diff --git a/mrdna/readers/.DS_Store b/mrdna/readers/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..ca5d0e6e71945129431efcf086d0e96e397046d8
Binary files /dev/null and b/mrdna/readers/.DS_Store differ
diff --git a/mrdna/readers/segmentmodel_from_cadnano.py b/mrdna/readers/segmentmodel_from_cadnano.py
index 23b78e9dbbf1c46a3d9f93985d80b4bb528d96f1..7021722e887437e5cdae7c3004e405fe0a47530d 100644
--- a/mrdna/readers/segmentmodel_from_cadnano.py
+++ b/mrdna/readers/segmentmodel_from_cadnano.py
@@ -204,7 +204,7 @@ def gen_prop_table(part):
return nt_prop
-def mrdna_model_from_cadnano(json_file,seq=None,return_nt=True,**model_parameters):
+def mrdna_model_from_cadnano(json_file,seq=None,**model_parameters):
part=read_json_file(json_file)
nt_prop=gen_prop_table(part)
@@ -222,7 +222,5 @@ def mrdna_model_from_cadnano(json_file,seq=None,return_nt=True,**model_parameter
stack=np.array((list(nt_prop["stack"])))
orientation=np.array(list(nt_prop["orientation"]))
model = model_from_basepair_stack_3prime( r, bp, stack, three_prime, seq, orientation, **model_parameters )
- if return_nt==True:
- return model,nt_prop
- else:
- return model
+ model._dataframe=nt_prop
+ return model
diff --git a/mrdna/readers/segmentmodel_from_oxdna_pinyi.py b/mrdna/readers/segmentmodel_from_oxdna_pinyi.py
index ebf52f2e2e8fb5831f3dfa271fd4a32fcc7568de..564065882e7bcc745f4c93a5b9a2a24789bba3dc 100644
--- a/mrdna/readers/segmentmodel_from_oxdna_pinyi.py
+++ b/mrdna/readers/segmentmodel_from_oxdna_pinyi.py
@@ -198,10 +198,8 @@ def mrdna_model_from_oxdna(coordinate_file, topology_file,virt2nuc=None,get_nt_p
simulate( model, output_name='test', directory='test4' )
"""
- if get_nt_prop is True:
- return nt_prop,model
- else:
- return model
+ model._dataframe=nt_prop
+ return model
if __name__ == "__main__":
mrdna_model_from_oxdna("0-from-collab/nanopore.oxdna","0-from-collab/nanopore.top")
diff --git a/mrdna/readers/segmentmodel_from_scadnano.py b/mrdna/readers/segmentmodel_from_scadnano.py
new file mode 100644
index 0000000000000000000000000000000000000000..71c70b81cbae68978857b6c88ca05ac6ad2d4265
--- /dev/null
+++ b/mrdna/readers/segmentmodel_from_scadnano.py
@@ -0,0 +1,145 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import os,sys
+from glob import glob
+import re
+import pandas as pd
+pd.options.mode.chained_assignment = None # default='warn'
+from .segmentmodel_from_lists import model_from_basepair_stack_3prime
+import scadnano as sc
+from ..arbdmodel.coords import readArbdCoords, readAvgArbdCoords, rotationAboutAxis
+from ..segmentmodel import SegmentModel, SingleStrandedSegment, DoubleStrandedSegment
+from ..model.dna_sequence import m13 as m13seq
+import json
+import re
+import pdb
+
+
+
+## TODO: separate SegmentModel from ArbdModel so multiple parts can be combined
+## TODO: catch circular strands in "get_5prime" cadnano calls
+## TODO: handle special motifs
+## - doubly-nicked helices
+## - helices that should be stacked across an empty region (crossovers from and end in the helix to another end in the helix)
+## - circular constructs
+def gen_id_series(domain,design):
+ df=pd.DataFrame(columns=["vh","zid","fwd","r"],index=range(domain.dna_length()),dtype=object)
+ df["loopout"]=False
+ hid=domain.helix
+ df["vh"]=hid
+ fwd=domain.forward
+ df["fwd"]=fwd
+ x=design.helices[domain.helix].calculate_position(design.grid,design.geometry).x*10
+ y=design.helices[domain.helix].calculate_position(design.grid,design.geometry).y*10
+ start=domain.start
+ end=domain.end
+ dels=domain.deletions
+ loops=domain.insertions
+ zids=[i for i in range(start,end)]
+ if len(dels)>0:
+ for d in dels:
+ zids.pop(zids.index(d))
+ if len(loops)>0:
+ for ind,ins in loops:
+ n=zids.index(ind)
+ zids.pop(n)
+ loop=[ind+round((j/(ins+1))*100)/100 for j in range(ins+1)]
+ loop.reverse()
+ for j in loop:
+ zids.insert(n,j)
+ df["zid"]=zids
+ if fwd==True:
+ start5=domain.start
+ end3=domain.end
+ df["zid"]=zids
+ else:
+ start5=domain.end
+ end3=domain.start
+ df["zid"]=zids[::-1]
+ df["x"]=x
+ df["y"]=y
+ df["z"]=df["zid"]*3.4
+ df["orientation_angle"]=[design.helices[hid].backbone_angle_at_offset(float(i),fwd,design.geometry) for i in df["zid"]]
+ df["stacked"]=[True]*(domain.dna_length()-1)+[False]
+ return [pd.Series(df.loc[i]) for i in df.index],(x,y,list(df["z"])[-1])
+
+
+def gen_strand(strand,design,group_index=0):
+ domain_list=[]
+ for do in strand.domains:
+
+ if type(do) is sc.scadnano.Domain:
+ L,(x,y,z)=gen_id_series(do,design)
+ if len(domain_list)>2:
+ if type(domain_list[-1]) is pd.DataFrame:
+ loop=domain_list.pop(-1)
+ inds=loop.index
+ loop["x"]=lx+(x-lx)*(loop.index+1)/(len(loop.index)+2)
+ loop["y"]=ly+(y-ly)*(loop.index+1)/(len(loop.index)+2)
+ loop["z"]=lz+(L[0]["z"]-lz)*(loop.index+1)/(len(loop.index)+2)
+ loop["orientation_angle"]=0
+ L=[pd.Series(loop.loc[i]) for i in loop.index]+L
+ domain_list=domain_list+L
+ elif type(do) is sc.scadnano.Loopout:
+ n=pd.DataFrame(columns=["loopout","x","y","z"],index=range(do.dna_length()),dtype=object)
+ n["loopout"]=True
+ n["stacked"]=False
+ lx,ly,lz=(x,y,z)
+ loop_out_length=do.dna_length()
+ domain_list.append(n)
+
+ strand_df=pd.DataFrame(domain_list).reset_index(drop=True)
+ strand_df["threeprime"]=list(strand_df.index[1:]+group_index)+[-1]
+ stack=np.where(strand_df["stacked"],strand_df["threeprime"],-1)
+ strand_df["stack"]=stack
+ if len(strand.dna_sequence)==len(strand_df.index):
+ strand_df["seq"]=list(strand.dna_sequence)
+ else:
+ strand_df["seq"]=-1
+ strand_df["index"]=strand_df.index+group_index
+ #strand_df["seg_index"]=index
+ return strand_df
+
+def gen_prop_table(fname):
+ sc_design=sc.Design.from_scadnano_file(fname)
+ nt_list=[]
+ group_index=0
+ for i in range(len(sc_design.strands)):
+ dfi=gen_strand(sc_design.strands[i],sc_design,group_index)
+ group_index+=len(dfi.index)
+ nt_list.append(dfi)
+
+ nt_prop=pd.concat(nt_list)
+ nt_prop.set_index("index",inplace=True)
+ ## Generate bps
+ bp_map={}
+ for i in nt_prop.index:
+ if nt_prop["loopout"][i]==False:
+ bp_map[(nt_prop["vh"][i],nt_prop["zid"][i],nt_prop["fwd"][i])]=i
+ ind_tuple=list(zip(nt_prop.index,nt_prop["vh"],nt_prop["zid"],nt_prop["fwd"]))
+ bp=-np.ones(len(nt_prop.index),dtype=int)
+ for i,j,k,l in ind_tuple:
+ try:
+ bp[i]=bp_map[(j,k,not(l))]
+ except:
+ continue
+ nt_prop["bp"]=bp
+ nt_prop["r"]=list(np.array([nt_prop["x"],nt_prop["y"],nt_prop["z"]],dtype="<f4").T)
+ return nt_prop
+
+def mrdna_model_from_scadnano(sc_file,seq=None,**model_parameters):
+
+ nt_prop=gen_prop_table(sc_file)
+
+ r=np.array(list(nt_prop['r']))
+ bp=np.array(list(nt_prop['bp']))
+ three_prime=np.array((list(nt_prop["threeprime"])))
+ stack=np.array((list(nt_prop["stack"])))
+ if nt_prop["seq"][0]==-1:
+ seq=None
+ else:
+ seq=nt_prop["seq"]
+ orientation=[rotationAboutAxis(np.array((0,0,1)),i) for i in list(nt_prop["orientation_angle"])]
+ model = model_from_basepair_stack_3prime( r, bp, stack, three_prime, seq, orientation, **model_parameters )
+ model._dataframe=nt_prop
+ return model
diff --git a/mrdna/readers/test/loopout.csv b/mrdna/readers/test/loopout.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ac096e7ca80778dd8fd5cc5889e5c17bf26f5edb
--- /dev/null
+++ b/mrdna/readers/test/loopout.csv
