diff --git a/mrdna/readers/.nfs000000000007668300005e88 b/mrdna/readers/.nfs000000000007668300005e88
new file mode 100644
index 0000000000000000000000000000000000000000..ed1479fd8b315ab84d5703322bf45d99a30438d1
Binary files /dev/null and b/mrdna/readers/.nfs000000000007668300005e88 differ
diff --git a/mrdna/readers/__init__.py b/mrdna/readers/__init__.py
index 1b61d775fcc4d8b7d5aa11dfd9a29bb9932471e8..8c0b352c5ad4b9b5ddc446475ee09778658b3d1e 100644
--- a/mrdna/readers/__init__.py
+++ b/mrdna/readers/__init__.py
@@ -11,6 +11,9 @@ def read_cadnano_input(json_file,sequence=None,**model_parameters):
from .segmentmodel_from_cadnano import mrdna_model_from_cadnano
return mrdna_model_from_cadnano(json_file,seq=sequence,**model_parameters)
+def read_scadnano(sc_file, **model_parameters):
+ from .segmentmodel_from_scadnano import mrdna_model_from_scadnano
+ return mrdna_model_from_scadnano(sc_file, **model_parameters)
def read_vhelix(maya_file, **model_parameters):
from .polygon_mesh import parse_maya_file, convert_maya_bases_to_segment_model
@@ -44,6 +47,5 @@ def read_atomic_pdb(pdb_file, **model_parameters):
return SegmentModelFromPdb(pdb_file)
def read_oxdna(coordinate_file, topology_file, virt2nuc=None, **model_parameters):
- """ Idealized coordinate file: coordinates for detecting stacks and base pairs, defaults to coordinate_file """
from .segmentmodel_from_oxdna_pinyi import mrdna_model_from_oxdna
return mrdna_model_from_oxdna(coordinate_file, topology_file,virt2nuc, **model_parameters)
diff --git a/mrdna/readers/segmentmodel_from_cadnano.bak.py b/mrdna/readers/segmentmodel_from_cadnano.bak.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfb56d9964fe0a45b3f72d62a8e833452892e79a
--- /dev/null
+++ b/mrdna/readers/segmentmodel_from_cadnano.bak.py
@@ -0,0 +1,225 @@
+# -*- 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
+
+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 get_lattice(part):
+ lattice_type = None
+ _gt = part.getGridType()
+ try:
+ lattice_type = _gt.name.lower()
+ except:
+ if _gt == 1:
+ lattice_type = 'square'
+ elif _gt == 2:
+ lattice_type = 'honeycomb'
+ else:
+ print(lattice_type)
+ return lattice_type
+
+
+def read_json_file(filename):
+ import cadnano
+ from cadnano.document import Document
+
+ try:
+ with open(filename) as ch:
+ json_data = json.load(ch)
+ except:
+ with open(filename) as ch:
+ content = ""
+ for l in ch:
+ l = re.sub(r"'", r'"', l)
+ # https://stackoverflow.com/questions/4033633/handling-lazy-json-in-python-expecting-property-name
+ # l = re.sub(r"{\s*(\w)", r'{"\1', l)
+ # l = re.sub(r",\s*(\w)", r',"\1', l)
+ # l = re.sub(r"(\w):", r'\1":', l)
+ content += l+"\n"
+ json_data = json.loads(content)
+
+ try:
+ doc = Document()
+ cadnano.fileio.v3decode.decode(doc, json_data)
+ decoder = 3
+ except:
+ doc = Document()
+ cadnano.fileio.v2decode.decode(doc, json_data)
+ decoder = 2
+
+ parts = [p for p in doc.getParts()]
+ if len(parts) != 1:
+ raise Exception("Only documents containing a single cadnano part are implemented at this time.")
+ part = parts[0]
+
+ if decoder == 2:
+ """ It seems cadnano2.5 (as of ce6ff019) does not set the EulerZ for square lattice structures correctly, doing so here """
+ l = get_lattice(part)
+ if l == 'square':
+ for id_num in part.getIdNums():
+ if part.vh_properties.loc[id_num,'eulerZ'] == 0:
+ part.vh_properties.loc[id_num,'eulerZ'] = 360*(6/10.5)
+ df=pd.DataFrame(json_data["vstrands"])
+ n_df=df.set_index("num")
+ return part
+
+def get_helix_angle(part, helix_id, indices):
+ """ Get "start_orientation" for helix """
+ # import ipdb
+ # ipdb.set_trace()
+
+ """ FROM CADNANO2.5
+ + angle is CCW
+ - angle is CW
+ Right handed DNA rotates clockwise from 5' to 3'
+ we use the convention the 5' end starts at 0 degrees
+ and it's pair is minor_groove_angle degrees away
+ direction, hence the minus signs. eulerZ
+ """
+
+ hp, bpr, tpr, eulerZ, mgroove = part.vh_properties.loc[helix_id,
+ ['helical_pitch',
+ 'bases_per_repeat',
+ 'turns_per_repeat',
+ 'eulerZ',
+ 'minor_groove_angle']]
+ twist_per_base = tpr*360./bpr
+ # angle = eulerZ - twist_per_base*indices + 0.5*mgroove + 180
+ angle = eulerZ + twist_per_base*indices - 0.5*mgroove
+ return rotationAboutAxis(np.array((0,0,1)),angle)
+
+def gen_id_series(strand,part):
+ df=pd.DataFrame(columns=["vh","zid","fwd","stack_tuple","threeprime_tuple","x","y","z"],index=range(strand.totalLength()),dtype=object)
+ df["vh"]=strand._id_num
+ df["fwd"]=strand.isForward()
+ df["x"]=part.getVirtualHelixOrigin(strand._id_num)[0]*10
+ df["y"]=part.getVirtualHelixOrigin(strand._id_num)[1]*10
+ id_lo,id_hi=strand.idxs()
+ zids=[str(i) for i in range(id_lo,id_hi+1)]
+ insert_dict={}
+ insert_dict=dict([(j.idx(),j.length()) for j in strand.insertionsOnStrand()])
+ z=np.arange(id_lo,id_hi+1)
+ zids=[str(i) for i in range(id_lo,id_hi+1)]
+ z=list(np.arange(id_lo,id_hi+1))
+ zids=[str(i) for i in range(id_lo,id_hi+1)]
+ for insert_base in insert_dict:
+ z_ind=zids.index(str(insert_base))
+ z_val=insert_dict[insert_base]
+ z_pos_ind=z.index(insert_base)
+ zids.pop(z_ind)
+ z.pop(z_pos_ind)
+ if z_val!=-1:
+ #l=[str(insert_base)+"."+str(i) for i in range(z_val+1)]
+ l=list(range(z_val+1))
+ l.reverse()
+ for k in l:
+ zids.insert(z_ind,str(insert_base)+"."+str(k))
+ z.insert(z_pos_ind,insert_base+k/(z_val+1))
+ df["zid"]=zids
+ df["z"]=np.array(z)*3.4
+
+
+ L=[(df["vh"][i],df["zid"][i],df["fwd"][i]) for i in df.index]
+ if strand.isForward()==True:
+ df["stack_tuple"]=L[1:]+[-1]
+ if strand.connection3p() is None:
+ df["threeprime_tuple"]=L[1:]+[-1]
+ else:
+ df["threeprime_tuple"]=L[1:]+[(strand.connection3p().idNum(),str(strand.connection3p().idx5Prime()),strand.connection3p().isForward())]
+
+
+ else:
+ df["stack_tuple"]=[-1]+L[0:-1]
+ if strand.connection3p() is None:
+ df["threeprime_tuple"]=[-1]+L[0:-1]
+ else:
+ df["threeprime_tuple"]=[(strand.connection3p().idNum(),str(strand.connection3p().idx5Prime()),strand.connection3p().isForward())]+L[0:-1]
+ ## cadnano 3.1 sequence assign is wrong if there is insertion or deletion.
