev.math_keys" ] @@ -342,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "55dcd0ad-35fc-4b0b-b3e9-8f0780c28f80", "metadata": {}, "outputs": [], @@ -355,10 +528,25 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "ab3e8393-a75a-4303-a0b0-38fb8e96f8e3", - "metadata": {}, - "outputs": [], + "execution_count": 13, + "id": "b46672d6-7446-489b-89ae-95f82fc0eca1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ev.xcol = 'delay'\n", "ev.clist = ['abs_mag']\n", @@ -396,10 +584,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "d557c3b2-81d6-47b7-93d4-8946e94ddf09", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ev.xcol = 'delay'\n", "ev.clist = ['abs_mag']\n", @@ -463,10 +677,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "16841687-d76a-4c34-bfae-ab22e4e06f82", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ev.xcol = 'delay'\n", "ev.clist = ['abs_mag']\n", @@ -496,10 +723,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "4df984f6-196b-4b7d-b077-70ad748d13cc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ev.xcol = 'delay'\n", "ev.clist = ['Unpumped']\n", @@ -542,10 +782,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "9be9fafd-ea9a-4660-9c12-4578ff7d68cc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "ev.xcol = 'delay'\n", "ev.clist = ['abs_mag']\n", @@ -582,10 +835,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "f3736aad-7494-463d-abe7-99b3a50aafaa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method plot_scans in module pyEvalData.evaluation:\n", + "\n", + "plot_scans(scan_list, xgrid=array([], dtype=float64), yerr='std', xerr='std', norm2one=False, binning=True, label_text='', fmt='-o', plot_separate=False, **kwargs) method of pyEvalData.evaluation.Evaluation instance\n", + " plot_scans\n", + " \n", + " Plot a list of scans from the source file.\n", + " \n", + " Args:\n", + " scan_list (list[int]): list of scan numbers.\n", + " xgrid (ndarray, optional): grid to bin the data to - default is\n", + " empty so use the x-axis of the first scan.\n", + " yerr (ndarray, optional): type of the errors in y: [err, std, none]\n", + " default is 'std'.\n", + " xerr (ndarray, optional): type of the errors in x: [err, std, none]\n", + " default is 'std'.\n", + " norm2one (bool, optional): normalize transient data to 1 for t < t0\n", + " default is False.\n", + " binning (bool, optional): enable binning of data - default is True\n", + " label_text (str, optional): Label of the plot - default is none.\n", + " fmt (str, optional): format string of the plot - defaults is -o.\n", + " plot_separate (bool, optional): use separate subplots for different\n", + " counters. Defaults to False.\n", + " \n", + " Returns:\n", + " (tuple):\n", + " - *y2plot (OrderedDict)* - y-data which was plotted.\n", + " - *x2plot (ndarray)* - x-data which was plotted.\n", + " - *yerr2plot (OrderedDict)* - y-error which was plotted.\n", + " - *xerr2plot (ndarray)* - x-error which was plotted.\n", + " - *name (str)* - Name of the data set.\n", + "\n" + ] + } + ], "source": [ "help(ev.plot_scans)" ] @@ -629,10 +920,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "d312e342-81f5-4c08-ab66-4ff8b3846996", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21:28:49\n", + "00:05:09\n", + "02:27:56\n", + "05:03:44\n", + "07:39:54\n", + "10:16:11\n" + ] + } + ], "source": [ "print(spec.scan1.time)\n", "print(spec.scan2.time)\n", @@ -655,7 +959,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "01f1a166-5bc5-47bb-9fdc-298d5213cba0", "metadata": {}, "outputs": [], @@ -682,10 +986,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "260ce0ef-a06a-4475-a643-9d2160483b54", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "ev.plot_scan_sequence(scan_sequence)\n", @@ -703,10 +1020,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "0e1b2e55-fdb7-4189-b10e-102c734b2114", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method plot_scan_sequence in module pyEvalData.evaluation:\n", + "\n", + "plot_scan_sequence(scan_sequence, xgrid=array([], dtype=float64), yerr='std', xerr='std', norm2one=False, binning=True, label_format='', fmt='-o', plot_separate=False, show_single=False, **kwargs) method of pyEvalData.evaluation.Evaluation instance\n", + " plot_scan_sequence\n", + " \n", + " Plot a scan sequence from the source file.\n", + " \n", + " Args:\n", + " scan_sequence (list[\n", + " list/tuple[list[int],\n", + " int/str]]): sequence of scan lists and parameters.\n", + " xgrid (ndarray, optional): grid to bin the data to - default is\n", + " empty so use the x-axis of the first scan.\n", + " yerr (ndarray, optional): type of the errors in y: [err, std, none]\n", + " default is 'std'.\n", + " xerr (ndarray, optional): type of the errors in x: [err, std, none]\n", + " default is 'std'.\n", + " norm2one (bool, optional): normalize transient data to 1 for t < t0\n", + " default is False.\n", + " binning (bool, optional): enable binning of data - default is True\n", + " label_format (str, optional): format string for label text - default\n", + " is empty.\n", + " fmt (str, optional): format string of the plot - defaults is -o.\n", + " plot_separate (bool, optional): use separate subplots for different\n", + " counters. Defaults to False.\n", + " show_single (bool, optional): show single figure for each sequence\n", + " element.\n", + " \n", + " Returns:\n", + " (tuple):\n", + " - *sequence_data (OrderedDict)* - dictionary of the averaged scan data.\n", + " - *parameters (list[str, float])* - parameters of the sequence.\n", + " - *names (list[str])* - list of names of each data set.\n", + " - *label_texts (list[str])* - list of labels for each data set.\n", + "\n" + ] + } + ], "source": [ "help(ev.plot_scan_sequence)" ] @@ -724,14 +1083,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "d64d1344-c05a-4113-8114-6afd2889d59e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "sequence_data, parameters, names, label_texts = \\\n", - " ev.plot_scan_sequence(scan_sequence, sequence_type='text')\n", + " ev.plot_scan_sequence(scan_sequence, label_format='{:s}')\n", "plt.show()" ] }, @@ -754,10 +1126,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "03bebe1e-6acc-4afb-ae23-bdd9ab39a468", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "odict_keys(['abs_mag', 'abs_magErr', 'delay', 'delayErr'])\n" + ] + } + ], "source": [ "print(sequence_data.keys())" ] @@ -772,10 +1152,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "9950e04a-798b-4305-ae19-fa539df64068", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "x = sequence_data['delay'][0]\n", "y = np.arange(6)\n", @@ -805,10 +1198,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "01e7e79f-dbfb-420f-af01-d4a9203713ff", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method fit_scan_sequence in module pyEvalData.evaluation:\n", + "\n", + "fit_scan_sequence(scan_sequence, mod, pars, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True, label_format='', fmt='o', select='', fit_report=0, weights=False, fit_method='leastsq', nan_policy='propagate', last_res_as_par=False, skip_plot=False, offset_t0=False, plot_separate=False, show_single=False) method of pyEvalData.evaluation.Evaluation instance\n", + " fit_scan_sequence\n", + " \n", + " Evaluate, fit, and plot the results of a given scan sequence from the\n", + " source file.\n", + " \n", + " Args:\n", + " scan_sequence (list[\n", + " list/tuple[list[int],\n", + " int/str]]): sequence of scan lists and parameters.\n", + " mod (lmfit.Model): fit model.\n", + " pars (lmfit.parameters): fit parameters.\n", + " xgrid (ndarray, optional): grid to bin the data to - default is\n", + " empty so use the x-axis of the first scan.\n", + " yerr (ndarray, optional): type of the errors in y: [err, std, none]\n", + " default is 'std'.\n", + " xerr (ndarray, optional): type of the errors in x: [err, std, none]\n", + " default is 'std'.\n", + " norm2one (bool, optional): normalize transient data to 1 for t < t0\n", + " default is False.\n", + " binning (bool, optional): enable binning of data - default is True\n", + " label_format (str, optional): format string for label text - default\n", + " is empty.\n", + " fmt (str, optional): format string of the plot - defaults is -o.\n", + " select (str, optional): evaluatable string to select x-range.\n", + " Defaults to empty string.\n", + " fit_report (uint, optional): Default is 0 - no report. 1 - fit\n", + " results. 2 - fit results and correlations.\n", + " weights (bool, optional): enable weighting by inverse of errors.\n", + " Defaults to False.\n", + " fit_method (str, optional): lmfit's fit method. Defaults to 'leastsq'.\n", + " nan_policy (str, optional): lmfit's NaN policy. Defaults to 'propagate'.