I went ahead and developed my own script to look at the 10th, 50th, and 90th percentiles for the particle sizing done on the SympaTEC HELOS laser diffractor device. As I suspected, the machine does in fact plot the cumulative distribution of particles and fits the data to a Sigmoid. It then calculates the 10th, 50th, and 90th percentiles and reports those values back to a txt file. The script is below the summary table. I will note that I got a NAN for one of the calculated measurements which, caused me to toss out that data point for my calculations. For completeness, I also removed it from the reported values.
Forgive the markdown table below, wordpress doesn’t recognize how to deal with markdown for some reason. It also doesn’t understand what WHITESPACE is supposed to mean. Nonetheless, it is clear that the machine is doing what one would expect as my calculated values are within error of the values reported. This is good as it is no longer a blackbox to me and I will report the values that it gives.
Pharmatose 150M | Percentile | Reported Average | Reported STD | Calculated Ave | Calculated STD | |:----------:|:----------------:|:------------:|:--------------:|:--------------:| | 10th | 121.9 µm | 6.6 µm | 117.1 µm | 6.5 µm | | 50th | 190.7 µm | 11.4 µm | 191.9 µm | 11.8 µm | | 90th | 268.2 µm | 22.6 µm | 260.0 µm | 20.8 µm | SuperTab 11SD | Percentile | Reported Average | Reported STD | Calculated Ave | Calculated STD | |:----------:|:----------------:|:------------:|:--------------:|:--------------:| | 10th | 119.7 µm | 7.9 µm | 116.3 µm | 6.1 µm | | 50th | 187.9 µm | 15.1 µm | 189.1 µm | 16.6 µm | | 90th | 271.4 µm | 39.9 µm | 262.4 µm | 32.9 µm | SuperTab 30GR | Percentile | Reported Average | Reported STD | Calculated Ave | Calculated STD | |:----------:|:----------------:|:------------:|:--------------:|:--------------:| | 10th | 117.9 µm | 26.5 µm | 117.9 µm | 18.1 µm | | 50th | 183.5 µm | 21.0 µm | 189.9 µm | 13.4 µm | | 90th | 257.4 µm | 28.3 µm | 256.4 µm | 19.2 µm |
# Import statements. import numpy as np from scipy.optimize import curve_fit # Input data. size = [ 4.50, 5.50, 6.50, 7.50, 9.00, 11.00, 13.00, 15.50, 18.50, 21.50, 25.00, 30.00, 37.50, 45.00, 52.50, 62.50, 75.00, 90.00, 105.00, 125.00, 150.00, 180.00, 215.00, 255.00, 305.00, 365.00, 435.00, 515.00, 615.00, 735.00, 875.00 ] # Pharmatose 150M pharmatose_150M_od43_63 = [ 2.16, 2.38, 2.54, 2.67, 2.82, 2.96, 3.07, 3.17, 3.28, 3.37, 3.46, 3.59, 3.77, 3.94, 4.10, 4.31, 4.67, 5.43, 6.96, 11.20, 21.73, 41.64, 67.86, 89.30, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] pharmatose_150M_od08_90 = [ 1.62, 1.87, 2.08, 2.26, 2.49, 2.73, 2.91, 3.08, 3.20, 3.27, 3.27, 3.27, 3.27, 3.27, 3.35, 3.52, 3.80, 4.40, 5.81, 10.75, 25.32, 51.46, 81.69, 97.52, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] pharmatose_150M_od15_83 = [ 2.27, 2.49, 2.63, 2.74, 2.84, 2.93, 2.93, 2.93, 2.93, 2.93, 2.93, 2.93, 2.93, 2.93, 3.01, 3.13, 3.36, 3.92, 5.31, 9.84, 21.84, 42.60, 67.85, 88.46, 99.02, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] pharmatose_150M_od15_52 = [ 1.69, 1.90, 2.06, 2.18, 2.33, 2.47, 2.57, 2.57, 2.57, 2.57, 2.57, 2.57, 2.57, 2.57, 2.68, 2.81, 2.98, 3.38, 4.42, 8.12, 18.74, 38.48, 63.38, 84.92, 94.39, 96.65, 100.00, 100.00, 100.00, 100.00, 100.00 ] pharmatose_150M_od09_31 = [ 2.25, 2.54, 2.77, 2.96, 3.17, 3.38, 3.53, 3.66, 3.77, 3.84, 3.84, 3.84, 3.84, 3.92, 4.05, 4.25, 4.55, 5.26, 7.09, 13.46, 29.42, 53.34, 79.60, 94.53, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] pharmatose_150M_od21_24 = [ 2.29, 2.53, 2.70, 2.82, 2.94, 3.03, 3.03, 3.03, 3.03, 3.03, 3.03, 3.09, 3.23, 3.38, 3.53, 3.73, 4.07, 4.78, 6.38, 11.15, 22.47, 40.47, 61.63, 80.36, 94.20, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] pharmatose_150M_od13_58 = [ 2.42, 2.66, 2.82, 2.93, 3.05, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.22, 3.36, 3.60, 4.11, 5.23, 8.67, 18.17, 36.99, 62.47, 83.56, 94.95, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] pharmatose_150M_od07_26 = [ 3.11, 3.51, 3.81, 4.03, 4.27, 4.48, 4.60, 4.70, 4.70, 4.70, 4.70, 4.76, 4.87, 5.04, 5.25, 5.57, 6.03, 6.97, 9.19, 16.09, 32.51, 57.07, 81.43, 94.38, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] pharmatose_150M_od15_40 = [ 2.57, 2.90, 3.15, 3.33, 3.51, 3.66, 3.75, 3.75, 3.75, 3.75, 3.75, 3.80, 3.92, 4.06, 4.19, 4.36, 4.59, 5.09, 6.23, 9.78, 19.14, 36.31, 58.95, 82.39, 98.64, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] pharmatose_150M_od16_94 = [ 2.60, 2.89, 3.09, 3.22, 3.35, 3.44, 3.44, 3.44, 3.44, 3.44, 3.44, 3.53, 3.66, 3.77, 3.87, 4.02, 4.26, 4.73, 5.79, 9.07, 17.62, 33.27, 53.49, 73.69, 89.55, 97.91, 100.00, 100.00, 100.00, 100.00, 100.00 ] pharmatose_150M_ave = [np.average(item) for item in zip(pharmatose_150M_od43_63, pharmatose_150M_od08_90, pharmatose_150M_od15_83, pharmatose_150M_od15_52, pharmatose_150M_od09_31, pharmatose_150M_od21_24, pharmatose_150M_od13_58, pharmatose_150M_od07_26, pharmatose_150M_od15_40, pharmatose_150M_od16_94)] pharmatose_150M_std = [np.std(item) for item in zip(pharmatose_150M_od43_63, pharmatose_150M_od08_90, pharmatose_150M_od15_83, pharmatose_150M_od15_52, pharmatose_150M_od09_31, pharmatose_150M_od21_24, pharmatose_150M_od13_58, pharmatose_150M_od07_26, pharmatose_150M_od15_40, pharmatose_150M_od16_94)] pharmatose_150M = [ pharmatose_150M_od43_63, pharmatose_150M_od08_90, pharmatose_150M_od15_83, pharmatose_150M_od15_52, pharmatose_150M_od09_31, pharmatose_150M_od21_24, pharmatose_150M_od13_58, pharmatose_150M_od07_26, pharmatose_150M_od15_40, pharmatose_150M_od16_94 ] # SuperTab 11SD supertab_11SD_od20_93 = [ 0.43, 0.50, 0.57, 0.63, 0.71, 0.81, 0.90, 0.99, 1.10, 1.19, 1.29, 1.43, 1.65, 1.88, 2.13, 2.49, 3.07, 4.27, 6.70, 13.32, 29.17, 56.35, 84.37, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_11SD_od12_18 = [ 0.55, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.68, 0.80, 0.99, 1.21, 1.45, 1.81, 2.36, 3.43, 5.67, 12.27, 28.07, 53.28, 81.44, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_11SD_od08_01 = [ 0.00, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.16, 0.30, 0.54, 0.83, 1.15, 1.63, 2.41, 3.92, 6.94, 15.44, 33.55, 