+ df["r"]=[np.array([df["x"][i],df["y"][i],df["z"][i]],dtype=np.float32) for i in df.index]
+
+ return [pd.Series(df.loc[i]) for i in df.index]
+
+
+def gen_prop_table(part):
+ strand_set=[]
+ for i in part.getidNums():
+ fwd,rev=part.getStrandSets(i)
+ [strand_set.append(i) for i in fwd.strands()]
+ [strand_set.append(i) for i in rev.strands()]
+ id_series=[]
+ for i in strand_set:
+ id_series=id_series+gen_id_series(i,part)
+
+ nt_prop=pd.DataFrame(id_series)
+ nt_prop.reset_index(inplace=True)
+ nt_prop["seq"]=-1
+ ind_tuple=list(zip(nt_prop["vh"],nt_prop["zid"],nt_prop["fwd"]))
+ stacks=[]
+ for i in list(nt_prop["stack_tuple"]):
+ if i ==-1:
+ stacks.append(i)
+ else:
+ stacks.append(ind_tuple.index(i))
+ nt_prop["stack"]=stacks
+ tprime=[]
+ for i in list(nt_prop["threeprime_tuple"]):
+ if i ==-1:
+ tprime.append(i)
+ else:
+ tprime.append(ind_tuple.index(i))
+ nt_prop["threeprime"]=tprime
+ vhzid=list(zip(nt_prop["vh"],nt_prop["zid"]))
+ nt_prop["orientation"]=[get_helix_angle(part, helix_id, int(float(indices))) for helix_id,indices in vhzid]
+ nt_prop=nt_prop.fillna(-1)
+ counter=-1
+ bp=-np.ones(len(nt_prop.index),dtype=int)
+ bp_map=dict(zip(ind_tuple,nt_prop.index))
+ for i,j,k in ind_tuple:
+ counter+=1
+ try:
+ bp[counter]=bp_map[(i,j,not(k))]
+ except:
+ pass
+ nt_prop["bp"]=bp
+
+ return nt_prop
+
+def mrdna_model_from_cadnano(json_file,seq=None,**model_parameters):
+ part=read_json_file(json_file)
+
+ nt_prop=gen_prop_table(part)
+
+
+ if seq is None:
+ if nt_prop["seq"][0]==-1:
+ seq=None
+ else:
+ seq=nt_prop["seq"]
+
+ 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"])))
+ orientation=np.array(list(nt_prop["orientation"]))
+ model = model_from_basepair_stack_3prime( r, bp, stack, three_prime, seq, orientation, **model_parameters )
+ return model
diff --git a/mrdna/readers/segmentmodel_from_cadnano.py b/mrdna/readers/segmentmodel_from_cadnano.py
index f8127e715c6cf43f558a5488d963f840b06c8e2f..bfb56d9964fe0a45b3f72d62a8e833452892e79a 100644
--- a/mrdna/readers/segmentmodel_from_cadnano.py
+++ b/mrdna/readers/segmentmodel_from_cadnano.py
@@ -171,7 +171,7 @@ def gen_prop_table(part):
id_series=id_series+gen_id_series(i,part)
nt_prop=pd.DataFrame(id_series)
- nt_prop.reset_index(names=list(range(len(nt_prop.index))),inplace=True)
+ nt_prop.reset_index(inplace=True)
nt_prop["seq"]=-1
ind_tuple=list(zip(nt_prop["vh"],nt_prop["zid"],nt_prop["fwd"]))
stacks=[]
diff --git a/mrdna/readers/segmentmodel_from_lists.py b/mrdna/readers/segmentmodel_from_lists.py
index 7e65b97b3a54b0dc72a558785cf2406fd247134d..c2571081f666f3e7ac05ca2ccc89693c06fcafc4 100644
--- a/mrdna/readers/segmentmodel_from_lists.py
+++ b/mrdna/readers/segmentmodel_from_lists.py
@@ -471,13 +471,9 @@ def model_from_basepair_stack_3prime(coordinate, basepair, stack, three_prime,
model._reader_list_hmap = hmap
model._reader_list_fwd = fwd
model._reader_list_hrank = hrank
- df=pd.DataFrame({"r":coordinate,"bp":basepair,"stack":stack,"threeprime":three_prime,"seq":sequence,"orientation":orientation,"fwd":fwd,"hmap":hmap,"hrank":hrank,})
if sequence is None:
for s in model.segments:
s.randomize_unset_sequence()
-
- model._dataframe=df
-
return model
def model_from_pandas(df,coordinate_col="r",bp_col="bp",stack_col="stack",three_prime_col="threeprime",
diff --git a/mrdna/readers/segmentmodel_from_oxdna_pinyi.py b/mrdna/readers/segmentmodel_from_oxdna_pinyi.py
index 564065882e7bcc745f4c93a5b9a2a24789bba3dc..3e7f8c86bc9fb093fce7332303e34665849aef03 100644
--- a/mrdna/readers/segmentmodel_from_oxdna_pinyi.py
+++ b/mrdna/readers/segmentmodel_from_oxdna_pinyi.py
@@ -3,8 +3,8 @@ from .segmentmodel_from_lists import model_from_basepair_stack_3prime
from ..arbdmodel.coords import rotationAboutAxis
import pickle
import pandas as pd
-
-from oxlibs import *
+import sys
+import mrdna.readers.libs as libs
import numpy as np
from scipy.spatial import distance_matrix
pd.options.mode.chained_assignment = None # default='warn'
@@ -89,10 +89,12 @@ def mrdna_model_from_oxdna(coordinate_file, topology_file,virt2nuc=None,get_nt_p
L.append(pd.Series({"index":index,"vh":vh,"zid":z,"strand":strand,"is_scaf":bool(is_scaf)}))
return L
def get_virt2nuc(virt2nuc,top_data):
- vh_vb,pattern=pd.read_pickle(virt2nuc)
+ sys.modules["libs"]=libs
+ virt_pickle=open(virt2nuc,"rb")
+ vh_vb,pattern=pickle.load(virt_pickle)
L1=_find_vh_vb_table(vh_vb._scaf,1)
L2=_find_vh_vb_table(vh_vb._stap,0)
- nt_prop=pd.DataFrame(L1+L2)
+ nt_prop=pd.DataFrame(L1+L2,dtype=object)
nt_prop.set_index("index",inplace=True)
nt_prop.sort_index(inplace=True)
nt_prop["threeprime"]=top_data[2]
@@ -126,9 +128,8 @@ def mrdna_model_from_oxdna(coordinate_file, topology_file,virt2nuc=None,get_nt_p
bp=nt_prop["bp"]
stack=nt_prop["stack"]
three_prime=nt_prop["threeprime"]
- nt_prop["r"]=r
nt_prop["orientation"]=orientation
-
+ nt_prop["r"]=list(r)
except:
## Reverse direction so indices run 5'-to-3'
top_data = [a[::-1] for a in top_data]
@@ -149,7 +150,7 @@ def mrdna_model_from_oxdna(coordinate_file, topology_file,virt2nuc=None,get_nt_p
five_prime = len(r) - top_data[3] -1
three_prime[three_prime >= len(r)] = -1
five_prime[five_prime >= len(r)] = -1
- nt_prop=pd.DataFrame({"r":r,"bp":bp,"stack":stack,"threeprime":three_prime, "seq":seq,"orientation":orientation})
+ nt_prop=pd.DataFrame({"r":list(r),"bp":list(bp),"stack":list(stack),"threeprime":list(three_prime), "seq":list(seq),"orientation":list(orientation)})
def _debug_write_bonds():
from ..arbdmodel import ParticleType, PointParticle, ArbdModel, Group
@@ -186,7 +187,6 @@ def mrdna_model_from_oxdna(coordinate_file, topology_file,virt2nuc=None,get_nt_p
logger.info(f'mrdna_model_from_oxdna: num_bp, num_ss_nt, num_stacked: {np.sum(bp>=0)//2} {np.sum(bp<0)} {np.sum(stack >= 0)}')
-
model = model_from_basepair_stack_3prime( r, bp, stack, three_prime, seq, orientation, **model_parameters )
"""
diff --git a/mrdna/readers/segmentmodel_from_scadnano.py b/mrdna/readers/segmentmodel_from_scadnano.py
index 7311227e03cba381200fa20bf36cad3122c4830b..da2d4c4fe4ab984432f6bf414b6ca002e9496590 100644
--- a/mrdna/readers/segmentmodel_from_scadnano.py
+++ b/mrdna/readers/segmentmodel_from_scadnano.py
@@ -85,9 +85,12 @@ def gen_strand(strand,design,group_index=0):
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:
+ try:
+ if len(strand.dna_sequence)==len(strand_df.index):
+ strand_df["seq"]=list(strand.dna_sequence)
+ else:
+ strand_df["seq"]=-1
+ except:
strand_df["seq"]=-1
strand_df["index"]=strand_df.index+group_index
#strand_df["seg_index"]=index
@@ -128,10 +131,12 @@ def mrdna_model_from_scadnano(sc_file,seq=None,**model_parameters):
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
diff --git a/mrdna/readers/test.ipynb b/mrdna/readers/test.ipynb
deleted file mode 100644
index a3bd160b751ec4f98fab99afeb23a4076371dfd4..0000000000000000000000000000000000000000
--- a/mrdna/readers/test.ipynb
+++ /dev/null
@@ -1,4745 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "03eb8540",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "pip3 install termcolor\n"
- ]
- }
- ],
- "source": [
- "import pandas as pd\n",
- "import pickle\n",
- "import numpy as np\n",
- "import json\n",
- "import re\n",
- "import cadnano\n",
- "from cadnano.document import Document\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "cb40f6b8",
- "metadata": {},
- "outputs": [],
- "source": [
- "from cadnano.views.pathview import pathstyles"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "ea7e8da0",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " _\n",
- " _____ ___ _| |___ ___\n",
- "| | _| . | | .'|\n",
- "|_|_|_|_| |___|_|_|__,| v1.0a.dev74 \n",
- "it/its\n",
- "\n"
- ]
- }
- ],
- "source": [
- "import cadnano\n",
- "from cadnano.document import Document\n",
