repaired data_vs_fit_err_clean in case of outliers, repaired single file…

repaired data_vs_fit_err_clean in case of outliers, repaired single file analysis, added extra note in results.txt

repaired data_vs_fit_err_clean in case of outliers, repaired single file…
repaired data_vs_fit_err_clean in case of outliers, repaired single file analysis, added extra note in results.txt
6c0d38ec · Peter Jansweijer · fd4c19d6 · 6c0d38ec
Commit 6c0d38ec authored Jun 04, 2018 by Peter Jansweijer
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Showing with 9 additions and 4 deletions

analyze_sellmeier.py sw/insitu_alpha/analyze_sellmeier.py +9 -4

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--- a/sw/insitu_alpha/analyze_sellmeier.py
+++ b/sw/insitu_alpha/analyze_sellmeier.py
@@ -494,10 +494,12 @@ def analyze_plot(insitu_file, analyse_single, x, y, name, tolerance, use_itu_cha
    x_clean = x  # in [nm]!
    y_clean = y
    t_clean = t
+    data_vs_fit_err_clean = data_vs_fit_err
    for i in sorted(outlier_idx, reverse=True):
      x_clean = numpy.delete(x_clean, i)
      y_clean = numpy.delete(y_clean, i)
      t_clean = numpy.delete(t_clean, i)
+      data_vs_fit_err_clean = numpy.delete(data_vs_fit_err_clean, i)
    # Tackle the case when all datapoints are regarded as outlier due to narrow tolerance
    if len(x_clean) == 0:
@@ -593,8 +595,8 @@ def analyze_plot(insitu_file, analyse_single, x, y, name, tolerance, use_itu_cha
    ax2.set_ylabel('data versus fit err [ps]')
    # Calcualte standard deviation of data to fit error and format string
-    stdev_str = "{0:.3f}".format(data_vs_fit_err.std(ddof=1))
+    stdev_str = "{0:.3f}".format(data_vs_fit_err_clean.std(ddof=1))
-    lns3 = ax2.plot(x_clean, data_vs_fit_err, color='grey', label='data vs. 5-term Sellmeier fit error\n   StDev: ' + stdev_str + '[ps]')
+    lns3 = ax2.plot(x_clean, data_vs_fit_err_clean, color='grey', label='data vs. 5-term Sellmeier fit error\n   StDev: ' + stdev_str + '[ps]')
    lns=lns+lns1+lns2+lns3
    labels=[l.get_label() for l in lns]
@@ -975,8 +977,9 @@ if __name__ == "__main__":
      t = crtt_array["temp"]
      tangent_array, temperature_array, result_line = analyze_plot(insitu_file, analyse_single, x, y, name, tolerance, use_itu_channels, ref_name, fixed_itu_channel, tangent_array, temperature_array)
-      numb_of_meas = numb_of_meas + 1
+      if not(analyse_single):
-      result_file.write(result_line)
+        numb_of_meas = numb_of_meas + 1
+        result_file.write(result_line)
  # After scanning all files in a directory (i.e. not a single file)
  # => add statistics:
@@ -987,6 +990,8 @@ if __name__ == "__main__":
    result_file.write("Mean:  " + str(tangent_array.mean()) +"\n")
    result_file.write("StDev: " + str(tangent_array.std(ddof=1)) +"\n")
    result_file.write("StErr: " + str(tangent_array.std(ddof=1)/(numb_of_meas**0.5)) +"\n")
+    result_file.write("Note: Individual files analysed. Statistics over individual results.\n")
+    result_file.write("      Averaged output is average of individual crtt measurements treated as single measurement leading to average tangent.\n")
  # Finally average all measurement files and analyse the average is if it was a single file
  x, arr_crtt,arr_temp = average_insitu_files(files)