\n", + " last_res_as_par (bool, optional): use last fit result as start value\n", + " for next fit. Defaults to False.\n", + " skip_plot (bool, optional): Skip plotting. Defaults to False.\n", + " offset_t0 (bool, optional): offset plot by t0 parameter of the fit\n", + " results. Defaults to False.\n", + " plot_separate (bool, optional): use separate subplots for different\n", + " counters. Defaults to False.\n", + " show_single (bool, optional): show single figure for each sequence\n", + " element.\n", + " Returns:\n", + " (tuple):\n", + " - *res (dict)* - fit result dictionary.\n", + " - *sequence_data (OrderedDict)* - dictionary of the averaged scan data.\n", + " - *parameters (list[str, float])* - parameters of the sequence.\n", + "\n" + ] + } + ], "source": [ "help(ev.fit_scan_sequence)" ] @@ -837,10 +1286,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "0a48e79c-e4e4-481b-b622-89e99f489617", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function doubleDecayConvScale in module ultrafastFitFunctions.dynamics:\n", + "\n", + "doubleDecayConvScale(x, mu, tau1, tau2, A, q, alpha, sigS, sigH, I0)\n", + "\n" + ] + } + ], "source": [ "help(ufff.doubleDecayConvScale)" ] @@ -857,7 +1317,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "f4911911-08b7-4c61-9c52-e25a373ead40", "metadata": {}, "outputs": [], @@ -889,14 +1349,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "13fa3147-d7d3-4c73-b958-94aadeabc8ec", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "ev.fit_scan_sequence(scan_sequence, mod, pars, xgrid=np.r_[-10:50:0.01],\n", - " sequence_type='text')\n", + " label_format='time: {:s}')\n", "plt.show()" ] }, @@ -910,18 +1383,140 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "a22f07c6-93a5-4b16-983b-4c35cdbbe883", "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", + "outputs": [ + { + "data": { + "image/png": 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\n", 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tv2fnWn50dADTNNjcEuEtHU1a2xrdIikVeSctpX3sNSsIQ7T7oWYq2pAsMMd6x/jmk6cLk7SSWZtvPnmaY71ji1swjWaeHOsd48HDZwG3ohRPa21rXBbEkIjIrSJyXES6ROTuaY4HROTb3vEDIrKh6Ngnvf3HRWS3t2+tiDwmIkdF5IiI/Lei9HUi8qiIdHr/L6mRvseO91MT9uGz3Edb4beoCft47Hj/IpdMcyk8/PDDdHR00N7ezr333jvleDqdBmgrF21Xh73ecBFqw36tbQ2wAIZEREzgi8BtwBbgfSKyZVKyO4ARpVQ78AXgc965W4C9wFbgVuBLXn454A+VUluAG4A7i/K8G/ixUmoT8GNve8nQP56mOugrdAEooDroo398+vklmqWLbdvceeedPPTQQxw9epQHHniAo0ePTkhz//33A+TKRduRgM/bcpvcWtsaWJgWyXVAl1LqhFIqA+wDbp+U5nbg69737wI3i4h4+/cppdJKqZNAF3CdUqpXKfUMgFIqCrwEtE6T19eBdy3APSwYTVUBxlJZ8l3JSinGUlma9ITEZcfBgwdpb2+nra0Nv9/P3r172b9//4Q03vaQt7nitZ3IuBNJ8l23WtsaWBhD0gqcKdru5vyLMSWNUioHjAH1cznX6yq4Bjjg7VqllOr1vvcBq6YrlIh8WEQOicihgYGBi7ylS+ctHU2MJrLkbPdNi6ayjCayvKWj6bKVQbMw9PT0sHbt2sL2mjVr6OnpmZIGyEB5aDu/dIJjO4wkMlrbGmCJD7aLSCXwz8DvK6XGJx9XSilgWr8opdRXlFI7lVI7Gxsv3+ptm1uq+e0b1mF5UVUDpslv37BOu0hqJrBctf3ua11baCNUBLS2NS4LYUh6gLVF22u8fdOmERELqMbtDpjxXBHx4b5o31JK/UtRmnMi0uKlaQGW3Ejf5pZqNjZWAnDba1r0i7ZMaW1t5cyZ842K7u5uWltbp6QB/FAe2r6y2dVyZcDio2/epLWtARbGkDwFbBKRjSLixx1gfHBSmgeBD3jf3w38xKtxPQjs9by6NgKbgINeH/P9wEtKqf89S14fAPazBPEaJHqthmXMrl276Ozs5OTJk2QyGfbt28eePXsmpPG28zFFVry2804kjp4gpSli3jPblVI5EbkLeAQwga8qpY6IyGeBQ0qpB3FfnG+ISBcwjGts8NJ9BziK681yp1LKFpHXA+8HXhCRw96l/kQp9UPgXuA7InIHcAp473zvoRSIfuEumYFoakII9I7myLSzr+ea7lKxLIv77ruP3bt3Y9s2H/zgB9m6dSv33HMPO3fuZM+ePdxxxx3cddddVrloW1eQ5sdcNFtqXZcCUWXwQ7dz50516NChy3rN//qNQzxy5Bx/81u/xm3bdCyiuZIPJJgPdZ4PJDg5BlRxukzOpqs/zmA8zXUb6ri+re6SX7zJL3F+jZrZXmoReVoptXNeN36JXG5tjyWzbP+zHxEJWLzwZ7sv23VXAnPR9uQ0vaNJugZirK0Lsa6u4pKNynTGCbigwZqrtnWsrRIhnJ9HstyZrYa00LWn431RKgMWFV7Qy/z/x/uiE/LNp8vaDkfOjhP2mzRW+jkxGCPnqGkNz1xqgvmXuL4iQO9okodf7OPX1tfQXBUikbF5omtoxsCG5UDBrX1xi7FgLDVtF6cZTWQ4MRjHNIRoKkcq60yrvwuVc7KuExmbh15wnQNX14QL++ajbW1ISoThjT4t9QZfXoSnhmKMp3JUh3wTaj7T/bg+0TXI2roQVUEfw/HMgokRYCSRob5i4ryEsN9kKJ6eNt2LPTHCfpOgz0QpGE9lqAxYE17O2e6h+F4nv+hD8Qw1YR+DsQwt1eEZjVo5UZhou8SFXfzj6pZYUKgJP7TFujBFeObUKD86eo7rNtTR3lTBsb7YhB/fy6Ht4jRnhhOE/SYBy2Q8lZlWf7PdQ75lPp0BG03kQBSbVs1eYZsr2pCUiKU8RlJsPLpHUqyqCtAfzWAIRFM5gj6TwVimsAjSTDWkwWiW8VSGpqogItaC/NDWhv0kMnYhr9FEhs5zMTI5m9rwQOFHIJ8ums5RHXTTpnI2lUHflJdzrrW8yS+6m7ePaCpb2DedUSsnlvoYyUA0xYETQxw8OUxDJMCqiJ/O/gSguHZ97YS/92yt2iNnx9i0qvKCLeOLoVjbo4kMZ4YTDMXcBcYGoqkJuq4IWAVt53UNU/U3l5b5dAYsazvAxGXB56NtbUhKRD5G6kIakotpas+UtrgGE0vZmIbwzOlRWmtC1FYESGZtBmMZ2hoqC+fPVENSCNUhH6eHE2xrdZdbDftNTg5GAS6pSyC/zDFAJmfzzOlRpvsRyKfzGQaprAPiBshsb6okkbGpLVr+da61vMlGLBKwGEtlqQr5CnlNzrvcyHfZLpauZ0uf1/bpoQRNkSAIPPHKMK01ISoCPs6MJNnWWgMwQdvTtWodRzEQTburR3oslLbHEhleGYxjCJimsKoqOEXXAJV+k9FkFgW0N7nTCWbS9mwt88m6BvCZxvl+So/5aFsbkhKx0OG2p+vnLG5qT27KF3c5FXfljCWzrKoKUhGwiGVsakI+XnUUY8kMDZUBgt4PbL52MmsNSUEm5xArqrH3jia9Vs703V0X+tEoXg722dPDVIUsrlwVoTp0XuDH+6K8flMjN7bXYxkUap/raoO83BcrDLpfbC3vhrb6wksc9pvUV/g5PZygrbECpdSMKwiWEws9RnIxus47P+S7nCZ35YA7vyXrOFQHLUQEx9N2fUWA8VQGYIq2p2vVVvgVw7GJtfOF0vZ3nz6DbdvURIKsqwtTHfITT+cm6Pp4X5TKoGtINq2qxHEUT50cnlHbs7XMJ+s6kbGp8YJvXmh1zLmypGe2L1cGoikGoq4IX+odZyCamneexd0zIkJFwCrUOPIvYyrrUF8R4MRAgtPDCXKOw1gyO6ErZzSepas/xlgyQyRgkc45VAV9jHuhL/IizNdOOpojxNI54ulcoYaUzNqsqwuzti7MaDKLZRoopTg7kuCx4/3E0llODMYYT2VnLWe+hTH5+TRGgrx+UyObW6rYub5ughEJ+01GEplCundsb+V3b95EW0MFR85GQRSvbasj6DMLec92D3C+JjZ5xcBV1UF++4Z1NEWCF1xBsFwYirl/K9t2eLxzYN7avhhdp7IO+w+fxVFOoSvHNCh05Rx8dZisbRd0DRS0XVxxmKztfKs2mbULumioDGAYBvF0jpF4mqdODvPQkT4cR5FznDmVdSZtr6kN86aOVWxrrSloe7KuX7+pkd+6YSMffP1G/KbBkyeGZtX2dPcwk66DPoPbtrVw27aWOa2OORd0i2SByQsqPxiZzU3vaXGxzDZQN3kwLes4hS4nFBO6cuorA4ynspweTrC2LsyLPWMEfAZG2r2GoxSrayoLtZPiFsLkGlJXf5zRZIaAZfBCzwijiRy1FX7W1YZJ5xxe6BljW2s1VUHftOWc3O88uUYnyJQm+XTN78ZIkJqwnzde2TQhbT7vi63lFZelsz9GbdjPDW3lbUDA1faTJ4YBd4xkJi+ii+FidF0RsApdTkJmSldOQ0WAzv4Ym5oivNjjrpFSFTIZSWYYTWS4dn0t8XRuirZnatVuXlVJ/3iSY31RGiIB1teFqQxaBV1Xh/yzlhWm13b3SIKs7UzoNpupW2mu2i6+h6tXVxFP5Qpuw493ut1qr9/UWPg7Fr9nC6FtbUgWmLyg8uuR+CxzihfRpTBdP2defJNfxkjAKnQ5KaTQlQMQS2V5uS+KZQpra0O0NVTQNRBja2sVjoLqkI+mSLDgZ/545wDD8QxVIR9v6ViFoxQHTg7xi65BasM+XruxHtMUnj09wsaGCqy4W8PxWyY52zUm6+vC+C2TE4MxakJ+klm7UFZHOfSMZqkKWhw6NUI4YBLymXSPJDg35tbmVlUHCflMklmbeNpm5/pazgwnCnkoBV3nYtSEfcTTbstKoVBK8dLZJOfGU4wnc1SFLLa0VLOlpZrDZ0b5RfcgtRU+2hvCjKey7H+2h2vXu0uAPH1qhHDAIuQzOD2c4FjvODdtXkWb11ddjhzvixIJnh8vKoVzBcysa4C6Cj/DsTR+nzWhKwcgm8vxXLdrQNbVBTk3niWRVbyhvYGqkJ+co4gEDdbUuuN/v3pliJqwj2vX13LNuhoOnBjmqVMj1IV9bF9TjWUKz54epa2xklVVQY6cHSeWyoHACz1jXNVcRSKTuzhtWyaWIfz85QG2rq6mKRKYVdcwd22/uaORkUSWrnMxesdTbKgPE/KZBf3Opu13bl/NqurQJf0NQRuSBScv/vNL7aoF8fQpHqgbjKUZjmcwDOH2HasBJryMa+vCPH1qhKqQRcA0GE1m6Y+miKdtDBHGkxmGE1n+96Mvu0bPNEjnHFJZh3TOJp1zSGZyJDMOWcdZsh46l5vO/jife/drFrsYi8ZIIkOdV2vOS2K+2p7sXFE8sXS6FmlDZYDRZLbQlZNTDqeH4sTTNjlHkcraPHasn/FUlrDPwjKFp0+NkM652k6kciRzDtmcQ3aRhf29w70XTnSZaKkJ8R9eow3JkiFfwyoea5+LN8RcBuo2N1e6fcSOQ11lgMZIgGN9MTY3V3KsLwa4L3Y84/brHusb59RQnL6xFFlnpivPjbzbp4j73UAwDNeHRwzBth0UEPJZOMo1Po5SGCKEfCaWaZCz3b5tEderTQE520EQbAUGCkRQgM8wMMR1UAz7z8t0Jh8G23ENoWG4eecch1TGwRDBZ5pYJpiGgaMUgnhlc920bdshpxSO98NiGgY+Uwou3ODNm5Dytqi14Yk1blAX1PZcB6CLXXZf21aHzzQ5O+rWzlfXhAn7TWLpHGfHU2RyNk+eGOKVgRjjKXumS88ZAU8LrleaaYincyFn24AQ9FsY4urMVgqUELAMTEOwTAPbdsgUaTvnOORs1zvKdrxAziL4DPccpdSctC3iviN5bTuOQ86BTNbBbwp+n4npTVqbj7ajqdy8nqE2JAtMR3OEh17oLQyyvTIQpbUmOGuYlNk8V4AJfaubVlVO6FuNp3MMxjJsrA/zzQOnebxrkN6xqQOgfkuoCljUVvhprQnRVBWkwm/iKHjL5iaqQj5CPpOAZRDwmfzseD9NkQB+y3BdBXHFn/d2SWWdCTXFsyMJugZiXLu+7oKhTYp/WEYTWYI+kxMDMbK2Q9DrwvJbBm0NlQR9RqFv90JMnh8TS+fc8RrbHYQsHq/J30PWdnixZ4yw38RRCgUkM/a0zznoK2/flLy28xw8OUxt2Dejtueqa9cQyZRxgNU1YZKZLK8MRHns2AAvnB0jnp5qOCr8JjVhi9XVIZprwlQF3e5RpeDmq1YRCfoI+AwClsHPXx5gVSSAzzLxmYJpGAui6/z9Fr+rq6qCtFSHeaF7lKxX0fJbBttaawp6uhhtFxvbsUSWmrCPVM4pjNdMvo/LqW1tSErExbj/zjRQ9+iRXs6MpAotkLOjKaKpHGG/VfD2ODeW5P883c1zPWOFPtWAKVzRVMm2NTW8ob2B69vqOd43PuUlmU3M6+rCpLIOPtMs7Cv2dpnsTmiaBrfvWM1gLFMQc35As5j8YHae7z/XQ9hvFgb+3fIbDEZTNEWCF+WOWJz3qqowJwZiZGyHkM+9h9PDCdoaKifcw+mhhPujA4WXMp7K0XkuRlXQvyCukSsVdQEn4Jl0feDEEMPxDKPJLNmcjc8yOTOc4KbNTeR/kmzH4eDJIf71cA8D0Uwhz/oKP5uaKrl6TTVv2tTAro31HHp1eEZtv669YUKZNjYkSGXdCkuehdA1TNTf95/rKYzv5LUd8plEk5kJA/5zZfKge9445cdAtrX6p9zH5dS2NiQLzPG+KKtrwjRVheBslPX1FayuCc86IDndoGImZ/OTYwNsbqkiYFp0notxYjBGUyRAOmtTEfTxoyN9hS4tv2nwpo4GNtRXcO26WqpCvoJIQE37kswmoNnSF3tyzfZyDURTPN45MOvkrXxXYE3Yz9Wt1ZwZTjAYS1NT4b9kb6D885zNOOXvoetcFBFFJOSnvamS6pCfqqCPVC5XcI2c7cejnMhr2xDXa2vn+jpSWWdGbc/kkfVEVz+mYVAT9mOKwemhBJ39MeJpmzd1NPD8mTH+7cU+RhPu/KTV1UF2b13Fqqog7U2Rgh6H4lmiqexFaXshdA0X7rIrdiLIa7vzXAylhKDPuCQ9FT/P2YzTYmhbG5IFpjDYXliz/cIDktN5rnT1xwn4TCyBV4fjBCyTurCPl/uiHDg5zFgiVwhwsKkxzH++cSPVIT9BnzmtC2KxC+xcBHShl2pyy2Iyc41vVfxiV4d8+BoraaoKzsuldK7GqTES5IYr6qfUZhMZm/X1lXPudigXJmjbGwObTdszeWQNxTNsaopg205B240Vbi37V68MMuLNaQpZwtVrqvnt69djGsaCaHu+uoa5xbeabLB8psG6+vCC6Houxulya1sbkgWmMNhetO9CA5LT1ZIG42muaKygZzRFwDKJpTKcHU3TN57Eq6hRETDZvWUVGxsqGIpnOH4uxmvb6ij+sxa/6HN5SYq52PTFXEwU04sxcHMJp3ExxuliW2rlzHltu5ZEMbu2Z3q2DRXuHKH+6Hltvzqcomc0VagcNVcFeOtVTTRVBTk1nGAwllkwbc9H1zD3yNMXo2u4sLYv1jhdTm1rQ7LA5P94OdvtP05mL/zHm050122oI521OdYXJWAKJ4fi9I6lC0akPmSxq62OkN8