- "from mrdna.arbdmodel.coords import readArbdCoords, readAvgArbdCoords, rotationAboutAxis"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "3a134bd9-0d8c-40b2-bf71-7b2310e09802",
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_lattice(part):\n",
- " lattice_type = None\n",
- " _gt = part.getGridType()\n",
- " try:\n",
- " lattice_type = _gt.name.lower()\n",
- " except:\n",
- " if _gt == 1:\n",
- " lattice_type = 'square'\n",
- " elif _gt == 2:\n",
- " lattice_type = 'honeycomb'\n",
- " else:\n",
- " print(lattice_type)\n",
- " return lattice_type\n",
- "\n",
- "\n",
- "def read_json_file(filename):\n",
- " import cadnano\n",
- " from cadnano.document import Document\n",
- "\n",
- " try:\n",
- " with open(filename) as ch:\n",
- " json_data = json.load(ch)\n",
- " except:\n",
- " with open(filename) as ch:\n",
- " content = \"\"\n",
- " for l in ch:\n",
- " l = re.sub(r\"'\", r'\"', l)\n",
- " # https://stackoverflow.com/questions/4033633/handling-lazy-json-in-python-expecting-property-name\n",
- " # l = re.sub(r\"{\\s*(\\w)\", r'{\"\\1', l)\n",
- " # l = re.sub(r\",\\s*(\\w)\", r',\"\\1', l)\n",
- " # l = re.sub(r\"(\\w):\", r'\\1\":', l)\n",
- " content += l+\"\\n\"\n",
- " json_data = json.loads(content)\n",
- "\n",
- " try:\n",
- " doc = Document()\n",
- " cadnano.fileio.v3decode.decode(doc, json_data)\n",
- " decoder = 3\n",
- " except:\n",
- " doc = Document()\n",
- " cadnano.fileio.v2decode.decode(doc, json_data)\n",
- " decoder = 2\n",
- "\n",
- " parts = [p for p in doc.getParts()]\n",
- " if len(parts) != 1:\n",
- " raise Exception(\"Only documents containing a single cadnano part are implemented at this time.\")\n",
- " part = parts[0]\n",
- "\n",
- " if decoder == 2:\n",
- " \"\"\" It seems cadnano2.5 (as of ce6ff019) does not set the EulerZ for square lattice structures correctly, doing so here \"\"\"\n",
- " l = get_lattice(part)\n",
- " if l == 'square':\n",
- " for id_num in part.getIdNums():\n",
- " if part.vh_properties.loc[id_num,'eulerZ'] == 0:\n",
- " part.vh_properties.loc[id_num,'eulerZ'] = 360*(6/10.5)\n",
- " df=pd.DataFrame(json_data[\"vstrands\"])\n",
- " n_df=df.set_index(\"num\")\n",
- " return part\n",
- "\n",
- "def get_helix_angle(part, helix_id, indices):\n",
- " \"\"\" Get \"start_orientation\" for helix \"\"\"\n",
- " # import ipdb\n",
- " # ipdb.set_trace()\n",
- "\n",
- " \"\"\" FROM CADNANO2.5\n",
- " + angle is CCW\n",
- " - angle is CW\n",
- " Right handed DNA rotates clockwise from 5' to 3'\n",
- " we use the convention the 5' end starts at 0 degrees\n",
- " and it's pair is minor_groove_angle degrees away\n",
- " direction, hence the minus signs. eulerZ\n",
- " \"\"\"\n",
- "\n",
- " hp, bpr, tpr, eulerZ, mgroove = part.vh_properties.loc[helix_id,\n",
- " ['helical_pitch',\n",
- " 'bases_per_repeat',\n",
- " 'turns_per_repeat',\n",
- " 'eulerZ',\n",
- " 'minor_groove_angle']]\n",
- " twist_per_base = tpr*360./bpr\n",
- " # angle = eulerZ - twist_per_base*indices + 0.5*mgroove + 180\n",
- " angle = eulerZ + twist_per_base*indices - 0.5*mgroove\n",
- " return angle\n",
- "\n",
- "def gen_id_series(strand,part):\n",
- " df=pd.DataFrame(columns=[\"vh\",\"zid\",\"fwd\",\"stack_tuple\",\"threeprime_tuple\",\"x\",\"y\",\"z\"],index=range(strand.totalLength()),dtype=object)\n",
- " df[\"vh\"]=strand._id_num\n",
- " df[\"fwd\"]=strand.isForward()\n",
- " df[\"x\"]=part.getVirtualHelixOrigin(strand._id_num)[0]*10\n",
- " df[\"y\"]=part.getVirtualHelixOrigin(strand._id_num)[1]*10\n",
- " id_lo,id_hi=strand.idxs()\n",
- " zids=[str(i) for i in range(id_lo,id_hi+1)]\n",
- " insert_dict={}\n",
- " insert_dict=dict([(j.idx(),j.length()) for j in strand.insertionsOnStrand()])\n",
- " z=np.arange(id_lo,id_hi+1)\n",
- " zids=[str(i) for i in range(id_lo,id_hi+1)]\n",
- " z=list(np.arange(id_lo,id_hi+1))\n",
- " zids=[str(i) for i in range(id_lo,id_hi+1)]\n",
- " for insert_base in insert_dict:\n",
- " z_ind=zids.index(str(insert_base))\n",
- " z_val=insert_dict[insert_base]\n",
- " z_pos_ind=z.index(insert_base)\n",
- " zids.pop(z_ind)\n",
- " z.pop(z_pos_ind)\n",
- " if z_val!=-1:\n",
- " #l=[str(insert_base)+\".\"+str(i) for i in range(z_val+1)]\n",
- " l=list(range(z_val+1))\n",
- " l.reverse()\n",
- " for k in l: \n",
- " zids.insert(z_ind,str(insert_base)+\".\"+str(k))\n",
- " z.insert(z_pos_ind,insert_base+k/(z_val+1))\n",
- " df[\"zid\"]=zids\n",
- " df[\"z\"]=np.array(z)*3.4\n",
- " \n",
- " \n",
- " L=[(df[\"vh\"][i],df[\"zid\"][i],df[\"fwd\"][i]) for i in df.index]\n",
- " if strand.isForward()==True:\n",
- " df[\"stack_tuple\"]=L[1:]+[-1]\n",
- " if strand.connection3p() is None:\n",
- " df[\"threeprime_tuple\"]=L[1:]+[-1]\n",
- " else:\n",
- " df[\"threeprime_tuple\"]=L[1:]+[(strand.connection3p().idNum(),str(strand.connection3p().idx5Prime()),strand.connection3p().isForward())]\n",
- " \n",
- " \n",
- " else:\n",
- " df[\"stack_tuple\"]=[-1]+L[0:-1]\n",
- " if strand.connection3p() is None:\n",
- " df[\"threeprime_tuple\"]=[-1]+L[0:-1]\n",
- " else:\n",
- " df[\"threeprime_tuple\"]=[(strand.connection3p().idNum(),str(strand.connection3p().idx5Prime()),strand.connection3p().isForward())]+L[0:-1]\n",
- " ## cadnano 3.1 sequence assign is wrong if there is insertion or deletion. \n",
- " df[\"r\"]=[np.array([df[\"x\"][i],df[\"y\"][i],df[\"z\"][i]],dtype=np.float32) for i in df.index]\n",
- " \n",
- " return [pd.Series(df.loc[i]) for i in df.index]\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "04c497ae",
- "metadata": {},
- "outputs": [],
- "source": [
- "def gen_prop_table(part):\n",
- " strand_set=[]\n",
- " for i in part.getidNums():\n",
- " fwd,rev=part.getStrandSets(i)\n",
- " [strand_set.append(i) for i in fwd.strands()]\n",
- " [strand_set.append(i) for i in rev.strands()]\n",
- " id_series=[]\n",
- " for i in strand_set:\n",
- " id_series=id_series+gen_id_series(i,part)\n",
- " \n",
- " nt_prop=pd.DataFrame(id_series)\n",
- " nt_prop.reset_index(inplace=True)\n",
- " nt_prop[\"seq\"]=-1\n",
- " ind_tuple=list(zip(nt_prop[\"vh\"],nt_prop[\"zid\"],nt_prop[\"fwd\"]))\n",
- " stacks=[]\n",
- " for i in list(nt_prop[\"stack_tuple\"]):\n",
- " if i ==-1:\n",
- " stacks.append(i)\n",
- " else:\n",
- " stacks.append(ind_tuple.index(i))\n",
- " nt_prop[\"stack\"]=stacks\n",
- " tprime=[]\n",
- " for i in list(nt_prop[\"threeprime_tuple\"]):\n",
- " if i ==-1:\n",
- " tprime.append(i)\n",
- " else:\n",
- " tprime.append(ind_tuple.index(i))\n",
- " nt_prop[\"threeprime\"]=tprime\n",
- " vhzid=list(zip(nt_prop[\"vh\"],nt_prop[\"zid\"]))\n",
- " nt_prop[\"orientation\"]=[get_helix_angle(part, helix_id, int(float(indices))) for helix_id,indices in vhzid]\n",
- " nt_prop=nt_prop.fillna(-1)\n",
- " counter=-1\n",
- " bp=-np.ones(len(nt_prop.index),dtype=int)\n",
- " bp_map=dict(zip(ind_tuple,nt_prop.index))\n",
- " for i,j,k in ind_tuple:\n",
- " counter+=1\n",
- " try:\n",
- " bp[counter]=bp_map[(i,j,not(k))]\n",
- " except:\n",
- " pass\n",
- " nt_prop[\"bp\"]=bp\n",
- "\n",
- " return nt_prop"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "11b0f5de-80f6-4845-b97f-a9d31e7be90a",
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- "</table>\n",
- "<p>415 rows × 15 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " index vh zid fwd stack_tuple threeprime_tuple x y \\\n",
- "0 0 0 5 True (0, 6, True) (0, 6, True) 0.000000 22.50 \n",
- "1 1 0 6 True (0, 7, True) (0, 7, True) 0.000000 22.50 \n",
- "2 2 0 7 True (0, 8, True) (0, 8, True) 0.000000 22.50 \n",
- "3 3 0 8 True (0, 9, True) (0, 9, True) 0.000000 22.50 \n",
- "4 4 0 9 True (0, 10, True) (0, 10, True) 0.000000 22.50 \n",
- ".. ... .. .. ... ... ... ... ... \n",
- "410 12 5 35 False (5, 34, False) (5, 34, False) 19.485574 11.25 \n",
- "411 13 5 36 False (5, 35, False) (5, 35, False) 19.485574 11.25 \n",
- "412 14 5 37 False (5, 36, False) (5, 36, False) 19.485574 11.25 \n",
