Debugging - Real case use

CHANGELOG

@@ -6,6 +6,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.2.0] - 2021-06-04
+### Added
+- None 
+
+### Changed
+- Use pickle5 to save data
+
+### Removed
+- None
+
 ## [0.1.2] - 2020-10-29
 ### Added
 - add OrderedDict() function to keep input ordered independent to Python version 
@@ -149,4 +159,4 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - None
 
 ### Removed
-- None
+- None
\ No newline at end of file

main.py

@@ -3,13 +3,14 @@ from collections import OrderedDict
 import pandas as pd
 import tooba_f as tbf
 import tooba_gmx as tbgmx
+import gc
 ###################################################
 #NOTICE:    resids of head in each subdomain may differ in tail case
 #           keep all atoms of group in the first occurent subdomain
 #           in case of tail is the one closest to the head, hence
 #           the code is a good approximation 
 ###################################################
-SYSTEM_NAME='Case4_20190909_5'
+SYSTEM_NAME="20190322_2"
 DISCET=[3.5, 3.5, 3.5]
 NUM_FR=750
 TRAJ=SYSTEM_NAME+'/eq_traj.trr'
@@ -41,29 +42,41 @@ TPR=SYSTEM_NAME+'/eq_run.tpr'
 #
 ###################################################
 GROUPS=OrderedDict()
-GROUPS={'ALL':['index+ALL_ndx',['save', ['pkl'],'index'],'density',['save', ['pkl'],'dens']],
-        'HD_GROUP':['surf',['save', ['pkl', 'json'],'surf'],'index+HD_ndx',['save', ['pkl'],'index'],'rdf',['save', ['pkl'],'rdf']],
-        'TL_GROUP':['vector',['save', ['pkl'],'vec']],
-        'ORDER_NS_SPH':['index_order+ORDER_NS_SPH_ndx',['save', ['pkl'],'index'],'order',['save',['pkl'], 'order']],
-        'ORDER_NS_ACYL':['index_order+ORDER_NS_ACYL_ndx',['save', ['pkl'],'index'],'order',['save',['pkl'], 'order']],
-        'ORDER_FFA':['index_order+ORDER_FFA_ndx',['save', ['pkl'],'index'],'order',['save',['pkl'], 'order']],
-        'ORDER_CHOL':['index_order+ORDER_CHOL_ndx',['save', ['pkl'],'index'],'order',['save',['pkl'], 'order']],
-        'COMBINE':[['HD_GROUP','surf'],['TL_GROUP','vector'],['COMB','tilt'],['save', ['pkl'],'tilt']]
-}
+GROUPS['ALL']=['index+ALL_ndx',['save', ['pkl'],'index'],'density',['save', ['pkl'],'dens']]
+GROUPS['HD_GROUP']=['surf',['save', ['pkl'],'surf'],'index+HD_ndx',['save', ['pkl'],'index'],'rdf',['save', ['pkl'],'rdf']]
+GROUPS['TL_GROUP']=['vector',['save', ['pkl'],'vec']]
+GROUPS['ORDER_NS_SPH']=['index_order+ORDER_NS_SPH_ndx',['save', ['pkl'],'index'],'order',['save',['pkl'], 'order']]
+GROUPS['ORDER_NS_ACYL']=['index_order+ORDER_NS_ACYL_ndx',['save', ['pkl'],'index'],'order',['save',['pkl'], 'order']]
+GROUPS['ORDER_FFA']=['index_order+ORDER_FFA_ndx',['save', ['pkl'],'index'],'order',['save',['pkl'], 'order']]
+GROUPS['ORDER_CHOL']=['index_order+ORDER_CHOL_ndx',['save', ['pkl'],'index'],'order',['save',['pkl'], 'order']]
+GROUPS['COMBINE']=[['HD_GROUP','surf'],['TL_GROUP','vector'],['COMB','tilt'],['save', ['pkl'],'tilt']]
+
 ALL=OrderedDict()
-ALL={'NS':['C1', 'C2', 'C3', 'C4', 'C5','C6', 'Na', 'P4', 'P3', 'C7','C3', 'C4', 'C5', 'C8', 'C9', 'C10'], 'CHOL':['ROH','R1', 'R2', 'R3', 'R4', 'R5'], 'FFA':['AC','C1', 'C2', 'C3', 'C4']}
+ALL['NS']=['C1', 'C2', 'C3', 'C4', 'C5','C6', 'Na', 'P4', 'P3', 'C7','C3', 'C4', 'C5', 'C8', 'C9', 'C10']
+ALL['CHOL']=['ROH','R1', 'R2', 'R3', 'R4', 'R5']
+ALL['FFA']=['AC','C1', 'C2', 'C3', 'C4']
+
 HD_GROUP=OrderedDict()
-HD_GROUP={'NS':['C6', 'Na', 'P4', 'P3', 'C7'], 'CHOL':['ROH'], 'FFA':['AC']}
+HD_GROUP['NS']=['C6', 'Na', 'P4', 'P3', 'C7']
+HD_GROUP['CHOL']=['ROH']
+HD_GROUP['FFA']=['AC']
+
 TL_GROUP=OrderedDict()
-TL_GROUP={'NS':['C3', 'C4', 'C5', 'C8', 'C9', 'C10'], 'CHOL':['R1', 'R2', 'R3', 'R4', 'R5'], 'FFA':['C1', 'C2', 'C3', 'C4']}
