With stats results

 # execution outputs:
 nohup.out
-jobstats.csv
 log_of_the_run.sh
 
+# results
+results/figures/wadley_plots/
+
 # temporary results files
-data/*.npy
-data/*.npz
-data/olddata
+data/
 
 # environment stuff
 .vscode/

-label,comp_time,max_mem
-Realign RF00001 + 733 chains,347.5666003227234,783781888
-Realign RF00002 + 138 chains,15.574181318283081,710549504
-Realign RF00004 + 10 chains,331.88619780540466,2516779008
-Realign RF00005 + 869 chains,2349.9712748527527,6085918720
-Realign RF00008 + 9 chains,7.597636461257935,247132160
-Realign RF00009 + 4 chains,423.78941464424133,22123020288
-Realign RF00010 + 3 chains,167.0309178829193,5554601984
-Realign RF00011 + 4 chains,10.090157508850098,996966400
-Realign RF00013 + 1 chains,17.571903228759766,474783744
-Realign RF00015 + 6 chains,98.247323513031,1385431040
-Realign RF00017 + 10 chains,2218.9181191921234,13771866112
-Realign RF00020 + 17 chains,23.84590220451355,431484928
-Realign RF00023 + 7 chains,1196.2392709255219,7625351168
-Realign RF00026 + 20 chains,82.25747513771057,518791168
-Realign RF00028 + 5 chains,240.64744520187378,11369852928
-Realign RF00029 + 1 chains,62.898540019989014,898707456
-Realign RF00032 + 9 chains,7.049402236938477,162136064
-Realign RF00037 + 2 chains,0.27519845962524414,108863488
-Realign RF00050 + 6 chains,9.991205930709839,397705216
-Realign RF00059 + 24 chains,52.07490301132202,532307968
-Realign RF00061 + 1 chains,0.3395853042602539,233058304
-Realign RF00080 + 4 chains,19.957021474838257,1301696512
-Realign RF00100 + 6 chains,415.4162850379944,4435156992
-Realign RF00162 + 27 chains,16.753626108169556,408281088
-Realign RF00164 + 1 chains,0.05605888366699219,83927040
-Realign RF00167 + 48 chains,4.422192573547363,264232960
-Realign RF00168 + 16 chains,17.653642892837524,796184576
-Realign RF00169 + 11 chains,9.363726615905762,226705408
-Realign RF00174 + 2 chains,171.14065551757812,2648383488
-Realign RF00177 + 498 chains,2885.531806945801,45187723264
-Realign RF00233 + 2 chains,0.16314435005187988,138911744
-Realign RF00234 + 37 chains,10.552204132080078,1207234560
-Realign RF00250 + 1 chains,0.08687877655029297,83755008
-Realign RF00379 + 7 chains,27.468972206115723,655532032
-Realign RF00380 + 3 chains,2.397320508956909,245669888
-Realign RF00442 + 1 chains,2.0599684715270996,222887936
-Realign RF00458 + 7 chains,0.24766230583190918,197394432
-Realign RF00488 + 3 chains,1.4626531600952148,850460672
-Realign RF00504 + 18 chains,12.249290227890015,366731264
-Realign RF00505 + 1 chains,0.06069207191467285,83628032
-Realign RF01051 + 17 chains,7.672087907791138,297189376
-Realign RF01510 + 16 chains,0.0939493179321289,83746816
-Realign RF01689 + 4 chains,1.2797768115997314,400691200
-Realign RF01725 + 2 chains,2.976431369781494,294690816
-Realign RF01734 + 5 chains,1.8893005847930908,163631104
-Realign RF01739 + 4 chains,1.6384203433990479,271265792
-Realign RF01750 + 6 chains,8.268307209014893,421974016
-Realign RF01763 + 13 chains,0.5894784927368164,135094272
-Realign RF01786 + 2 chains,0.8764479160308838,182689792
-Realign RF01807 + 1 chains,0.19919967651367188,166801408
-Realign RF01826 + 1 chains,0.06825041770935059,83787776