- "413 15 5 38 False (5, 37, False) (5, 37, False) 19.485574 11.25 \n",
- "414 16 5 39 False (5, 38, False) (5, 38, False) 19.485574 11.25 \n",
- "\n",
- " z r seq stack threeprime orientation \\\n",
- "0 17.0 [0.0, 22.5, 17.0] -1 1 1 81.428571 \n",
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- "4 30.6 [0.0, 22.5, 30.6] -1 5 5 218.571429 \n",
- ".. ... ... ... ... ... ... \n",
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- "411 122.4 [19.485573, 11.25, 122.4] -1 410 410 1144.285714 \n",
- "412 125.8 [19.485573, 11.25, 125.8] -1 411 411 1178.571429 \n",
- "413 129.2 [19.485573, 11.25, 129.2] -1 412 412 1212.857143 \n",
- "414 132.6 [19.485573, 11.25, 132.6] -1 413 413 1247.142857 \n",
- "\n",
- " bp \n",
- "0 38 \n",
- "1 39 \n",
- "2 40 \n",
- "3 41 \n",
- "4 42 \n",
- ".. ... \n",
- "410 375 \n",
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- "412 377 \n",
- "413 378 \n",
- "414 379 \n",
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- "[415 rows x 15 columns]"
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- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "gen_prop_table(p)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f2116b88",
- "metadata": {},
- "outputs": [],
- "source": [
- "import mrdna\n",
- "from mrdna.readers import read_list"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "4c954133",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(415,)"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.array(list(nt_prop['bp'])).shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 560,
- "id": "5ee54071",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([ 12, 13, 14, ..., 13920, 13921, 13922]),)"
- ]
- },
- "execution_count": 560,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.where(np.array(nt_prop[\"bp\"])!=-1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "e1588c54",
- "metadata": {},
- "outputs": [
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- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>410</th>\n",
- " <td>5</td>\n",
- " <td>35</td>\n",
- " <td>False</td>\n",
- " <td>[1.948557375, 1.125, 11.9]</td>\n",
- " <td>205</td>\n",
- " <td>-1</td>\n",
- " <td>411</td>\n",
- " <td>-1</td>\n",
- " <td>[[0.8660254037844375, -0.5000000000000019, 0.0...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>411</th>\n",
- " <td>5</td>\n",
- " <td>36</td>\n",
- " <td>False</td>\n",
- " <td>[1.948557375, 1.125, 12.24]</td>\n",
- " <td>206</td>\n",
- " <td>-1</td>\n",
- " <td>412</td>\n",
- " <td>-1</td>\n",
- " <td>[[0.4338837391175605, -0.900968867902418, 0.0]...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>412</th>\n",
- " <td>5</td>\n",
- " <td>37</td>\n",
- " <td>False</td>\n",
- " <td>[1.948557375, 1.125, 12.58]</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>413</td>\n",
- " <td>-1</td>\n",
- " <td>[[-0.14904226617617078, -0.9888308262251292, 0...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>413</th>\n",
- " <td>5</td>\n",
- " <td>38</td>\n",
- " <td>False</td>\n",
- " <td>[1.948557375, 1.125, 12.920000000000002]</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>414</td>\n",
- " <td>-1</td>\n",
- " <td>[[-0.6801727377709186, -0.7330518718298275, 0....</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>414</th>\n",
- " <td>5</td>\n",
- " <td>39</td>\n",
- " <td>False</td>\n",
- " <td>[1.948557375, 1.125, 13.260000000000002]</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>-1</td>\n",
- " <td>[[-0.9749279121818233, -0.222520933956317, 0.0...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>415 rows × 9 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " vh zid is_scaf r bp stack \\\n",
- "0 0 5 True [0.0, 2.25, 1.7000000000000002] 213 -1 \n",
- "1 0 6 True [0.0, 2.25, 2.04] 214 -1 \n",
- "2 0 7 True [0.0, 2.25, 2.3800000000000003] 215 -1 \n",
- "3 0 8 True [0.0, 2.25, 2.72] 216 -1 \n",
- "4 0 9 True [0.0, 2.25, 3.06] 217 -1 \n",
- ".. .. ... ... ... ... ... \n",
- "410 5 35 False [1.948557375, 1.125, 11.9] 205 -1 \n",
- "411 5 36 False [1.948557375, 1.125, 12.24] 206 -1 \n",
- "412 5 37 False [1.948557375, 1.125, 12.58] -1 -1 \n",
- "413 5 38 False [1.948557375, 1.125, 12.920000000000002] -1 -1 \n",
- "414 5 39 False [1.948557375, 1.125, 13.260000000000002] -1 -1 \n",
- "\n",
- " threeprime seq orientation \n",
- "0 1 -1 [[0.14904226617617466, -0.9888308262251284, 0.... \n",
- "1 2 -1 [[-0.4338837391175583, -0.900968867902419, 0.0... \n",
- "2 3 -1 [[-0.8660254037844388, -0.49999999999999994, 0... \n",
- "3 4 -1 [[-0.9972037971811805, 0.07473009358642399, 0.... \n",
- "4 5 -1 [[-0.7818314824680299, 0.6234898018587334, 0.0... \n",
- ".. ... ... ... \n",
- "410 411 -1 [[0.8660254037844375, -0.5000000000000019, 0.0... \n",
- "411 412 -1 [[0.4338837391175605, -0.900968867902418, 0.0]... \n",
- "412 413 -1 [[-0.14904226617617078, -0.9888308262251292, 0... \n",
- "413 414 -1 [[-0.6801727377709186, -0.7330518718298275, 0.... \n",
- "414 -1 -1 [[-0.9749279121818233, -0.222520933956317, 0.0... \n",
- "\n",
- "[415 rows x 9 columns]"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nt_prop"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 468,
- "id": "156dcda2",
- "metadata": {},
- "outputs": [],
- "source": [
- "scaf_id=[nttype(vslist['scaf'][i]) for i in vslist.index]\n",
- "stap_id=[nttype(vslist['stap'][i]) for i in vslist.index]\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 500,
- "id": "6413d856",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1"
- ]
- },
- "execution_count": 500,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nttype(vslist[\"scaf\"][30])[146]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 498,
- "id": "fe8797db",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0"
- ]
- },
- "execution_count": 498,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "vhi,zidi=np.where(np.array(scaf_id)==1)\n",
- "scaf_id[30][146]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 480,
- "id": "9f1b975f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 480,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "scaf_id[30][146]==np.array(scaf_id)[0][9]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 549,
- "id": "a16f3fd0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Int64Index([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
- " ...\n",
- " 39, 39, 39, 39, 39, 39, 39, 39, 39, 39],\n",
- " dtype='int64', name='num', length=7560)"
- ]
- },
- "execution_count": 549,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "def nttype(scafs):\n",
- " def judge(i):\n",
- " if i ==[-1,-1,-1,-1]:\n",
- " return 0\n",
- " else: return 1\n",
- " n=np.array([judge(i) for i in scafs])\n",
- " return n\n",
- "d={}\n",
- "vslist.index[vhi]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 544,
- "id": "b9f25d41",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([7394, 7395, 7396, 7397, 7398, 7399, 7400, 7401, 7402, 7403, 7404,\n",
- " 7405, 7406, 7407, 7408, 7409, 7410, 7411, 7412, 7413, 7414, 7415,\n",
- " 7416, 7417, 7418, 7419, 7420, 7421, 7422, 7423, 7424, 7425, 7426,\n",
- " 7427, 7428, 7429, 7430, 7431, 7432, 7433]),)"
- ]
- },
- "execution_count": 544,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.where(vslist.index[vhi]!=vhi)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 550,
- "id": "976095ce",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "vh 29\n",
- "zid 83\n",
- "is_scaf True\n",
- "r [-17.537016375, 28.125, 28.220000000000002]\n",
- "bp -1\n",
- "stack -1\n",
- "threeprime -1\n",
- "seq -1\n",
- "orientation [[-0.5633200580636211, 0.8262387743159955, 0.0...\n",
- "Name: 7394, dtype: object"
- ]
- },
- "execution_count": 550,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nt_prop.loc[7394]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 548,