+TL_GROUP['NS']=['C3', 'C4', 'C5', 'C8', 'C9', 'C10']
+TL_GROUP['CHOL']=['R1', 'R2', 'R3', 'R4', 'R5']
+TL_GROUP['FFA']=['C1', 'C2', 'C3', 'C4']
+
 ORDER_NS_SPH=OrderedDict()
-ORDER_NS_SPH={'NS':['C1', 'C2', 'C3', 'C4', 'C5']} #propable problem with the same atomname of NS, FFA
+ORDER_NS_SPH['NS']=['C1', 'C2', 'C3', 'C4', 'C5'] #propable problem with the same atomname of NS, FFA
+
 ORDER_NS_ACYL=OrderedDict()
-ORDER_NS_ACYL={'NS':['C8', 'C9', 'C10']}
+ORDER_NS_ACYL['NS']=['C8', 'C9', 'C10']
+
 ORDER_FFA=OrderedDict()
-ORDER_FFA={'FFA':['C1', 'C2', 'C3', 'C4']}
+ORDER_FFA['FFA']=['C1', 'C2', 'C3', 'C4']
+
 ORDER_CHOL=OrderedDict()
-ORDER_CHOL={'CHOL':['R2', 'R3', 'R4', 'R5']}
+ORDER_CHOL['CHOL']=['R2', 'R3', 'R4', 'R5']
 ###################################################
 ###################################################
 print(' ')
@@ -95,12 +108,15 @@ ST_FR=MAX_FR-NUM_FR
 ###################################################
 #--------------------------------------------------
 if os.path.isfile('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_data.pkl'):
+    print('_data.pkl exist !')
     pass
 else:
     #Read .trr file
     data_all=tbf.fr_export(trajfile=TRAJ,num_frames=NUM_FR)
+    #tbf.tojson(fl=data_all, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_data')
     tbf.topickle(fl=data_all, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_data')
     del data_all
+    gc.collect()
 ###################################################
 #--------------------------------------------------
 #Check save files if exist in order to skip functions
@@ -161,13 +177,17 @@ for i in GROUPS.keys():
             print('++++++++++++++++++++++++')
             group_ndx=tbf.atomid_data(res_num, res_type, atom_type, atom_num, group=locals()[i])
             tbf.topickle(fl=group_ndx, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_ndx')
+            #tbf.tojson(fl=group_ndx, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_ndx')
             del group_ndx
+            gc.collect()
 #--------------------------------------------------
         if os.path.isfile('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.pkl'):
             pass            
         else:
             data_all=tbf.frompickle('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_data.pkl')
+            #data_all=tbf.fromjson('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_data.json')
             group_ndx=tbf.frompickle('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_ndx.pkl')
+            #group_ndx=tbf.fromjson('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_ndx.json')
             #Create subdomains coordinates
             box_p=tbf.domain_decomposition(data=data_all,dx=DISCET[0],dy=DISCET[1],dz=DISCET[2])
             #Assign desired atoms (from above function) to subdomains
@@ -175,23 +195,31 @@ for i in GROUPS.keys():
             ##result2: {step:{res:{atom_type:{(subX,subYsubZ):[atom_num]}}}}
             #todo keep fixed the initial domain name and the molecules that are grouped for all the steps
             _,box_res=tbf.atom2group(data_all,box_p, group_ndx)
-            tbf.topickle(fl=box_res, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box')
             del data_all
             del group_ndx
             del box_p
+            gc.collect()
+            tbf.topickle(fl=box_res, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box')
+            #tbf.tojson(fl=box_res, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box')
             del box_res
+            gc.collect()
 ###################################################
         if os.path.isfile('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_FR'+str(NUM_FR)+'.pkl'):