-Realign RF01831 + 10 chains,2.0323476791381836,254255104
-Realign RF01846 + 2 chains,15.989834308624268,1073623040
-Realign RF01852 + 16 chains,4.523370265960693,249016320
-Realign RF01854 + 3 chains,8.060775518417358,647757824
-Realign RF01857 + 1 chains,3.9880683422088623,587083776
-Realign RF01960 + 140 chains,3388.5226855278015,56313212928
-Realign RF02001 + 26 chains,22.095701456069946,1335533568
-Realign RF02012 + 3 chains,10.277246713638306,796667904
-Realign RF02253 + 1 chains,0.2654685974121094,104386560
-Realign RF02348 + 2 chains,0.11346197128295898,82419712
-Realign RF02519 + 1 chains,0.039333343505859375,81330176
-Realign RF02540 + 67 chains,726.7017936706543,48769855488
-Realign RF02545 + 3 chains,0.451732873916626,513720320
-Realign RF02546 + 1 chains,0.3498055934906006,405676032
-Realign RF02553 + 1 chains,1.2360577583312988,281141248
-Realign RF02680 + 1 chains,0.09950971603393555,80687104
-Realign RF02683 + 1 chains,1.070310115814209,282808320
-Realign RF02796 + 6 chains,0.0940089225769043,81862656

-rfam_acc,n_seq,maxlength,n_pdb_seqs,total_seqs
-RF00001,70460,345,733,71193
-RF00002,11746,289,138,11884
-RF00004,10251,342,10,10261
-RF00005,436080,293,869,436949
-RF00008,2383,132,9,2392
-RF00009,1217,1029,4,1221
-RF00010,6473,812,3,6476
-RF00011,787,436,4,791
-RF00013,3502,254,1,3503
-RF00015,5016,310,6,5022
-RF00017,3733,806,10,3743
-RF00020,4459,188,17,4476
-RF00023,6656,784,7,6663
-RF00026,23130,431,20,23150
-RF00028,2051,892,5,2056
-RF00029,8804,341,1,8805
-RF00032,16724,88,9,16733
-RF00037,1607,56,2,1609
-RF00050,3746,347,6,3752
-RF00059,9846,255,24,9870
-RF00061,80,261,1,81
-RF00080,788,241,4,792
-RF00100,7822,636,6,7828
-RF00162,4049,375,27,4076
-RF00164,63,43,1,64
-RF00167,1765,156,48,1813
-RF00168,1889,334,16,1905
-RF00169,6295,121,11,6306
-RF00174,9480,476,2,9482
-RF00177,25969,3531,498,26467
-RF00233,49,87,2,51
-RF00234,930,380,37,967
-RF00250,63,60,1,64
-RF00379,2637,324,7,2644
-RF00380,921,282,3,924
-RF00442,770,226,1,771
-RF00458,16,215,7,23
-RF00488,40,824,3,43
-RF00504,3582,249,18,3600
-RF00505,21,65,1,22
-RF01051,3217,270,17,3234
-RF01510,5,63,16,21
-RF01689,344,215,4,348
-RF01725,767,158,2,769
-RF01734,1748,159,5,1753
-RF01739,761,273,4,765
-RF01750,1513,203,6,1519
-RF01763,640,82,13,653
-RF01786,496,122,2,498
-RF01807,12,218,1,13
-RF01826,14,93,1,15
-RF01831,614,249,10,624
-RF01846,616,537,2,618
-RF01852,4469,112,16,4485
-RF01854,1707,302,3,1710
-RF01857,442,343,1,443
-RF01960,27108,5325,140,27248
-RF02001,2268,340,26,2294
-RF02012,838,191,3,841
-RF02253,677,63,1,678
-RF02348,77,105,2,79
-RF02519,6,33,1,7
-RF02540,34679,9019,67,34746
-RF02541,35613,8885,689,36302
-RF02543,38161,11046,147,38308
-RF02545,16,628,3,19
-RF02546,18,572,1,19
-RF02553,116,188,1,117
-RF02680,34,103,1,35
-RF02683,229,187,1,230
-RF02796,13,70,6,19

-#!/usr/bin/python3
+#!/usr/bin/python3.8
 # This file is supposed to propose regression models on the computation time and mem usage of the re-alignment jobs.
 # Light jobs are monitored by the Monitor class in RNAnet.py, and the measures are saved in jobstats.csv.
 # This was done to guess the amount of memory required to re-align the large ribosomal subunit families RF02541 and RF02543.