@@ -0,0 +1,456 @@
+index,vh,zid,fwd,r,loopout,x,y,z,orientation_angle,stacked,threeprime,stack,seq,bp
+0,5,22.0,False,[441.67294 555. 74.8 ],False,441.6729559300637,555.0,74.8,184.28571428571422,True,1,1,-1,293
+1,5,21.0,False,[441.67294 555. 71.4 ],False,441.6729559300637,555.0,71.39999999999999,150.0,True,2,2,-1,292
+2,5,20.0,False,[441.67294 555. 68. ],False,441.6729559300637,555.0,68.0,115.71428571428567,True,3,3,-1,291
+3,5,19.0,False,[441.67294 555. 64.6 ],False,441.6729559300637,555.0,64.6,81.42857142857144,True,4,4,-1,290
+4,5,18.0,False,[441.67294 555. 61.2 ],False,441.6729559300637,555.0,61.199999999999996,47.14285714285711,True,5,5,-1,289
+5,5,17.0,False,[441.67294 555. 57.8 ],False,441.6729559300637,555.0,57.8,12.85714285714289,True,6,6,-1,288
+6,5,16.0,False,[441.67294 555. 54.4 ],False,441.6729559300637,555.0,54.4,338.57142857142856,True,7,7,-1,287
+7,5,15.0,False,[441.67294 555. 51. ],False,441.6729559300637,555.0,51.0,304.2857142857142,True,8,8,-1,286
+8,5,14.0,False,[441.67294 555. 47.6 ],False,441.6729559300637,555.0,47.6,270.0,True,9,9,-1,285
+9,5,13.0,False,[441.67294 555. 44.2 ],False,441.6729559300637,555.0,44.199999999999996,235.71428571428578,True,10,10,-1,284
+10,5,12.0,False,[441.67294 555. 40.8 ],False,441.6729559300637,555.0,40.8,201.42857142857144,True,11,11,-1,283
+11,5,11.0,False,[441.67294 555. 37.4 ],False,441.6729559300637,555.0,37.4,167.1428571428571,True,12,12,-1,282
+12,5,10.0,False,[441.67294 555. 34. ],False,441.6729559300637,555.0,34.0,132.85714285714283,True,13,13,-1,281
+13,5,9.0,False,[441.67294 555. 30.6 ],False,441.6729559300637,555.0,30.599999999999998,98.57142857142856,False,14,-1,-1,280
+14,4,9.0,True,[441.67294 585. 30.6 ],False,441.6729559300637,585.0,30.599999999999998,308.57142857142856,True,15,15,-1,414
+15,4,10.0,True,[441.67294 585. 34. ],False,441.6729559300637,585.0,34.0,342.85714285714283,True,16,16,-1,413
+16,4,11.0,True,[441.67294 585. 37.4 ],False,441.6729559300637,585.0,37.4,17.14285714285711,True,17,17,-1,412
+17,4,12.0,True,[441.67294 585. 40.8 ],False,441.6729559300637,585.0,40.8,51.428571428571445,True,18,18,-1,411
+18,4,13.0,True,[441.67294 585. 44.2 ],False,441.6729559300637,585.0,44.199999999999996,85.71428571428572,True,19,19,-1,410
+19,4,14.0,True,[441.67294 585. 47.6 ],False,441.6729559300637,585.0,47.6,120.0,True,20,20,-1,409
+20,4,15.0,True,[441.67294 585. 51. ],False,441.6729559300637,585.0,51.0,154.28571428571422,False,21,-1,-1,408
+21,3,15.0,False,[415.6922 600. 51. ],False,415.69219381653056,600.0,51.0,304.2857142857142,True,22,22,-1,397
+22,3,14.0,False,[415.6922 600. 47.6 ],False,415.69219381653056,600.0,47.6,270.0,True,23,23,-1,396
+23,3,13.0,False,[415.6922 600. 44.2 ],False,415.69219381653056,600.0,44.199999999999996,235.71428571428578,True,24,24,-1,395
+24,3,12.0,False,[415.6922 600. 40.8 ],False,415.69219381653056,600.0,40.8,201.42857142857144,True,25,25,-1,394
+25,3,11.0,False,[415.6922 600. 37.4 ],False,415.69219381653056,600.0,37.4,167.1428571428571,True,26,26,-1,393
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+41,2,8.0,True,[389.71143 585. 27.2 ],False,389.71143170299734,585.0,27.2,274.2857142857143,True,42,42,-1,368
+42,2,9.0,True,[389.71143 585. 30.6 ],False,389.71143170299734,585.0,30.599999999999998,308.57142857142856,True,43,43,-1,367
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+44,2,12.0,True,[389.71143 585. 40.8 ],False,389.71143170299734,585.0,40.8,51.428571428571445,True,45,45,-1,365
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+47,2,15.0,True,[389.71143 585. 51. ],False,389.71143170299734,585.0,51.0,154.28571428571422,True,48,48,-1,362
+48,2,16.0,True,[389.71143 585. 54.4 ],False,389.71143170299734,585.0,54.4,188.57142857142856,True,49,49,-1,361
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+50,2,18.0,True,[389.71143 585. 61.2 ],False,389.71143170299734,585.0,61.199999999999996,257.1428571428571,False,51,-1,-1,359
+51,1,18.0,False,[389.71143 555. 61.2 ],False,389.71143170299734,555.0,61.199999999999996,47.14285714285711,True,52,52,-1,227
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+58,1,11.0,False,[389.71143 555. 37.4 ],False,389.71143170299734,555.0,37.4,167.1428571428571,True,59,59,-1,220
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+60,1,10.0,False,[389.71143 555. 34. ],False,389.71143170299734,555.0,34.0,132.85714285714283,True,61,61,-1,218
+61,1,9.0,False,[389.71143 555. 30.6 ],False,389.71143170299734,555.0,30.599999999999998,98.57142857142856,True,62,62,-1,217
+62,1,8.0,False,[389.71143 555. 27.2 ],False,389.71143170299734,555.0,27.2,64.28571428571428,True,63,63,-1,216
+63,1,7.0,False,[389.71143 555. 23.8 ],False,389.71143170299734,555.0,23.8,30.0,True,64,64,-1,215
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+65,1,5.0,False,[389.71143 555. 17. ],False,389.71143170299734,555.0,17.0,321.42857142857144,False,66,-1,-1,213
+66,0,5.0,True,[415.6922 540. 17. ],False,415.69219381653056,540.0,17.0,171.42857142857142,True,67,67,-1,255
+67,0,6.0,True,[415.6922 540. 20.4 ],False,415.69219381653056,540.0,20.4,205.71428571428572,True,68,68,-1,254
+68,0,7.0,True,[415.6922 540. 23.8 ],False,415.69219381653056,540.0,23.8,240.0,True,69,69,-1,253
+69,0,8.0,True,[415.6922 540. 27.2 ],False,415.69219381653056,540.0,27.2,274.2857142857143,True,70,70,-1,252
+70,0,9.0,True,[415.6922 540. 30.6 ],False,415.69219381653056,540.0,30.599999999999998,308.57142857142856,True,71,71,-1,251
+71,0,10.0,True,[415.6922 540. 34. ],False,415.69219381653056,540.0,34.0,342.85714285714283,True,72,72,-1,250
+72,0,11.0,True,[415.6922 540. 37.4 ],False,415.69219381653056,540.0,37.4,17.14285714285711,True,73,73,-1,249
+73,0,12.0,True,[415.6922 540. 40.8 ],False,415.69219381653056,540.0,40.8,51.428571428571445,True,74,74,-1,248
+74,0,13.0,True,[415.6922 540. 44.2 ],False,415.69219381653056,540.0,44.199999999999996,85.71428571428572,True,75,75,-1,247
+75,0,13.09,True,[415.6922 540. 44.506 ],False,415.69219381653056,540.0,44.506,88.79999999999995,True,76,76,-1,246
+76,0,13.18,True,[415.6922 540. 44.812 ],False,415.69219381653056,540.0,44.812,91.88571428571424,True,77,77,-1,245
+77,0,13.27,True,[415.6922 540. 45.118 ],False,415.69219381653056,540.0,45.117999999999995,94.97142857142853,True,78,78,-1,244
+78,0,13.36,True,[415.6922 540. 45.424 ],False,415.69219381653056,540.0,45.424,98.05714285714282,True,79,79,-1,243
+79,0,13.45,True,[415.6922 540. 45.73 ],False,415.69219381653056,540.0,45.73,101.14285714285711,True,80,80,-1,242
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diff --git a/mrdna/readers/test/sc.csv b/mrdna/readers/test/sc.csv
new file mode 100644
index 0000000000000000000000000000000000000000..77ee3c5d241f58c125276905321d8a2517aea065
--- /dev/null
+++ b/mrdna/readers/test/sc.csv
@@ -0,0 +1,70 @@
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+68,1.0,24,False,False,False,G,0,-1,-1
diff --git a/mrdna/readers/test/test.csv b/mrdna/readers/test/test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c59ee2f1285a69e8766e333d63a4125ad767d5ea
--- /dev/null
+++ b/mrdna/readers/test/test.csv
@@ -0,0 +1,70 @@
+,vh,zid,fwd,offset_angle,stacked,seq,seg_index,threeprime,stack
+0,1.0,23.0,False,,True,A,0,1,1
+1,1.0,22.0,False,,True,A,0,2,2
+2,1.0,21.0,False,,True,C,0,3,3