             pass   
         else:
             #Creates dictionary with coordinates per subdomain for each frame
             data_all=tbf.frompickle('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_data.pkl')
+            #data_all=tbf.fromjson('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_data.json')
             box_res=tbf.frompickle('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.pkl')
+            #box_res=tbf.fromjson('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.json')
             _,coord_vector=tbf.sub_coord(box=box_res, data=data_all, res_num=res_num)
-            tbf.topickle(fl=coord_vector, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_FR'+str(NUM_FR))
             del data_all
             del box_res
+            gc.collect()
+            tbf.topickle(fl=coord_vector, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_FR'+str(NUM_FR))
+            #tbf.tojson(fl=coord_vector, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_FR'+str(NUM_FR))
             del coord_vector
+            gc.collect()
 ###################################################
         for j in GROUPS[i]:
             if len(j) > 1:
@@ -200,57 +228,73 @@ for i in GROUPS.keys():
                         surf={}
                     #Creates dictionary with c, normal per subdomain for each frame
                     coord_vector=tbf.frompickle('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_FR'+str(NUM_FR)+'.pkl')
+                    #coord_vector=tbf.fromjson('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_FR'+str(NUM_FR)+'.json')
                     surf[i]=tbf.coord2norm2cg(coord_vector,img=False)
                     del coord_vector
+                    gc.collect()
                     sv_data=surf[i]
                 elif j=='surf' and sv_index[i][j]['status']=='exist':
                     if j not in locals():
                         surf={}
                     surf[i]=tbf.frompickle(sv_index[i][j]['name'])
+                    #surf[i]=tbf.fromjson(sv_index[i][j]['name'])
 #--------------------------------------------------   
                 if j=='vector' and sv_index[i][j]['status']=='not exist':
                     if j not in locals():
                         vector={}
                     coord_vector=tbf.frompickle('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_FR'+str(NUM_FR)+'.pkl')
+                    #coord_vector=tbf.fromjson('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_FR'+str(NUM_FR)+'.json')
                     vector[i]=tbf.coord2vector(coord_vector)
                     del coord_vector
+                    gc.collect()
                     sv_data=vector[i]
                 elif j=='vector' and sv_index[i][j]['status']=='exist':
                     if j not in locals():
                         vector={}
                     vector[i]=tbf.frompickle(sv_index[i][j]['name'])
+                    #vector[i]=tbf.fromjson(sv_index[i][j]['name'])
 #--------------------------------------------------
 #ToDo: make more generic file with ndx files and ndx for order parameter
 #As for now the hash value is generic (system+domain coord), but needs to run for every input group
                 if j=='index' and sv_index[i][j]['status']=='not exist':
                     box_res=tbf.frompickle('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.pkl')
+                    #box_res=tbf.fromjson('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.json')
                     tbf.togmxndx(box_res, fld='./'+SYSTEM_NAME+'/'+ndx_fl[i][j], sv_name=SYSTEM_NAME+'_'+i)
                     del box_res
+                    gc.collect()
 