- "id": "ac8f5067",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
- " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 29, 31, 32, 33,\n",
- " 34, 35, 36, 37, 38, 39, 41, 40, 42, 44, 46, 48, 50],\n",
- " dtype='int64', name='num')"
- ]
- },
- "execution_count": 548,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "vslist.index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 527,
- "id": "1006fc48",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([7394, 7395, 7396, 7397, 7398, 7399, 7400, 7401, 7402, 7403, 7404,\n",
- " 7405, 7406, 7407, 7408, 7409, 7410, 7411, 7412, 7413, 7414, 7415,\n",
- " 7416, 7417, 7418, 7419, 7420, 7421, 7422, 7423, 7424, 7425, 7426,\n",
- " 7427, 7428, 7429, 7430, 7431, 7432, 7433]),)"
- ]
- },
- "execution_count": 527,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "n=list(nt_prop[\"zid\"])\n",
- "np.where(np.array(list(nt_prop[\"vh\"]))==29)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 503,
- "id": "09c7e7d4",
- "metadata": {},
- "outputs": [
- {
- "ename": "ValueError",
- "evalue": "((30, 146), True) is not in list",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-503-1b9956d4cdaf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtprime_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnt_prop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m146\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m: ((30, 146), True) is not in list"
- ]
- }
- ],
- "source": [
- "vhzid=list(zip(nt_prop[\"vh\"],nt_prop[\"zid\"]))\n",
- "index2=list(zip(vhzid,nt_prop[\"is_scaf\"]))\n",
- "tprime_list=-np.ones(len(nt_prop.index),dtype=int)\n",
- " \n",
- "print(index2.index(((30,146),(True))))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 537,
- "id": "5f0c5266",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "11 135 30 146 3010\n"
- ]
- },
- {
- "ename": "ValueError",
- "evalue": "((30, 146), True) is not in list",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-537-f07d5cbf0867>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mtprime_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mValueError\u001b[0m: ((30, 146), True) is not in list"
- ]
- }
- ],
- "source": [
- " \n",
- "for i in range(len(nt_prop.index)):\n",
- " ((m,n),p)=list(zip(vhzid,nt_prop[\"is_scaf\"]))[i]\n",
- " if p==True:\n",
- " k,l=(vslist[\"scaf\"][m])[n][2:]\n",
- " if k==30 and l==146:\n",
- " print(m,n,k,l,i)\n",
- " if k!=-1 and l!=-1:\n",
- " n=index2.index(((k,l),True))\n",
- " tprime_list[i]=int(n)\n",
- "\n",
- " else:\n",
- " k,l=(vslist[\"stap\"][m])[n][2:]\n",
- " if k!=-1 and l!=-1:\n",
- " n=index2.index(((k,l),False))\n",
- " tprime_list[i]=int(n)\n",
- "nt_prop[\"threeprime\"]=tprime_list\n",
- "(n,)=np.where(nt_prop[\"threeprime\"]==-1)\n",
- "stackid=nt_prop[\"bp\"][[list(nt_prop[\"threeprime\"]).index(i) for i in n]]\n",
- "nt_prop[\"stack\"][stackid.index[np.where(np.array(stackid)!=-1)]]=nt_prop[\"threeprime\"][stackid.index[np.where(np.array(stackid)!=-1)]]\n",
- " ## Todo: sequence "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 491,
- "id": "fec987da",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[11, 135, 30, 147]"
- ]
- },
- "execution_count": 491,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "list(vslist.loc[30][\"scaf\"])[146]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 493,
- "id": "f332ad87",
- "metadata": {},
- "outputs": [
- {
- "ename": "ValueError",
- "evalue": "(30, 146) is not in list",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-493-d1dd239124c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvhzid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m146\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m: (30, 146) is not in list"
- ]
- }
- ],
- "source": [
- "vhzid.index((30,146))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "dd3cd839",
- "metadata": {},
- "outputs": [],
- "source": [
- "df=pd.DataFrame(data=d)\n",
- "df=df.set_index(\"num\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "41b3d9af",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "9ec18edc",
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_lattice(part):\n",
- " lattice_type = None\n",
- " _gt = part.getGridType()\n",
- " try:\n",
- " lattice_type = _gt.name.lower()\n",
- " except:\n",
- " if _gt == 1:\n",
- " lattice_type = 'square'\n",
- " elif _gt == 2:\n",
- " lattice_type = 'honeycomb'\n",
- " else:\n",
- " print(\"WARNING: unable to determine cadnano part lattice type\")\n",
- " return lattice_type\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "2fa31a78",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found cadnano version 2 file\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "NucleicAcidPart_-1_2800"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "p=read_json_file(\"test/test.json\")\n",
- "p"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 441,
- "id": "64eb309f",
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " <th>col</th>\n",
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- " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [3, 2, 2,...</td>\n",
- " <td>[[2, 1, -1, -1], [2, 2, 2, 0], [2, 3, 2, 1], [...</td>\n",
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- " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
- " <td>[]</td>\n",
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- " <td>[[0, 13369344]]</td>\n",
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- " <td>17</td>\n",
- " <td>[[-1, -1, 4, 1], [4, 0, 4, 2], [4, 1, 4, 3], [...</td>\n",
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- " <td>[]</td>\n",
- " <td>[]</td>\n",
- " <td>[[9, 0]]</td>\n",
- " </tr>\n",
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- ],
- "text/plain": [
- " row col scaf \\\n",
- "num \n",
- "0 12 16 [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ... \n",
- "1 12 15 [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ... \n",
- "2 13 15 [[-1, -1, -1, -1], [-1, -1, -1, -1], [3, 2, 2,... \n",
- "3 13 16 [[-1, -1, -1, -1], [-1, -1, -1, -1], [3, 3, 2,... \n",
- "4 13 17 [[-1, -1, 4, 1], [4, 0, 4, 2], [4, 1, 4, 3], [... \n",
- "5 12 17 [[5, 1, -1, -1], [5, 2, 5, 0], [5, 3, 5, 1], [... \n",
- "\n",
- " stap \\\n",
- "num \n",
- "0 [[-1, -1, -1, -1], [-1, -1, -1, -1], [0, 3, -1... \n",
- "1 [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ... \n",
- "2 [[2, 1, -1, -1], [2, 2, 2, 0], [2, 3, 2, 1], [... \n",
- "3 [[-1, -1, 3, 1], [3, 0, 3, 2], [3, 1, 3, 3], [... \n",
- "4 [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ... \n",
- "5 [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ... \n",
- "\n",
- " loop \\\n",
- "num \n",
- "0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n",
- "1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n",
- "2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n",
- "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n",
- "4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n",
- "5 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n",
- "\n",
- " skip scafLoop stapLoop \\\n",
- "num \n",
- "0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "5 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "\n",
- " stap_colors \n",
- "num \n",
- "0 [[23, 13369809], [38, 12060012]] \n",
- "1 [[3, 1501302]] \n",
- "2 [[34, 8947848]] \n",
- "3 [[0, 13369344]] \n",
- "4 [[39, 8947848]] \n",
- "5 [[9, 0]] "
- ]
- },
- "execution_count": 441,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "f"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 199,
- "id": "bda3cddd",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0. , 2.25, 3.4 ])"
- ]
- },
- "execution_count": 199,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "p.getCoordinate(0,10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 434,