-# INFO: Our home hardware was a 24-core VM with 50GB RAM + 8GB Swap.
+# INFO: Our home hardware was a 32-core VM with 50GB RAM + 8GB Swap.
 
 import matplotlib.pyplot as plt
 import pandas as pd
 import numpy as np
-import scipy
+import scipy, os
 from sklearn.linear_model import LinearRegression
 from mpl_toolkits.mplot3d import Axes3D
 
@@ -31,105 +31,109 @@ for index, fam in jobstats.iterrows():
         maxlengths.append(
             families.loc[families["rfam_acc"] == rfam_acc, "maxlength"].values[0])
 
-nchains = [x/1000 for x in nchains]  # compte en milliers de séquences
 comptimes = [x/3600 for x in comptimes]  # compte en heures
 maxlengths = [x/1000 for x in maxlengths]  # compte en kB
 maxmem = [x/1024/1024 for x in maxmem]  # compte en MB
 
 summary = pd.DataFrame({"family": computed_families, "n_chains": nchains,
-                        "max_length": maxlengths, "comp_time": comptimes, "max_mem": maxmem})
-summary.sort_values("max_length", inplace=True)
-summary.to_csv("summary.csv")
+                        "max_length(kB)": maxlengths, "comp_time(h)": comptimes, "max_mem(MB)": maxmem})
+summary.sort_values("max_length(kB)", inplace=True)
+summary.to_csv("results/summary.csv")
 
 # ========================================================
 # Plot the data
 # ========================================================
 
-fig = plt.figure(dpi=100)
+fig = plt.figure(figsize=(12,8), dpi=100)
+
 plt.subplot(231)
-plt.scatter(summary.n_chains, summary.max_mem)
-plt.xlabel("Number of sequences (x1000 seqs)")
+plt.scatter(summary.n_chains, summary["max_mem(MB)"])
+plt.xlabel("Number of sequences")
 plt.ylabel("Peak memory (MB)")
+
 plt.subplot(232)
-plt.scatter(summary.max_length, summary.max_mem)
+plt.scatter(summary["max_length(kB)"], summary["max_mem(MB)"])
 plt.xlabel("Maximum length of sequences (kB)")
 plt.ylabel("Peak memory (MB)")
+
 ax = fig.add_subplot(233, projection='3d')
-ax.scatter(summary.n_chains, summary.max_length, summary.max_mem)
-ax.set_xlabel("Number of sequences (x1000 seqs)")
+ax.scatter(summary.n_chains, summary["max_length(kB)"], summary["max_mem(MB)"])
+ax.set_xlabel("Number of sequences")
 ax.set_ylabel("Maximum length of sequences (kB)")
 ax.set_zlabel("Peak memory (MB)")
+
 plt.subplot(234)
-plt.scatter(summary.n_chains, summary.comp_time)
-plt.xlabel("Number of sequences (x1000 seqs)")
+plt.scatter(summary.n_chains, summary["comp_time(h)"])
+plt.xlabel("Number of sequences")
 plt.ylabel("Computation time (h)")
+
 plt.subplot(235)
-plt.scatter(summary.max_length, summary.comp_time)
+plt.scatter(summary["max_length(kB)"], summary["comp_time(h)"])
