updated ui added new features

third_party/codec2/stm32/unittest/lib/octave/ofdm_demod_check.m

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+% ofdm_demod_check.m
+%
+% Load results from reference and stm32 run and compare
+
+addpath("../../lib/octave")
+
+% Constants (would prefer parameters)
+err_limit = 0.001;
+
+% Reference
+load("ofdm_demod_ref_log.txt");
+
+% DUT
+load("ofdm_demod_log.txt");
+
+% Eliminate trailing rows of all zeros (unused)
+sums_ref = sum(payload_syms_log_c, 2);
+last_ref = find(sums_ref, 1, 'last');
+sums_dut = sum(payload_syms_log_stm32, 2);
+last_dut = find(sums_dut, 1, 'last');
+last_all = max(last_ref, last_dut);
+
+syms_ref = payload_syms_log_c(1:last_all,:);
+syms_dut = payload_syms_log_stm32(1:last_all,:);
+
+% error values
+err = abs(syms_ref - syms_dut);
+err_max = max(max(err));
+printf("MAX_ERR %f\n", err_max);
+
+err_vals = err - err_limit;
+err_vals(err_vals<0) = 0;
+errors = err_vals > 0;
+num_errors = nnz(errors);
+
+%% TODO, print errors info (count locations,...)
+if (num_errors > 0)
+    printf("%d ERRORS\n", num_errors);
+else
+    printf("PASSED\n");
+end
+
+% EVM 
+evm = sqrt(meansq(err, 2));
+evm_avg = mean(evm);
+printf("AVG_EVM %f\n", evm_avg);
+evm_max = max(evm);
+printf("MAX_EVM %f\n", evm_max);
+
+% Standard deviation
+sdv = std(err, 0, 2);
+sdv_max = max(sdv);
+printf("MAX_SDV %f\n", sdv_max);
+
+% Plot
+figure(1)
+figure(1, "visible", true)
+scatter(real(syms_ref), imag(syms_ref), "g", "+")
+hold on
+scatter(real(syms_dut), imag(syms_dut), "b", "o")
+print(1, "syms_plot.png", "-dpng")
+hold off

third_party/codec2/stm32/unittest/lib/python/sum_profiles.py

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+#!/usr/bin/env python3
+""" sum_profiles """
+
+def sum_profiles(fin, frames):
+    data = {}
+    total_time = 0.0
+    active = False
+
+    for line in fin:
+        if   (not active): active = line.startswith("Start Profile Data")
+        elif line.startswith("End Profile Data"): active = False
+        else:
+            words = line.strip().split()
+            if (len(words) == 3):
+                part = words[0]
+                time_str = words[1]
+                time = float(time_str)
+                total_time += time
+                if (not part in data): data[part] = 0.0
+                data[part] += time
+            # end else 
+        # end for line
+
+    data_sorted = [(p, data[p]) for p in sorted(data, key=data.get, reverse=True)]
+
+    print("Total time = {:.1f} ms".format(total_time))
+    if (frames):
+        print("{:.1f} ms per frame".format(total_time / frames))
+        print("")
+
+    for part, time in data_sorted:
+        percent = int(100*(time / total_time))
+        print('{:2d}% - {:10.3f} - {}'.format(percent, time, part))
+
+    return(data)
+    # end sum_profiles()
+
+
+########################################
+if __name__ == "__main__":
+    import argparse
+
+    #### Options 
+    argparser = argparse.ArgumentParser()
+    argparser.add_argument("-f", "--frames", action="store", type=int, default=0,
+                                             help="Number of frames")
+    argparser.add_argument("file", metavar="FILE", help="file to read")
+    args = argparser.parse_args()
+
+    fin = open(args.file, "r")
+    sum_profiles(fin, args.frames)