k5LMmTMjateF8d8LFxs9ZqNDZFxPFdK4v9oXCaeS5mJf4Ul74cqXww+QNxsVTOTK2mlHbMz1by4DDZ8YYS2bxm3C8L8arQwkcwBJoqQ6ybU01DixpbV8o8vTFGKy5aPtitXo5ta0NyQKT/+P94PmzAFiGzKk5O1l0eWFtrK/g2dMjnB5KEsu49bW2+jABn0Em55DM2IXwD2tqQjx5YuiS4+fM9Yd6LlxsFNO5MNe1SuDy1lDLhby2DUPAVvh9Brs21sz67KZ7tte31TMczzASz9DVP86rgwlygE9gS2sVmZxDKmsveW3PJfL0XJmrti9nr8LFUN7+jCXkfPTfSzs//9Kurw/RPZo6b0QaQjgKQn6T8UQG05BCbB2/ZXLdxrpLjp8zW9yji2Wu8a0uhpGEGxajmOIYRZrLw/nJtpd2fmPEdYe/dl01nQOuEbEEtrZWMZbILRttzxbf6mJZ7trWLZIFZiCacueReMHtzgzHeeiFXm7b1jKnQbzJTe+KgJ9+b17I6uoAa2vD+E2D4USGVE5RF/Zz9eoqLMOY0b99rsx1Uam5MFOMrqqg75LcH2H2cBqa0pPXtu3Vjp49PULvaPKStX28P1YYT+xorqI65KOxMrBstD05vlW+nJcyBrHcta1bJAvMgRPDnB5OYIr7aA0RTg8nOOAFu5uJ6aKH/uLlQf7qxy+TA9bXhrl9x2rqwn7GUznSOYd3bm9h25oaco5akMi0eTEXMx8xN0YmRjGdbxTd4laOUqpgkPJxwTSlJa/tvEeiaVy6tv/5mR6++avTAOzeuoqd66sxRBiMZ3AU3PmWNt5wZeOS1nY+8vSvrauddzmXu7Z1i2SBOdY7TnXIh+XFFLFMd/GnY73jvGP76hnPK/YEebEnRjSdo/PcOCcGE1QGTD52Uxs9w0niWZvmqhDr6oNc0VQ175paMaX08liIvlo9ML645LVteP6/AcvEbxqXpO1/faYHG3hdWx1v6Wjkia4hwt74RX2lj7Qt7JznGunFlErbCzUGsdy1rQ3JAqNw492IkV/b2o3fc6FZwCOJDKZIwZ3Qtt3gcwCvaa3CZxpUBNxFnhSwtbV6wdcQv5xivlQPGj0wvnhM0Lat3PBRl6DtV85F6R5NIsC6+jAnBuNcuSpSWOVyW2s1lmEsmK5Ba7vUaEOywFzVUsXhM2PYjjs4nsk5jCaz7FhbPet5tWE/z5waJex3w6//+NgAaRuqgyZXt1bReS7GSCLNxoZK1tdXTFgQZyHXEL8cYl5IDxrN5SOv7fwoeyLjuv9ejLbTOZufdg4CsH1NhOqQxYmBGOvqwqyqDhVmYOfjRi0kWtulQ4+RLDDXt9Wzri6EUm6LxFawri7E9W2zN6E7miMMxtM4StE9muSVfjf0yS1bVhH0WVy7vo719RVc0Thx2dnlNCCXZyE9aDSXj7y2815btlIXre2nXh1xB5V9Jm/c1ETAMtnWWkt12D9hxcDlqGsoX21rQ7LA5F0bW6rd2kdrTWhOXi0AkYDJS73j/LJriJyClqoAGxsqC/7pVUFrWQ/I5Vnuro7lSl7bPsv9213dWnPR2n7m9AgAr2uvx+8zqQz6aG+qYDCaXva6hvLVtjYkJaAxEmRdvTtfYl1deM6xe9oaKllXH2bQcx2+qiXCy+ei9I+nOPTqCNUh/5S1l5djk3mhPWg0l4/GSJCAFzn3uo11F6VtQUjnFBbgt+D57lFi6RzJjD2vOSJLiXLVth4jKRH5MPJzmbNV3Bzu7I+StiFgwnjKrZWF/RZjyQzDcffPtdyDCer4Vssboygg6YUo1vbpEXehqtpKPyOxLNe11ZFzFM+eGeW3b1jH5pbZx1qWA+Wqbd0iKRH5FcicObxtxc3hAyfdpv/O9XU0RQL4THehqZ0b6grh6Jc7eQ+alVADLUeMS9D2SCLDkbPjALx92yqaqoI4CqpCPn5tfQ2DsZXR9VOu2l4QQyIit4rIcRHpEpG7pzkeEJFve8cPiMiGomOf9PYfF5HdRfu/KiL9IvLipLw+IyI9InLY+7x9Ie5hIRmIpjg9HAfg1aF4YbXEmcg3h4diaQ6fGcU04IqmSravreG1VzQWBiFXUl9rfrLiO7e38vpNjUv2RXv44Yfp6Oigvb2de++9d8rxdDoN0FZO2s54y+0eODk0Z23/7OUBHAVXNIZpqAyypbW6oO3mqtCK0TUsH20vJPM2JCJiAl8EbgO2AO8TkS2Tkt0BjCil2oEvAJ/zzt0C7AW2ArcCX/LyA/iat286vqCU2uF9fjjfe1hI8n3CjtdNmsu5IdNne+Hys1p//vIACtixxp0j0hiZGNKhHPpaZyK/SNb3n+vh8c6BC/6ALQS2bXPnnXfy0EMPcfToUR544AGOHj06Ic39998PkCsnbefbIemsPWdt/+Jld07UtjU1jCWzhZhrUN66hsXR9kKzEC2S64AupdQJpVQG2AfcPinN7cDXve/fBW4Wt+/ndmCfUiqtlDoJdHn5oZT6OTB77IUlSL5POOCtgWyZxozuf3kBPXliCMuAJ191+1Zf197I7TtWY4ixIjxZ5st0ITYu9AO2EBw8eJD29nba2trw+/3s3buX/fv3T0jjbQ95m2WhbcMbJAn5ZnZtLdb2YDTF2bE0IZ/JW65sZF1dGMtbK72cdQ2Lp+2FZiEMSStwpmi729s3bRqlVA4YA+rneO503CUiz3tdBLXTJRCRD4vIIRE5NDAwMLc7WQDyfcKTV0ic3HQ/1jvGVx8/yS9e7ufsaJJzY2lODyXxm/Cea1sZjGWIpzN09o/z6lCsbPpap2OxfPN7enpYu3ZtYXvNmjX09PRMSQNkoHy0XTxGMlnbA9EUP3iuh7/+cSfPnB7BMoQXe9yxkRuvqKO+MoBpoHXtsVLmnSzHwfa/Aa4AdgC9wP87XSKl1FeUUjuVUjsbGy+fl1NhFbkir63JTfeBaIoHDp7m7EiK/miGF86M8e1DbgC71poQPz0+QCrrsLEhwqamKsJ+3yUvwrMSKCPf/GWi7fP7irWdr12/0D1GIm3zwplR/vFXp3js+Dmv3GhdT2KlaHsh3H97gLVF22u8fdOl6RYRC6jG7Q6Yy7kTUEqdy38Xkb8DfnDJJS8Befe/bM4LkWJPdf87cGKI08NJKv0GZ4eTdI+mGYi64SBCfpNjveMMxjNunC6lSOccus5FueGK+rJ88RYrxHZraytnzpxvVHR3d9Pa2jolzZEjR/wA5aLtvLNW3Ksw5bV9vC+KbTucHIqjHIdTQwlGU1nGUu6AYSKTYyie4sRAjmg6RyZr03lunM0tVfNasXA5s9zDx+dZiBbJU8AmEdkoIn7cAcYHJ6V5EPiA9/3dwE+UUsrbv9fz6toIbAIOznYxEWkp2vx14MWZ0i4Gefc/n+U+WlOmrpD4zKlh4qksz54a4eVzceLpDDnv5RxNZDk9HKd3NImJomsgzpmRJIlMdtn2n86XxQqxvWvXLjo7Ozl58iSZTIZ9+/axZ8+eCWm87XwtoSy0bXi/Gj5zorZHEhlODccZTWR45tQIZ0eTjMbdtaFNYCiW4icv9ZO1HUwUZ0aSHD07jmWI1vYyHwudtyHx+oXvAh4BXgK+o5Q6IiKfFZH8W3c/UC8iXcDHgbu9c48A3wGOAg8DdyqlbAAReQD4FdAhIt0icoeX1+dF5AUReR54C/AH872HhaYxEmRVxK1RJDM2x/uihRdkIJqiayDOQCzJcDJLMmeTyJz3x3ccRd+4Gy5iIJYhErTwWwbJrLNs+0/ny2L55luWxX333cfu3bu56qqreO9738vWrVu55557ePBBt650xx13AFjlom0AIx9ta9I0kmgyy89fHuTcWJLxjE3GUWQd7xwDhhNZ+qNpgj6TgVgGv2XQEAlwZiSptb3M550syMx2z03xh5P23VP0PQW8Z4Zz/xz482n2v2+G9O+fV2EvAwPRFK8OubN4A5ZRqG3d2F7PgRPDmALJtCJomcTSuUJrJGgKtlLYtkMslSNgWVgmKKUKTd+Fjva7XFisENtvf/vbefvbJ07n+OxnP1v4HgwGAU4opXZOPnelabvg/uv1bRXrGtyuLRHIOW4N1bbP25qw3yCdVSTSWZSCsWQW0xDW1IaJpbJeGq3t5cpyHGxf8hw4McyoN1g2GMuQc5xCbetY77gb9iRgIpOqdJVBN4R8hd8iHDBRuNW51toQTVWu0JZj/6lmZXC8L4qjHLK2q9szI3Ec5XC8L8rxvihBn8nOdTUoQNTEBktlwIfCwWeZjKcyVAZ9rK4JYRoGlUEfoLW9nNGGZIEZiKY4+Opwofmfcxxe6Bkja9uMJDIoFJUBP5ubI/h9JpZ1/k9gGQZVAYvqsI8tq6t525ZmqkN+MjmHtbWhZdt/qlkZnB6O09UfI28isjlFV3+M08NxRhIZ6ir81FQE2LGm2qsIueSdklZFAjRXBXnNmlreuKmBrK0YTWS0tlcAOmjjAnO8L0pDRYDuYbdryzINQj6Tzv4Yv7autrA4UGMkQNAyGVduy8UA1tSG2NJaRSJt09ZQia3yiwYJOUcRCRrLavlNzcpiLJnFEMHwRtv9PsEQ8WaqV5DJupUmQ4SgZQKut1ZN0KKpKsCVqyJsbAgT9Bkkszmt7RWENiQLzEgiQ3tTBU+96k5cdhy3jT8YTRdqW6eG4rzYk6Qm7Gc0lSWezdEQ8XNlc4SGihBtG8KzroGt0SwGVUGLaMouLK2bytr4THednI7mCCcGYmRyDkGfxarqIGejbiVpc0s1O9bXsr42zKrq4LKPXq2ZijYkC0xt2E8q67C6xq1ZpW0HR01cu2F9fSW2gtU1AV4ZcFdCXFcbIp11MA0FCN9/rqdsfes1S5P19ZUELasw2dYyDK5oqGBVtTtYXFcRoLkmS852CPtNnu0exydQG/aRzuZ4vmeUDjvC451oXa8w9BjJApP3C7dM92Wrr/Czrj48YTlShWLn+jquWVvLeNpt/hsivNQ7xq9ODDEYSy3ruDualUlHcwTTNAh5KySurgljmkahpZ3X9WuvaCQ//b0m7OPEYIwDJ4Y5M5zAELSuVyDakCwwhQmJpvtohakTEvOzWZ865XZ/hXyCZZogQibn8KuuIcZT2bL1rdcsTfLa9upIWCYTtF28OuDzPaMAVIV9KNwxk1TO4bGXBiZ4MWpWBtqQlIDGSJArV1UB0FIz0Ud8IJpiNJHh5y/3F8ZRgj4T23u5Qj6T/liKo2fHgOUZd0ezcmmMBKnyXHS3r60taHuiroc4PeSux4Ny51JVBE38plHQttb1ykIbkhKRX47Ucc7vy0/oCvosbmirJ+bV3iJ+i7V1YaqCPs+Dxc9pb0Kj9q3XLDXOL7XrDrpP1rXtKIYTOcDtDqsN+7G99yCvba3rlYU2JCUiHyG1eDnS4pDRtRUBVJGR6RtLEkvniKZyVAQMlEL71muWJOeXkXa3J+t6dU0IAFOgfzxJKmsznsySzNhUh0wyttK6XmFor60SEUu5NbJTwwke7xygodLPk68MIaIQERzHDVoHYCtFzlb4LRMrKKQyDs2ev732rdcsJQaiKaJJt0vquTOjRAJmQdeRkJ/qkMWjR9wgxkELbAXpnOMdt4ilHba0VC3LeFKamdGGpAQMRFPeDGDwm8K5sRQPv9hHRcAklc5x9FyM8aQbX8gnUFvhp6EqgGUIyhFqwhYfe8sV+kXTLCnOx9pyWyRDsRTffPI0FQGTSMBHz3Cch3rGOeN1yzZFQlQGfFSHfeAoAj6T7WtruG1bi9b2CkMbkhJwvC9KMu22SE4MxPj+8z20VIfI2cLzZ8cJmEZhUlfIb3BVSwTTMGmMBKj0m1QGTf2iaZYcx/uijCbSjCTcwIq/fGWYLS0RApaPwViao70x/KYQz7iVpNpKP22NFaRzisbKAI5ytBFZoWhDUgKOnB2jc8BdXtRnCLG0zbHecWrCfiIBi/Fkhv4x92WsqwzgM00aKv289ooG4ukcQZ8eutIsPY6cHePgyfNLzSczrq5FqmmoDJCzHdI5h6Rbh6KxIkDWVjRF/LxmTQ1Bn6GNyApF/2KVgBMDMXdeCCCGQU3IR07B8XNR+saSJLI2adv12EplHV4diFIRsPTgumZJc2IgRsAyMb05UhUBk5xyDczzZ0aIpTMkMrlC+v5YmnPjSSzT0Lpe4egWSYlIpd3m/WA0jSkOiYwbfjtY4eNcNEsy67m8KMWJoQTXprJ6cF2z5ImnMkRT7mB772gCEaEm7HdbImmHqGdIAgZ0jyQZiWe4efMqPbi+wtGGpASEfAbjade3V3DIOUI2ayOGQd94BlU0ucQ0oCrkYzyZ0/GHNEuakM+gbzxdWGgk54CJwjKgbzyFjQJvDR3DEEwDKgIGOUfNnKlmRaC7tkqAIYLp+doHfRaGCEqE6pDFmtpQYY6JAayvr6AxEuTMSIIDJ4YWr9AazQUwRPBZBj6v2zZgGViWIGLQFAmyvi5Mxlv0qjLkY2NDmMqAn9PDSa3tFY42JCVAIXQ0VwCQcxSCYk1dmIqgj6DPJOvV0II+AxGD2rCfuooAL/WOL2axNZpZUQg3tNVheFPb/ZZBU1UI0xBaa0MoKMxgr/BbOI67vybk09pe4WhDUgKaqgL4fZb3Pcjm5ipMgaBpUB00iXuhUXym0FDpR0RojPgRb1VFjWYp0lQVIOizqKtwQ5tsW1NNznHwGaAcRSxt40kb05sftb6+AoXS2l7haENSAt7S0UTcCw/vOA6WZWAAIb+FaZoErfM1OkOE1TVBsrZic0vVIpZao5mdt3Q0MZpw1xsByNkQtEx8lkHWUayvCxXSNlQGvPlR7gqKWtsrG21ISsDmlmredGUDAMmcYm1tkJoKP7FUliNnx4im3a6tmpCPRCZH0Geyri7M9W11i1lsjWZWNrdU89s3rMPyurYCPoO3XtVEUyTIwHiaZ0+PAiBAJufG13KU0touA7QhKRHr6isB2Nwc4Z3bWxmMpRmIp0llnbzTC5YhjCYztDVU6hm/mmXB5pZq2prc+SC3Xt2CEqFnJMG5aLLQZWsJxDM5xlM5re0yQRuSElEII6/gseP9hPw+KgNWIXS2gesFs6YmTE3Yp180zbKhOIz8iYEYCoOqkJ+Q3/Xmqgr58Fsma+u0tssFPY+kRBSHke8fT1MTtBiMprA9jy2/TxhPZomnc/z4JTdaqp5HolkO5AfO89NDHOV44VHcFokpkEjnONIzSjpra12XAbpFUiLy0X3PjaVIZHIo3AHITM59+0wBv88k4DNprg7qdaw1y4KBaIqhuBsn7ujZcRoqg9RXBDANIZX1JiOaBiGfRSToI+Azta7LAG1ISsBANFXwmzcN2LyqirNjCUbjGaJe6BQDoSkSoM5zkdTrs2uWOoUw8l5TJJuzCfsNcraNoxTpXD7sj0NdpZ9I0MeVqyJa12WANiQl4HhftNDuH0tm6R5NELBM0rYik3Wb/0G/xZaWKq5vq6c65I6b6HWsNUuZ431RHOUUBtX7xlMopcg6DqYUIqdQE/LRsSpS0LbW9cpnQQyJiNwqIsdFpEtE7p7meEBEvu0dPyAiG4qOfdLbf1xEdhft/6qI9IvIi5PyqhORR0Wk0/u/diHuYSE5PRzn7Ljb/FeO4vnucRwHNjSECfjdYan2xkosUwpGBPT67EuRhx9+mI6ODtrb27n33nunHE+n0wBt5aDt08NxuvpjhbXaY6ksPz42gGkY7FjrFtVvwobGSsIBs6BtreuVz7wNiYiYwBeB24AtwPtEZMukZHcAI0qpduALwOe8c7cAe4GtwK3Al7z8AL7m7ZvM3cCPlVKbgB9720uKsWSWrDfwOJ7KEUtnyToOfWMpEt6CVyG/yZGz4/SOJVBK6RDySxDbtrnzzjt56KGHOHr0KA888ABHjx6dkOb+++8HyJWDtse8ddeTnrbPjqXJ2A6ZrM1xb0XQSMBkKJ7lhe4xRhNpresyYSFaJNcBXUqpE0qpDLAPuH1SmtuBr3vfvwvcLCLi7d+nlEorpU4CXV5+KKV+DgwzleK8vg68awHuYUERFOeibovEUQoB+sczGCJkHHfCVthnsLW1inPj7uBl0GfoUNtLjIMHD9Le3k5bWxt+v5+9e/eyf//+CWm87XxEwhWtbUFxejiB7Y2FpDI5Uukc4+kcmZw70F5XEaQmaFJX4edIz7jWdZmwEO6/rcCZou1u4PqZ0iilciIyBtR7+5+cdG7rBa63SinV633vA1ZNl0hEPgx8GGDdunUXvosFRCE0VwfgDIgIVUGLWNom6Y2PhP0Ghul2B+QcxTu3X+iWNYtBT08Pa9euLWyvWbOGAwcOTEkDZGDla1shrK8L88pAHICAz6Sx0k/veIpkxjUkfssgFPBxy5ZV5BzF6zc1XrbyaRaPZT3YrtzO2mkXO1BKfUUptVMptbOx8fKKuTrkw++F2g6ago2AOMQ9j62Q3+LG9np8pqn7jjXTshS1XR3yEfSbVHgTD32mMJLMErSk4I0Y8Jla22XIQhiSHmBt0fYab9+0aUTEAqpxuwPmcu5kzolIi5dXC9B/ySUvEevqKlhbFwYg7ShWRfxc2RTBMNzHvaEuSH1FUPcdL3FaW1s5c+Z8Y7u7u5vW1tYpaQA/rHxtr6uroL2pEttbmM1vGly7roZ1dRXEvIXcbtrcqLVdhiyEIXkK2CQiG0XEjzvA+OCkNA8CH/C+vxv4iVfjehDY63l1bQQ2AQcvcL3ivD4A7J8l7aLQ0RwhmXUH1QUI+XysratgkxejyDBM