 plt.xlabel("Maximum length of sequences (kB)")
 plt.ylabel("Computation time (h)")
+
 ax = fig.add_subplot(236, projection='3d')
-ax.scatter(summary.n_chains, summary.max_length, summary.comp_time)
-ax.set_xlabel("Number of sequences (x1000 seqs)")
+ax.scatter(summary.n_chains, summary["max_length(kB)"], summary["comp_time(h)"])
+ax.set_xlabel("Number of sequences")
 ax.set_ylabel("Maximum length of sequences (kB)")
 ax.set_zlabel("Computation time (h)")
-plt.show()
-
-# ========================================================
-# Linear Regression of max_mem as function of max_length
-# ========================================================
 
-# With scikit-learn
-model = LinearRegression(normalize=True, n_jobs=-1)
-model.fit(np.array(summary.max_length).reshape(-1, 1), summary.max_mem)
-b0 = model.intercept_
-b1 = model.coef_[0]
-print(f"peak_mem = {b0:.0f} + {b1:.0f} * max_length")
-
-# with scipy
-coeffs = scipy.optimize.curve_fit(lambda t, B0, B1: B0+np.exp(B1*t),
-                                  np.array(summary.max_length[:-3]), np.array(summary.max_mem[:-3]))[0]
-print(f"peak_mem = {coeffs[0]:.0f} + e^({coeffs[1]:.0f} * max_length)")
-coeffs_log = scipy.optimize.curve_fit(lambda t, B0, B1: B0+B1*np.log(t),
-                                      np.array(summary.max_length), np.array(summary.max_mem), p0=(400, 12000))[0]
-print(
-    f"peak_mem = {coeffs_log[0]:.0f} + {coeffs_log[1]:.0f} * log(max_length)")
-
-# Re-plot
-x = np.linspace(0, 10, 1000)
-plt.figure()
-plt.scatter(summary.max_length, summary.max_mem)
-plt.xlabel("Maximum length of sequences (kB)")
-plt.ylabel("Peak memory (MB)")
-plt.plot(x, b0 + b1*x, "-r", label="linear fit")
-plt.plot(x, coeffs[0] + np.exp(coeffs[1]*x), "-g", label="expo fit on [:-3]")
-plt.plot(x, coeffs_log[0] + coeffs_log[1]*np.log(x), "-b", label="log fit")
-plt.ylim(0, 60000)
-plt.legend()
-plt.show()
-
-print("Estimated mem required to compute RF02543 and its 11kB sequences:",
-      model.predict(np.array([11]).reshape(-1, 1)))
-
-# ========================================================
-# Linear Regression of comp_time as function of n_chains
-# ========================================================
-
-# With scikit-learn
-model = LinearRegression(normalize=True, n_jobs=-1)
-model.fit(np.array(summary.n_chains).reshape(-1, 1), summary.comp_time)
-b0 = model.intercept_
-b1 = model.coef_[0]
-print(f"comp_time = {b0:.3f} + {b1:.3f} * n_chains")
-print("Estimated computation time required for RF02543 and its 38k sequences:",
-      model.predict(np.array([38]).reshape(-1, 1)))
-
-# Re-plot
-x = np.linspace(0, 500, 1000)
-plt.figure()