+3,1.0,19.0,False,,True,G,0,4,4
+4,1.0,18.0,False,,True,T,0,5,5
+5,1.0,17.0,False,,True,A,0,6,6
+6,1.0,16.0,False,,True,A,0,7,7
+7,1.0,15.0,False,,True,C,0,8,8
+8,1.0,14.0,False,,True,G,0,9,9
+9,1.0,13.0,False,,True,T,0,10,10
+10,1.0,12.0,False,,True,A,0,11,11
+11,1.0,11.0,False,,True,A,0,12,12
+12,1.0,10.0,False,,True,C,0,13,13
+13,1.0,9.0,False,,True,G,0,14,14
+14,1.0,8.0,False,,False,T,0,15,-1
+15,0.0,8.0,True,,True,A,0,16,16
+16,0.0,9.0,True,,True,A,0,17,17
+17,0.0,10.0,True,,True,C,0,18,18
+18,0.0,11.0,True,,True,G,0,19,19
+19,0.0,12.0,True,,True,T,0,20,20
+20,0.0,13.0,True,,True,A,0,21,21
+21,0.0,14.0,True,,True,A,0,22,22
+22,0.0,14.5,True,,True,C,0,23,23
+23,0.0,15.0,True,,True,G,0,24,24
+24,0.0,16.0,True,,True,T,0,25,25
+25,0.0,17.0,True,,True,A,0,26,26
+26,0.0,18.0,True,,True,A,0,27,27
+27,0.0,19.0,True,,True,C,0,28,28
+28,0.0,20.0,True,,True,G,0,29,29
+29,0.0,21.0,True,,True,T,0,30,30
+30,0.0,22.0,True,,True,A,0,31,31
+31,0.0,23.0,True,,True,A,0,32,32
+32,0.0,24.0,True,,True,C,0,33,33
+33,0.0,25.0,True,,True,G,0,34,34
+34,0.0,26.0,True,,True,T,0,35,35
+35,0.0,26.33,True,,True,A,0,36,36
+36,0.0,26.67,True,,True,A,0,37,37
+37,0.0,27.0,True,,True,C,0,38,38
+38,0.0,28.0,True,,True,G,0,39,39
+39,0.0,29.0,True,,True,T,0,40,40
+40,0.0,30.0,True,,True,A,0,41,41
+41,0.0,31.0,True,,True,A,0,42,42
+42,0.0,32.0,True,,True,C,0,43,43
+43,0.0,33.0,True,,True,G,0,44,44
+44,0.0,34.0,True,,True,T,0,45,45
+45,0.0,35.0,True,,True,A,0,46,46
+46,0.0,36.0,True,,True,A,0,47,47
+47,0.0,37.0,True,,True,C,0,48,48
+48,0.0,38.0,True,,True,G,0,49,49
+49,0.0,39.0,True,,False,T,0,50,-1
+50,0.5,40.0,False,,,A,0,51,51
+51,0.5,40.0,False,,,A,0,52,52
+52,0.5,40.0,False,,,C,0,53,53
+53,1.0,39.0,False,,True,G,0,54,54
+54,1.0,38.0,False,,True,T,0,55,55
+55,1.0,37.0,False,,True,A,0,56,56
+56,1.0,36.0,False,,True,A,0,57,57
+57,1.0,35.0,False,,True,C,0,58,58
+58,1.0,34.0,False,,True,G,0,59,59
+59,1.0,33.0,False,,True,T,0,60,60
+60,1.0,32.0,False,,True,A,0,61,61
+61,1.0,31.0,False,,True,A,0,62,62
+62,1.0,30.0,False,,True,C,0,63,63
+63,1.0,29.0,False,,True,G,0,64,64
+64,1.0,28.0,False,,True,T,0,65,65
+65,1.0,27.0,False,,True,A,0,66,66
+66,1.0,26.0,False,,True,A,0,67,67
+67,1.0,25.0,False,,True,C,0,68,68
+68,1.0,24.0,False,,False,G,0,-1,-1
diff --git a/mrdna/readers/test/test.ipynb b/mrdna/readers/test/test.ipynb
index e6aff5ff10d28e647d5337c78b99ddb632756bb4..270b98c02dd96d0b2ed26defc0c0193c92b9de32 100644
--- a/mrdna/readers/test/test.ipynb
+++ b/mrdna/readers/test/test.ipynb
@@ -3247,23 +3247,35 @@
},
{
"cell_type": "code",
- "execution_count": 171,
+ "execution_count": 263,
"id": "170a2918-f113-475c-b0a3-edbf33153e33",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "False"
+ "0 -1\n",
+ "1 -1\n",
+ "2 -1\n",
+ "3 -1\n",
+ "4 -1\n",
+ " ..\n",
+ "450 77\n",
+ "451 76\n",
+ "452 75\n",
+ "453 74\n",
+ "454 73\n",
+ "Name: bp, Length: 455, dtype: int64"
]
},
- "execution_count": 171,
+ "execution_count": 263,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "not(True)"
+ "coordinate_col=\"bp\"\n",
+ "dg[coordinate_col]"
]
},
{
diff --git a/mrdna/readers/test/test2.ipynb b/mrdna/readers/test/test2.ipynb
index 2ebc69d8abad1e817d8e18950e7739adf839d85a..825e255b883e0a49cde86e0bb75fda657d1bd01f 100644
--- a/mrdna/readers/test/test2.ipynb
+++ b/mrdna/readers/test/test2.ipynb
@@ -2,10 +2,18 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 584,
+ "execution_count": 1,
"id": "9ea65628",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "pip3 install termcolor\n"
+ ]
+ }
+ ],
"source": [
"\n",
"from cadnano.document import Document\n",
@@ -2835,11 +2843,18 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 648,
"id": "38a3dacc-7991-4475-b356-44043fd90984",
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "hp, bpr, tpr, eulerZ, mgroove = part.vh_properties.loc[0,\n",
+ " ['helical_pitch',\n",
+ " 'bases_per_repeat',\n",
+ " 'turns_per_repeat',\n",
+ " 'eulerZ',\n",
+ " 'minor_groove_angle']]"
+ ]
},
{
"cell_type": "code",
@@ -2920,140 +2935,2002 @@
},
{
"cell_type": "code",
- "execution_count": 70,
+ "execution_count": 2,
"id": "dbcb2c6d-2e3f-4952-b21e-c624ca2026dc",
"metadata": {},
"outputs": [],
"source": [
- "import scadnano as sc"
+ "import scadnano as sc\n"
]
},
{
"cell_type": "code",
- "execution_count": 204,
+ "execution_count": 3,
"id": "e7b1dda8-1c43-4f22-b7bb-54e006134cb5",
"metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['__abstractmethods__',\n",
+ " '__annotations__',\n",
+ " '__class__',\n",
+ " '__dataclass_fields__',\n",
+ " '__dataclass_params__',\n",
+ " '__delattr__',\n",
+ " '__dict__',\n",
+ " '__dir__',\n",
+ " '__doc__',\n",
+ " '__eq__',\n",
+ " '__format__',\n",
+ " '__ge__',\n",
+ " '__getattribute__',\n",
+ " '__gt__',\n",
+ " '__hash__',\n",
+ " '__init__',\n",
+ " '__init_subclass__',\n",
+ " '__le__',\n",
+ " '__lt__',\n",
+ " '__module__',\n",
+ " '__ne__',\n",
+ " '__new__',\n",
+ " '__post_init__',\n",
+ " '__reduce__',\n",
+ " '__reduce_ex__',\n",
+ " '__repr__',\n",
+ " '__setattr__',\n",
+ " '__sizeof__',\n",
+ " '__slots__',\n",
+ " '__str__',\n",
+ " '__subclasshook__',\n",
+ " '__weakref__',\n",
+ " '_abc_impl',\n",
+ " '_add_new_excel_plate_sheet',\n",
+ " '_assign_color_to_strand',\n",
+ " '_assign_colors_to_strands',\n",
+ " '_assign_default_helices_view_orders_to_groups',\n",
+ " '_build_domains_on_helix_lists',\n",
+ " '_cadnano_v2_color_of_stap',\n",
+ " '_cadnano_v2_fill_blank',\n",
+ " '_cadnano_v2_import_circular_strands_merge_first_last_domains',\n",
+ " '_cadnano_v2_import_explore_domains',\n",
+ " '_cadnano_v2_import_explore_strand',\n",
+ " '_cadnano_v2_import_extract_deletions',\n",
+ " '_cadnano_v2_import_extract_insertions',\n",
+ " '_cadnano_v2_import_find_5_end',\n",
+ " '_cadnano_v2_import_find_strand_color',\n",
+ " '_cadnano_v2_place_crossover',\n",
+ " '_cadnano_v2_place_strand',\n",
+ " '_cadnano_v2_place_strand_segment',\n",
+ " '_check_helix_offsets',\n",
+ " '_check_legal_design',\n",
+ " '_check_loopout_not_singleton',\n",
+ " '_check_loopouts_length',\n",
+ " '_check_loopouts_not_consecutive_or_singletons_or_zero_length',\n",
+ " '_check_loopouts_not_first_or_last_substrand',\n",
+ " '_check_mutually_exclusive_fields',\n",
+ " '_check_strand_has_legal_offsets_in_helices',\n",
+ " '_check_strand_references_legal_helices',\n",
+ " '_check_strands_overlap_legally',\n",
+ " '_check_strands_reference_helices_legally',\n",
+ " '_check_strands_with_same_name_agree_on_other_idt_fields',\n",
+ " '_check_two_consecutive_loopouts',\n",
+ " '_check_types',\n",
+ " '_default_group',\n",
+ " '_ensure_helices_distinct_objects',\n",
+ " '_ensure_helix_groups_exist',\n",
+ " '_ensure_mods_have_unique_vendor_codes',\n",
+ " '_ensure_strands_distinct_objects',\n",
+ " '_get_default_group',\n",
+ " '_get_multiple_of_x_sup_closest_to_y',\n",
+ " '_groups_and_grid_from_json',\n",
+ " '_has_default_groups',\n",
+ " '_helices_and_groups_and_grid_from_json',\n",
+ " '_helices_from_json',\n",
+ " '_helices_to_string',\n",
+ " '_idt_strands_to_export',\n",
+ " '_inline_deletions_insertions_on_helix',\n",
+ " '_normalize_helices_as_dict',\n",
+ " '_num_helix_groups',\n",
+ " '_prepare_nicks_for_full_crossover',\n",
+ " '_set_helices_grid_positions_or_positions',\n",