3Xe8DNi1axednZ2cPHmSTCbDvn372LNnz4Q03na9t7mitd3RHMEQA8fz2so4ipFkjjdtbkK8xlN1yKe1XYbM25AopXLAXcAjwEvAd5RSR0TksyKSf+vuB+pFpAv4OJ43ilLqCPAd4CjwMHCnUsoGEJEHgF8BHSLSLSJ3eHndC7xVRDqBW7ztJYd4j9ZxHM6NJ3mpd5zjvWMAxFI5RhPZxSyeZg5YlsV9993H7t27ueqqq3jve9/L1q1bueeee3jwQbeudMcddwBY5aDtxkiQzc2VxLwlEtJZm9F4hn95uod4xrnA2ZqVjOR9wlcyO3fuVIcOHbps13u8c4CjveP8xQ+PUeE32NZaxZnhBH1jGWzgyqYwOzfUs7k5oiOjrgBE5Gml1M7FuPZiaPuz3z/Cy/1xNjeFMQyDwViK/lgOAd7xmmauXV9LTTigWyUrgLlqe1kPti9VRhIZcp6vfTrncGYkSSrj4C0eh88wGE1k6RlJ6NARmmXF6eE4qYzbbdsbzZDO2oXQKD4DbEfxzOlRHOVobZcROvpvCRCE53tG3Q0HxpK5QmgUgIytqAxY9I2ldOgIzbJhIJrizHCSjBf+J5nJcTabK6zTbpkG6ZwbLmUgmsZn6npquaD/0iVgPJlmYPy8gXAcp/CyAcTSWVK5HFkH7SKpWTYc74vSVBkgm2+BmG44eS/oL46CgfEUkZCf4Vhaa7uM0IakBPSMptjQ4Lr/2rjxtoqHIuPpHJ29URorA9pFUrNsGElkyNgO9RU+ABxb4ajzk11ytsOpoQSJdBbDMLS2ywhtSEqAeP/AfclS9sSZZY6C0VSOW69u0oORmmVDbdjPcDxD0JuQmLUhbRclUOCg6OyP8cZNeqC9nNCGpASsrgnyshfEDtxFrPIELaE66KM+7MNWMs3ZGs3SpKM5QiprM+DFkRNx12fPEwlZVIf81Ghtlx3akJSAqpAfQ4peJIH8limCQmGI6IF2zbKiMRKkozlSCNDoKLCKf0EEKgImqYyttV1maENSAsaSaZQ6Pyoixf1aAgG/ia3Or32t0SwXoulsoZvWNJngRBKyTBxHkcoqre0yQ7v/loCzo2mG4+drZHbRgGSFzyQS8NFQGWCGmHwazZJkIJrihe7xQqwt22aCE0nAMgj5La3tMkQbkhLQ1T8+wZAUv1I5R1EZMNm9tUm/applxfG+KKlMjoQXoNFhoraDPpPmqiA3X9WotV1m6K6tEnB6KEF1yFfYLn6pDEMYimUYjGo/e83yYiSR4Vwsjc9b53GysUhmbbaujlBfGdLaLjO0ISkBOUcxlpx+sLE6ZKGU4tFj/TRU6pdNs3yoDfuJp2y81aKnoFD8omuQs6MJPYekzNCGpAQELIPMDC/beDIHApV+i8GY9mzRLB86miPYto09w/GxZJZk1qauwq/nkJQZ2pCUgEjQRGZ4skG/ic8wWFMb0i6SmmVFYyRIQ1Vg2mMmEPZZNFYE9PhIGaINSQnwWxZh3/SPNpGxSdkOm1uqdD+yZtnhN6xpfzRs3Im39ZV+resyRBuSEpDJ2cRS0y/0U+Ezaa4KYIjofmTNsmIgmqI/mmSmJaxqK/1UBnxa12WINiQlYDSRnbEfuabCz9WtNbofWbPsOHBiiFRuemVbgIFbOdK6Lj+0ISkBY6kZRtqBugofb7pSzyHRLD9e6h3HmGHGumVCddgiUuT2rikftCEpAcpRMz7YWCpHImPrfmTNskOQGVvaiLuAm9Z1eaINSQlYXRvCmCHUUH80w/G+Md2PrFl2bG6pImBN3yZRjuvarudGlSfakJSAG9oaaKic2sT3GVATtugfTy9CqTSa+XF9Wx1rakN4y5FMIOAzaKkO0FW0fIKmfNCGpAS8c3sLdZVT/e1rQj4qAz4cx+F4X3QRSqbRXDqNkSBvaG+gwj/1ZyMcsKit8HPo1eFFKJlmsdGGpATUVwaoCkxtkUSCFrFMjrFUjlNDuuamWX5UhnxTxkFMIGAKtlL0jCQZiKYWp3CaRUMbkhJw4MQwKXvqsGQq55C1HRojAcZn8ezSaJYqPSMpkrmJPoc+U7AdYSiWpa2xUre2yxBtSErAsd5xqoNTWyTD8QyWGNiOmhAdWKNZLiTSOXL2REOSsRXjKTfO1sbGsA79U4ZoQ1ICFIr0NBO3cjlFNJXl1FBCr/ujWZYoFLYzUdsOkLVtLEN4/syYXh2xDNGGpAS01gTpPDd1DMQQMA2wTIPjfVHdl6xZdhgCyczUSpJjg6MUZ8fSjCe1V2K5sSCGRERuFZHjItIlIndPczwgIt/2jh8QkQ1Fxz7p7T8uIrsvlKeIfE1ETorIYe+zYyHuYWER0vbUiEQ5BQOxNBvqwwR9hu5LXgY8/PDDdHR00N7ezr333jvleDqdBmgrF20nMja5aYJtZRX0j6dYVxekZ1RXkMqNeRsSETGBLwK3AVuA94nIlknJ7gBGlFLtwBeAz3nnbgH2AluBW4EviYg5hzz/SCm1w/scnu89LDRnR1OEfFOd7RXuOtcnh+L4LUP3JS9xbNvmzjvv5KGHHuLo0aM88MADHD16dEKa+++/HyBXLtoejmdmnGybyjn0jCZJTNNi0axsFqJFch3QpZQ6oZTKAPuA2yeluR34uvf9u8DNIiLe/n1KqbRS6iTQ5eU3lzyXLAo17cumABF3BnAmp3Q4iSXOwYMHaW9vp62tDb/fz969e9m/f/+ENN72kLdZBtqeHgcwRRiJZwn7rctZJM0SYCEMSStwpmi729s3bRqlVA4YA+pnOfdCef65iDwvIl8QkWlX2hGRD4vIIRE5NDAwcPF3NQ+uaqmatvkP4DiKqqBJMmvrMClLnJ6eHtauXVvYXrNmDT09PVPSABkoD22vqQ3js6b/2XAch0jAZHXN9ItfaVYuy3Gw/ZPAZmAXUAd8YrpESqmvKKV2KqV2NjY2Xs7ycX1bPfY080gMQIm7FO91G+t0uG3NZJa8tluqAiQyU2tJJuAoWFcXZn195WUtk2bxWQhD0gOsLdpe4+2bNo2IWEA1bnfATOfOmKdSqle5pIF/wO0qWFIcOjlILD31ZVOAzxAaKkNc31Z/+QumuShaW1s5c+Z846G7u5vW1tYpaQA/rHxtH+sd42fH+7Gn6d/yW2AYgmGYuqVdhiyEIXkK2CQiG0XEjzvA+OCkNA8CH/C+vxv4iVJKefv3el5dG4FNwMHZ8hSRFu9/Ad4FvLgA97Cg7DvUjTnNk1VApd/kyuYK3RpZBuzatYvOzk5OnjxJJpNh37597NmzZ0IabztfK1jR2n7seD8Dscy0s0SyNtSHfVrbZcq8R8WUUjkRuQt4BLeF+1Wl1BER+SxwSCn1IHA/8A0R6QKGcV8evHTfAY4COeBOpZQNMF2e3iW/JSKNgACHgY/M9x4WmrMjyRmnZNVVBHTTf5lgWRb33Xcfu3fvxrZtPvjBD7J161buuecedu7cyZ49e7jjjju46667rHLQ9qmhOIPxNCbuDRVjCWxqjmhtlyniVp5WNjt37lSHDh26bNd751//nJfPRklP82g7VlXwzd+5QdfaVhAi8rRSaudiXPtyavsT3z3MIy/2EU3ZUxa4ClnCO7av5o9v3ay1vYKYq7aX42D7kmfnumqcaZ6sH6j0WfpF0yxLNja4HlsyTXO7wm9yRWNYa7tM0YakBNy8ZTUtVRNdIAOmEAyYxDJZHRpFsyy5urWWjqYqqoPnJ9uaQMRvYJjCKwMJre0yRRuSEtDRHGF9XQUVPjd8nYUbhyjnOCjlhpnXaJYbHc0RrmiqoCbsJ29KDAMQCFomQ9G01naZog1JCWiMBFlbFyKTX7dB3Fm/IpBzFE+fGlncAmo0l0BjJMgNbbWMJN3QPvkerqytCPlMxHCXUNCUHzqWQQkYiKZ4ZSBBwG/isx0QwRAI+02ytsNQTEdH1SxPXjwbo8JnkTAdRBSmaWIJxNM26ayD0usjlCXakJSA431R0rkc2YxNTgHixt5yHIUR9lFXoRe10ixPjvSMkbIdcrbCAQzbxmeCrRSKEFe1VC12ETWLgO7aKgGnh+OMJrM4uEEaBXAcSOcUJrBzQ90il1CjuTTOjSeIpbL4LXfuiFKQyoHtOLQ3RnTEhjJFG5ISMJbMkssp/CZYxvm+ZIUbJbW9SU/a0ixPklkHQwTTMLDM8z8gOQcdGqWM0YakBFQFLRAIByxMz73FEAj5hEjA4lhfTLtJapYlIZ9JTdDnLbkLYkLIgqBlEPSbPNE1pLVdhmhDUgLW11fSWBnAMg3CPj/1lX6aa4JUhfw0RAJUBiy9OqJmWbJpVSWmz6S+Mkh12E9t2E8k5Gd1TYjBWEZru0zRhqQEdDRHaK0NUxPy47dMfKZgO1AdstjQUEnYb+rVETXLkvddtw7TcJdC8JsGPlPwWQY719cSS2W1tssUbUhKQGMkyOuuqKelJoDCwXZgTW2Q69vq2dBQQSJj69URNcuS17Y38c5tLfgtg0zOxjIMbthYR3NNmMqgT2u7TNHuvyVgIJoi5yjaGqvoWFXFqeEEWdtBgIZKP7F0ju1rtXeLZvkxEE3REAlyQ1sDPgNODScYS2YZjKZ4zdoare0yRRuSEnC8L8rqGjeA3ZnhBGnbYSyRQ0RoigTpaI7o4HaaZYnWtmY6tCEpASOJDPUVAcaS7iqJFQEfqyJBKoMmr990eZdG1WgWkpFEBlOE7pEk0XSO5qoQO9eHsJXS2i5j9BhJCagN++kdTfJizxhZ26E6aBFN5+geSWnXSM2yRoBnTo8WdJ21HZ45PTrjQm6a8kC3SEpAR3OEJ7oGSWdtzo4k6B1LkXUctqyu4sCJId6xvfXCmWg0SxIhkckxlsgSS2WIZWwcpQhYBte3pXS3VpmiWyQloDESpCbs48xIgleH4vh9Bq01IYZiGX7WOaBbJZply3gqg98UktksA7E0jlJU+E3GUxk9GbGM0S2SEuEoRchvcWVzAL/l2utoKodjK473RXXNTbMsGUtmqQz6yNqKioAPv2UQTeXI2aowGVFru/zQLZISURW0SGVtlFIopcjk3EWtqkOWnrClWbZUBS0c5RoUy6Cg67Df1JMRyxjdIikR6+sr2VifZDieIZ6xCftMVlcHCQVMPWFLs2xZX19J0LIYjmcYTWapCflYXR2kttKvJyOWMdqQlIiO5ggnBmLYCmpCbpC7sWSWllBQR0nVLFs6miMMxjLsWl/LK4Nxd50dpfRE2zJHG5IS0RgJctu2Fg6cGOKl3nEEYcfaWq5vq9N9yJplS2MkyI3t9Rzvi5LK5RhP5agO+fRkxDJHG5IS0hgJ8o7trdrdV7OiaIwEaYwE9QRETQFtSErIQDTF8b4oI4kMtWG/rrFpVgxa25pitNdWiRiIpniia4hU1qG+IkAq62g/e82KQGtbMxndIikRx/ui2LbDiYEY0XSOSMCivsKv/ew1yx6tbc1kdIukRJwaivHKYHxCTKJXBuOcGootdtE0mnmhta2ZzIIYEhG5VUSOi0iXiNw9zfGAiHzbO35ARDYUHfukt/+4iOy+UJ4istHLo8vLc0k6ro+nchgCQZ+JiBD0mRji7tcsHx5++GE6Ojpob2/n3nvvnXI8nU4DtGlta22XM/M2JCJiAl8EbgO2AO8TkS2Tkt0BjCil2oEvAJ/zzt0C7AW2ArcCXxIR8wJ5fg74gpfXiJf3ksMQ4dRQnMOnh3llIMZQ3I1LVB3yLXbRNHPEtm3uvPNOHnroIY4ePcoDDzzA0aNHJ6S5//77AXJa21rb5cxCtEiuA7qUUieUUhlgH3D7pDS3A1/3vn8XuFlExNu/TymVVkqdBLq8/KbN0zvnJi8PvDzftQD3sKAMRFOcHU2StW2GExlODMR4pT9GUyTAurqKxS6eZo4cPHiQ9vZ22tra8Pv97N27l/37909I420PeZsrXtvHesd4oXuMaNKN/DsQTdM9ktTaLnMWwpC0AmeKtru9fdOmUUrlgDGgfpZzZ9pfD4x6ecx0LQBE5MMickhEDg0MDFzCbV06B04Mk805GGLSWlPB2rowjlKcHEjoWe3LiJ6eHtauXVvYXrNmDT09PVPSABlY+doeiKbYf/gsjZEA1eEA1SE/Yb9JY6Wfc+Npre0yZsUOtiulvqKU2qmU2tnYeHknTh3rHaelJkTHqkp8pmDbUBX0YQjaq0UzbxZL28f7ojiOorUmRFtDBRV+E9tRxDI2a2qDWttlzEIYkh5gbdH2Gm/ftGlExAKqcbsDZjp3pv1DQI2Xx0zXWnQUCpm0ZpwgKNQilUhzKbS2tnLmzPnGQ3d3N62trVPSAH5Y+doeSWSoq/AzFE/TH02TzDhUBX34xQ3mqClfFsKQPAVs8jxO/LgDjA9OSvMg8AHv+7uBnyillLd/r+fVtRHYBBycKU/vnMe8PPDynNhpvQS4qqWKs6NJjp+LkbUdLBPGUlmUQk/aWkbs2rWLzs5OTp48SSaTYd++fezZs2dCGm87H6lwRWu7NuzHbxp0nouRyNiE/MJoMsvZ8TQNlUvSwUxzmZi3IfH6dO8CHgFeAr6jlDoiIp8Vkfxbdz9QLyJdwMeBu71zjwDfAY4CDwN3KqXsmfL08voE8HEvr3ov7yXF9W31+CzBZxhkbYVSwpraENvX1XC8L7rYxdPMEcuyuO+++9i9ezdXXXUV733ve9m6dSv33HMPDz7o1pXuuOMOAKsctN3RHKE/lmZdXZiQz2Q8mcMyhTdsamAwptchKWfErQitbHbu3KkOHTp0Wa/5TwdeJZrKEU/nqAz6WFcXpiroYyie5p06iOOKQkSeVkrtXIxrX25ta12XF3PVtg6RUiLW1VWQyjpUBM4/4ng6pxf+0SxrtK4106ENSYnoaI7w0Au9jCazZHM2PsukJuTjtm0ti100jeaS0brWTMeKdf9dMigBDO9/jWaFoHWtKUK3SErE8b4oq2vCbFo1sQtAR0jVLGe0rjXToVskJWIkkSHsNyfsC/tNRhLau0WzfNG61kyHNiQlojbsJ5GxJ+xLZGw9KKlZ1mhda6ZDG5IS0dEcIZZ23SSVUsTTOWLpnI5HpFnWaF1rpkMbkhLRGAlyY3s9QZ/BUDxN0GdwY3u97kfWLGu0rjXToQfbS0hjRAey06w8tK41k9EtEo1Go9HMC21INBqNRjMvtCHRaDQazbzQhkSj0Wg080IbEo1Go9HMC+21VWIGoimO90UZSWSoDfvpaI5ojxdNSclms3R3d5NKlW4RNdtRZG0HRykMEXymgWnouFsrhWAwyJo1a+acXhuSEjIQTfFE1xCVAYv6igCJjM0TXUPa715TUrq7u4lEImzYsAGRhf9xz9oOsXQOUwRDwFFgK0VlwMJn6k6O5Y5SiqGhIbq7u+d8jv6rl5DjfVEqAxYVAQsRoSJgURmw9CqJmpKSSqWor68viREBSGVtz4gI4P5vipDK2hc8V7P0ERHq6+svqkWrDUkJ0QHuNIvFxRqRI2fHOHJ2bE5pbUcxuRfLEHe/ZmVwsfrRhqSE6AB3mpWIaQiTbYaj0GMkZYw2JCVEB7jTLAcGoimePT3Cz18e4PHOAQais3dpBH0mtlI4SgHu/7ZSBH3mrOdpVi7akJQQHeBOs9TJO4Rkcg7VIYtU1uGJrqFZjYnPNKgMWIhAzlGIMGWg/eGHH6ajo4P29nbuvffewv6TJ09y/fXX097ezm/8xm+QyUzfzfs//+f/pL29nY6ODh555JHC/g0bNrBt2zZ27NjBzp07pz1XKcXv/d7v0d7ezmte8xqeeeaZwrFPfOITXH311Vx99dV8+9vfnvb8r33ta3zmM5+Z9titt95KTU0N73jHOyZcDyick9+eiTe/+c0cOnRo1jTLDe21VWJ0gDvNUiQ/HvLs6REyOacwbiciJDM2jxzp45p1tWxdXT3t+T7TmNFDy7Zt7rzzTh599FHWrFnDrl272LNnD1u2bOETn/gEf/AHf8DevXv5yEc+wv33389HP/rRCecfPXqUffv2ceTIEc6ePcstt9zCyy+/jGm6LZ7HHnuMhoaGGe/toYceorOzk87OTg4cOMBHP/pRDhw4wL/927/xzDPPcPjwYdLpNG9+85u57bbbqKqqmvNz+6M/+iMSiQR/+7d/W9h3+PBh/uEf/gGA733vexw8eJC/+Iu/mHOeKwHdIikhA9EUj3cO8P3neubUZaDRXG6iqRxB38SfgaDPIJrKzXhO1naIprKMJjJEU1mytjPh+MGDB2lvb6etrQ2/38/evXvZv38/Sil+8pOf8O53vxuAD3zgA3zve9+bkv/+/fvZu3cvgUCAjRs30t7ezsGDB+d8T/v37+c//af/hIhwww03MDo6Sm9vL0ePHuWNb3wjlmVRUVHBa17zGh5++OE55wtw8803E4lM7Jq+5ppr+NjHPsY3vvENHnnkkYIR+exnP8uuXbu4+uqr+fCHPzyhpfKNb3yDHTt2cPXVVxfu7Wc/+xk7duxgx44dXHPNNUSj03t3/vSnP+VNb3oTt99+O21tbdx9991861vf4rrrrmPbtm288sorAHz/+9/n+uuv55prruGWW27h3LlzAAwMDPDWt76VrVu38ju/8zusX7+ewcHBi3oOk9GGpETkuwxSWYf6isCcugw0msvF1tXVbF1dzbbWapoiIVbXuJ8N9ZU0RUJsa62etjWSn0OiFFiGoBTE0rkJxqSnp4e1a9cWttesWUNPTw9DQ0PU1NRgWdaE/QAPPvgg99xzz6zng9tietvb3sa1117LV77ylUKaL3/5