- "id": "a86cfa84",
- "metadata": {},
- "outputs": [],
- "source": [
- "def mrdna_model_from_cadnano(json_file,**model_parameters):\n",
- " part,vslist=read_json_file(json_file)\n",
- " props = part.getModelProperties().copy()\n",
- " try:\n",
- " if props.get('point_type') == PointType.ARBITRARY:\n",
- " # TODO add code to encode Parts with ARBITRARY point configurations\n",
- " raise NotImplementedError(\"Not implemented\")\n",
- " except:\n",
- " try:\n",
- " vh_props, origins = part.helixPropertiesAndOrigins()\n",
- " except:\n",
- " origins = {hid:part.getVirtualHelixOrigin(hid)[:2] for hid in part.getidNums()}\n",
- " scaf_id=[nttype(vslist['scaf'][i]) for i in vslist.index]\n",
- " stap_id=[nttype(vslist['stap'][i]) for i in vslist.index]\n",
- " cad_bps=part.getIndices(0)\n",
- " vslist[\"scafnt\"]=np.sum(np.array(scaf_id),axis=1)\n",
- " vslist[\"stapnt\"]=np.sum(np.array(stap_id),axis=1)\n",
- " totnt=np.sum(vslist[\"scafnt\"])+np.sum(vslist[\"stapnt\"])\n",
- " is_scaf=np.zeros(totnt,dtype=bool)\n",
- " is_scaf[0:np.sum(vslist[\"scafnt\"])]=1\n",
- " nt_prop=pd.DataFrame(index=range(totnt),columns=[\"vh\",\"zid\",\"is_scaf\",\"r\",\"bp\",\"stack\",\"threeprime\",\"seq\",\"orientation\"])\n",
- " nt_prop[\"is_scaf\"]=is_scaf\n",
- " tot_id=scaf_id+stap_id\n",
- " vhi,zidi=np.where(np.array(scaf_id)==1)\n",
- " vhj,zidj=np.where(np.array(stap_id)==1)\n",
- " nt_prop[\"vh\"]=list(vhi)+list(vhj)\n",
- " nt_prop[\"zid\"]=list(zidi)+list(zidj)\n",
- " vhzid=list(zip(nt_prop[\"vh\"],nt_prop[\"zid\"]))\n",
- " nt_prop[\"r\"]=[part.getCoordinate(i,j) for i,j in zip(nt_prop[\"vh\"],nt_prop[\"zid\"])]\n",
- " nt_prop[\"orientation\"]=[get_helix_angle(part, helix_id, indices) for i,j in zip(nt_prop[\"vh\"],nt_prop[\"zid\"])]\n",
- " nt_prop=nt_prop.fillna(-1)\n",
- " for i in range(int(len(vhzid)/2)):\n",
- " try:\n",
- " bp1,bp2=(i,1+i+vhzid[i+1:].index(vhzid[i]))\n",
- " nt_prop[\"bp\"][bp1]=bp2\n",
- " nt_prop[\"bp\"][bp2]=bp1\n",
- " except:\n",
- " pass\n",
- " tprime_list=-np.ones(len(nt_prop.index),dtype=int)\n",
- " for i in range(len(nt_prop.index)):\n",
- " ((m,n),p)=list(zip(vhzid,nt_prop[\"is_scaf\"]))[i]\n",
- " if p==True:\n",
- " k,l=(vslist[\"scaf\"][m])[n][2:]\n",
- " if k!=-1 and l!=-1:\n",
- " n=index2.index(((k,l),True))\n",
- " tprime_list[i]=int(n)\n",
- "\n",
- " else:\n",
- " k,l=(vslist[\"stap\"][m])[n][2:]\n",
- " if k!=-1 and l!=-1:\n",
- " n=index2.index(((k,l),False))\n",
- " tprime_list[i]=int(n)\n",
- " nt_prop[\"threeprime\"]=tprime_list\n",
- " (n,)=np.where(nt_prop[\"threeprime\"]==-1)\n",
- " stackid=nt_prop[\"bp\"][[list(nt_prop[\"threeprime\"]).index(i) for i in n]]\n",
- " nt_prop[\"stack\"][stackid.index[np.where(np.array(stackid)!=-1)]]=nt_prop[\"threeprime\"][stackid.index[np.where(np.array(stackid)!=-1)]]\n",
- "\n",
- "\n",
- " return nt_prop\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 442,
- "id": "be5de5ba",
- "metadata": {},
- "outputs": [
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- "data": {
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- "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
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- " -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
- " -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
- " -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
- " -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
- " -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
- " -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
- " -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
- " -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
- " -1, -1, -1, -1, -1, -1, -1])"
- ]
- },
- "execution_count": 442,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.array(nt_prop[\"seq\"])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 302,
- "id": "0ce6701d",
- "metadata": {},
- "outputs": [],
- "source": [
- "tprime_list=-np.ones(len(nt_prop.index),dtype=int)\n",
- "for i in range(len(nt_prop.index)):\n",
- " ((m,n),p)=list(zip(vhzid,nt_prop[\"is_scaf\"]))[i]\n",
- " if p==True:\n",
- " k,l=(vslist[\"scaf\"][m])[n][2:]\n",
- " if k!=-1 and l!=-1:\n",
- " n=index2.index(((k,l),True))\n",
- " tprime_list[i]=int(n)\n",
- " \n",
- " else:\n",
- " k,l=(vslist[\"stap\"][m])[n][2:]\n",
- " if k!=-1 and l!=-1:\n",
- " n=index2.index(((k,l),False))\n",
- " tprime_list[i]=int(n)\n",
- "nt_prop[\"threeprime\"]=tprime_list"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 368,
- "id": "9d0e49cf",
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_helix_angle(part, helix_id, indices):\n",
- " \"\"\" Get \"start_orientation\" for helix \"\"\"\n",
- " # import ipdb\n",
- " # ipdb.set_trace()\n",
- "\n",
- " \"\"\" FROM CADNANO2.5\n",
- " + angle is CCW\n",
- " - angle is CW\n",
- " Right handed DNA rotates clockwise from 5' to 3'\n",
- " we use the convention the 5' end starts at 0 degrees\n",
- " and it's pair is minor_groove_angle degrees away\n",
- " direction, hence the minus signs. eulerZ\n",
- " \"\"\"\n",
- "\n",
- " hp, bpr, tpr, eulerZ, mgroove = part.vh_properties.loc[helix_id,\n",
- " ['helical_pitch',\n",
- " 'bases_per_repeat',\n",
- " 'turns_per_repeat',\n",
- " 'eulerZ',\n",
- " 'minor_groove_angle']]\n",
- " twist_per_base = tpr*360./bpr\n",
- " # angle = eulerZ - twist_per_base*indices + 0.5*mgroove + 180\n",
- " angle = eulerZ + twist_per_base*indices - 0.5*mgroove\n",
- " return rotationAboutAxis(np.array((0,0,1)),angle)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 429,
- "id": "c1d77642",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "(n,)=np.where(nt_prop[\"threeprime\"]==-1)\n",
- "\n",
- "stackid=nt_prop[\"bp\"][[list(nt_prop[\"threeprime\"]).index(i) for i in n]]\n",
- "\n",
- "nt_prop[\"stack\"][stackid.index[np.where(np.array(stackid)!=-1)]]=nt_prop[\"threeprime\"][stackid.index[np.where(np.array(stackid)!=-1)]]\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 430,
- "id": "2c2d1227",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "33 -1\n",
- "68 -1\n",
- "102 -1\n",
- "136 -1\n",
- "142 -1\n",
- "176 -1\n",
- "194 399\n",
- "211 -1\n",
- "233 20\n",
- "281 -1\n",
- "284 -1\n",
- "351 -1\n",
- "354 145\n",
- "413 -1\n",
- "Name: bp, dtype: int64"
- ]
- },
- "execution_count": 430,
- "metadata": {},
- "output_type": "execute_result"
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- "stackid"
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- "cell_type": "code",
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- "data": {
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- "232"
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- "execution_count": 431,
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- "source": [
- "nt_prop[\"stack\"][233]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 433,
- "id": "4a62f5d9",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "353"
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- "execution_count": 433,
- "metadata": {},
- "output_type": "execute_result"
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- "source": [
- "nt_prop[\"stack\"][354]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 167,
- "id": "d80ab792",
- "metadata": {},
- "outputs": [],
- "source": [
- "scaf_id=[nttype(vslist[\"scaf\"][i]) for i in vslist.index]\n",
- "stap_id=[nttype(vslist[\"stap\"][i]) for i in vslist.index]\n",
- "nts=scaf_id+stap_id"
- ]
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- {
- "cell_type": "code",
- "execution_count": 360,
- "id": "8e009bc9",
- "metadata": {},
- "outputs": [],
- "source": [
- "nt_prop[\"orientation\"]=[get_helix_angle(p,i,j) for i,j in zip(nt_prop[\"vh\"],nt_prop[\"zid\"])]\n"
- ]
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- {