+ " '_set_helices_min_max_offsets',\n",
+ " '_setup_excel_file',\n",
+ " '_warn_if_strand_names_not_unique',\n",
+ " '_write_plates_assuming_explicit_plates_in_each_strand',\n",
+ " '_write_plates_default',\n",
+ " 'add_deletion',\n",
+ " 'add_full_crossover',\n",
+ " 'add_half_crossover',\n",
+ " 'add_helix',\n",
+ " 'add_insertion',\n",
+ " 'add_nick',\n",
+ " 'add_strand',\n",
+ " 'append_domain',\n",
+ " 'assign_dna',\n",
+ " 'assign_m13_to_scaffold',\n",
+ " 'assign_modifications_to_strands',\n",
+ " 'automatically_assign_color',\n",
+ " 'base_pairs',\n",
+ " 'color_cycler',\n",
+ " 'domain_at',\n",
+ " 'domains_at',\n",
+ " 'draw_strand',\n",
+ " 'from_cadnano_v2',\n",
+ " 'from_scadnano_file',\n",
+ " 'from_scadnano_json_map',\n",
+ " 'from_scadnano_json_str',\n",
+ " 'geometry',\n",
+ " 'grid',\n",
+ " 'groups',\n",
+ " 'helices',\n",
+ " 'helices_idxs_in_group',\n",
+ " 'helices_view_order',\n",
+ " 'inline_deletions_insertions',\n",
+ " 'insert_domain',\n",
+ " 'ligate',\n",
+ " 'modifications',\n",
+ " 'move_strand_offsets',\n",
+ " 'move_strands_on_helices',\n",
+ " 'pitch_of_helix',\n",
+ " 'plate_maps',\n",
+ " 'relax_helix_rolls',\n",
+ " 'remove_domain',\n",
+ " 'remove_strand',\n",
+ " 'reverse_all',\n",
+ " 'roll_of_helix',\n",
+ " 'scaffold',\n",
+ " 'set_end',\n",
+ " 'set_grid',\n",
+ " 'set_helices_view_order',\n",
+ " 'set_helix_idx',\n",
+ " 'set_major_tick_distance',\n",
+ " 'set_start',\n",
+ " 'strand',\n",
+ " 'strand_with_name',\n",
+ " 'strands',\n",
+ " 'strands_ending_on_helix',\n",
+ " 'strands_starting_on_helix',\n",
+ " 'to_cadnano_v2_json',\n",
+ " 'to_cadnano_v2_serializable',\n",
+ " 'to_idt_bulk_input_format',\n",
+ " 'to_json',\n",
+ " 'to_json_serializable',\n",
+ " 'to_oxdna_format',\n",
+ " 'to_oxview_format',\n",
+ " 'to_oxview_json',\n",
+ " 'write_cadnano_v2_file',\n",
+ " 'write_idt_bulk_input_file',\n",
+ " 'write_idt_plate_excel_file',\n",
+ " 'write_oxdna_files',\n",
+ " 'write_oxview_file',\n",
+ " 'write_scadnano_file',\n",
+ " 'yaw_of_helix']"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s=sc.Design.from_scadnano_file(\"loopout.sc\")\n",
+ "dir(s)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "id": "898974c3-dcbb-4374-bf1d-86c87a643d6e",
+ "metadata": {},
"outputs": [],
"source": [
- "s=sc.Design.from_scadnano_file(\"loopout.sc\")"
+ "def gen_id_series(domain,design):\n",
+ " df=pd.DataFrame(columns=[\"vh\",\"zid\",\"fwd\",\"r\"],index=range(domain.dna_length()),dtype=object)\n",
+ " df[\"loopout\"]=False\n",
+ " hid=domain.helix\n",
+ " df[\"vh\"]=hid\n",
+ " fwd=domain.forward\n",
+ " df[\"fwd\"]=fwd\n",
+ " x=design.helices[domain.helix].calculate_position(design.grid,design.geometry).x*10\n",
+ " y=design.helices[domain.helix].calculate_position(design.grid,design.geometry).y*10 \n",
+ " start=domain.start\n",
+ " end=domain.end\n",
+ " dels=domain.deletions\n",
+ " loops=domain.insertions\n",
+ " zids=[i for i in range(start,end)]\n",
+ " if len(dels)>0:\n",
+ " for d in dels:\n",
+ " zids.pop(zids.index(d))\n",
+ " if len(loops)>0:\n",
+ " for ind,ins in loops:\n",
+ " n=zids.index(ind)\n",
+ " zids.pop(n)\n",
+ " loop=[ind+round((j/(ins+1))*100)/100 for j in range(ins+1)]\n",
+ " loop.reverse()\n",
+ " for j in loop:\n",
+ " zids.insert(n,j)\n",
+ " df[\"zid\"]=zids\n",
+ " if fwd==True:\n",
+ " start5=domain.start\n",
+ " end3=domain.end\n",
+ " df[\"zid\"]=zids\n",
+ " else:\n",
+ " start5=domain.end\n",
+ " end3=domain.start\n",
+ " df[\"zid\"]=zids[::-1]\n",
+ " df[\"x\"]=x\n",
+ " df[\"y\"]=y\n",
+ " df[\"z\"]=df[\"zid\"]*3.4\n",
+ " df[\"orientation_angle\"]=[design.helices[hid].backbone_angle_at_offset(float(i),fwd,design.geometry) for i in df[\"zid\"]]\n",
+ " df[\"stacked\"]=[True]*(domain.dna_length()-1)+[False]\n",
+ " return [pd.Series(df.loc[i]) for i in df.index],(x,y,list(df[\"z\"])[-1])\n"
]
},
{
"cell_type": "code",
- "execution_count": 213,
- "id": "4e2dd66e-fe4d-4728-8233-60cb1d9b156a",
+ "execution_count": 66,
+ "id": "722d5ad3-52bc-4c95-856c-a0766e28c701",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(pd.DataFrame({\"seq\":np.arange(0,3),\"vh\":0})) is pd.DataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "325c75d6-6951-439d-b2b3-ff2e74cc2245",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0, 1, 2]"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "list(pd.DataFrame({\"seq\":np.arange(0,3),\"vh\":0}).index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "id": "76d1979d-74c2-4ffb-8dc5-659b889d52b6",
"metadata": {},
"outputs": [],
"source": [
- "tot=np.sum([i.dna_length() for i in s.strands])"
+ "\n",
+ "def gen_strand(strand,design,group_index=0):\n",
+ " domain_list=[]\n",
+ " for do in strand.domains:\n",
+ " \n",
+ " if type(do) is sc.scadnano.Domain:\n",
+ " L,(x,y,z)=gen_id_series(do,design)\n",
+ " if len(domain_list)>2:\n",
+ " if type(domain_list[-1]) is pd.DataFrame:\n",
+ " loop=domain_list.pop(-1)\n",
+ " inds=loop.index\n",
+ " loop[\"x\"]=lx+(x-lx)*(loop.index+1)/(len(loop.index)+2)\n",
+ " loop[\"y\"]=ly+(y-ly)*(loop.index+1)/(len(loop.index)+2)\n",
+ " loop[\"z\"]=lz+(L[0][\"z\"]-lz)*(loop.index+1)/(len(loop.index)+2)\n",
+ " loop[\"orientation_angle\"]=0\n",
+ " L=[pd.Series(loop.loc[i]) for i in loop.index]+L\n",
+ " domain_list=domain_list+L \n",
+ " elif type(do) is sc.scadnano.Loopout:\n",
+ " \"\"\"\n",
+ " for i in range(do.dna_length()):\n",
+ " n=pd.Series({\"seq\":do.dna_sequence[i],\"fwd\":False,\"vh\":helix+0.5,\"zid\":end3,\"loopout\":True})\n",
+ " domain_list.append(n)\n",
+ " \"\"\"\n",
+ " n=pd.DataFrame(columns=[\"loopout\",\"x\",\"y\",\"z\"],index=range(do.dna_length()),dtype=object)\n",
+ " n[\"loopout\"]=True\n",
+ " n[\"stacked\"]=False\n",
+ " lx,ly,lz=(x,y,z)\n",
+ " loop_out_length=do.dna_length()\n",
+ " domain_list.append(n)\n",
+ " \n",
+ " \n",
+ " strand_df=pd.DataFrame(domain_list).reset_index(drop=True)\n",
+ " strand_df[\"threeprime\"]=list(strand_df.index[1:]+group_index)+[-1]\n",
+ " stack=np.where(strand_df[\"stacked\"],strand_df[\"threeprime\"],-1)\n",
+ " strand_df[\"stack\"]=stack\n",
+ " strand_df[\"seq\"]=-1\n",
+ " if strand.dna_sequence is not None:\n",
+ " if len(strand.dna_sequence)==len(strand_df.index):\n",
+ " strand_df[\"seq\"]=list(strand.dna_sequence)\n",
+ " \n",
+ " strand_df[\"index\"]=strand_df.index+group_index\n",
+ " #strand_df[\"seg_index\"]=index\n",
+ " return strand_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "id": "45e6434e-58a9-4aa2-b1f7-6b871daedba3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_prop_table(fname):\n",
+ " sc_design=sc.Design.from_scadnano_file(fname)\n",
+ " nt_list=[]\n",
+ " group_index=0\n",
+ " for i in range(len(sc_design.strands)):\n",
+ " dfi=gen_strand(sc_design.strands[i],sc_design,group_index)\n",
+ " group_index+=len(dfi.index)\n",
+ " nt_list.append(dfi)\n",
+ "\n",
+ " nt_prop=pd.concat(nt_list)\n",
+ " nt_prop.set_index(\"index\",inplace=True)\n",
+ " ## Generate bps\n",
+ " bp_map={}\n",
+ " for i in nt_prop.index:\n",
+ " if nt_prop[\"loopout\"][i]==False:\n",
+ " bp_map[(nt_prop[\"vh\"][i],nt_prop[\"zid\"][i],nt_prop[\"fwd\"][i])]=i\n",
+ " ind_tuple=list(zip(nt_prop.index,nt_prop[\"vh\"],nt_prop[\"zid\"],nt_prop[\"fwd\"]))\n",