y3z5y1+e9fzt27fz8MMPk0gkGBwc5LHHHuPMmTPzfpaHDx/mb/7mb3j/+9/P7t27+dSnPgXAXXfdxVNPPcWLL75IMpnkBz/4QeGcRCLB4cOH+dKXvsQHP/hBAP7X//pffPGLX+Tw4cP84he/IBQKzXjN5557ji9/+cu89NJLfOMb3+Dll1/m4MGD/M7v/A5//dd/DcDrX/96nnzySZ599ln27t3L5z//eQD+7M/+jJtuuokjR47w7ne/m9OnT8/7GeiurRJRPIcEKPx/vC+qu7o0S4aO5ghPdA2RzNgEfUbBIWT72vpp00+cQ0LBDTiVtec1GXHPnj3s2bPngukef/xxWltb6e/v561vfSubN2/mjW98Ix/5yEcueO7b3vY2nnrqKV73utfR2NjIa1/72kJ32XzYvn07f/VXf8VnPvMZ3vWud3H77bcDbhfc5z//eRKJBMPDw2zdupV3vvOdALzvfe8D4I1vfCPj4+OMjo5y44038vGPf5zf+q3f4j/+x/8468zyXbt20dLSAsAVV1zB2972NgC2bdvGY489BrgTU3/jN36D3t5eMpkMGzduBNxn+K//+q+AO+ZTW1s772egWyQl4vRwnFcGovzqlQFe6BllLJnRc0g0S468Q4jfMhhL5i7oEGI7CqUUyWyOWDpLMut6JBbPIWltbZ1Q0+/u7qa1tZX6+npGR0fJ5XIT9k9mpvPzxwCampr49V//9Wm7vGY7/0//9E85fPgwjz76KEoprrzyyjk/q5nIz7nID7aLCKlUio997GN897vf5YUXXuBDH/rQhAl+k+dpiAh33303f//3f08ymeTGG2/k2LFjM14zEAgUvhuGUdg2DKPwfH/3d3+Xu+66ixdeeIG//du/LWnIHG1ISsBANMWZ4STRVI6qoJ9MzuGFnjH6xpN6DolmydEYCXLNulreeGUjr9/UeMEWczxjoxSYhoFS7nYxu3btorOzk5MnT5LJZNi3bx979uxBRHjLW97Cd7/7XQC+/vWvF2rvxezZs4d9+/aRTqc5efIknZ2dXHfddcTj8cK4QTwe50c/+hFXX331tOf/4z/+I0opnnzySaqrq2lpacG2bYaGhgB4/vnnef755ws1+YUm/6Pd0NBALBYr3HOevMfY448/TnV1NdXV1bzyyits27aNT3ziE+zatWtWQzIXxsbGCgb061//emH/jTfeyHe+8x0AfvSjHzEyMjKv64Du2ioJx/uitDdWcmIwTjpnE7QMUlmbznMx3rCpcbGLp9FMYSbvrNmZ3s3Vsizuu+8+du/ejW3bfPCDH2Tr1q0AfO5zn2Pv3r186lOf4pprruGOO+4A3DGSQ4cO8dnPfpatW7fy3ve+ly1btmBZFl/84hcxTZNz587x67/+6wDkcjl+8zd/k1tvvRWgMD7ykY98hLe//e388Ic/pL29nXA4XPCoymazvOENbwCgqqqKb37zm4Xxmrnyhje8gWPHjhGLxVizZg33338/u3fvnpKupqaGD33oQ1x99dU0Nzeza9euCceDwSDXXHMN2WyWr371qwD85V/+JY899hiGYbB161Zuu+22iyrbZD7zmc/wnve8h9raWm666SZOnjwJwKc//Wne97738Y1vfIPXvva1NDc3T3EguGiUUpf8AeqAR4FO7//aGdJ9wEvTCXygaP+1wAtAF/D/ATJbvsCbgTHgsPe5Zy7lvPbaa9Xl5MHD3eqJzgH1w+fPqr/9aZf6X48cU19+rFN981cnLms5NJfO0NCQuuWWW1R7e7u65ZZb1PDw8LTpvva1rykgtZS0ffTo0YV/IB4j8bQajadV/3hS9Y4mVf94Uo3G02okni7ZNS83//AP/6A+/elPL3YxSkYqlVLZbFYppdQvf/lLtX379mnTHT16VAGH1Bx0ON+urbuBHyulNgE/9rYnICJ1wKeB64HrgE+LSH5052+ADwGbvM+tc8j3F0qpHd7ns/Msf0nIh0apCfvZtqaG113RwBVNEdbXVy520TRz5N577+Xmm2+ms7OTm2++ecKkujzDw8P82Z/9GcBLlIm2TUMQEUI+NwBpyOcGJNXhUZYPp0+fZteuXWzfvp3f+73f4+/+7u/mned8DcntQL7z7evAu6ZJsxt4VCk1rJQawa2F3SoiLUCVUupJpZQC/rHo/Lnku2TRoVGWP/v37+cDH/gAMPN8h0ceeYS3vvWtAHa5aLscwqPs2LGDN7/5zYtdDF544YXCvJL85/rrr593vps2beLZZ5/lueee46mnnprS7XYpzHeMZJVSqtf73gesmiZNK1DsrN3t7Wv1vk/ef6F8XysizwFngf+ulDoyXcFE5MPAhwHWrVs35xtaCPKeMMf7ogzF09SG/Wxfq0OjLCfOnTtXcK9sbm4uTOYqZvJ8BZaQtpVSJQkjnw+Pksra5ByFaQiV/pW1DsmOHTsWuwiA68p7+PDhRbm2ukCYl8lc0JCIyL8DzdMc+tNJF1YisuBxpCfl+wywXikVE5G3A9/D7TaY7ryvAF8B2Llz52WPb61Doyx9brnlFvr6+qbs//M///MJ2yJSkh/lUmk7GAwyNDRUsjVJZguPoln+KG9hq2Bw7r9fFzQkSqlbZjomIudEpEUp1es15/unSdaDO5CYZw3wU2//mkn7e7zv0+arlBovKtcPReRLItKglJrf/H5NWfLv//7vMx5btWoVvb29tLS00NvbS1NT05Q0ra2t/PSnPy3etSS0vWbNGrq7uxkYGLjYUzUa4PIvtfsgrkfWvd7/+6dJ8wjwF0WDkG8DPqmUGhaRcRG5ATgA/Cfgr2fLV0SagXNeTe463DGeoXneg0YzhT179vD1r3+du+++e8b5Drt37+ZP/uRPAExP30tC2z6frzCLWaO5LMzFtWumD1CP63nSCfw7UOft3wn8fVG6D+K6QXYB/6Vo/07gReAV4D7Ou0jOlO9dwBHgOeBJ4HVzKefldv/VLH8GBwfVTTfdpNrb29XNN9+shoaGlFJKPfXUU+qOO+4opLv//vvz7r9a25oVB3N0/82Le0Wzc+dOtdLi/2uWDiLytFJq+sUxSozWtqaUzFXbesRMo9FoNPOiLFokIjIAnFqkyzcAy9EZYLmWGy5/2dcrpRYl9s0ialvrY3FYktouC0OymIjIocXq9pgPy7XcsLzLvlxYzs9Yl33h0V1bGo1Go5kX2pBoNBqNZl5oQ1J6vnLhJEuS5VpuWN5lXy4s52esy77A6DESjUaj0cwL3SLRaDQazbzQhkSj0Wg080IbkhIhIreKyHER6RKRKQt+LSVEZK2IPCYiR0XkiIj8N29/nYg8KiKd3v+1F8prsRARU0SeFZEfeNsbReSA9/y/LSL+xS7jSkFr+/KxXHStDUkJEBET+CJwG7AFeJ+IbFncUs1KDvhDpdQW4AbgTq+8F1wBcwnx33BXKszzOeALSql2YAS4Y1FKtcLQ2r7sLAtda0NSGq4DupRSJ5RSGWAf7sp4SxKlVK9S6hnvexRXuK0sk9X8RGQN8B+Av/e2BbgJ+K6XZMmWfRmitX2ZWE661oakNMy0KuSSR0Q2ANfghj+fywqYS4G/BP4YcLztemBUKZXztpfN818GaG1fPv6SZaJrbUg0BUSkEvhn4PdV0UJL4K7mByw5X3EReQfQr5R6erHLolm6LDdtLzddz3dhK8309ADFi3kXr5C3JBERH+6L9i2l1L94u+eyAuZicyOwx1ueNghUAX8F1IiI5dXelvzzX0ZobV8elpWudYukNDwFbPI8LPzAXtyV8ZYkXt/r/cBLSqn/XXQov5ofzLwC5qKilPqkUmqNUmoD7nP+iVLqt4DHgHd7yZZk2ZcpWtuXgeWma21ISoBXW7gLd5nhl4DvKKWOLG6pZuVG4P3ATSJy2Pu8HXc52LeKSCdwi7e9XPgE8HER6cLtW75/kcuzItDaXnSWpK51iBSNRqPRzAvdItFoNBrNvNCGRKPRaDTzQhsSjUaj0cwLbUg0Go1GMy+0IdFoNBrNvNCGpMwQkc+IyH+/1OMazVJFa3vx0IZEo9FoNPNCG5IyQET+VEReFpHHgQ5v3xUi8rCIPC0ivxCRzdOc9yEReUpEnhORfxaRsIhEROSkF3YCEakq3tZoLida20sDbUhWOCJyLW6IhR3A24Fd3qGvAL+rlLoW+O/Al6Y5/V+UUruUUttxZzHf4YXi/ilueGu8vP9FKZUt2U1oNNOgtb100EEbVz5vAP5VKZUAEJEHcYPAvQ74P24oIgAC05x7tYj8D6AGqMQNiwHu+gh/DHwP+C/Ah0pUdo1mNrS2lwjakJQnBu66BjsukO5rwLuUUs+JyH8G3gyglHpCRDaIyJsBUyn1YslKqtFcHFrbi4Du2lr5/Bx4l4iERCQCvBNIACdF5D3gRkgVke3TnBsBer0+4t+adOwfgX8C/qF0RddoZkVre4mgDckKx1tm9NvAc8BDuGHAwX157hCR54AjTL9c6v+Fu5rcE8CxSce+BdQCD5Sg2BrNBdHaXjro6L+aS0JE3g3crpR6/2KXRaNZSLS2Lx49RqK5aETkr4HbcD1lNJoVg9b2paFbJBqNRqOZF3qMRKPRaDTzQhsSjUaj0cwLbUg0Go1GMy+0IdFoNBrNvNCGRKPRaDTz4v8HDwX7h/bwmFgAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "╒════════════════╤═══════════╤══════════╤═════════╤══════════╤═════════╤═════════╤════════╤════════╤════════════╕\n", + "│ counter │ mu │ tau1 │ tau2 │ A │ q │ alpha │ sigS │ sigH │ I0 │\n", + "╞════════════════╪═══════════╪══════════╪═════════╪══════════╪═════════╪═════════╪════════╪════════╪════════════╡\n", + "│ >> 21:28:49 << │ │ │ │ │ │ │ │ │ │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ abs_mag │ -0.112822 │ 0.143747 │ 2.76829 │ 0.185717 │ 5.14362 │ 1 │ 0.05 │ 0 │ 0.00223483 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ 1*abs_mag │ -0.112822 │ 0.143747 │ 2.76829 │ 0.185717 │ 5.14362 │ 1 │ 0.05 │ 0 │ 0.00223483 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ >> 00:05:09 << │ │ │ │ │ │ │ │ │ │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ abs_mag │ -0.112677 │ 0.144236 │ 3.12348 │ 0.181572 │ 5.3156 │ 1 │ 0.05 │ 0 │ 0.00220192 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ 1*abs_mag │ -0.112677 │ 0.144236 │ 3.12348 │ 0.181572 │ 5.3156 │ 1 │ 0.05 │ 0 │ 0.00220192 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ >> 02:27:56 << │ │ │ │ │ │ │ │ │ │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ abs_mag │ -0.114335 │ 0.146801 │ 2.95593 │ 0.186009 │ 5.2447 │ 1 │ 0.05 │ 0 │ 0.00217351 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ 1*abs_mag │ -0.114335 │ 0.146801 │ 2.95593 │ 0.186009 │ 5.2447 │ 1 │ 0.05 │ 0 │ 0.00217351 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ >> 05:03:44 << │ │ │ │ │ │ │ │ │ │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ abs_mag │ -0.124481 │ 0.145896 │ 2.76218 │ 0.184166 │ 5.57045 │ 1 │ 0.05 │ 0 │ 0.00216463 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ 1*abs_mag │ -0.124481 │ 0.145896 │ 2.76218 │ 0.184166 │ 5.57045 │ 1 │ 0.05 │ 0 │ 0.00216463 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ >> 07:39:54 << │ │ │ │ │ │ │ │ │ │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ abs_mag │ -0.125993 │ 0.164335 │ 2.98283 │ 0.177329 │ 5.53992 │ 1 │ 0.05 │ 0 │ 0.00222822 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ 1*abs_mag │ -0.125993 │ 0.164335 │ 2.98283 │ 0.177329 │ 5.53992 │ 1 │ 0.05 │ 0 │ 0.00222822 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ >> 10:16:11 << │ │ │ │ │ │ │ │ │ │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ abs_mag │ -0.126334 │ 0.19582 │ 2.56944 │ 0.194156 │ 5.62727 │ 1 │ 0.05 │ 0 │ 0.0022361 │\n", + "├────────────────┼───────────┼──────────┼─────────┼──────────┼─────────┼─────────┼────────┼────────┼────────────┤\n", + "│ 1*abs_mag │ -0.126334 │ 0.19582 │ 2.56944 │ 0.194156 │ 5.62727 │ 1 │ 0.05 │ 0 │ 0.0022361 │\n", + "╘════════════════╧═══════════╧══════════╧═════════╧══════════╧═════════╧═════════╧════════╧════════╧════════════╛\n" + ] + } + ], + "source": [ + "# plt.figure()\n", + "\n", + "ev.clist = ['abs_mag', '1*abs_mag']\n", "res, parameters, sequence_data = ev.fit_scan_sequence(scan_sequence, mod, pars,\n", " xgrid=np.r_[-10:50:0.01],\n", - " sequence_type='text',\n", + " label_format='{:s}',\n", + " skip_plot=False,\n", " show_single=True,\n", + " plot_separate=True,\n", " fit_report=1)\n", - "plt.show()" + "# plt.show()" ] }, { @@ -937,10 +1532,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "1aaf7e66-6a47-40dc-a0ea-b9b91a781cae", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['abs_mag', '1*abs_mag'])\n" + ] + } + ], "source": [ "print(res.keys())" ] @@ -958,10 +1561,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "c3d8b61b-138e-437d-b208-4a06b46f5754", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['mu', 'muErr', 'tau1', 'tau1Err', 'tau2', 'tau2Err', 'A', 'AErr', 'q', 'qErr', 'alpha', 'alphaErr', 'sigS', 'sigSErr', 'sigH', 'sigHErr', 'I0', 'I0Err', 'chisqr', 'redchi', 'CoM', 'int', 'fit'])\n" + ] + } + ], "source": [ "print(res['abs_mag'].keys())" ] @@ -977,10 +1588,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "953397e2-d8ac-436d-abe5-9e57d6895415", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", "plt.errorbar(parameters, res['abs_mag']['A'], yerr=res['abs_mag']['AErr'], fmt='-o')\n", @@ -999,13 +1623,110 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "9ef4dd4b-4721-4a46-8598-59ac2d5737db", "metadata": {}, "outputs": [], "source": [ "# to be done" ] + }, + { + "cell_type": "markdown", + "id": "f3c8e604-310f-4793-814b-9db77d9f031c", + "metadata": { + "tags": [] + }, + "source": [ + "# new syntax" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "419c0029-3481-4f76-a6ea-54ffdb45d31d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "ev.clist = ['abs_mag']\n", + "ev.scans([1,2,3,4]).plot(alpha=0.75)\n", + "plt.legend()\n", + "plt.xlim(-10, 50)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "91ecd9c6-2f56-4bda-8df9-cdc5b1fb723b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "ev.clist = ['abs_mag']\n", + "ev.scans([1,2,3,4], xgrid=np.r_[-10:50:0.05]).fit(mod, pars).plot(alpha=0.25)\n", + "plt.legend()\n", + "plt.xlim(-10, 50)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "dc50616b-1ee1-4172-89ff-bff27f999900", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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kJMfrL2rjBy8OkoyGODVemjFweEDYtjg2kuX6nfNaL0WpC053OsZ1Ozp56MAAEwW/hHHl1oU3gk9MTNDe3k4ikeDll1/m8ccfB8DzPO69915uvfVWvvnNb3L99dfjeR4nTpzgl37pl7j++uu55557yGaztLW1ndd7VwNEV1cX2WyWe++9l3e84x20tbWRTqd54oknuOaaa2ZsN1lMayJoxO0QrgdF1/DmHV20xCNMBI3Or7+ojcFMmUzBIRaxiIQt4naIq3raOT1Zoeh4bG6Ls6ktxsBkmZLjkoqEyFU82hKRqeNOjZfoTEbpTEcAGJgoMpZ32Lk+eVavjOt3dk/1uKr+wd50xUYADg5k6ExlOTSYZX06SjwSZkt7jGyxTK7oMFI4t10DIGxZvGZTC5PFhdfJKnUh6E7HeN1F7cDidW2/8cYb+fKXv8xll13Grl27eOMb3whAMpnkySef5NOf/jTr1q3jW9/6Fq7r8t73vpeJiQmMMXz0ox8974AB0NbWxgc/+EFe85rXsGHDBq666qqpfV/96lf54Ac/iGVZ/OIv/iKtrUvblV+MmaVeZJXYefmV5mv/64fndJutDq47NpJlsujQGrdpidnUdsntSkV4eSBLKhqm7Lj0DuYYzpXYvT5FSzxyznGjuRIx26JY8chXXN61b+u8f8HUjvEYy5Z49NVhhjMFjg5lyVU4p8Txtsu6uWR9mi3tcX7zmp6Ff2BKrTIvvfQSl1122Uonoylls1lSqRQAn/3sZ+nv7+dP//RPzzmu3mcoIk8bY/bN5/3WREmjNW7XrbbpTsfoTsfmrNLpTEU5OJChUHF4/ba2GcdsdKaiUyWI2cZ2zKX6y6ca1Equw9PHYH1rnJ3rUhTKLoOZMvc/P0AyYvGG7Z10pSKs0/YMpdQ0DzzwAP/1v/5XHMdh27Zt3H333Uv6fmsiaCxUNbg0ctx739iz6O87PagNZYr8r2dPcv/zA0TCFhd3pciWnKmBhUqp1edrX/vaOSWA6667jjvvvHNB133Xu9411XtrOWjQaELd6VgwFQmAELMXpyFPqdXMGLOqZ7r9wAc+wAc+8IEVee/FbIbQWW6b1Ia2OADhkMX1O7s1YKgLWiwWY2RkZFFvfheK6iJMsdji3EO0pNGkwpb/i8pxvRVOiVIrb8uWLfT19TE0NLTSSVmVqsu9LgYNGk0qHPILgY6rv6yUsm17UZYqVQun1VNNKhIEjbKWNJRSTUSDRpMKh/zqqYoGDaVUE9Gg0aSqbRqewZ/PSimlmkBDQUNEbhSRgyLSKyLnzL0rIlER+Vaw/wkR6anZ9/Fg+0EReVuwbauIPCwiL4rIARH5WM3xHSLyAxE5FPy/fRHyueqIyFTgqHha2lBKNYc5g4aIhIA7gZuAPcC7RWTPtMNuA8aMMTuAO4DPBefuAW4FLgduBL4UXM8Bft8Yswd4I/DhmmveDvzIGLMT+FHw+oJkBX3StZehUqpZNFLSuBroNcYcNsaUgXuAm6cdczPw9eD5vcBbxR+FczNwjzGmZIw5AvQCVxtj+o0xzwAYYzLAS8DmOtf6OnDLeeVsDaiOY3K1ekop1SQaCRqbgRM1r/s4c4M/5xhjjANMAJ2NnBtUZb0OeCLYtN4Y0x88HwDW10uUiHxIRPaLyP612ne7WtJwtaihlGoSK9oQLiIp4G+A3zXGTE7fb/zhn3XvmMaYrxhj9hlj9nV3r801Jqzg2zHapKGUahKNBI2TwNaa11uCbXWPEZEw0AqMzHauiNj4AeMbxpi/rTnmtIhsDI7ZCAw2mpm1RksaSqlm00jQeArYKSLbRSSC37B937Rj7gPeHzx/B/DjoJRwH3Br0LtqO7ATeDJo7/gq8JIx5k9mudb7ge/ON1NrxVTQ0DYNpVSTmDNoBG0UHwEewm+w/rYx5oCIfEpEfi047KtAp4j0Av+WoMeTMeYA8G3gReDvgA8bY1zgOuB9wA0i8lzw+NXgWp8FfkVEDgG/HLy+4AxlijhBV9vHXx1hKLMy6wErpVSthuaeMsZ8H/j+tG2fqHleBN45w7mfAT4zbdujQN05jo0xI8BbG0nXWjWUKfJY78hURC86Do/1jpy1rKxSamUcODUBLN4ysov5XsuRNh0R3oQODmRIRcNY4n89UTtEKhrm4EBmhVOmltKBUxNT/+jV0qj3Ga/1z32x86dBowmN5cskIqGzek8lIiHG8uWVTZhaM9b6jXK5XUifp06N3oTaExHyZXfqtWcM+bJLeyKygqm68CxnNcRiaDS9Q5kizx4fI1N0GMuVz3ut+9Wm2fO90L+3evkDFj3PWtJoQrs2pMmWHKpDVHJlV9cIv0A1+gu2esN45JUhHj00NGPHiWp7WdnxaI2HKVY8Huttro4WS/GrfbHy3ejnXLVc1WH18vfg8/08+Hz/on/XWtJoQt3pGNft6Jxq07At0UbwJjCUKXJwIMNYvkx7InLev9oWuwQz0w2x3t9Mtb2sUHEASEbDU9tny8tylbpmKg0MZYo8dGCATNHhis2t8/7sZ8t3e/LcEny97xqY8XNeinzDzKWE6d9HvfyN5x0QQ3c6ek6eF3Iv0aDRpLrTMVLRMCO5MnsvateAscKqN+bRXImYbRG3w2fdmBdaNbSQqoX5BIKxfJnOZPSsbYlIiJFc6fw+mEU0U/DbvSHFywPZhoLiTGbL9/SgMdN3Hbak7uf8xOERxguVhqqAZvqe65USABzP1A1Q069RL3/+WjxnTyexGN+1Bo0mphMWNo/z/YVeaz43xdluGtOD1HwCwfT2MmDF2sum/5ofz1fqfsYPHxxk57qWGT/7RgL2fPI903f93PEx3rSj66xjy47Lk0dG6elKzhnQZvr+6wWjmUoJTxwewfGoe43p+bND1pmbyBx5ng8NGk2sOiLc02lEVtz5/EJvpAoB6t8UZ6tagLN/aQrn3jCqN4fpN+auVISXB7IUyi4x2yJXcsiWHK7cOv8qloVUWdX7Nf/k0VGuvbjjrOMSkRCDkyWu3BI6Z/t8fjHv2pDmsd6RuvkezJx9nZm+a4M553PuHczRlY4Sj/jpm+17miko1gtGM5USnj0+yusu6jjnGsWKn5/a/LUl/H2L8V3X0qDRxCxr7QaNxaojn+k6i90TZbYbc6NmuhnVuynOdNM4MpxhOFs+65fmaHDzdDxz1s1hS3uKx3pHSEXDdCaj5MsuLw9k2b0hxXA2wli+TMy2uHJr54x15fU+i/m2J9Rrj6gXQLuSUQ4NZqcCJfif8bqW6Lw/++n5qLYTPnRggImCw0UdZ/I9PWjMVCq5bGPLOTfm4VyJay/uYLxQmTp2pu9ppqBYLxjNVEoQhETk3ABaqDjn5O+mKzYC1M3zQmjQaGKWVk+tiHrVCNUb86a2BIlI6Lx+tc10M6p3U5zppjFZdFjfkjjrZrupLUGx4jBeqJx1c6jemIeyfm+Zns4UAMPZMtfvbGxm6Nka2Rdyfr5cmUpP1Y51SR4/7Ae52uD3S7vWLUrpqDsd43UX+QuBzvZjoloq6U7Fzvquq3muvQlf3dOBHQoBZ4LGTN/TTEGxXjCaqZSwe2PLjAF0pvw1kuf50KDRxLR6amXU+xVcvTHHbMtvPE1Epn61zdWIPZotsXNdiu1dCR57dZSJQoVY2GI0VyJbdLhmewcHB7KMF8pEwhZDmSJRWwBhNOtvG5jIky25WCKUXZdM0U/bWL6M5xnG8mXeetkGDGCMoeR49A5maU/YjOb8QaHJSBFjDH1jFbZ1JuvmfWDCDzAtMRuAp46OUnZcChX/kYraFMoOj7wyTGvCPyYdtc+6xiuDftXMpevSU+fnK/4jGfHPPzZcQBBGggGrcbtAvuyyvStJS9xmsuAwUShzcVeSeCTMto44x0dynJ5wiIZDXNyVJF92OTaSo3+8AEAqeuZ21j9ewADJyNm3uOrro8M5AEZzJf7x0DC5ksNLpya5uDtJRzLKRR1xDg/lOD7q0BILc3F3klzJxQDrWmKsa4HN7QnikRLPHBtjvFAhGrYwHuTKDmXXMFkoM5z18xcLF2iJhXiub4KS45117Ou3+Tf1oyM5+icc7FCIyzf7N/h/PDQ8te3ibv87q/d+r9/WzpHhHCeDzyJRk+/q85LjEg2fXUo5Hxo0mtiZoLHCCVkEi1EdNd9rlByX/okC2aL/K+3AqUmKFZeOZIRcySFXdsmVHIoVl7LjUXY9ShWPwyNZLIRsycFxDeGQUHY8So5HIhLC9QyuMXgelCsuRcfFcT08/AnVHNfgGX+UzXJ+df/5uy82fOwnv9f4sWpteOCj1y9KaUODRhPT3lPn8oxhcLLIqYki/eMFnjk+xnC2hGdgNFdmLF9mLFdhOFui5DTP6lWCX90YClkIZ75bQRDx93vG3x6yZGo2z+q2sCWICJ7n4XhmqvQZtiwwhkjYIhwKTV1LRHBcPxBWt/mlEIiGLcKhs8f1Oq4fFD1j/LRa4q9NbwxSfY6ftorr4npn0hkKCZ4HMdu/bjn43CNhi0LZxeAHWIBwSIL3EOyQUHH9vFgiRELnpqsRte83fVvUnvl61bRV/33ZIWsqbfHIzL/IK7O8X3Wb43oUKx6u8YLPyC8VxGxr6kdg7fnzydts22cTOY/Pth4NGk0sHfO/Hm8VBo3aUsGBUxO8OpTlku7UHGedUXY8Dg5kODSY4dWhLK8O5nixf5JT4wWcBj+PsCV0JCOkY2GiIaFY8bAsYV06xobWKF3pGBjDsdEcjmfY1p5ka0cCOyy8OpSjWHGJ2xbd6Rhlx+OqHn+8TMgSQpZgifDwwdN0JaOcHC9gibCtK8lTR0awRNjUHsdCuLg7iTEwkivx9iunr5Rc/zObzXwGujU6IPHRQ0MUK95Z7R9+KczB8Zjq5bQuHefpY6PsXJ+aCsrVY2O2xfU7u8/Kx/ReUuvS8an2gcUaezT9c2v08/nez07SmYxybDQ3lQ9jzJzfUyNpmC0d8ykxN+NUNho0mljIWl3VU7P1vOkdzDJZqLAuHa37D/jEaJ6fHh7h533j/Lxvgpf7M5Td+iWFVDTEptYY61vilF2PWDjE7o1pLtuYZntXCoxh//FRyhWPK7a0TXUznX7jqo6P2NAaO+eGBjR047m4K0Wx4pEKAnwyEiYZsUEM4WpPBoR82Vm0sRDd6RjvfWNPw8c2cnOeqWdXvV45WzvibGiJT91sq8fW6wJb7bVUDVzTe2sttvmMjl/MMSv1buozNUw3UwA4Hxo0mthqX+51LFfi0Gl/0FoqGuL4SI5v7T/Bu/ZtpVB22X9sjL/86TEee3WYE6OFs84V4JLuJLs3tLChNUrJ8YiHLTa1xdnakeTUeB440820esOPhoWXB3IIQmvCpljx+O5zp9i5PnVOX/rZBo1dv7N7Xj1tlqN//FKa7QY6/eb36CHmdbNtNHAthvkMwpxt7EazaMYAo0GjiU01hK+WosY0R0fybGiJU6g45MdcBieLPHN8nO88c5JjwzmcmmzFbb/74nU7u0jHwuzoTnHVdv8f7/Sqk9kGv9ULBJ5nGMqUaImf6eWzWIPG6vX/X6r+8UtpPjfQ2Y5d6Zlk5zMIc7axG2pmGjSaWGgVD+4by5V45JVBOhJRxvIlftY3yeBk6azhauvSEV6zuZXdG9Ls3dpOvuxy3Y7GRujONPitXiDoSEYYzZ4dNM530Fg9i9U/fiV/Vc51A61N20zHwswT+i1lddRCBmE2OnbjfDVjSWGhNGg0sdUyuG8oU+Tep0/wzLFx2pM2e7e0cXwsz1iuwrPHxxnKnhn41JW0ecO2dpLRMPFIiNcE/dHTMRtLpO6so/WqTmYa/FYvEHSloowXKuf8Ml6sQWNrxXxuoPWOffTQ0ILn55qP2QZhTh8df6F+p0tBg0YTWw2D+4YyRR58vp9XBjLYISEasvjOs6d4eWCSTMm/eVtAVyrMupY4eza3csOudfzk4Glc9+x8VadfODoiZ1VvzKfdoF4gCIUsbt67if3Hxs75Fd2Zijb06/p8rMVfmbNZ7hl0ZxuEOX10vFY5LR4NGk1sNfSeOjiQYTzvkI6HyYwX+ckrQ5wY89seIiHh2ks68TyPk+NFcmWXasVRyDq3z3j/eIG+sSLd6eg51RvzaTeYKRDs3th4Dxc1t+mf13LPoDtbjy/9TpeOBo0mNtV7qomjxli+TMlxeOFkhmePj2Pwez51pW3Wp+NsbIlhwB/E5XmELeHvDgzgOA5bO5JnlQh6h7Jn9f9vpDdTvW3zDQSLcWNZKzenheRjuXsjNdM07xcSXe61ia2GWW4Pnc7yzSeO80wQMLZ3Jbjukg46kxHWt0SpuH49c0s8THsyymSpwmS+zGTRpSVusy4dIx2zidln+v/XSkRCjAXzE6nmVm0gj4QtJgr+YL+lbASvLotcKLsYY6aClC6LvLS0pNHEqg3hyx00Gh2FeufDvXzp4V4cA7GwxZVbWkhHw4glXBRN0JGMMpar0BqLYIf9qRmyJRc7KRweznPodI7JgsPNezexe+Pc/f/Xyq/5tWw5q/vmM925WjwaNJpYaGqcxgonZBrH9fj0Ay9x9z8dBeBNF3fwG6/fzFNHxzg2kuOqng7S8TDpmE2m4FB2vKnZNzuTNs/35bEtoSMZJmQJ333uFJ2p6KoYbKWay1oddd3MNGg0MWmyEeEHTk3wUv8k9z7dx+OHRwlbwm+8fjP/57XbeM3mNi7b1MoDPz9FezIyNfp6a0eCp4+NYVmQLztM5A1GoCMVRURoi0cYyhSm2i0Wa7CV3jSUWhoaNJpYdVLK5RwRXp1csKp2ErjHeof5i0cOM5Qtk4qG+MTb9xCzQ1PBDWBTW4zxgt8FMmZb2CGLizri5MoOR4fzFB2XbR0JChW/+FR0XDpS0al2C+3NpJab/p3NjwaNJraUXW7nM3vmUKbID188zd2PHWEoW6YlGuZ3fukSNrXGGM2fGbhXvdZYrsTRkfxZ3WMHMyWu2Fzisd4RxnJlf6Ggikch7LKpLXnePV70H3zz0e9kbdOg0cTGgxvyclRPVYNIPff97BR3P3qUUxMl0rEw/+Ytl9CdinLfz04ylnNoT9r8ws51XBOsf9yejNIe9J+v3kAGMyXak1Fu3ruJ7z53isHJEiFLuKQ7iSWW9nhRapXQoNHEquM0zAq2aTiux189fowT4wWSEYtrtrfz+KvDlMoOBdewuS1ONGTx3IkxRnMlLu5OTgWMenZvbJ0afJcpOqxLx86Z1E5/qSrVvDRoNLHlWrlvejtGrU8/8BJHhvNELfjFS9chAvmSS6bsIiIYIBYJ0RqPcHI8z+HhLOtb4qRjYXo6E3Wvqe0WSq1eOrivia30NCLff76fu//pKAJcu6OTllgYOyQUKi6egY6kzWTBr0JzHI8jQzkyBZfWeJiy4/Fc3wRDmeLKJF4ptSS0pNHEptaJXsSoUVuquKQ7xVCmyCOvDPLMsXFEDJva4uxYl2I87/Dn/3AYgPe88SLaYmHScZuXTk1SrLh4niFbdKjOOdg3XiAeCdOasBE5s8byUs1wqpRaGQ2VNETkRhE5KCK9InJ7nf1REflWsP8JEemp2ffxYPtBEXlbzfa7RGRQRF6Ydq1PishJEXkuePzqAvK3qi31NCJjuRJ3PXqYJ4+MYocE2xKODuf5x1eG+LN/6MXxDNds72D3hhQb2uJ0