                     uniq_id=tbgmx.ndx_index(SYSTEM_NAME,ndx_fl[i][j])
                     sv_data=uniq_id
                 elif j=='index' and sv_index[i][j]['status']=='exist':
                     box_res=tbf.frompickle('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.pkl')
+                    #box_res=tbf.fromjson('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.json')
                     tbf.togmxndx(box_res, fld='./'+SYSTEM_NAME+'/'+ndx_fl[i][j], sv_name=SYSTEM_NAME+'_'+i)
                     del box_res
+                    gc.collect()
 
                     uniq_id=tbf.frompickle(sv_index[i][j]['name'])
+                    #uniq_id=tbf.fromjson(sv_index[i][j]['name'])
 #--------------------------------------------------
                 if j=='index_order' and sv_index[i][j]['status']=='not exist':
                     box_res=tbf.frompickle('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.pkl')
+                    #box_res=tbf.fromjson('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.json')
                     for mol, atoms in locals()[i].items():
                         tbgmx.order_ndx(box_res, fld='./'+SYSTEM_NAME+'/'+ndx_fl[i][j], atoms=atoms, sv_name=SYSTEM_NAME+'_'+i)
                     del box_res
+                    gc.collect()
 
                     uniq_id=tbgmx.ndx_index(SYSTEM_NAME,ndx_fl[i][j])
                     sv_data=uniq_id
                 elif j=='index_order' and sv_index[i][j]['status']=='exist':
                     box_res=tbf.frompickle('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.pkl')
+                    #box_res=tbf.fromjson('./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_box.json')
                     for mol, atoms in locals()[i].items():
                         tbgmx.order_ndx(box_res, fld='./'+SYSTEM_NAME+'/'+ndx_fl[i][j], atoms=atoms, sv_name=SYSTEM_NAME+'_'+i)
                     del box_res
+                    gc.collect()
 