+ " bp=-np.ones(len(nt_prop.index),dtype=int)\n",
+ " for i,j,k,l in ind_tuple:\n",
+ " try:\n",
+ " bp[i]=bp_map[(j,k,not(l))]\n",
+ " except:\n",
+ " continue\n",
+ " nt_prop[\"bp\"]=bp\n",
+ " nt_prop[\"r\"]=list(np.array([nt_prop[\"x\"],nt_prop[\"y\"],nt_prop[\"z\"]],dtype=\"<f4\").T)\n",
+ " return nt_prop"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "id": "44f994b6-6c49-4b8a-916b-7ac224558ded",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n=gen_prop_table(\"test_insert.sc\")\n",
+ "n.to_csv(\"loopout.csv\")"
]
},
{
"cell_type": "code",
- "execution_count": 214,
- "id": "786068b5-fa4b-4c02-bdb8-a48af896cd09",
+ "execution_count": 150,
+ "id": "fae9db1a-6148-4cd6-9d30-6c6d8ccbcec6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "135"
+ "vh 1.0\n",
+ "zid 23.0\n",
+ "fwd True\n",
+ "r [0.0, 30.0, 78.2]\n",
+ "loopout False\n",
+ "x 0.0\n",
+ "y 30.0\n",
+ "z 78.2\n",
+ "orientation_angle 68.571429\n",
+ "stacked False\n",
+ "threeprime 84\n",
+ "stack -1\n",
+ "seq T\n",
+ "bp 0\n",
+ "Name: 83, dtype: object"
]
},
- "execution_count": 214,
+ "execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "tot\n"
+ "n.loc[83]"
]
},
{
"cell_type": "code",
- "execution_count": 207,
- "id": "4952a867-7839-4d65-b3d3-228b401fa4de",
+ "execution_count": 117,
+ "id": "6db39eca-d003-4690-a140-18e6abb6b246",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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+ " (1.0, 22.0, True): 82,\n",
+ " (1.0, 23.0, True): 83,\n",
+ " (0.0, 23.0, False): 84,\n",
+ " (0.0, 22.0, False): 85,\n",
+ " (0.0, 21.0, False): 86,\n",
+ " (0.0, 20.0, False): 87,\n",
+ " (0.0, 19.0, False): 88,\n",
+ " (0.0, 18.0, False): 89,\n",
+ " (0.0, 17.0, False): 90,\n",
+ " (0.0, 16.0, False): 91,\n",
+ " (0.0, 15.0, False): 92,\n",
+ " (0.0, 14.5, False): 93,\n",
+ " (0.0, 14.0, False): 94,\n",
+ " (0.0, 13.0, False): 95,\n",
+ " (0.0, 12.0, False): 96,\n",
+ " (0.0, 11.0, False): 97,\n",
+ " (0.0, 10.0, False): 98,\n",
+ " (0.0, 9.0, False): 99,\n",
+ " (0.0, 8.0, False): 100,\n",
+ " (0.0, 39.0, False): 101,\n",
+ " (0.0, 38.0, False): 102,\n",
+ " (0.0, 37.0, False): 103,\n",
+ " (0.0, 36.0, False): 104,\n",
+ " (0.0, 35.0, False): 105,\n",
+ " (0.0, 34.0, False): 106,\n",
+ " (0.0, 33.0, False): 107,\n",
+ " (0.0, 32.0, False): 108,\n",
+ " (0.0, 31.0, False): 109,\n",
+ " (0.0, 30.0, False): 110,\n",
+ " (0.0, 29.0, False): 111,\n",
+ " (0.0, 28.0, False): 112,\n",
+ " (0.0, 27.0, False): 113,\n",
+ " (0.0, 26.67, False): 114,\n",
+ " (0.0, 26.33, False): 115,\n",
+ " (0.0, 26.0, False): 116,\n",
+ " (0.0, 25.0, False): 117,\n",
+ " (0.0, 24.0, False): 118,\n",
+ " (1.0, 24.0, True): 119,\n",
+ " (1.0, 25.0, True): 120,\n",
+ " (1.0, 26.0, True): 121,\n",
+ " (1.0, 27.0, True): 122,\n",
+ " (1.0, 28.0, True): 123,\n",
+ " (1.0, 29.0, True): 124,\n",
+ " (1.0, 30.0, True): 125,\n",
+ " (1.0, 31.0, True): 126,\n",
+ " (1.0, 32.0, True): 127,\n",
+ " (1.0, 33.0, True): 128,\n",
+ " (1.0, 34.0, True): 129,\n",
+ " (1.0, 35.0, True): 130,\n",
+ " (1.0, 36.0, True): 131,\n",
+ " (1.0, 37.0, True): 132,\n",
+ " (1.0, 38.0, True): 133,\n",
+ " (1.0, 39.0, True): 134}"
]
},
- "execution_count": 207,
+ "execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "s.strands[1].dna_length()"
+ "bp_map={}\n",
+ "for i in nt_prop.index:\n",
+ " if nt_prop[\"loopout\"][i]==False:\n",
+ " bp_map[(nt_prop[\"vh\"][i],nt_prop[\"zid\"][i],nt_prop[\"fwd\"][i])]=i\n",
+ "bp_map"
]
},
{
"cell_type": "code",
- "execution_count": 208,
- "id": "5d1d1bea-7d71-476b-928b-18b5cf0bd2e5",
+ "execution_count": null,
+ "id": "4e4ae946-464f-4033-8462-5c5931198123",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "id": "8c52f71c-0333-4d50-a9f7-92dbe60226cb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "34"
+ "[(0, 1.0, 23.0, False),\n",
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+ " (22, 0.0, 14.5, True),\n",
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+ " (27, 0.0, 19.0, True),\n",
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+ " (33, 0.0, 25.0, True),\n",
+ " (34, 0.0, 26.0, True),\n",
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+ " (36, 0.0, 26.67, True),\n",
+ " (37, 0.0, 27.0, True),\n",
+ " (38, 0.0, 28.0, True),\n",
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+ " (45, 0.0, 35.0, True),\n",
+ " (46, 0.0, 36.0, True),\n",
+ " (47, 0.0, 37.0, True),\n",
+ " (48, 0.0, 38.0, True),\n",
+ " (49, 0.0, 39.0, True),\n",
+ " (50, nan, nan, nan),\n",
+ " (51, nan, nan, nan),\n",
+ " (52, nan, nan, nan),\n",
+ " (53, 1.0, 39.0, False),\n",
+ " (54, 1.0, 38.0, False),\n",
+ " (55, 1.0, 37.0, False),\n",
+ " (56, 1.0, 36.0, False),\n",
+ " (57, 1.0, 35.0, False),\n",
+ " (58, 1.0, 34.0, False),\n",
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+ " (60, 1.0, 32.0, False),\n",
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+ " (64, 1.0, 28.0, False),\n",
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+ " (77, 1.0, 16.0, True),\n",
+ " (78, 1.0, 17.0, True),\n",
+ " (79, 1.0, 18.0, True),\n",
+ " (80, 1.0, 19.0, True),\n",
+ " (81, 1.0, 21.0, True),\n",
+ " (82, 1.0, 22.0, True),\n",
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+ " (84, 0.0, 23.0, False),\n",
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+ " (86, 0.0, 21.0, False),\n",
+ " (87, 0.0, 20.0, False),\n",
+ " (88, 0.0, 19.0, False),\n",
+ " (89, 0.0, 18.0, False),\n",
+ " (90, 0.0, 17.0, False),\n",
+ " (91, 0.0, 16.0, False),\n",
+ " (92, 0.0, 15.0, False),\n",
+ " (93, 0.0, 14.5, False),\n",
+ " (94, 0.0, 14.0, False),\n",
+ " (95, 0.0, 13.0, False),\n",
+ " (96, 0.0, 12.0, False),\n",
+ " (97, 0.0, 11.0, False),\n",
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+ " (99, 0.0, 9.0, False),\n",
+ " (100, 0.0, 8.0, False),\n",
+ " (101, 0.0, 39.0, False),\n",
+ " (102, 0.0, 38.0, False),\n",
+ " (103, 0.0, 37.0, False),\n",
+ " (104, 0.0, 36.0, False),\n",
+ " (105, 0.0, 35.0, False),\n",
+ " (106, 0.0, 34.0, False),\n",
+ " (107, 0.0, 33.0, False),\n",
+ " (108, 0.0, 32.0, False),\n",
+ " (109, 0.0, 31.0, False),\n",
+ " (110, 0.0, 30.0, False),\n",
+ " (111, 0.0, 29.0, False),\n",
+ " (112, 0.0, 28.0, False),\n",
+ " (113, 0.0, 27.0, False),\n",
+ " (114, 0.0, 26.67, False),\n",
+ " (115, 0.0, 26.33, False),\n",
+ " (116, 0.0, 26.0, False),\n",
+ " (117, 0.0, 25.0, False),\n",
+ " (118, 0.0, 24.0, False),\n",
+ " (119, 1.0, 24.0, True),\n",
+ " (120, 1.0, 25.0, True),\n",
+ " (121, 1.0, 26.0, True),\n",
+ " (122, 1.0, 27.0, True),\n",
+ " (123, 1.0, 28.0, True),\n",
+ " (124, 1.0, 29.0, True),\n",
+ " (125, 1.0, 30.0, True),\n",
+ " (126, 1.0, 31.0, True),\n",
+ " (127, 1.0, 32.0, True),\n",
+ " (128, 1.0, 33.0, True),\n",
+ " (129, 1.0, 34.0, True),\n",
+ " (130, 1.0, 35.0, True),\n",
+ " (131, 1.0, 36.0, True),\n",
+ " (132, 1.0, 37.0, True),\n",
+ " (133, 1.0, 38.0, True),\n",
+ " (134, 1.0, 39.0, True)]"
]
},
- "execution_count": 208,
+ "execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "s.strands[2].dna_length()"
+ "ind_tuple=list(zip(nt_prop.index,nt_prop[\"vh\"],nt_prop[\"zid\"],nt_prop[\"fwd\"]))\n",
+ "ind_tuple"
]
},
{
"cell_type": "code",
- "execution_count": 187,
- "id": "91b1be83-f406-47f3-8e63-11a0262a653a",
+ "execution_count": 355,