-plt.scatter(summary.n_chains, summary.comp_time)
-plt.xlabel("Number of sequences (x1000)")
-plt.ylabel("Computation time (h)")
-plt.plot(x, b0 + b1*x, "-r", label="linear fit")
-plt.ylim(0, 10)
-plt.legend()
-plt.show()
+plt.subplots_adjust(wspace=0.4)
+plt.savefig("results/realign_jobs_performance.png")
+
+# # ========================================================
+# # Linear Regression of max_mem as function of max_length
+# # ========================================================
+
+# # With scikit-learn
+# model = LinearRegression(normalize=True, n_jobs=-1)
+# model.fit(summary["max_length(kB)"].values.reshape(-1, 1), summary["max_mem(MB)"])
+# b0 = model.intercept_
+# b1 = model.coef_[0]
+# print(f"peak_mem = {b0:.0f} + {b1:.0f} * max_length")
+
+# # with scipy
+# coeffs = scipy.optimize.curve_fit(  lambda t, B0, B1: B0+np.exp(B1*t), 
+#                                     summary["max_length(kB)"].values, 
+#                                     summary["max_mem(MB)"].values
+#                                  )[0]
+# print(f"peak_mem = {coeffs[0]:.0f} + e^({coeffs[1]:.0f} * max_length)")
+# coeffs_log = scipy.optimize.curve_fit(  lambda t, B0, B1: B0+B1*np.log(t),
+#                                         summary["max_length(kB)"].values, 
+#                                         summary["max_mem(MB)"].values,
+#                                         p0=(400, 12000)
+#                                      )[0]
+# print(f"peak_mem = {coeffs_log[0]:.0f} + {coeffs_log[1]:.0f} * log(max_length)")
+
+# # Re-plot
+# x = np.linspace(0, 10, 1000)
+# plt.figure()
+# plt.scatter(summary["max_length(kB)"], summary["max_mem(MB)"])
+# plt.xlabel("Maximum length of sequences (kB)")
+# plt.ylabel("Peak memory (MB)")
+# plt.plot(x, b0 + b1*x, "-r", label="linear fit")
+# plt.plot(x, coeffs[0] + np.exp(coeffs[1]*x), "-g", label="expo fit")
+# plt.plot(x, coeffs_log[0] + coeffs_log[1]*np.log(x), "-b", label="log fit")
+# plt.legend()
+# plt.savefig("results/regression/memory_linear_model.png")
+
+# # ========================================================
+# # Linear Regression of comp_time as function of n_chains
+# # ========================================================
+
+# # With scikit-learn
+# model = LinearRegression(normalize=True, n_jobs=-1)
+# model.fit(summary.n_chains.values.reshape(-1, 1), summary["comp_time(h)"])
+# b0 = model.intercept_
+# b1 = model.coef_[0]
+# print(f"comp_time = {b0:.3f} + {b1:.3f} * n_chains")
+
+# # Re-plot
+# x = np.linspace(0, 500000, 1000)
+# plt.figure()
+# plt.scatter(summary.n_chains, summary["comp_time(h)"])
+# plt.xlabel("Number of sequences")