- "cell_type": "code",
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- " row col scaf \\\n",
- "num \n",
- "0 12 16 [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ... \n",
- "1 12 15 [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ... \n",
- "2 13 15 [[-1, -1, -1, -1], [-1, -1, -1, -1], [3, 2, 2,... \n",
- "3 13 16 [[-1, -1, -1, -1], [-1, -1, -1, -1], [3, 3, 2,... \n",
- "4 13 17 [[-1, -1, 4, 1], [4, 0, 4, 2], [4, 1, 4, 3], [... \n",
- "5 12 17 [[5, 1, -1, -1], [5, 2, 5, 0], [5, 3, 5, 1], [... \n",
- "\n",
- " stap \\\n",
- "num \n",
- "0 [[-1, -1, -1, -1], [-1, -1, -1, -1], [0, 3, -1... \n",
- "1 [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ... \n",
- "2 [[2, 1, -1, -1], [2, 2, 2, 0], [2, 3, 2, 1], [... \n",
- "3 [[-1, -1, 3, 1], [3, 0, 3, 2], [3, 1, 3, 3], [... \n",
- "4 [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ... \n",
- "5 [[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ... \n",
- "\n",
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- "2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n",
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- "\n",
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- "0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "5 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... [] [] \n",
- "\n",
- " stap_colors \n",
- "num \n",
- "0 [[23, 13369809], [38, 12060012]] \n",
- "1 [[3, 1501302]] \n",
- "2 [[34, 8947848]] \n",
- "3 [[0, 13369344]] \n",
- "4 [[39, 8947848]] \n",
- "5 [[9, 0]] "
- ]
- },
- "execution_count": 190,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "vslist"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 200,
- "id": "3d019b73",
- "metadata": {},
- "outputs": [],
- "source": [
- "def mrdna_model_from_cadnano(json_data,**model_parameters):\n",
- " part,vslist=decode_cadnano_part(json_data)\n",
- " props = part.getModelProperties().copy()\n",
- "\n",
- " if props.get('point_type') == PointType.ARBITRARY:\n",
- " # TODO add code to encode Parts with ARBITRARY point configurations\n",
- " raise NotImplementedError(\"Not implemented\")\n",
- " else:\n",
- " try:\n",
- " vh_props, origins = part.helixPropertiesAndOrigins()\n",
- " except:\n",
- " origins = {hid:part.getVirtualHelixOrigin(hid)[:2] for hid in part.getidNums()}\n",
- " scaf_id=np.array([nttype(vslist['scaf'][i]) for i in vslist.index])\n",
- " stap_id=np.array([nttype(vslist['stap'][i]) for i in vslist.index])\n",
- " cad_bps=part.getIndices(0)\n",
- " vslist[\"scafnt\"]=np.sum(scaf_id,axis=1)\n",
- " vslist[\"stapnt\"]=np.sum(stap_id,axis=1)\n",
- " totnt=np.sum(vslist[\"scafnt\"])+np.sum(vslist[\"stapnt\"])\n",
- " is_scaf=np.zeros(totnt)\n",
- " is_scaf[0:np.sum(vslist[\"scafnt\"])]=1\n",
- " nt_prop=pd.DataFrame(index=range(totnt),columns=[\"vh\",\"zid\",\"is_scaf\",\"r\",\"bp\",\"stack\",\"threeprime\",\"seq\",\"orientation\"])\n",
- " nt_prop[\"is_scaf\"]=is_scaf\n",
- " vhi,zids=np.where(np.array(scaf_id+stap_id)==1)\n",
- " nt_prop[\"vh\"]=vhi\n",
- " nt_prop[\"zid\"]=zids\n",
- " nt_prop[\"r\"] =part.getCoordinate(nt_prop[\"vh\"],nt_prop[\"zid\"])\n",
- " return nt_prop\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 201,
- "id": "fb789ffb",
- "metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'decode_cadnano_part' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-201-c5d589a8b80d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmrdna_model_from_cadnano\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"test.json\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m<ipython-input-200-181a924488ad>\u001b[0m in \u001b[0;36mmrdna_model_from_cadnano\u001b[0;34m(json_data, **model_parameters)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmrdna_model_from_cadnano\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mmodel_parameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpart\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvslist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_cadnano_part\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprops\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetModelProperties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'point_type'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mPointType\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mARBITRARY\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mNameError\u001b[0m: name 'decode_cadnano_part' is not defined"
- ]
- }
- ],
- "source": [
- "mrdna_model_from_cadnano(\"test.json\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 146,
- "id": "98703867",
- "metadata": {},
- "outputs": [],
- "source": [
- "a,b=np.where(np.array(nts)==1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 148,
- "id": "c316fead",
- "metadata": {},
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- " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
- " 34, 35, 36, 37, 38, 39])"
- ]
- },
- "execution_count": 148,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "nt_prop=pd.DataFrame(index)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 137,
- "id": "0718e41e",
- "metadata": {},
- "outputs": [],
- "source": [
- "def nttype(scafs):\n",
- " def judge(i):\n",
- " if i ==[-1,-1,-1,-1]:\n",
- " return 0\n",
- " else: return 1\n",
- " n=np.array([judge(i) for i in scafs])\n",
- " return n\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1a61115e",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 84,
- "id": "12198835",
- "metadata": {},
- "outputs": [],
- "source": [
- "b[\"scafnt\"]=[ntcount(b['scaf'][i]) for i in b.index]\n",
- "b[\"stapnt\"]=[ntcount(b['stap'][i]) for i in b.index]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 156,
- "id": "1e5c9807",
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- "i=range(5)\n",
- "col=[\"vh\",\"zid\",\"r\",\"bp\",\"stack\",\"threeprime\",\"seq\",\"orientation\"]\n",
- "d=pd.DataFrame(index=i,columns=col)\n",
- "d['vh']=[0,1,2,3,1]\n",
- "d['zid']=[0,3,1,2,8]\n",
- "d.set_index([\"vh\",\"zid\"],inplace=True)\n",
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- "s=[True,False,True,False,False]\n",
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- " r bp stack threeprime seq orientation\n",
- "vh zid \n",
- "0 0 NaN NaN NaN NaN NaN NaN\n",
- "1 3 NaN NaN NaN NaN NaN NaN\n",
- "2 1 NaN NaN NaN NaN NaN NaN\n",
- "3 2 NaN NaN NaN NaN NaN NaN\n",
- "1 8 NaN NaN NaN NaN NaN NaN"
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- "ename": "KeyError",
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- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-100-a349feadc600>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m/data/server1/cmaffeo2/miniconda3/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3509\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3510\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3511\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3513\u001b[0m \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/data/server1/cmaffeo2/miniconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m 5780\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5782\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_missing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5783\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5784\u001b[0m \u001b[0mkeyarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/data/server1/cmaffeo2/miniconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m 5840\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5841\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5842\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5843\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5844\u001b[0m \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mKeyError\u001b[0m: \"None of [Int64Index([0, 0], dtype='int64')] are in the [columns]\""
