Merge pull request #679 from aodn/fix_676

fixup for 676, fixes 676

AutomaticQC/imosTimeSeriesSpikeQC.m

@@ -47,9 +47,17 @@
 
 if (is_profile || time_missing), return, end
 
-has_burst_duration = (isfield(sample_data.meta, 'instrument_burst_duration') &&~isempty(sample_data.meta.instrument_burst_duration)) || (isfield(sample_data, 'instrument_burst_duration') &&~isempty(sample_data.instrument_burst_duration));
-has_burst_interval = (isfield(sample_data.meta, 'instrument_burst_interval') &&~isempty(sample_data.meta.instrument_burst_interval)) || (isfield(sample_data, 'instrument_burst_interval') &&~isempty(sample_data.instrument_burst_interval));
-
+has_burst_duration = false;
+has_burst_interval = false;
+try
+    has_burst_duration = sample_data.meta.instrument_burst_duration;
+    has_burst_interval = sample_data.meta.instrument_burst_interval;
+catch
+    try
+        has_burst_duration = sample_data.instrument_burst_duration;
+        has_burst_interval = sample_data.meta.instrument_burst_interval;
+    end
+end
 has_burst = has_burst_duration || has_burst_interval;
 
 if has_burst
@@ -71,8 +79,8 @@
 
 if auto
     user_interaction = struct();
-    spike_methods = load_classifiers(opts_file, has_burst);
-    method = readProperty(opts_file, 'auto_method');
+    spike_methods = loadSpikeClassifiers(opts_file, has_burst);
+    method = readProperty('auto_function',opts_file);
 
     for k = 1:length(ts_variables)
         user_interaction.(ts_variables{k}) = spike_methods(method);
@@ -129,7 +137,6 @@
 % Construct the SpikeQC log strings for each variable
 % and each parameters used.
 %
-nspikes = length(user_input.spikes);
 fun_name = func2str(user_input.fun);
 nargs = length(user_input.args);
 arg_string = cell(1, nargs);
@@ -160,12 +167,11 @@
 c = 1;
 
 for k = 1:length(ts_dims)
-    not_empty = ~isempty(ts_dims{k});
+    no_empty_dims = ~isempty(ts_dims{k});
     not_depth = ~strcmpi(varnames{k}, 'DEPTH');
     var = getVar(sample_data.variables,varnames{k});
-    is_vector = var && isvector(var);
-
-    if not_empty && not_depth && is_vector
+    is_vector = var && isvector(sample_data.variables{var}.data);
+    if no_empty_dims && not_depth && is_vector
         ts_variables{c} = varnames{k};
         c = c + 1;
     end
@@ -185,7 +191,7 @@
 % sample_data - the toolbox data struct.
 % ts_variables - the time-series variables to process.
 % has_burst - if the data is in burst-mode.
-% 
+%
 % Outputs:
 %
 % postqc - A data structure that is a copycat of sample_data but with clean timeSeries variables only.
@@ -260,19 +266,3 @@
 end
 
 end
-
-function [valid_burst_range, max_burst_len] = cut_burst_range(burst_index_ranges, l, r)
-%function [valid_burst_range, max_burst_len] = cut_burst_range(burst_index_ranges, l, r)
-% find the valid burst range and the maximum burst length
-%
-find_burst_ind = @(ind, x)(any(x == ind));
-find_l = @(x)(find_burst_ind(l, x));
-find_r = @(x)(find_burst_ind(r, x));
-first_valid_burst = find(cellfun(find_l, burst_index_ranges));
-last_valid_burst = find(cellfun(find_r, burst_index_ranges));
-
-valid_burst_range = burst_index_ranges(first_valid_burst:last_valid_burst);
-
-compute_burst_len = @(x)(max(x) - min(x));
-max_burst_len = max(cellfun(compute_burst_len, valid_burst_range));
-end

AutomaticQC/imosTimeSeriesSpikeQC.txt

@@ -1,5 +1,7 @@
 %If auto is on, use the default options from named method
-auto_function = hampel
+auto_function = Hampel
+
+%Hampel
 
 hampel_function = imosSpikeClassifierHampel
 %window of data at each side
@@ -9,7 +11,7 @@ hampel_madfactor = 5
 %clipping_mad
 hampel_lower_mad_limit = 0.0
 
-%SavGol Filter with OTSU Threshold - despiking2
+%OTSU-Savgol/despiking1 
 
 otsu_savgol_function = imosSpikeClassifierNonBurstSavGolOTSU
 %odd window filtering size
@@ -21,7 +23,7 @@ otsu_savgol_nbins = 100
 %scale to reduce the otsu threshold
 otsu_savgol_oscale = 1
 
-%OTSU Threhsold - despiking1
+%OTSU-Thresholding/despiking2
 
 otsu_threshold_function = imosSpikeClassifierOTSU
 %number of histogram bins in otsu thresholding

AutomaticQC/imosTimeSeriesSpikeQCBurst.txt

@@ -1,7 +1,7 @@
 %If auto is on, use the default options from named method
-auto_function = burst_hampel
+auto_function = Hampel-Burst
 
-% burst Hampel filter
+% Hampel-Burst
 burst_hampel_function = imosSpikeClassifierBurstHampel
 % boolean use windows as bursts instead of samples
 burst_hampel_use_burst_window = 0
@@ -15,7 +15,7 @@ burst_hampel_lower_mad_limit = 0
 %detect all not only repeated
 burst_hampel_repeated_only = 0
 
-%Running Statistics
+%Running-Statistics/imosBurstQC
 burst_runningstats_function = imosSpikeClassifierBurstRunningStats
 
 % the scale statistic function

test/UI/test_imosTimeSeriesSpikeQC.m

@@ -0,0 +1,34 @@
+classdef test_imosTimeSeriesSpikeQC < matlab.unittest.TestCase
+
+    methods (Test)
+        function test_no_ndvars_processed(~)
+            sample_data = create_sample_data([100,1],{'2dvar','3dvar','TEMP','PITCH','ROLL'});
+            dummy = sample_data.variables{1}.data;
+            sample_data.variables{1}.data = repmat(dummy,1,6);
+            sample_data.variables{1}.dimensions = 2;
+            sample_data.variables{2}.data = repmat(dummy,1,6,12);
+            sample_data.variables{2}.dimensions = 3;
+            z = imosTimeSeriesSpikeQC(sample_data,true);
+            procvars = fieldnames(z);
+            assert(~inCell(procvars,'2dvar'));
+            assert(~inCell(procvars,'3dvar'));
+        end
+
+    end
+end
+
+function sample_data = create_sample_data(asize,varnames)
+sample_data = struct();
+sample_data.variables = cell(1, 1);
+sample_data.dimensions = cell(1, 1);
+dummy = randn(asize);
+
+for k = 1:length(varnames)
+    sample_data.variables{k} = struct();
+    sample_data.variables{k}.name = varnames{k};
+    sample_data.variables{k}.data = dummy;
+    sample_data.variables{k}.flags = zeros(asize);
+    sample_data.variables{k}.dimensions = 1;
+end
+sample_data.dimensions{1} = struct('name','TIME','data',linspace(0,length(dummy),1),'flags',zeros(asize));
+end