+# plt.ylabel("Computation time (h)")
+# plt.plot(x, b0 + b1*x, "-r", label="linear fit")
+# plt.legend()
+# plt.savefig("results/regression/comp_time_linear_model.png")

+,cWW,tSH,tWH,tHS,other,tWW,tSS,tHW,cSH,cSW,cSS,tSW,cWH,cWS,tWS,tHH,cHW,cHH,cHS
+RF00001,61.87%,4.31%,3.21%,1.98%,3.33%,0.42%,0.97%,2.64%,5.30%,5.61%,0.11%,4.14%,0.61%,3.04%,0.93%,0.53%,0.89%,<.01%,0.10%
+RF00002,62.36%,5.36%,2.71%,6.11%,1.72%,2.25%,1.23%,2.54%,1.87%,4.10%,0.63%,1.50%,1.14%,0.68%,0.57%,3.20%,1.38%,0.59%,0.05%
+RF00004,85.28%,3.30%,5.23%,0.96%,0.69%,0.14%,0 %,0 %,0.28%,0.28%,0 %,0.69%,0.55%,0 %,0 %,0 %,0.28%,0.28%,2.06%
+RF00005,70.47%,0.91%,6.92%,0.09%,1.74%,3.56%,0.08%,3.29%,0.53%,0.52%,0.22%,1.75%,1.24%,2.00%,2.31%,1.71%,0.65%,0.48%,1.53%
+RF00008,64.74%,4.62%,8.09%,2.89%,1.16%,0 %,0 %,0 %,1.16%,5.20%,0 %,1.16%,0.58%,4.05%,4.62%,1.73%,0 %,0 %,0 %
+RF00009,81.68%,0.58%,2.53%,0.58%,0.97%,0 %,0.39%,1.36%,1.17%,2.73%,0.97%,2.34%,0.58%,0.78%,0.78%,0 %,1.36%,0.39%,0.78%
+RF00010,69.24%,2.58%,4.60%,0.37%,3.31%,0.55%,1.29%,0.92%,2.03%,2.76%,2.39%,2.76%,0.18%,1.84%,1.66%,0.55%,2.21%,0 %,0.74%
+RF00011,64.71%,4.50%,4.50%,1.04%,3.46%,2.08%,2.42%,2.77%,3.11%,1.04%,1.38%,2.08%,2.08%,1.04%,1.04%,1.04%,1.73%,0 %,0 %
+RF00013,89.66%,3.45%,0 %,0 %,3.45%,3.45%,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %
+RF00015,86.76%,4.18%,0.70%,3.48%,0.70%,0 %,0 %,0 %,0.70%,0.35%,0 %,1.74%,0.35%,0 %,0 %,0.35%,0 %,0.70%,0 %
+RF00017,75.15%,2.90%,3.05%,0.76%,3.35%,2.74%,0.46%,1.68%,1.07%,0.30%,2.13%,2.59%,1.68%,0.30%,0 %,0 %,0.91%,0.91%,0 %
+RF00020,88.26%,0.73%,2.39%,0.37%,0.55%,0.73%,0 %,0 %,0.73%,1.10%,1.28%,1.10%,0.37%,1.28%,0 %,0 %,0.73%,0 %,0.37%
+RF00023,73.83%,1.87%,12.15%,0.93%,1.87%,0.93%,0 %,0.93%,0 %,1.87%,0 %,0 %,0 %,1.87%,3.74%,0 %,0 %,0 %,0 %
+RF00026,81.41%,3.66%,6.15%,1.17%,0.44%,1.17%,0 %,0 %,0.29%,0.44%,0.15%,1.02%,0.29%,0.29%,0.44%,0.15%,0.15%,0.29%,2.49%
+RF00028,65.73%,2.86%,2.64%,3.83%,2.16%,1.62%,2.91%,2.05%,3.12%,1.29%,1.94%,0.38%,1.67%,0.54%,1.45%,0.22%,4.58%,0.86%,0.16%
+RF00029,80.70%,6.14%,0 %,0 %,0 %,3.51%,0 %,3.51%,0 %,0.88%,0 %,0 %,0.88%,0.88%,0 %,0 %,0.88%,0 %,2.63%
+RF00032,100.00%,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %
+RF00037,100.00%,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %
+RF00050,68.39%,3.87%,7.74%,3.87%,2.26%,0.32%,5.48%,0 %,0 %,0 %,5.81%,0 %,0 %,0.32%,0 %,0 %,1.94%,0 %,0 %
+RF00059,60.28%,1.50%,4.97%,3.70%,2.54%,1.85%,5.31%,0 %,0 %,0 %,7.16%,4.97%,4.50%,0.35%,0.12%,1.85%,0.23%,0.69%,0 %
+RF00061,77.86%,3.05%,2.29%,2.29%,0 %,2.29%,0 %,1.53%,2.29%,0 %,0 %,0.76%,0.76%,2.29%,0 %,1.53%,2.29%,0 %,0.76%