+ "id": "039b65c8-5a0d-4127-a2dc-2808ce11fba1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[20]"
+ "(array([50, 51, 52]),)"
]
},
- "execution_count": 187,
+ "execution_count": 355,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "s.strands[0].domains[0].deletions"
+ "x=s.helices[0].calculate_position(s.grid,s.geometry)\n",
+ "np.where(m[\"loopout\"])"
]
},
{
"cell_type": "code",
- "execution_count": 183,
- "id": "e616db25-a0fa-47ea-917d-28f2ec18c67e",
+ "execution_count": 368,
+ "id": "570999f4-a7b9-400b-a61d-c1d66eaf0999",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_pos(design,vh,zid,fwd,loopout=False):\n",
+ " if loopout==False:\n",
+ " x=design.helices[int(vh)].calculate_position(design.grid,design.geometry).x\n",
+ " y=design.helices[int(vh)].calculate_position(design.grid,design.geometry).y\n",
+ " z=zid*3.4\n",
+ " orientation_angle=design.helices[int(vh)].backbone_angle_at_offset(float(zid),True,design.geometry)\n",
+ " return np.array([x,y,z]),orientation_angle\n",
+ " else:\n",
+ " return np.array([np.nan]*3),0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 370,
+ "id": "b0e5cb3c-8cb4-4fd6-8fcc-1b63c7733acd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[Domain(, helix=1, forward=False, start=8, end=24, deletions=[20]),\n",
- " Domain(, helix=1, forward=False, start=24, end=40),\n",
- " Domain(, helix=1, forward=True, start=8, end=24, deletions=[20]),\n",
- " Domain(, helix=1, forward=True, start=24, end=40)]"
+ "(array([ 0. , 0. , 40.8]), 51.428571428571445)"
]
},
- "execution_count": 183,
+ "execution_count": 370,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 371,
+ "id": "837c9a06-1e50-434d-b97b-70bfb8d20c42",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'vh' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[371], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m s\u001b[38;5;241m.\u001b[39mhelices[\u001b[38;5;28mint\u001b[39m(\u001b[43mvh\u001b[49m)]\u001b[38;5;241m.\u001b[39mbackbone_angle_at_offset(\u001b[38;5;28mfloat\u001b[39m(zid),\u001b[38;5;28;01mTrue\u001b[39;00m,s\u001b[38;5;241m.\u001b[39mgeometry)\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'vh' is not defined"
+ ]
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 225,
+ "id": "998f4321-f520-45f7-bffb-92177b6a0fec",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>vh</th>\n",
+ " <th>zid</th>\n",
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+ "metadata": {},
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+ "True"
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+ "execution_count": 48,
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+ {
+ "data": {
+ "text/plain": [
+ "20"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "n=list(range(8,25))\n",
+ "n.pop(-5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "ef80053e-350c-4c5a-ac94-c1ffbedc1e46",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "Length of values (16) does not match length of index (14)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgen_id_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrands\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomains\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+ "Cell \u001b[0;32mIn[20], line 22\u001b[0m, in \u001b[0;36mgen_id_series\u001b[0;34m(domain)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m loop:\n\u001b[1;32m 21\u001b[0m zids\u001b[38;5;241m.\u001b[39minsert(n,j)\n\u001b[0;32m---> 22\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzids\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m=\u001b[39mzids\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fwd\u001b[38;5;241m==\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 24\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstack_tuple\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvh\u001b[39m\u001b[38;5;124m\"\u001b[39m],df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzid\u001b[39m\u001b[38;5;124m\"\u001b[39m],df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfwd\u001b[39m\u001b[38;5;124m\"\u001b[39m]))[\u001b[38;5;241m1\u001b[39m:]\u001b[38;5;241m+\u001b[39m[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n",
+ "File \u001b[0;32m/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/frame.py:3950\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 3947\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_array([key], value)\n\u001b[1;32m 3948\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 3949\u001b[0m \u001b[38;5;66;03m# set column\u001b[39;00m\n\u001b[0;32m-> 3950\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_item\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/frame.py:4143\u001b[0m, in \u001b[0;36mDataFrame._set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 4133\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_set_item\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, value) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4134\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4135\u001b[0m \u001b[38;5;124;03m Add series to DataFrame in specified column.\u001b[39;00m\n\u001b[1;32m 4136\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4141\u001b[0m \u001b[38;5;124;03m ensure homogeneity.\u001b[39;00m\n\u001b[1;32m 4142\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4143\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sanitize_column\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 4146\u001b[0m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m 4147\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m value\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 4148\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_extension_array_dtype(value)\n\u001b[1;32m 4149\u001b[0m ):\n\u001b[1;32m 4150\u001b[0m \u001b[38;5;66;03m# broadcast across multiple columns if necessary\u001b[39;00m\n\u001b[1;32m 4151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mis_unique \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns, MultiIndex):\n",
+ "File \u001b[0;32m/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/frame.py:4870\u001b[0m, in \u001b[0;36mDataFrame._sanitize_column\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m 4867\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _reindex_for_setitem(Series(value), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex)\n\u001b[1;32m 4869\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_list_like(value):\n\u001b[0;32m-> 4870\u001b[0m \u001b[43mcom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequire_length_match\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4871\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sanitize_array(value, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, allow_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+ "File \u001b[0;32m/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/common.py:576\u001b[0m, in \u001b[0;36mrequire_length_match\u001b[0;34m(data, index)\u001b[0m\n\u001b[1;32m 572\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;124;03mCheck the length of data matches the length of the index.\u001b[39;00m\n\u001b[1;32m 574\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(index):\n\u001b[0;32m--> 576\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 577\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of values \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 578\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 579\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdoes not match length of index \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 580\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(index)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 581\u001b[0m )\n",