JaNE7BCb2uJ0JG2yRYfJfIWxXImRXJn2hM2G1jPtGTHb0ilA1JK7fFOrVnMuozmDhoiEgDuBm4A9wLtFZM+0w24DxowxO4A7gM8F5+4BbgUuB24EvhRcD+DuYFs9dxhj9gaP788vS2vHVJvGIgWNA6cm+IdXBukb85dKPTqSZ7LokIiEidohPAMT+RKPHBohV/LoTtq89bJ1HBzI0hYP0zuUxQLSsTCb2xO0pyLEYyGGMmX2bGzhtVvbSMf8hY56OlOsS8d18jil1phGShpXA73GmMPGmDJwD3DztGNuBr4ePL8XeKv4I75uBu4xxpSMMUeA3uB6GGMeAUYXIQ9rVmiJl3vNFB1cz8MOCcWyw1C2zOlMeWrg3VXbOwBD3LYYLzhs7YgTj4QoVDxa4hHeetl6bti1jjde0sVvXdeDJZZOHqfUGtdI0NgMnKh53Rdsq3uMMcYBJoDOBs+t5yMi8vOgCqu9gePXpDOLMC3+tfvG8hwZzjJZqFBxDRNFFzAMTPgTvW3vStCVijIwUSIStsgUHS7qSLK1I8Gl61PsXJ8iHbMpOx7pWHjOGU61CkGptaEZe0/9GXAJsBfoB/643kEi8iER2S8i+4eGhpYxecunWj01MFFY8LWqVVOnJ8/0ZupKRUhEwuTLDuP5EgcHJvGAsAU9nQkiYb/kUA0MuzakyVdcShW/NFEouxQq3lTX2mpX2l+4tJvrd3ZrA7hSa1AjQeMksLXm9ZZgW91jRCQMtAIjDZ57FmPMaWOMa4zxgL8gqM6qc9xXjDH7jDH7uru7G8jG6jNV0lii66diNnsvaqMrFWEkWyFT8os0PZ0JRnNlxvMlLIupwNCdjvGufVu5qDPJRMEhErbYtSE162A+pdTa0kiX26eAnSKyHf+Gfyvwm9OOuQ94P/BT4B3Aj40xRkTuA74pIn8CbAJ2Ak/O9mYistEY0x+8/HXghdmOX8um1tNYhPqpsVyJo8M5BiaKjGb9Kqii4xELh+gbK5Ap+Nu2dcRpi9uM5Mo8fyrDL+9ex8a22FRgmD4wb7bpR5RSa8+cQcMY44jIR4CHgBBwlzHmgIh8CthvjLkP+CrwlyLSi9+4fWtw7gER+TbwIuAAHzbGuAAi8tfAW4AuEekD/osx5qvA50VkL2CAo8BvL2J+V42hTJH+Cb8qqX+iwFCmeN7VPUOZIs/1TeC4BsHQP1Gi7DiELIt16SiDk0Xyjj8uJBUN4RrDhtYo7Yko8ej8hvJou4VSa1tDd4Sg2+v3p237RM3zIvDOGc79DPCZOtvfPcPx72skTWuZPw35yNT4DNc1PNY7ct5LZx4cyJCwQ7iex6nxIpZlkStWKHkeY4Uyp4LG757OOCJCT1eKiuOSitr0jxf4+YkiIKxLR+lOx3SxG6UuYM3YEH7BOziQIRUNEw37X49lWaSiYQ4OZM7resdHcxwcmOTAqUnGCw6e55CvuBTLDpO5CgawQxCPhnA8l4rjMllwyFccShWH4WyZZ46Nc9ejR3i5X6ujlLqQadBoQmP5MolICOHMLLeJSOi8RlcPZYqcGC1werJIzA5hh4TRvIPgB6Oxgr8md09HnIlchfG8w0iujGVBJCwMTJTOWZpV55NS6sKlQaMJtSci5MsuVnXlPgz5snteo6sPDmTY0Z2iWDGUKy6u5wVdZR2KRZdqE3um5FFxPLqSEVpiYYazZU5PlM5ZmtXzvPMu8dTScRtKrU4aNJrQrg1psiUHJ+g1VXG88x5dPZYvs7EtzvqWCPmKCwjJSAhLhKCHLcmIEA0LsUh1hhfBtixG8mW2dSSI2f726UuzKqUuPBo0mlB1dHU4KGqIyHk3gk8UKrw8MEmx4mFZFulYmI5kZGqUuQDtiSitCZuudBREiNkhbrxiPZta4ngeZ5Zmrbh0p6M6n5RSFzANGk2qOx2bGmndmYqed3fbns4E+YqL4xraYiEM4BihWPGjRiQEjmdI2mHidgjXeBQdDzsU4obd3bjGMFl0dGlWpRSg62k0NVmECQvbk1Ha4mFy5QrjBQcrJIQwU6PM2xM2bbEwY4UKkbBFOmLTlfKrst5+pT/6+59eHSEds+suzaqUurBo0Ghi1RHhhoWPCE/GbFIxf+qPakN2whbCIYtw2CIdCTGQKRFvibKlPc4Vm1vpTscYzJTYvbGFS7pT2nCtlNKg0czOlDTmf27t9B69g1mKZRfjGUZzFQpB1dTmthgxO4TjGaJ2iK2dCf7ZnvW87qKORUm/Umrt0aDRxKbW0ziPRZiqS7p2JGxeHcwhwIbWOAdOTQKQigiO53en3dga59L1aSJha2peKaWUqkeDRhOrTo1+vk0ak4UyzxwbJVNycFyPqC0MZ/3usnE7jGfAtiw2tcWCNoxNdKdjOgmhUmpG2nuqiS1kjfDJQpmDA1kmCy7r0xHClnBipIgHRC1IRENsaovREo8wmiuzd0urNnArpeakJY0mZp1n9dSBUxP8vG+CtrhNxfU4OVbBGDM1KK89FWFTW5xtnUleu6WVdMzm+p3rFj39Sqm1R0saTcxaQPVUoeJScT1yJYeS4yGWECz9zfqWGOvS/voYxYqng/WUUg3ToNHEqiUNcx7VU3E7xCunc3gGNrbGGM1XAEjYFp3JCFs7ErTEwuQrrg7WU0o1TINGE5MGV+47cGrinMbrrlSE4WyRiXyZqB1iLGgAf93WVkKWRbbkEg5Z2pahlJoXbdNoYtu7ksD5rRGeitmsa43huYbhTJlMycW2YNfGFmJ2iN0bWwDmXN9bB/QppWpp0GhijU4jUh2TUXuDzxYr4BnyZYfJogPA9q4UFdfQnQ7xcv8k+bLLZKEytSKfUkrNRaunmlhoqk1jfueN5UocGy1ghy22tMc4Ne4vmrR3ayub2qKcGi/huB6Xrk+xoSXOY70jurCSUqohGjSa2Hy63PaN5afaNY6O5ImGLOyQRdkTChWPhG1x+eZW0rEI27uSRO0QIkIyGj5nKVldIEkpNROtnmpiUyv3NVjUqFZTZYoOdsgPOL2n/W17NrWQL/tLu7bGz/7aE5EQI7nSYiRZKbXGadBoYmfmnprvmYa+sQKj2RInR3MApKIhwJCO2RQrZzetz7WUrJY6lFJVWj3VxBqdRqRvLM/pSb9NYixXYrJQYSxbYiBTpOgXLjDGMFmo0Bb3x2aUKi7GGHIl57yXklVKXXg0aDQxa54N4X1jeX740mlOTxYZzpUpBNVRLVGLWDjMzvVp2hJR9m5pJRyymCg4xGzrvJeSVUpdeLR6qomdmUaksaiRLVZ4rm+CiEAkHMIYfxT4prY4kbAwlClhhywu7k7pwkpKqfOiJY0mFrLm16YxnC2TtMOUPQiHhFJQNRUJhUjHI4xmSzrPlFJqQTRoNDFrHmuEj+bKDEwU6UyGsQRKQdVUxIKy5xK3LSzL0rYLpdSCaNBoYlNtGg2uER4JW4RDIS5dl6Lo+kGjLWGzuTVBJBzi5r2btO1CKbUg2qbRxKbGaTQw+VQx6BF1JFtiY2ucQlA3dcm6NL/++i1cc3GHBgyl1IJp0Ghi1XEa7hwN4dliheFMifZkhK2JOJMll6ILAvz2L1zMW3avX4bUKqUuBBo0mlh1nEbZmb2oMZwt4wHj+QqFijvV1XZDa5TuFi1dKKUWj7ZpNLHxYHnWfNnh0UNDdScVHMoUeal/gqNDOU6M5RnPlTkcTCcChmePj+lkhEqpRaNBo0kNZYo8e2wc8KuZihXvnNlohzJFHny+n6HJMq7nUSi7nBjNM1n0SyapSJiyc+55Sil1vjRoNKmDAxlSsRAABurORntwIMPPToyTLVeouAZjDG5N99x03CYeCZ1znlJKna+GgoaI3CgiB0WkV0Rur7M/KiLfCvY/ISI9Nfs+Hmw/KCJvq9l+l4gMisgL067VISI/EJFDwf/bF5C/VWssXyYZ8ZucqmEgEQkxFlRZVY8ZyZaIhkOELBCxcIKDbQtaYnbd8y7f1Mol3allyYdSam2ZM2iISAi4E7gJ2AO8W0T2TDvsNmDMGLMDuAP4XHDuHuBW4HLgRuBLwfUA7g62TXc78CNjzE7gR8HrC057IkIxaAA3Qe+p6bPRticiVDywLcGyQoQtwQtGgVsCJcclU6zMOYutUko1qpGSxtVArzHmsDGmDNwD3DztmJuBrwfP7wXeKv5apTcD9xhjSsaYI0BvcD2MMY8Ao3Xer/ZaXwduaTw7a8euDWkKZX+ZVs+rPxvtrg1pLAwl12CJwXEdgphBSzxMSIQX+zOcGs/rSHCl1KJoJGhsBk7UvO4LttU9xhjjABNAZ4PnTrfeGNMfPB8A6g4yEJEPich+Edk/NDTUQDZWl+50jGsu7gTAg7qz0XanY2xoi+K4LhXHw3HPnL+hNUYyGubizhQdyYgO7FNKLYqmbgg3fr1M3ZFtxpivGGP2GWP2dXd3L3PKlse64EYftiyu39l9zo1/KFMkXzG0J6K0J6N+NysgHQ1hh0Jsbo+zr6e9wUlIlFJqbo0EjZPA1prXW4JtdY8RkTDQCow0eO50p0VkY3CtjcBgA2lck86sp1H/tv/E4VHGc2VKrkep4kyVNGK2hW0JJ0YLDEwWtD1DKbVoGgkaTwE7RWS7iETwG7bvm3bMfcD7g+fvAH4clBLuA24NeldtB3YCT87xfrXXej/w3QbSuCadWSP83H1DmSJPHh1FAOMZChXvTM+pkEXFNSQiFodOZ7U9Qym1aOYMGkEbxUeAh4CXgG8bYw6IyKdE5NeCw74KdIpIL/BvCXo8GWMOAN8GXgT+DviwMcYFEJG/Bn4K7BKRPhG5LbjWZ4FfEZFDwC8Hry9I1fU06hU0Dg5k6AqqpFxzdh1ea8LGAIWyx5b2mLZnKKUWTUNzTxljvg98f9q2T9Q8LwLvnOHczwCfqbP93TMcPwK8tZF0rXVT62nUiRpj+TI71iX5ycEBimWXcsU/JmxBT2eSaNgiJBbbOnU8hlJq8eiEhU3szHoa55ooVHj51AT5igvWmWMilmCMoVB2aIlHtGpKKbWomrr31IVu+hrhB05NcODUBAA9nQkGJv01v5N2CAm+ydaEzcBECdeDX7y0S6umlFKLSksaTWy2No32ZJSOVISRXAkRwQ1mT9/YFmfnujRvv3Kj3w1XKaUWkQaNJiZB9RTUXye8KxVlOFticNKfwdYOwYaWKD1diTkDxuWbWhc3sUqpC4JWTzW5atyot3rfprYYmYI/txRAKhomX/aYLFQYy5WWM5lKqQuEBo0mNpQpTlVNfePxo+cEgpZ4hFg0TCWom2pLRLi4O0l3OsbRkfxyJ1cpdQHQoNGkhjJFHusdqc4MQtHxeK5v4qzA8fSxUYYmS1SC9oyezgTJaJiYbZEpOsufaKXUmqdBo0kdHMiQioanRoXHwhYJOzRVgnh1KMtorkzIglIwFPzS9X732mLFIx3T5iql1OLToNGkxvJlEpEQVlDWcA3nlCCKFRc3WHMjGhbKjkup4pKvuPR0JlYk3UqptU2DRpNqT0TIl12soNut65mzShB/f+AUzxwd49SEX10VDgkv9k9Sdj32bmnV7rZKqSWhQaNJ7dqQJltyqI71zpecqRLEXz1+hP3Hxqm4Z9YEj4dDdCQjrG+JacBQSi0ZDRpNqjsd47odnVMD/CyxpkoQp8aLVFxDyBKKQc+pVCxM2fE4MpxbyWQrpdY4DRpNrDsdIxr2l1SPRy3ak1FeHcpyYiyPGDCYqZHgjusxMFGkWHZnuaJSSi2MBo0mV9umAUE320yJiG1Rrlnf1UJwjcEDHdinlFoyGjSaXCgYEt4/UZjalo6FCVtQDjpSWUDZ89jWnuDS9Skd2KeUWjIaNJpcdZyGO23uKTsUmhr4Z4eFVNRm18YW9m3rpDVuL28ilVIXDA0aTS40tRDTmQF9maJDzA5NBZTudJSNrTGyJZd82dU1wZVSS0aHDTe56kJMw9kSfWN+tVPZ8bAEqm3eYUsICUzkK2RLDldu7dR1NJRSS0JLGk2u2hA+UahMbYuELfJlZ2q1PtfzGC9USMdDXLdDA4ZSaulo0GhyoaDhwtRMjR4JCaM5P4hELPxuuQL7trVrwFBKLSkNGk2uWtLIFh1OB4stlV1DOKi2itgh7LCwqSXGeEFntlVKLS0NGk2u2hBe23eq7HhTizJ1JG3aEhHScVunQ1dKLTkNGk1sKFMkX/FbuzNFh3wwMCNXcqYChGf8INIat7lisy7hqpRaWtp7qklVF2GqFjFKFZfe01nSsTAVz5taeClswWi2zFi+zK4N6ZVLsFLqgqBBo0lVF2EKBy3hIkI4JAxmStihMwXEsGVhCSQiYW0EV0otOQ0aTWosX6YzGZ0ap2GMv57GZMEhZvtBI2ZbdLdEMcYgIrNdTimlFoW2aTSp6iJM1UKFMX77RSRsUSj7dVOxsL8zEQmzY11qpZKqlLqAaNBoUtVFmLyg7cIzhlzRIRoSJgtlwG/nOD6SZzRX0uVdlVLLQoNGk6ouwpSOn6lBTMVCFB2PYsVvHQ+HBOMZJvIO4/nySiVVKXUB0aDRxLrTMTa1xgGIBIsxjeUqVFfRiIZDtKUipGJh9h8bW6FUKqUuJNoQ3uRaY/4055liGc8YijULL2UKDu1Jm61dyalpRZRSailpSaOJDWWKnAhmti05hlyxTO1qrpbllzyOj+aJ2/pVKqWWnpY0mtgTh0fJlfyR38aD6ZOEOK6h4nlkSy5dKV1DQym19PTnaRN7uX+SZNSP6x7geNMOEBBgc2uMVEyDhlJq6TUUNETkRhE5KCK9InJ7nf1REflWsP8JEemp2ffxYPtBEXnbXNcUkbtF5IiIPBc89i4si6uXwUyNCDf4gaPKAuyQsL4lRkcqSjqmhUal1NKbM2iISAi4E7gJ2AO8W0T2TDvsNmDMGLMDuAP4XHDuHuBW4HLgRuBLIhJq4Jp/YIzZGzyeW0gGV7PLNraQLZ7pSls7060ArmsYz1cYyZXIFCsMZYrLnkal1IWlkZLG1UCvMeawMaYM3APcPO2Ym4GvB8/vBd4q/rwWNwP3GGNKxpgjQG9wvUauecHbsS5FdobpziX4z4bWGHs2thANh3isd0QDh1JqSTUSNDYDJ2pe9wXb6h5jjHGACaBzlnPnuuZnROTnInKHiETrJUpEPiQi+0Vk/9DQUAPZWH16B7NYoRnmlLKgPWnzhm0dpGI28UiIVDTMwYHM8iZSKXVBacaG8I8Du4GrgA7gP9Q7yBjzFWPMPmPMvu7u7uVM37J5qX+SuH2mraI2fBgPio7Bqqm0SkRCjOnIcKXUEmokaJwEtta83hJsq3uMiISBVmBklnNnvKYxpt/4SsDX8KuyLkiCnBU0ats0whak7BCHhnJT3XLzZZf2hPaiUkotnUaCxlPAThHZLiIR/Ibt+6Ydcx/w/uD5O4AfG2NMsP3WoHfVdmAn8ORs1xSRjcH/BbgFeGEB+VvVdm9sqTtoLySQjIawwxaVisdwpkSh7JItOboQk1JqSc3ZT9MY44jIR4CHgBBwlzHmgIh8CthvjLkP+CrwlyLSC4ziBwGC474NvIg/Nu3DxhgXoN41g7f8hoh049fGPAf860XL7SpzzcUdHDg1Di+d3WYTCQsGIRISUrEwEwWHSNjiuh2duhCTUmpJNdS53xjzfeD707Z9ouZ5EXjnDOd+BvhMI9cMtt/QSJouBN3pGJdvakE4u2rKAlzjUXIMr9nSQrHi8bqL2jVgKKWWXDM2hKsa4wUHO3T2topnKJU9Ko5HoeyyqU2DhVJqeWjQaHKZol/1VGUJxCN+e0ZEJylUSi0zves0uXQsTCpypqgRCUEkZBGLhLioM0F3OsapcR3Qp5RaHho0mlxPZ4KOlD++UYCQCAZoi4eJ2SFitkW+dr50pZRaQjrLXZO7fuc6Lt98kmLFJVt0cY1HdyqCHazkV6x4JCKhOa6ilFKLQ0saq0AiEiYRCRGPWLieIVd2KVccKq5HvqIN4Uqp5aNBYxXIlx2yRZeQZRELWyDCRNGh4hn2bmmlJa6jwJVSy0Orp1aBgckSuXKFiguuZ7DDhnUtMTa2xGhPRhnN6/rgSqnloSWNJjeUKXJyNE+pYhCBsCVUXI9iuf6U6UoptZQ0aDS5gwMZYnaYqG3heYaY7U+BbomlDeBKqWWnQaPJjeXLxMMCBowx/gMoux7JiNYuKqWWl951mlx7IkIsYhMJl/AA1wDG0J6MsLEtvtLJU0pdYDRoNLldG9LEIhYI2CEhGraIhEO0xiP0dCZWOnlKqQuMVk81ue50jF3rU6SjITwDdshix7okuzakaE/WXQlXKaWWjJY0VoFUzGZda5x1rbC9K8n6lhhb2hNcvqmVA6cmuKQ7xeWbWlc6mUqpC4CWNFaZasBQSqmVoCWNVaI1bq90EpRSSksaq8GW9gQdSZ0qRCm18jRoKKWUapgGDaWUUg3ToLEKXNKdYn2LTn+ulFp52hC+ymlXW6XUctKSxipw+aZW7WarlGoKGjSUUko1TIPGKnFJd0q73SqlVpwGDaWUUg3ToKGUUqphGjSUUko1TIOGUkqphuk4jVXoku7USidBKXWB0pKGUkqphmnQUEop1TANGkoppRqmQWMV0VX7lFIrraGgISI3ishBEekVkdvr7I+KyLeC/U+ISE/Nvo8H2w+KyNvmuqaIbA+u0RtcU4dBK6VUk5gzaIhICLgTuAnYA7xbRPZMO+w2YMwYswO4A/hccO4e4FbgcuBG4EsiEprjmp8D7giuNRZcWymlVBNopKRxNdBrjDlsjCkD9wA3TzvmZuDrwfN7gbeKiATb7zHGlIwxR4De4Hp1rxmcc0NwDYJr3nLeuVtjtrQntLutUmpFNRI0NgMnal73BdvqHmOMcYAJoHOWc2fa3gmMB9eY6b0AEJEPich+Edk/NDTUQDaUUkot1KptCDfGfMUYs88Ys6+7u3ulk6OUUheERkaEnwS21rzeEmyrd0yfiISBVmBkjnPrbR8B2kQkHJQ26r3XBU9X61NKrZRGShpPATuDXk0R/Ibt+6Ydcx/w/uD5O4AfG2NMsP3WoHfVdmAn8ORM1wzOeTi4BsE1v3v+2Vs7Lt/Uqu0ZSqkVN2dJwxjjiMhHgIeAEHCXMeaAiHwK2G+MuQ/4KvCXItILjOIHAYLjvg28CDjAh40xLkC9awZv+R+Ae0Tk08CzwbWVUko1AfF/3K9u+/btM/v371/pZCil1KoiIk8bY/bN55xV2xCulFJq+WnQUEop1TANGkoppRqmQUMppVTDNGgopZRqmAYNpZRSDdOgoZRSqmEaNJRSSjVMg4ZSSqmGrYkR4SKSAQ6udDqWUBcwvNKJWEJrOX9rOW+g+Vvtdhlj0vM5oZFZbleDg/MdCr+aiMh+zd/qtJbzBpq/1U5E5j3/klZPKaWUapgGDaWUUg1bK0HjKyudgCWm+Vu91nLeQPO32s07f2uiIVwppdTyWCslDaWUUstAg4ZSSqmGreqgISLvFJEDIuKJyL5p+z4uIr0iclBE3rZSaVwIEbkxSH+viNy+0ulZKBG5S0QGReSFmm0dIvIDETkU/L99JdO4ECKyVUQeFpEXg7/LjwXb10QeRSQmIk+KyM+C/P1RsH27iDwR/J1+S0QiK53W8yUiIRF5VkTuD16vpbwdFZHnReS5alfb8/nbXNVBA3gB+A3gkdqNIrIHf53yy4EbgS+JSGj5k3f+gvTeCdwE7AHeHeRrNbsb//uodTvwI2PMTuBHwevVygF+3xizB3gj8OHgO1sreSwBNxhjrgT2AjeKyBuBzwF3GGN2AGPAbSuXxAX7GPBSzeu1lDeAXzLG7K0ZezLvv81VHTSMMS8ZY+qNBL8ZuMcYUzLGHAF6gauXN3ULdjXQa4w5bIwpA/fg52vVMsY8AoxO23wz8PXg+deBW5YzTYvJGNNvjHkmeJ7Bv/lsZo3k0fiywUs7eBjgBuDeYPuqzZ+IbAH+OfDfg9fCGsnbLOb9t7mqg8YsNgMnal73BdtWk7WQh0asN8b0B88HgPUrmZjFIiI9wOuAJ1hDeQyqb54DBoEfAK8C48YYJzhkNf+d/jfg3wNe8LqTtZM38AP834vI0yLyoWDbvP82m34aERH5IbChzq4/NMZ8d7nTo5aOMcaIyKrvAy4iKeBvgN81xkz6P1h9qz2PxhgX2CsibcB3gN0rm6LFISL/Ahg0xjwtIm9Z4eQsleuNMSdFZB3wAxF5uXZno3+bTR80jDG/fB6nnQS21rzeEmxbTdZCHhpxWkQ2GmP6RWQj/i/YVUtEbPyA8Q1jzN8Gm9dUHgGMMeMi8jBwLdAmIuHgF/lq/Tu9Dvg1EflVIAa0AH/K2sgbAMaYk8H/B0XkO/hV4PP+21yr1VP3AbeKSFREtgM7gSdXOE3z9RSwM+i9EcFv2L9vhdO0FO4D3h88fz+wakuPQR34V4GXjDF/UrNrTeRRRLqDEgYiEgd+Bb/d5mHgHcFhqzJ/xpiPG2O2GGN68P+t/dgY8x7WQN4ARCQpIunqc+Cf4Xckmv/fpjFm1T6AX8evZywBp4GHavb9IX5960HgppVO63nm71eBV4J8/OFKp2cR8vPXQD9QCb632/DrjX8EHAJ+CHSsdDoXkL/r8euNfw48Fzx+da3kEXgt8GyQvxeATwTbL8b/UdYL/E8gutJpXWA+3wLcv5byFuTjZ8HjQPV+cj5/mzqNiFJKqYat1eoppZRSS0CDhlJKqYZp0FBKKdUwDRpKKaUapkFDKaVUwzRoKHWeROSTIvLvzne/UquRBg2llFIN06Ch1DyIyB+KyCsi8iiwK9h2iYj8XTAR3D+KyDnzMYnIB0XkqWAtir8RkYSIpEXkSDD1CCLSUvtaqWakQUOpBonIG/CnmNiLP9L7qmDXV4D/2xjzBuDfAV+qc/rfGmOuMv5aFC8Btxl/+vSf4E/HTXDtvzXGVJYsE0otUNNPWKhUE3kz8B1jTB5ARO7Dn9zuTcD/rJnNNlrn3NeIyKeBNiAFPBRs/+/403H/L+ADwAeXKO1KLQoNGkotjIW/5sLeOY67G7jFGPMzEfkt/PmNMMY8JiI9wXTcIWPMCzNdQKlmoNVTSjXuEeAWEYkHM4a+HcgDR0TkneDPdCsiV9Y5Nw30B+0V75m2738A3wS+tnRJV2pxaNBQqkHGX8r1W/gzhT6IP309+EHgNhGpziBab1ne/4y/it9jwMvT9n0DaMefBVippqaz3Cq1wkTkHcDNxpj3rXRalJqLtmkotYJE5P8HbsLvjaVU09OShlJKqYZpm4ZSSqmGadBQSinVMA0aSimlGqZBQymlVMM0aCillGrY/wYj78hS8lxUNAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "ev.fit_scans([1,2,3,4], mod, pars, xgrid=np.r_[-10:50:0.05])\n", + "plt.legend()\n", + "plt.xlim(-10, 50)\n", + "plt.show()" + ] } ], "metadata": { diff --git a/pyEvalData/evaluation.py b/pyEvalData/evaluation.py index 87d1dd7..d702da9 100644 --- a/pyEvalData/evaluation.py +++ b/pyEvalData/evaluation.py @@ -464,8 +464,7 @@ def eval_scan_sequence(self, scan_sequence, xgrid=[], yerr='std', xerr='std', no parameters = [] for i, (scan_list, parameter) in enumerate(scan_sequence): - # traverse the scan sequence - + # iterate the scan sequence parameters.append(parameter) # get the data for the current scan list y2plot, x2plot, yerr2plot, xerr2plot, name = self.eval_scans( @@ -783,7 +782,7 @@ def _plot_fit_scans(self, y2plot, x2plot, yerr2plot, xerr2plot, name, res, offse if plot_separate: # use subplot for separate plotting plt.subplot(1, len(self.clist), i+1) - # plot the fit and the data as errorbars + # plot the fit x2plotFit = np.linspace( np.min(x2plot), np.max(x2plot), 10000) plt.plot(x2plotFit-offsetX, res[counter]['fit'].eval(x=x2plotFit), '-', lw=2, alpha=1, @@ -1039,3 +1038,162 @@ def clist(self, clist): else: clist = list(clist) self._clist = clist + + def scans(self, scan_list, xgrid=[], yerr='std', xerr='std', norm2one=False, binning=True): + y2plot, x2plot, yerr2plot, xerr2plot, name = self.eval_scans( + scan_list, xgrid=xgrid, yerr=yerr, xerr=xerr, norm2one=norm2one, binning=binning) + + return Scans(y2plot, x2plot, yerr2plot, xerr2plot, name, self.xcol) + + +class Scans(): + def __init__(self, y2plot, x2plot, yerr2plot, xerr2plot, name, xcol): + """__init__ + + Evaluate a list of scans for a given set of external parameters. + + Args: + - *y2plot (OrderedDict)* - evaluated y-data. + - *x2plot (ndarray)* -evaluated x-data. + - *yerr2plot (OrderedDict)* - evaluated y-error. + - *xerr2plot (ndarray)* - evaluated x-error. + - *name (str)* - name of the data set. + + """ + self.log = logging.getLogger(__name__) + self.y2plot = y2plot + self.x2plot = x2plot + self.yerr2plot = yerr2plot + self.xerr2plot = xerr2plot + self.name = name + self.xcol = xcol + self.fit_result = [] + self.fit_report = [] + + def fit(self, mod, pars, select='', weights=False, fit_method='leastsq', + nan_policy='propagate'): + """_fit_scans + + Internal method to fit a given data set. + + Args: + y2plot (OrderedDict): y-data to plot. + x2plot (ndarray): x-data to plot. + yerr2plot (OrderedDict): y-error to plot. + xerr2plot (ndarray): x-error which was plot. + mod (lmfit.Model): fit model. + pars (lmfit.parameters): fit parameters. + select (str, optional): evaluatable string to select x-range. + Defaults to empty string. + weights (bool, optional): enable weighting by inverse of errors. + Defaults to False. + fit_method (str, optional): lmfit's fit method. Defaults to 'leastsq'. + nan_policy (str, optional): lmfit's NaN policy. Defaults to 'propagate'. + + Returns: + (tuple): + - *res (dict)* - fit result dictionary. + - *report (list[dict, report])* - list of lmfit's best value + dictionary and fit report object + """ + res = {} # initialize the results dict + report_1 = [] + report_2 = {} + + for counter in self.y2plot: + res[counter] = {} + # get the fit models and fit parameters if they are lists/tuples + + # evaluate the select statement + if select == '': + # select all + sel = np.ones_like(self.y2plot[counter], dtype=bool) + else: + sel = eval(select) + + # execute the select statement + _y2plot = self.y2plot[counter][sel] + _x2plot = self.x2plot[sel] + _yerr2plot = self.yerr2plot[counter][sel] + _xerr2plot = self.xerr2plot[sel] + + # remove nans + _y2plot = _y2plot[~np.isnan(_y2plot)] + _x2plot = _x2plot[~np.isnan(_y2plot)] + _yerr2plot = _yerr2plot[~np.isnan(_y2plot)] + _xerr2plot = _xerr2plot[~np.isnan(_y2plot)] + + # do the fitting with or without weighting the data + if weights: + out = mod.fit(_y2plot, pars, x=_x2plot, weights=1/_yerr2plot, method=fit_method, + nan_policy=nan_policy) + else: + out = mod.fit(_y2plot, pars, x=_x2plot, method=fit_method, nan_policy=nan_policy) + + best_values = list(out.best_values.values()) + best_values.insert(0, counter) + report_1.append(best_values) + + report_2[counter] = out.fit_report() + # add the fit results to the returns + for pname, par in pars.items(): + res[counter][pname] = out.best_values[pname] + res[counter][pname + 'Err'] = out.params[pname].stderr + + res[counter]['chisqr'] = out.chisqr + res[counter]['redchi'] = out.redchi + res[counter]['CoM'] = sum(_y2plot*_x2plot)/sum(_y2plot) + res[counter]['int'] = np.trapz(_y2plot, x=_x2plot) + res[counter]['fit'] = out + + self.fit_result = res + self.fit_report = [report_1, report_2] + + return self + + def plot(self, label_text='', fmt='-o', plot_separate=False, offset_t0=False, **kwargs): + if len(self.fit_result) > 0: + fmt = 'o' + offsetX = 0 + if offset_t0: + try: + offsetX = self.fit_result['t0'] + except KeyError: + self.log.warning('No parameter \'t0\' present in model!') + else: + offsetX = 0 + + # plot all keys in the clist + for i, counter in enumerate(self.y2plot.keys()): + # iterate the counter list + + if plot_separate: + # use subplot for separate plotting + plt.subplot(1, len(self.clist), i+1) + + if len(label_text) == 0: + # if no label_text is given use the counter name + lt = counter + else: + if len(self.y2plot.keys()) > 1: + # for multiple counters add the counter name to the label + lt = label_text + ' | ' + counter + else: + # for a single counter just use the label_text + lt = label_text + + # plot the errorbar for each counter + if (self.xerr2plot is None) & (self.yerr2plot is None): + plot = plt.plot(self.x2plot, self.y2plot[counter], fmt, label=lt, **kwargs) + else: + plot = plt.errorbar(self.x2plot, self.y2plot[counter], fmt=fmt, label=lt, + xerr=self.xerr2plot, yerr=self.yerr2plot[counter], **kwargs) + + if len(self.fit_result) > 0: + x2plotFit = np.linspace(np.min(self.x2plot), np.max(self.x2plot), 10000) + plt.plot(x2plotFit-offsetX, self.fit_result[counter]['fit'].eval(x=x2plotFit), '-', + lw=2, alpha=1, color=plot[0].get_color()) + + plt.xlabel(self.xcol) + plt.title(self.name) + return self