new script to prepare MOARNA basepair parameters

+#/usr/bin/python3
+import json
+import os
+import numpy as np
+
+runDir = os.getcwd()
+
+def get_best(i):
+    weights = [ float(x.strip("[]")) for x in i["weights"] ]
+    means = [ float(x.strip("[]")) for x in i["means"] ]
+    s = sorted(zip(weights, means), reverse=True)
+    return s[0][1]
+
+def get_k(lw, bp):
+    if lw == "cWW":
+        if bp in ["GC", "CG"]:
+            return 3.9
+        if bp in ["AU", "UA"]:
+            return 3.3
+        if bp in ["GU", "UG"]:
+            return 3.15
+        return 2.4
+    if lw == "tWW":
+        return 2.4
+    return 0.8
+
+if __name__ == "__main__":
+    print("processing HRNA jsons...")
+
+    lws = []
+    for c in "ct":
+        for nt1 in "WHS":
+            for nt2 in "WHS":
+                lws.append(c+nt1+nt2)
+
+    bps = []
+    for nt1 in "ACGU":
+        for nt2 in "ACGU":
+            bps.append(nt1+nt2)
+    
+    fullresults = dict()
+    fullresults["A"] = dict()
+    fullresults["C"] = dict()
+    fullresults["G"] = dict()
+    fullresults["U"] = dict()
+    counts = dict()
+    for lw in lws:
+        counts[lw] = 0
+    for bp in bps:
+        fullresults[bp[0]][bp[1]] = []
+
+        # open json file
+        with open(runDir + f"/results/geometry/json/hirerna_{bp}_basepairs.json", "rb") as f:
+            data = json.load(f)
+        
+        # consider each BP type
+        for lw in lws:
+            this = dict()
+
+            # gather params
+            distance = 0
+            a1 = 0
+            a2 = 0
+            for i in data:
+                if i["measure"] == f"Distance between {lw} {bp} tips":
+                    distance = np.round(get_best(i), 2)
+                if i["measure"] == f"{lw}_{bp}_alpha_1":
+                    a1 = np.round(np.pi/180.0*get_best(i), 2)
+                if i["measure"] == f"{lw}_{bp}_alpha_2":
+                    a2 = np.round(np.pi/180.0*get_best(i), 2)
+
+            if distance == 0 and a1 == 0 and a2 == 0:
+                # not found
+                continue
+            
+            counts[lw] += 1
+
+            # create entry
+            this["rho"] = distance
+            this["a1"] = a1
+            this["a2"] = a2
+            this["k"] = get_k(lw, bp)
+            this["canonical"] = 1.0 if lw=="cWW" and bp in ["GC", "CG", "GU", "UG", "AU", "UA"] else 0.0
+            this["LW"] = lw
+
+            # store entry
+            fullresults[bp[0]][bp[1]].append(this)
+
+    with open(runDir + "/results/geometry/json/hirerna_basepairs_processed.json", "w") as f:
+        json.dump(fullresults, f, indent=None)

@@ -2026,9 +2026,9 @@ def GMM_histo(data_ori, name_data, toric=False, hist=True, col=None, save=True)
         mean = means[i]
         mean = means[i]
         sigma = np.sqrt(covariances[i])
         sigma = np.sqrt(covariances[i])
         weight = weights[i]
         weight = weights[i]
-        summary_data["means"].append(str(mean))
-        summary_data["std"].append(str(sigma))
-        summary_data["weights"].append(str(weight))
+        summary_data["means"].append("{:.2f}".format(float(str(mean).strip("[]"))))
+        summary_data["std"].append("{:.2f}".format(float(str(sigma).strip("[]"))))
+        summary_data["weights"].append("{:.2f}".format(str(weight).strip("[]")))
 
 
         # compute the right x and y data to plot
         # compute the right x and y data to plot
         y = weight*st.norm.pdf(x, mean, sigma)
         y = weight*st.norm.pdf(x, mean, sigma)
@@ -2730,6 +2730,8 @@ def process_jobs(joblist):
         print("Something went wrong")
         print("Something went wrong")
 
 
 if __name__ == "__main__":
 if __name__ == "__main__":
+    merge_jsons()
+    exit(0)
     os.makedirs(runDir + "/results/figures/", exist_ok=True)
     os.makedirs(runDir + "/results/figures/", exist_ok=True)
 
 
     # parse options
     # parse options