+ "\u001b[0;31mValueError\u001b[0m: Length of values (16) does not match length of index (14)"
+ ]
+ }
+ ],
+ "source": [
+ "gen_id_series(s.strands[0].domains[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "4e2dd66e-fe4d-4728-8233-60cb1d9b156a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tot=np.sum([i.dna_length() for i in s.strands])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "4952a867-7839-4d65-b3d3-228b401fa4de",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Domain(, helix=1, forward=True, start=8, end=24, deletions=[20]),\n",
+ " Domain(, helix=0, forward=False, start=8, end=24, insertions=[(14, 1)])]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.strands[1].domains"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 626,
+ "id": "5d1d1bea-7d71-476b-928b-18b5cf0bd2e5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Strand(domains=[Domain(, helix=0, forward=False, start=24, end=40, insertions=[(26, 2)]), Domain(, helix=1, forward=True, start=24, end=40)], circular=False, color=Color(r=87, g=187, b=0), vendor_fields=None, is_scaffold=False, modification_5p=Modification5Prime(display_text='B', vendor_code='/5Biosg/', connector_length=4), modification_3p=None, modifications_int={}, name=None, label=None)"
+ ]
+ },
+ "execution_count": 626,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.strands[2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "91b1be83-f406-47f3-8e63-11a0262a653a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'AACGTAACGTAACGTAACGTAACGTAACGTAACGT'"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.helices[0].domains[0].dna_sequence"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 723,
+ "id": "e616db25-a0fa-47ea-917d-28f2ec18c67e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0"
+ ]
+ },
+ "execution_count": 723,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.helices[0].domains[0].helix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 721,
+ "id": "216ec8a1-d779-4725-b3c2-4a2f01c3dd52",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Position3D(x=0.0, y=3.0, z=0)"
+ ]
+ },
+ "execution_count": 721,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.helices[1].calculate_position(s.grid,s.geometry)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 700,
+ "id": "0d5d750d-f978-4096-b819-8a26ca2be63d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[20]"
+ ]
+ },
+ "execution_count": 700,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.helices[1]._domains[0].deletions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 710,
+ "id": "855c45bb-5744-48ff-9ea3-4a7ab6221208",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "'Domain' object has no attribute 'backbone_angle_at_offset'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[710], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhelices\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomains\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackbone_angle_at_offset\u001b[49m(\u001b[38;5;241m12\u001b[39m,\u001b[38;5;28;01mTrue\u001b[39;00m,s\u001b[38;5;241m.\u001b[39mgeometry)\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'Domain' object has no attribute 'backbone_angle_at_offset'"
+ ]
+ }
+ ],
+ "source": [
+ "s.helices[0].domains[0].backbone_angle_at_offset(12,True,s.geometry)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 726,
+ "id": "7af4b044-e9e9-43e3-ab29-1cac35a9ac46",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_id_series(domain):\n",
+ "\n",
+ " df=pd.DataFrame(columns=[\"vh\",\"zid\",\"fwd\",\"stack_tuple\",\"threeprime_tuple\"],index=range(domain.dna_length()),dtype=object)\n",
+ " df[\"vh\"]=domain.helix\n",
+ " fwd=domain.forward\n",
+ " df[\"fwd\"]=fwd\n",
+ " start=domain.start\n",
+ " end=domain.end\n",
+ " dels=domain.deletions\n",
+ " loops=domain.insertions\n",
+ " zids=[str(i) for i in range(start,end+1)]\n",
+ " if len(dels)>0:\n",
+ " for d in dels:\n",
+ " zids.pop(zids.index(str(d)))\n",
+ " if len(loops)>0:\n",
+ " for ind,ins in loops:\n",
+ " n=zids.index(str(ind))\n",
+ " zids.pop(n)\n",
+ " loop=[str(ind)+\".\"+str(j) for j in range(ins+1)]\n",
+ " loop.reverse()\n",
+ " for j in loop:\n",
+ " zids.insert(n,j)\n",
+ " print(zids)\n",
+ " df[\"zids\"]=zids\n",
+ " if fwd==True:\n",
+ " df[\"stack_tuple\"]=list(zip(df[\"vh\"],df[\"zid\"],df[\"fwd\"]))[1:]+[-1]\n",
+ " else:\n",
+ " df[\"stack_tuple\"]=[-1]+list(zip(df[\"vh\"],df[\"zid\"],df[\"fwd\"]))[0:-1]\n",
+ " \n",
+ " if len(domain.dna_sequence)==domain.dna_length():\n",
+ " df[\"seq\"]=list(domain.dna_sequence)\n",
+ " else:\n",
+ " df[\"seq\"]=-1\n",
+ "\n",
+ " return [pd.Series(df.loc[i]) for i in df.index]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 734,
+ "id": "3c488e25-5333-4a34-bbe4-6467dc291894",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Domain(, helix=1, forward=False, start=8, end=24, deletions=[20]),\n",
+ " Domain(, helix=0, forward=True, start=8, end=40, insertions=[(14, 1), (26, 2)]),\n",
+ " Loopout(3, ),\n",
+ " Domain(, helix=1, forward=False, start=24, end=40)]"
+ ]
+ },
+ "execution_count": 734,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.strands[0].domains"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 741,
+ "id": "d9c3d962-831f-4b5f-b889-e6173f7e2c5c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Domain(, helix=1, forward=False, start=8, end=24, deletions=[20])"
+ ]
+ },
+ "execution_count": 741,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.strands[0].first_domain()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eff8f4a8-a228-40d5-8487-e091b1c425d9",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 728,
+ "id": "eb4f45b4-3064-4238-830b-0dfea9a0e99d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(14, 1), (26, 2)]"
+ ]
+ },
+ "execution_count": 728,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.helices[0].domains[0].insertions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 729,
+ "id": "42960c56-4516-4021-989a-4e2848ad4215",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Domain(, helix=0, forward=True, start=8, end=40, insertions=[(14, 1), (26, 2)])"
+ ]
+ },
+ "execution_count": 729,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.helices[0].domains[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 727,