+RF00080,84.19%,6.45%,0 %,0 %,2.26%,0 %,1.94%,0 %,4.19%,0 %,0 %,0.65%,0 %,0 %,0 %,0 %,0 %,0 %,0.32%
+RF00100,65.22%,0 %,4.35%,0 %,5.07%,0.72%,0 %,8.70%,0 %,0 %,0 %,2.90%,13.04%,0 %,0 %,0 %,0 %,0 %,0 %
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+RF00164,76.19%,4.76%,0 %,0 %,0 %,0 %,0 %,0 %,4.76%,4.76%,9.52%,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %
+RF00167,67.80%,0 %,7.77%,0.23%,2.51%,0 %,0 %,2.63%,2.22%,3.10%,2.63%,2.98%,0 %,5.14%,2.63%,0.29%,0.06%,0 %,0 %
+RF00168,76.92%,4.74%,1.95%,2.41%,0.45%,1.20%,1.20%,2.41%,3.23%,1.20%,0.68%,1.43%,0.98%,0 %,0 %,1.20%,0 %,0 %,0 %
+RF00169,70.92%,9.56%,3.19%,0.80%,4.78%,0 %,0.40%,9.16%,0 %,0 %,0 %,0 %,0.80%,0 %,0.40%,0 %,0 %,0 %,0 %
+RF00174,71.01%,2.90%,5.07%,4.35%,2.90%,0.72%,1.45%,2.17%,0 %,2.17%,2.90%,1.45%,0.72%,2.17%,0 %,0 %,0 %,0 %,0 %
+RF00177,63.05%,3.95%,4.48%,2.84%,3.20%,2.13%,2.18%,2.57%,2.50%,2.24%,2.00%,1.72%,2.02%,1.58%,1.44%,0.78%,0.70%,0.34%,0.29%
+RF00233,72.06%,1.47%,7.35%,2.94%,0 %,2.94%,0 %,0 %,4.41%,0 %,2.94%,1.47%,2.94%,0 %,0 %,0 %,1.47%,0 %,0 %
+RF00234,73.03%,1.96%,0.68%,0.64%,1.28%,1.96%,2.42%,5.29%,2.92%,0.59%,0.41%,7.07%,1.32%,0 %,0.23%,0 %,0.18%,0 %,0 %
+RF00250,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %
+RF00379,71.10%,6.46%,1.46%,7.07%,1.10%,0.12%,3.29%,0.24%,2.93%,1.46%,1.95%,1.59%,0.61%,0 %,0 %,0.12%,0.49%,0 %,0 %
+RF00380,64.46%,5.37%,1.24%,2.07%,6.20%,3.31%,2.89%,4.96%,2.48%,1.24%,2.07%,0 %,0 %,1.24%,1.24%,0 %,1.24%,0 %,0 %
+RF00382,50.00%,0 %,0 %,0 %,20.59%,0 %,0 %,0 %,0 %,0 %,0 %,2.94%,20.59%,0 %,0 %,0 %,0 %,5.88%,0 %
+RF00390,55.17%,0 %,0 %,0 %,6.90%,0 %,0 %,0 %,13.79%,6.90%,0 %,0 %,17.24%,0 %,0 %,0 %,0 %,0 %,0 %
+RF00442,56.52%,6.52%,6.52%,2.17%,8.70%,2.17%,2.17%,2.17%,0 %,4.35%,2.17%,0 %,4.35%,0 %,0 %,2.17%,0 %,0 %,0 %
+RF00458,70.22%,3.37%,5.06%,0 %,5.34%,1.97%,0 %,1.40%,1.97%,1.97%,0.28%,0.28%,2.81%,1.97%,0.84%,0.84%,0.56%,0.84%,0.28%
+RF00488,91.95%,0.20%,0 %,0.20%,0.80%,1.41%,0.10%,0.50%,0.91%,1.21%,0.10%,0.30%,0.70%,0.70%,0 %,0 %,0.30%,0.50%,0.10%
+RF00504,72.66%,3.88%,2.59%,7.77%,3.02%,0 %,2.45%,0.29%,2.59%,0 %,1.58%,0 %,0 %,0 %,0.14%,0.14%,2.88%,0 %,0 %
+RF00505,100.00%,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %
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+RF01725,71.25%,7.50%,0 %,0 %,1.25%,0 %,5.00%,0 %,5.00%,0 %,5.00%,2.50%,0 %,0 %,2.50%,0 %,0 %,0 %,0 %
+RF01734,75.76%,8.08%,0 %,0 %,0 %,5.05%,3.03%,5.05%,0 %,0 %,1.01%,2.02%,0 %,0 %,0 %,0 %,0 %,0 %,0 %
+RF01739,61.06%,3.54%,4.42%,3.54%,7.96%,3.54%,0 %,0 %,3.54%,1.77%,0 %,0 %,3.54%,0 %,0 %,3.54%,3.54%,0 %,0 %
+RF01750,79.22%,4.55%,0 %,3.90%,1.30%,0 %,0 %,1.30%,0 %,0 %,3.90%,0 %,1.30%,0 %,0 %,0 %,4.55%,0 %,0 %