                     uniq_id=tbf.frompickle(sv_index[i][j]['name'])
+                    #uniq_id=tbf.fromjson(sv_index[i][j]['name'])
 #--------------------------------------------------
                 if j=='density' and sv_index[i][j]['status']=='not exist':
                     dens_dict={}
@@ -277,10 +321,13 @@ for i in GROUPS.keys():
                         sv_data=dens_dict
                         mrg_data[i][j]=[dens_dict,[]]
                     del dens_dict
+                    gc.collect()
                 elif j=='density' and sv_index[i][j]['status']=='exist':
                     dens_dict=tbf.frompickle(sv_index[i][j]['name'])
+                    #dens_dict=tbf.fromjson(sv_index[i][j]['name'])
                     mrg_data[i][j]=[dens_dict,[]]
                     del dens_dict
+                    gc.collect()
 #--------------------------------------------------
                 if j=='rdf' and sv_index[i][j]['status']=='not exist':
                     rdf_dict={}
@@ -301,17 +348,22 @@ for i in GROUPS.keys():
                                 cnt2=cnt2+1
                                 peaks = tbgmx.rdf_peaks(TRR=TRAJ,TPR=TPR,IND=fl,ST=time_index[ST_FR],EN=-1,fld='./'+uniq_id[iidd]['system'],arg1=cnt1,arg2=cnt2,dist_pk=20)
                             rdf_nm=mol1+'-'+mol2+'_rdf_'+uniq_id[iidd]['fld']
-                            if len(peaks)==0:
-                                peaks.append(0)
-                           
+                            #if len(peaks)==0:
+                             #   print(peaks)
+                              #  print(type(peaks))
+                               # peaks=np.ndarray((0))
+                                
                             rdf_dict[iidd][rdf_nm]=peaks
                         sv_data=rdf_dict
                         mrg_data[i][j]=[rdf_dict,[]]
                     del rdf_dict
+                    gc.collect()
                 elif j=='rdf' and sv_index[i][j]['status']=='exist':
                     rdf_dict=tbf.frompickle(sv_index[i][j]['name'])
+                    #rdf_dict=tbf.fromjson(sv_index[i][j]['name'])
                     mrg_data[i][j]=[rdf_dict,[]]
                     del rdf_dict
+                    gc.collect()
 #--------------------------------------------------
                 if j=='order' and sv_index[i][j]['status']=='not exist':
                     order_dict={}
@@ -335,8 +387,10 @@ for i in GROUPS.keys():
                         sv_data=order_dict
                         mrg_data[i][j]=[order_dict,[]]
                     del order_dict
+                    gc.collect()
                 elif j=='order' and sv_index[i][j]['status']=='exist':
                     order_dict=tbf.frompickle(sv_index[i][j]['name'])
+                    #order_dict=tbf.fromjson(sv_index[i][j]['name']) 
                     mrg_data[i][j]=[order_dict,[]]
                     del order_dict
 #--------------------------------------------------
@@ -354,6 +408,7 @@ for i in GROUPS.keys():
                             if k=='json':    
                                 tbf.tojson(fl=sv_data, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_'+j[2])
                         del sv_data
+                        gc.collect()
 ###################################################
 #COMBINE section
     else:
@@ -373,6 +428,7 @@ for i in GROUPS.keys():
                     #ToDo: check  "if str(value['domain']).strip() == str(sub).strip():"
                     del surf_inuse
                     del vector_inuse
+                    gc.collect()
                     #Loop over timesteps and keep avgs tilts for each step
                     avg={}
                     for step in tilt.keys():
@@ -399,10 +455,13 @@ for i in GROUPS.keys():
                     sv_data=tot_avg
                     mrg_data[i][j[1]]=[tot_avg,['Tilt[degrees]']]
                     del tot_avg
+                    gc.collect()
                 elif j[1]=='tilt' and sv_index[i][str(j)]['status']=='exist':
                     tot_avg=tbf.frompickle(sv_index[i][str(j)]['name'])
+                    #tot_avg=tbf.fromjson(sv_index[i][str(j)]['name'])
                     mrg_data[i][j[1]]=[tot_avg,['Tilt[degrees]']]
                     del tot_avg
+                    gc.collect()
 #--------------------------------------------------
             # Save module
             if len(j)==3:
@@ -417,10 +476,12 @@ for i in GROUPS.keys():
                                 tbf.topickle(fl=sv_data, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_'+j[2])
                             if k=='json':    
                                 tbf.tojson(fl=sv_data, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_'+i+'_'+j[2])   
-                        del sv_data          
+                        del sv_data
+                        gc.collect()          
 ###################################################
 #Merge data
 tbf.topickle(fl=mrg_data, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_merge')
+#tbf.tojson(fl=mrg_data, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_merge')
 print(' ')
 print('Merging data of:')
 print('==============================')
@@ -436,6 +497,7 @@ for grp in mrg_data.keys():
             data_df=df.copy()
             continue
 tbf.topickle(fl=data_df, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_dataset')
+#tbf.tojson(fl=data_df, sv_name='./'+SYSTEM_NAME+'/'+SYSTEM_NAME+'_dataset')
 print(data_df.head())
 ###################################################
-###################################################
+###################################################
\ No newline at end of file

tooba_f.py

@@ -3,11 +3,12 @@ import numpy as np
 #from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.pyplot as plt
 import scipy.optimize
-import pickle as pkl
+import pickle5 as pkl
 import json
 import re
 import pandas as pd
 from progress.bar import Bar
+import joblib
 
 from pytrr import (
     read_trr_header,
@@ -66,15 +67,47 @@ def center_gravity(a):
     cg = np.sum(a)/m
     return cg
 