+ "id": "2c4153a3-88a1-4b6a-ac1d-0bdd3d9ef621",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['8', '9', '10', '11', '12', '13', '14.0', '14.1', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26.0', '26.1', '26.2', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40']\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "Length of values (36) does not match length of index (35)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[727], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgen_id_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhelices\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomains\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+ "Cell \u001b[0;32mIn[726], line 24\u001b[0m, in \u001b[0;36mgen_id_series\u001b[0;34m(domain)\u001b[0m\n\u001b[1;32m 22\u001b[0m zids\u001b[38;5;241m.\u001b[39minsert(n,j)\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28mprint\u001b[39m(zids)\n\u001b[0;32m---> 24\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzids\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m=\u001b[39mzids\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fwd\u001b[38;5;241m==\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 26\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstack_tuple\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvh\u001b[39m\u001b[38;5;124m\"\u001b[39m],df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzid\u001b[39m\u001b[38;5;124m\"\u001b[39m],df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfwd\u001b[39m\u001b[38;5;124m\"\u001b[39m]))[\u001b[38;5;241m1\u001b[39m:]\u001b[38;5;241m+\u001b[39m[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n",
+ "File \u001b[0;32m/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/frame.py:3950\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 3947\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_array([key], value)\n\u001b[1;32m 3948\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 3949\u001b[0m \u001b[38;5;66;03m# set column\u001b[39;00m\n\u001b[0;32m-> 3950\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_item\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/frame.py:4143\u001b[0m, in \u001b[0;36mDataFrame._set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 4133\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_set_item\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, value) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4134\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4135\u001b[0m \u001b[38;5;124;03m Add series to DataFrame in specified column.\u001b[39;00m\n\u001b[1;32m 4136\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4141\u001b[0m \u001b[38;5;124;03m ensure homogeneity.\u001b[39;00m\n\u001b[1;32m 4142\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4143\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sanitize_column\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 4146\u001b[0m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m 4147\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m value\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 4148\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_extension_array_dtype(value)\n\u001b[1;32m 4149\u001b[0m ):\n\u001b[1;32m 4150\u001b[0m \u001b[38;5;66;03m# broadcast across multiple columns if necessary\u001b[39;00m\n\u001b[1;32m 4151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mis_unique \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns, MultiIndex):\n",
+ "File \u001b[0;32m/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/frame.py:4870\u001b[0m, in \u001b[0;36mDataFrame._sanitize_column\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m 4867\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _reindex_for_setitem(Series(value), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex)\n\u001b[1;32m 4869\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_list_like(value):\n\u001b[0;32m-> 4870\u001b[0m \u001b[43mcom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequire_length_match\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4871\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sanitize_array(value, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, allow_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+ "File \u001b[0;32m/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/common.py:576\u001b[0m, in \u001b[0;36mrequire_length_match\u001b[0;34m(data, index)\u001b[0m\n\u001b[1;32m 572\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;124;03mCheck the length of data matches the length of the index.\u001b[39;00m\n\u001b[1;32m 574\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(index):\n\u001b[0;32m--> 576\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 577\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of values \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 578\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 579\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdoes not match length of index \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 580\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(index)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 581\u001b[0m )\n",
+ "\u001b[0;31mValueError\u001b[0m: Length of values (36) does not match length of index (35)"
+ ]
+ }
+ ],
+ "source": [
+ "gen_id_series(s.helices[0].domains[0])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "83578dcc-15c5-4112-9225-ba93370bfd3c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 672,
+ "id": "09e48bf8-ae24-4d32-817e-f576a9ca019c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "51.428571428571445"
+ ]
+ },
+ "execution_count": 672,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.helices[0].backbone_angle_at_offset(12,True,s.geometry)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 642,
+ "id": "9517ec2a-d5f9-4a7f-8af9-1d042486b6c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['__abstractmethods__',\n",
+ " '__annotations__',\n",
+ " '__class__',\n",
+ " '__dataclass_fields__',\n",
+ " '__dataclass_params__',\n",
+ " '__delattr__',\n",
+ " '__dict__',\n",
+ " '__dir__',\n",
+ " '__doc__',\n",
+ " '__eq__',\n",
+ " '__format__',\n",
+ " '__ge__',\n",
+ " '__getattribute__',\n",
+ " '__gt__',\n",
+ " '__hash__',\n",
+ " '__init__',\n",
+ " '__init_subclass__',\n",
+ " '__le__',\n",
+ " '__lt__',\n",
+ " '__module__',\n",
+ " '__ne__',\n",
+ " '__new__',\n",
+ " '__post_init__',\n",
+ " '__reduce__',\n",
+ " '__reduce_ex__',\n",
+ " '__repr__',\n",
+ " '__setattr__',\n",
+ " '__sizeof__',\n",
+ " '__slots__',\n",
+ " '__str__',\n",
+ " '__subclasshook__',\n",
+ " '__weakref__',\n",
+ " '_abc_impl',\n",
+ " '_domains',\n",
+ " 'backbone_angle_at_offset',\n",
+ " 'calculate_major_ticks',\n",
+ " 'calculate_position',\n",
+ " 'compute_relaxed_roll_delta',\n",
+ " 'crossover_addresses',\n",
+ " 'default_grid_position',\n",
+ " 'domains',\n",
+ " 'from_json',\n",
+ " 'grid_position',\n",
+ " 'group',\n",
+ " 'idx',\n",
+ " 'major_tick_distance',\n",
+ " 'major_tick_distance_is_default',\n",
+ " 'major_tick_periodic_distances',\n",
+ " 'major_tick_periodic_distances_is_default',\n",
+ " 'major_tick_start',\n",
+ " 'major_tick_start_is_default',\n",
+ " 'major_ticks',\n",
+ " 'major_ticks_is_default',\n",
+ " 'max_offset',\n",
+ " 'min_offset',\n",
+ " 'min_offset_is_default',\n",
+ " 'position',\n",
+ " 'relax_roll',\n",
+ " 'roll',\n",
+ " 'to_json_serializable']"
+ ]
+ },
+ "execution_count": 642,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "s.helices[1]._domains"
+ "dir(s.helices[0])"
]
},
{