58.19, 83.75, 97.68, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_11SD_od06_29 = [ 0.00, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.15, 0.31, 0.56, 0.83, 1.12, 1.53, 2.09, 2.92, 4.35, 8.68, 20.48, 40.37, 63.77, 85.46, 99.23, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_11SD_od15_59 = [ 0.00, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.16, 0.32, 0.59, 0.88, 1.18, 1.59, 2.17, 3.14, 4.93, 9.76, 21.00, 38.97, 60.11, 79.39, 94.27, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_11SD_od10_27 = [ 0.00, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.14, 0.27, 0.49, 0.74, 0.98, 1.29, 1.69, 2.39, 3.84, 8.10, 18.11, 34.17, 53.57, 71.78, 85.51, 94.19, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_11SD_od09_51 = [ 0.61, 0.76, 0.90, 1.04, 1.24, 1.51, 1.75, 2.03, 2.32, 2.57, 2.85, 3.25, 3.86, 4.46, 4.99, 5.60, 6.31, 7.52, 10.15, 17.87, 34.59, 58.89, 83.86, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_11SD_od07_94 = [ 0.52, 0.60, 0.60, 0.60, 0.60, 0.60, 0.60, 0.70, 0.81, 0.92, 1.04, 1.21, 1.50, 1.81, 2.14, 2.61, 3.26, 4.33, 6.32, 11.81, 24.04, 42.31, 63.21, 81.04, 92.89, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_11SD_od08_40 = [ 0.51, 0.59, 0.59, 0.59, 0.59, 0.59, 0.59, 0.59, 0.59, 0.69, 0.82, 1.01, 1.32, 1.63, 1.93, 2.31, 2.80, 3.64, 5.22, 9.51, 19.31, 34.87, 53.46, 71.73, 86.80, 96.02, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_11SD_ave = [np.average(item) for item in zip(supertab_11SD_od20_93, supertab_11SD_od12_18, supertab_11SD_od08_01, supertab_11SD_od06_29, supertab_11SD_od15_59, supertab_11SD_od10_27, supertab_11SD_od09_51, supertab_11SD_od07_94, supertab_11SD_od08_40) ] supertab_11sd_std = [np.std(item) for item in zip(supertab_11SD_od20_93, supertab_11SD_od12_18, supertab_11SD_od08_01, supertab_11SD_od06_29, supertab_11SD_od15_59, supertab_11SD_od10_27, supertab_11SD_od09_51, supertab_11SD_od07_94, supertab_11SD_od08_40)] supertab_11SD = [ supertab_11SD_od20_93, supertab_11SD_od12_18, supertab_11SD_od08_01, supertab_11SD_od06_29, supertab_11SD_od15_59, supertab_11SD_od10_27, supertab_11SD_od09_51, supertab_11SD_od07_94, supertab_11SD_od08_40 ] # SuperTab 30GR supertab_30GR_od05_40 = [ 0.89, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 1.03, 1.14, 1.26, 1.43, 1.63, 1.90, 2.53, 4.99, 13.15, 30.51, 55.09, 80.77, 98.84, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_30GR_od14_59 = [ 0.82, 0.91, 0.91, 0.91, 0.91, 0.91, 0.91, 0.91, 0.91, 0.91, 0.99, 1.11, 1.29, 1.47, 1.64, 1.88, 2.23, 2.82, 3.96, 7.42, 17.30, 37.26, 64.08, 86.29, 97.81, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_30GR_od14_16 = [ 0.81, 0.90, 0.90, 0.90, 0.90, 0.90, 0.90, 0.90, 0.90, 0.90, 0.97, 1.07, 1.23, 1.40, 1.57, 1.83, 2.19, 2.83, 4.08, 8.10, 19.27, 38.78, 62.03, 80.56, 93.90, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_30GR_od12_68 = [ 1.11, 1.23, 1.32, 1.32, 1.32, 1.32, 1.32, 1.32, 1.32, 1.32, 1.41, 1.55, 1.76, 2.01, 2.29, 2.71, 3.30, 4.15, 5.64, 10.08, 22.32, 43.58, 68.81, 86.73, 96.04, 99.37, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_30GR_od19_84 = [ 1.08, 1.19, 1.28, 1.28, 1.28, 1.28, 1.28, 1.28, 1.28, 1.28, 1.37, 1.50, 1.70, 1.91, 2.14, 2.45, 2.89, 3.59, 4.96, 9.26, 21.13, 42.65, 69.46, 90.32, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_30GR_od16_64 = [ 1.34, 1.48, 1.59, 1.68, 1.78, 1.88, 1.97, 1.97, 1.97, 2.06, 2.16, 2.30, 2.52, 2.75, 2.98, 3.30, 3.73, 4.44, 5.85, 10.46, 23.76, 47.74, 76.14, 92.11, 97.37, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_30GR_od07_38 = [ 8.09, 9.59, 10.86, 11.92, 13.20, 14.50, 15.46, 6.35, 17.12, 17.71, 18.27, 18.98, 20.05, 21.20, 2.48, 24.29, 26.55, 29.80, 34.86, 45.30, 62.11, 80.83, 93.74, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_30GR_od22_13 = [ 1.32, 1.48, 1.60, 1.70, 1.82, 1.93, 2.02, 2.12, 2.12, 2.21, 2.32, 2.46, 2.68, 2.90, 3.14, 3.47, 3.93, 4.70, 6.09, 10.07, 20.37, 38.47, 60.39, 79.95, 92.87, 98.84, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_30GR_od14_21 = [ 1.99, 2.23, 2.41, 2.56, 2.74, 2.92, 3.06, 3.20, 3.34, 3.46, 3.59, 3.78, 4.06, 4.36, 4.65, 5.03, 5.49, 6.19, 7.54, 11.87, 24.41, 48.74, 80.55, 98.71, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_30GR_od09_15 = [ 3.94, 4.61, 5.17, 5.65, 6.22, 6.80, 7.23, 7.62, 7.97, 8.25, 8.53, 8.89, 9.44, 10.01, 10.60, 11.41, 12.38, 13.78, 16.37, 23.89, 41.22, 66.35, 88.63, 98.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00 ] supertab_30GR_ave = [np.average(item) for item in zip(supertab_30GR_od05_40, supertab_30GR_od14_59, supertab_30GR_od14_16, supertab_30GR_od12_68, supertab_30GR_od19_84, supertab_30GR_od16_64, supertab_30GR_od07_38, supertab_30GR_od22_13, supertab_30GR_od14_21, supertab_30GR_od09_15) ] supertab_30GR_std = [np.std(item) for item in zip(supertab_30GR_od05_40, supertab_30GR_od14_59, supertab_30GR_od14_16, supertab_30GR_od12_68, supertab_30GR_od19_84, supertab_30GR_od16_64, supertab_30GR_od07_38, supertab_30GR_od22_13, supertab_30GR_od14_21, supertab_30GR_od09_15) ] supertab_30GR = [ supertab_30GR_od05_40, supertab_30GR_od14_59, supertab_30GR_od14_16, supertab_30GR_od12_68, supertab_30GR_od19_84, supertab_30GR_od16_64, supertab_30GR_od22_13, supertab_30GR_od14_21, supertab_30GR_od09_15 ] # Define the Sigmoidal function. def sigmoid(x, a, b, k, t): """ Sigmoidal function. x = Input data. a = Minimum value of input data. b = Maximum value of input data. k = Midpoint of the Sigmoid. t = Rate of increase. """ y = a + (b/(1 + np.exp((k - x)/t))) return y # Get the 10, 50, and 90th percentiles. pharmatose_150M_percentiles = [] for item in pharmatose_150M: popt, pcov = curve_fit(sigmoid, size, item) _10 = popt[2] - popt[3]*np.log(popt[1]/(10 - popt[0]) - 1) _50 = popt[2] - popt[3]*np.log(popt[1]/(50 - popt[0]) - 1) _90 = popt[2] - popt[3]*np.log(popt[1]/(90 - popt[0]) - 1) temp = _10, _50, _90 pharmatose_150M_percentiles.append(temp) supertab_11SD_percentiles = [] for item in supertab_11SD: popt, pcov = curve_fit(sigmoid, size, item) _10 = popt[2] - popt[3]*np.log(popt[1]/(10 - popt[0]) - 1) _50 = popt[2] - popt[3]*np.log(popt[1]/(50 - popt[0]) - 1) _90 = popt[2] - popt[3]*np.log(popt[1]/(90 - popt[0]) - 1) temp = _10, _50, _90 supertab_11SD_percentiles.append(temp) supertab_30GR_percentiles = [] for item in supertab_30GR: popt, pcov = curve_fit(sigmoid, size, item) _10 = popt[2] - popt[3]*np.log(popt[1]/(10 - popt[0]) - 1) _50 = popt[2] - popt[3]*np.log(popt[1]/(50 - popt[0]) - 1) _90 = popt[2] - popt[3]*np.log(popt[1]/(90 - popt[0]) - 1) temp = _10, _50, _90 supertab_30GR_percentiles.append(temp) # Create a list of the generated percentile values. pharmatose_150M_10 = [ 119.34, 121.96, 125.33, 129.43, 114.13, 120.18, 128.50, 107.36, 125.58, 127.73 ] pharmatose_150M_50 = [ 191.16, 178.32, 190.26, 196.19, 175.81, 195.76, 197.87, 171.37, 201.16, 208.96 ] pharmatose_150M_90 = [ 258.28, 236.00, 262.29, 281.83, 242.86, 289.82, 283.27, 241.46, 278.42, 308.22 ] pharmatose_150M_reported = [ pharmatose_150M_10, pharmatose_150M_50, pharmatose_150M_90 ] supertab_11SD_10 = [ 114.96, 118.13, 112.20, 127.81, 125.54, 129.74, 104.16, 118.42, 126.26 ] supertab_11SD_50 = [ 172.99, 176.09, 170.03, 194.41, 198.26, 208.57, 169.02, 192.88, 208.49 ] supertab_11SD_90 = [ 229.41, 233.45, 232.95, 271.49, 290.66, 336.04, 230.22, 292.81, 325.82 ] supertab_11SD_reported = [ supertab_11SD_10, supertab_11SD_50, supertab_11SD_90 ] supertab_30GR_10 = [ 140.35, 131.52, 129.25, 124.65, 126.56, 123.02, 124.67, 116.38, 44.88 ] # 5.82 supertab_30GR_50 = [ 207.76, 196.63, 196.89, 188.90, 189.60, 182.79, 131.99, 198.41, 181.39, 160.48 ] supertab_30GR_90 = [ 280.54, 271.11, 290.39, 272.56, 254.39, 249.72, 204.86, 293.91, 235.82, 220.86 ] supertab_30GR_reported = [ supertab_30GR_10, supertab_30GR_50, supertab_30GR_90 ] for i in range(0, 3): rept_ave = np.average(pharmatose_150M_reported[i]) calc_ave = np.average([item[i] for item in pharmatose_150M_percentiles]) rept_std = np.std(pharmatose_150M_reported[i]) calc_std = np.std([item[i] for item in pharmatose_150M_percentiles]) print 'Calculated average for Pharmatose 150M = %f' % calc_ave print 'Reported average for Pharmatose 150M = %f' % rept_ave print 'Calculated STD for Pharmatose 150M = %f' % calc_std print 'Reported STD for Pharmatose 150M = %f' % rept_std for i in range(0, 3): rept_ave = np.average(supertab_11SD_reported[i]) calc_ave = np.average([item[i] for item in supertab_11SD_percentiles]) rept_std = np.std(supertab_11SD_reported[i]) calc_std = np.std([item[i] for item in supertab_11SD_percentiles]) print 'Calculated average for SuperTab 11SD = %f' % calc_ave print 'Reported average for SuperTab 11SD = %f' % rept_ave print 'Calculated STD for SuperTab 11SD = %f' % calc_std print 'Reported STD for SuperTab 11SD = %f' % rept_std for i in range(0, 3): rept_ave = np.average(supertab_30GR_reported[i]) calc_ave = np.average([item[i] for item in supertab_30GR_percentiles]) rept_std = np.std(supertab_30GR_reported[i]) calc_std = np.std([item[i] for item in supertab_30GR_percentiles]) print 'Calculated average for SuperTab 30GR = %f' % calc_ave print 'Reported average for SuperTab 30GR = %f' % rept_ave print 'Calculated STD for SuperTab 30GR = %f' % calc_std print 'Reported STD for SuperTab 30GR = %f' % rept_std
Andy Maloney 