+class StreamFile(object):
+    def __init__(self, f):
+        self.f = f
+    def __getattr__(self, item):
+        return getattr(self.f, item)
+    def read(self, n):
+        # print("reading total_bytes=%s" % n, flush=True)
+        if n >= (1 << 31):
+            buffer = bytearray(n)
+            idx = 0
+            while idx < n:
+                batch_size = min(n - idx, 1 << 31 - 1)
+                # print("reading bytes [%s,%s)..." % (idx, idx + batch_size), end="", flush=True)
+                buffer[idx:idx + batch_size] = self.f.read(batch_size)
+                # print("done.", flush=True)
+                idx += batch_size
+            return buffer
+        return self.f.read(n)
+    def write(self, buffer):
+        n = len(buffer)
+        print("writing total_bytes=%s..." % n, flush=True)
+        idx = 0
+        while idx < n:
+            batch_size = min(n - idx, 1 << 31 - 1)
+            print("writing bytes [%s, %s)... " % (idx, idx + batch_size), end="", flush=True)
+            self.f.write(buffer[idx:idx + batch_size])
+            print("done.", flush=True)
+            idx += batch_size
+
+
+
 def topickle(fl, sv_name):
     print(' ')
     print('Save to pickle |################################| 1/1')
     with open(sv_name+'.pkl', 'wb') as handle:
         pkl.dump(fl, handle, protocol=pkl.HIGHEST_PROTOCOL)
-
+        #joblib.dump(fl, handle)
 def frompickle(fl):
     with open(fl, 'rb') as handle:
         b = pkl.load(handle)
+        #b = joblib.load(handle)
     return b
 
 
@@ -83,6 +116,14 @@ def tojson(fl, sv_name):
     print('Save to json |################################| 1/1')
     with open(sv_name+'.json', 'w') as file:
         file.write(json.dumps(str(fl)))
+        #file.write(json.dumps(fl))
+def fromjson(fl):
+    print(' ')
+    print('Load to json |################################| 1/1')
+    with open(fl, 'r') as file:
+        data = file.read()
+        b = json.dumps(data)
+    return b
 
 def plot_surf(data, normal, c, save_name):
     #Plot surface
@@ -866,4 +907,4 @@ def togmxndx(box_res, fld, sv_name):
     bar.finish()
 
 def dict2pd(d, col=[]):
-    return pd.DataFrame.from_dict(d, orient='index', columns=col)
+    return pd.DataFrame.from_dict(d, orient='index', columns=col)
\ No newline at end of file

tooba_gmx.py

@@ -97,13 +97,18 @@ def rdf_peaks(TRR,TPR,IND,ST,EN,fld,arg1,arg2,dist_pk):
     p.communicate(cmd.encode('UTF-8'))
     p.send_signal('<Ctrl>-D')
     p.wait()
-
-    x,y=read_xvg(XVG=fld+'/tmp.xvg')
-    yhat = savgol_filter(y, 15, 4) # window size 15, polynomial order 4
-    peaks, _ = find_peaks(yhat, distance=dist_pk)
-
-    pathname = os.path.abspath(os.path.join(fld, 'tmp.xvg'))
-    os.remove(pathname)
+    try:
+      f = open(fld+'/tmp.xvg')
+      f.close()
+    # Do something with the file
+      x,y=read_xvg(XVG=fld+'/tmp.xvg')
+      yhat = savgol_filter(y, 15, 4) # window size 15, polynomial order 4
+      peaks, _ = find_peaks(yhat, distance=dist_pk)
+
+      pathname = os.path.abspath(os.path.join(fld, 'tmp.xvg'))
+      os.remove(pathname)
+    except IOError:
+      peaks=np.ndarray((0))
 
     return peaks
 
@@ -190,4 +195,4 @@ def order(TRR,TPR,IND,ST,EN,normal,fld,dist_pk=1):
     pathname = os.path.abspath(os.path.join(fld, 'tmp2.xvg'))
     os.remove(pathname)
 
-    return y
+    return y
\ No newline at end of file