+RF01763,42.70%,0.28%,5.23%,0 %,12.67%,3.58%,0 %,0 %,2.20%,0 %,3.03%,2.75%,20.94%,6.61%,0 %,0 %,0 %,0 %,0 %
+RF01786,76.39%,2.78%,5.56%,2.78%,1.39%,0 %,0 %,2.78%,5.56%,0 %,0 %,0 %,0 %,0 %,2.78%,0 %,0 %,0 %,0 %
+RF01807,74.12%,3.53%,2.35%,0 %,2.35%,4.71%,2.35%,1.18%,0 %,1.18%,0 %,1.18%,2.35%,1.18%,0 %,1.18%,0 %,0 %,2.35%
+RF01826,50.00%,0 %,8.33%,4.17%,4.17%,4.17%,4.17%,0 %,0 %,0 %,4.17%,0 %,20.83%,0 %,0 %,0 %,0 %,0 %,0 %
+RF01831,78.61%,1.19%,2.97%,1.98%,1.19%,0 %,3.56%,3.96%,1.78%,2.38%,0 %,0 %,0 %,0 %,2.38%,0 %,0 %,0 %,0 %
+RF01846,86.57%,3.14%,0.43%,1.71%,1.00%,0.57%,0.29%,1.43%,0.29%,1.14%,0 %,1.00%,0.43%,0.57%,0.29%,0.29%,0.86%,0 %,0 %
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+RF01854,68.87%,5.96%,4.64%,3.97%,3.97%,1.99%,2.65%,2.65%,0 %,0 %,1.99%,0 %,1.32%,0 %,0.66%,0 %,1.32%,0 %,0 %
+RF01857,71.35%,4.21%,2.81%,0 %,3.93%,2.25%,2.53%,5.34%,0 %,0.56%,1.97%,1.69%,0.56%,1.12%,1.69%,0 %,0 %,0 %,0 %
+RF01960,66.53%,3.35%,3.47%,2.51%,3.10%,2.23%,1.24%,2.17%,1.66%,2.49%,1.75%,1.64%,2.30%,1.38%,1.71%,0.42%,1.34%,0.49%,0.22%
+RF01998,56.65%,4.92%,4.37%,6.74%,3.10%,0.91%,7.10%,4.01%,2.73%,1.09%,0 %,0.36%,3.64%,0.36%,0 %,3.46%,0.55%,0 %,0 %
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+RF02012,76.03%,5.48%,0 %,4.11%,1.37%,0.68%,0 %,0 %,2.74%,0 %,0 %,0 %,1.37%,2.05%,0 %,0 %,4.11%,1.37%,0.68%
+RF02253,100.00%,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %
+RF02348,80.00%,5.00%,0 %,3.33%,0 %,0 %,0 %,1.67%,1.67%,3.33%,0 %,0 %,0 %,0 %,0 %,0 %,5.00%,0 %,0 %
+RF02519,66.67%,0 %,0 %,0 %,16.67%,0 %,8.33%,0 %,8.33%,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %
+RF02540,60.17%,5.14%,3.83%,3.92%,2.79%,2.53%,3.11%,1.90%,2.22%,1.96%,2.38%,2.25%,1.45%,1.79%,1.50%,1.94%,0.55%,0.28%,0.31%
+RF02541,62.00%,4.13%,3.68%,3.79%,2.68%,2.55%,2.84%,2.12%,2.25%,1.87%,2.18%,1.89%,1.71%,1.78%,1.53%,1.61%,0.65%,0.35%,0.38%
+RF02543,66.82%,3.48%,2.88%,3.00%,2.51%,2.52%,1.61%,2.09%,1.74%,2.13%,1.88%,1.84%,1.95%,1.51%,1.25%,1.41%,0.74%,0.36%,0.26%
+RF02545,65.43%,0.82%,4.12%,2.88%,1.23%,3.70%,1.65%,1.65%,2.47%,2.47%,1.23%,1.23%,0.82%,2.47%,3.70%,2.47%,0.82%,0.82%,0 %
+RF02546,82.61%,0 %,8.70%,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,0 %,4.35%,0 %,0 %,0 %,0 %,4.35%
+RF02553,73.68%,2.63%,7.89%,0 %,0 %,2.63%,0 %,0 %,2.63%,0 %,0 %,5.26%,0 %,2.63%,0 %,2.63%,0 %,0 %,0 %
+RF02680,88.89%,0 %,2.78%,0 %,2.78%,0 %,0 %,0 %,0 %,0 %,0 %,0 %,5.56%,0 %,0 %,0 %,0 %,0 %,0 %
+RF02683,80.56%,2.78%,0 %,5.56%,2.78%,0 %,0 %,0 %,0 %,0 %,0 %,0 %,2.78%,2.78%,0 %,2.78%,0 %,0 %,0 %
+RF02796,78.69%,4.92%,0 %,4.92%,4.92%,0 %,0 %,0 %,4.92%,0 %,0 %,1.64%,0 %,0 %,0 %,0 %,0 %,0 %,0 %
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