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- "df[\"vstrands\"][0][\"stap_colors\"]"
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- "id": "00f1513e",
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- "vh_vb,pattern=pd.read_pickle(\"test.virt2nuc\")\n",
- "len(vh_vb._scaf)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 198,
- "id": "aaa65658",
- "metadata": {},
- "outputs": [
- {
- "ename": "TypeError",
- "evalue": "file must have 'read' and 'readline' attributes",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[198], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpickle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtest.virt2nuc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
- "\u001b[0;31mTypeError\u001b[0m: file must have 'read' and 'readline' attributes"
- ]
- }
- ],
- "source": [
- "df = pickle.load(\"test.virt2nuc\")"
- ]
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- "execution_count": 15,
- "id": "cbddf07f",
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- {
- "data": {
- "text/plain": [
- "{0: (12, 16), 1: (12, 15), 2: (13, 15), 3: (13, 16), 4: (13, 17), 5: (12, 17)}"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
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- "pattern"
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- {
- "cell_type": "code",
- "execution_count": 173,
- "id": "fac8699e",
- "metadata": {},
- "outputs": [],
- "source": [
- "class strands():\n",
- " def __init__(self):\n",
- " self.row=0 \n",
- " self.col=0\n",
- " self.num=0\n",
- " self.scaf=[]\n",
- " self.stap=[]\n",
- " self.loop=[]\n",
- " self.skip=[]\n",
- " self.scafLoop=[]\n",
- " self.stapLoop=[]\n",
- " self.stap_colors=[]\n",
- " self.scaf_contact={}\n",
- " self.stap_connect={}\n",
- " def to_dict(self):\n",
- " d={}\n",
- " d['row']=self.row\n",
- " d['col']=self.col\n",
- " d['num']=self.num\n",
- " d['scaf']=self.scaf\n",
- " d['stap']=self.stap\n",
- " d['loop']=self.loop\n",
- " d['skip']=self.skip\n",
- " d['scafLoop']=self.scafLoop\n",
- " d['stapLoop']=self.stapLoop\n",
- " d['stap_colors']=self.stap_colors\n",
- " return d\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 177,
- "id": "308cd6c1",
- "metadata": {},
- "outputs": [],
- "source": [
- "def find_segs(vir2nuc_scaf):\n",
- " oligos={}\n",
- " for i in range(len(vir2nuc_scaf)):\n",
- " oligo,ox_ind=list(vir2nuc_scaf.values())[i]\n",
- " if oligo not in oligos.keys():\n",
- " oligos[oligo]=[]\n",
- " oligos[oligo].append(list(vir2nuc_scaf.keys())[i])\n",
- " return oligos\n",
- "\n",
- "#class\n",
- "def decode_vh_vb(virt2nuc):\n",
- " vh_list={}\n",
- " vh_vb,pattern=pd.read_pickle(virt2nuc)\n",
- " for i in pattern.keys():\n",
- " s=strands()\n",
- " s.row,s.col=pattern[i]\n",
- " s.num=i\n",
- " vh_list[s.num]=s\n",
- " scafs=vh_vb._scaf\n",
- " staps=vh_vb._stap\n",
- " scaf_strands=find_segs(scafs)\n",
- " scaf_oligos=list(scaf_strands.keys())\n",
- " for i in scaf_oligos:\n",
- " pass\n",
- " \n",
- " \n",
- " return vh_list"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 187,
- "id": "bc032680",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
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- " [(5, 9),\n",
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- " [(3, 0),\n",
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- " (3, 32),\n",
- " (3, 33),\n",
- " (3, 34)]]"
- ]
- },
- "execution_count": 187,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s1=decode_vh_vb(\"test.virt2nuc\")\n",
- "vh_vb,pattern=pd.read_pickle(\"test.virt2nuc\")\n",
- "list(find_segs(vh_vb._stap).values())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 142,
- "id": "29ff7990",
- "metadata": {},
- "outputs": [],
- "source": [
- "def find_segs(vir2nuc_scaf):\n",
- " oligos={}\n",
- " for i in range(len(vir2nuc_scaf)):\n",
- " oligo,ox_ind=list(vir2nuc_scaf.values())[i]\n",
- " if oligo not in oligos.keys():\n",
- " oligos[oligo]=[]\n",
- " oligos[oligo].append(list(vir2nuc_scaf.keys())[i])\n",
- " return oligos\n",
- "\n",
- "def decode_vh_vb(virt2nuc):\n",
- " vh_vb,pattern=pd.read_pickle(virt2nuc)\n",
- " vi={'row':0, 'col':0, 'num':0, 'scaf':dict(), 'stap':dict(), 'loop':[], 'skip':[], 'scafLoop':[],'stapLoop':[], 'stap_colors':[],\"scaf53\":True}\n",
- " vs=[] \n",
- " for i in range(len(pattern.keys())):\n",
- " vhi=vi.copy()\n",
- " vhi[\"row\"],vhi[\"col\"]=list(pattern.values())[i]\n",
- " vhi[\"num\"]=list(pattern.keys())[i]\n",
- " vs.append(vhi)\n",
- " vhelices=pd.DataFrame(vs)\n",
- " vhelices=vhelices.set_index('num')\n",
- " scafs=vh_vb._scaf\n",
- " staps=vh_vb._stap\n",
- " scaf_strands=find_segs(scafs)\n",
- " stap_strands=find_segs(staps)\n",
- " scaf_oligos=list(scaf_strands.keys())\n",
- "\n",
- " \n",
- " return vhelices"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 117,
- "id": "3cb9542c",
- "metadata": {},
- "outputs": [],
- "source": [
- "def find_base_map(oligo,i,vhx,scaf=True):\n",
- " vh0,vb0=oligo[i]\n",
- " vh1,vb1=oligo[i+1]\n",
- " if scaf==True:\n",
- " if vb0 not in vhx[\"scaf\"][vh0].keys():\n",
- " \n",
- " if vh0==vh1 and scaf==True:\n",
- " if vb0>vb1:\n",
- " vhx[vh0][\"scaf\"][vb0]=\n",
- " \n",
- "\n",
- " \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 116,
- "id": "4219838b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
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- " (5, 38),\n",
- " (5, 37),\n",
- " (5, 36),\n",
- " (5, 35),\n",
- " (5, 34),\n",
- " (5, 33),\n",
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- " (5, 23)]"
- ]
- },
- "execution_count": 116,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "scafs=vh_vb._scaf\n",
- "s=list(scafs.values())\n",
- "len(scafs)\n",
- "ss=find_segs(scafs)[7]\n",
- "ss"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 157,
- "id": "45168e4b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[(1, 2)]"
- ]
- },
- "execution_count": 157,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "oligos[0]=[]\n",
- "L=[]\n",
- "L.append((1,2))\n",
- "L"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 152,
- "id": "33e5c80d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(0, 39)"
- ]
- },
- "execution_count": 152,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "list(scafs.keys())[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "id": "9b79e902",
- "metadata": {},
- "outputs": [
- {
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- " (1, 29): (9, [291]),\n",
- " (1, 30): (9, [290]),\n",
- " (1, 31): (9, [289]),\n",
- " (1, 32): (9, [288]),\n",
- " (1, 33): (9, [287]),\n",
- " (1, 34): (9, [286]),\n",
- " (1, 35): (9, [285]),\n",
- " (1, 36): (9, [284]),\n",
- " (1, 37): (9, [283]),\n",
- " (1, 38): (9, [282]),\n",
- " (5, 9): (10, [325]),\n",
- " (5, 10): (10, [324]),\n",
- " (5, 11): (10, [323]),\n",
- " (5, 12): (10, [322]),\n",
- " (5, 13): (10, [321]),\n",
- " (5, 14): (10, [320]),\n",
- " (5, 15): (10, [319]),\n",
- " (5, 16): (10, [318]),\n",
- " (5, 17): (10, [317]),\n",
- " (5, 18): (10, [316]),\n",
- " (5, 19): (10, [315]),\n",
- " (5, 20): (10, [314]),\n",
- " (5, 21): (10, [313]),\n",
- " (5, 22): (10, [312]),\n",
- " (5, 23): (10, [311]),\n",
- " (5, 24): (10, [310]),\n",
- " (5, 25): (10, [309]),\n",
- " (5, 26): (10, [308]),\n",
- " (5, 27): (10, [307]),\n",
- " (0, 27): (10, [306]),\n",
- " (0, 26): (10, [305]),\n",
- " (0, 25): (10, [304]),\n",
- " (0, 24): (10, [303]),\n",
- " (0, 38): (11, [348]),\n",
- " (0, 37): (11, [347]),\n",
- " (0, 36): (11, [346]),\n",
- " (0, 35): (11, [345]),\n",
- " (0, 34): (11, [344]),\n",
- " (0, 33): (11, [343]),\n",
- " (0, 32): (11, [342]),\n",
- " (0, 31): (11, [341]),\n",
- " (0, 30): (11, [340]),\n",
- " (0, 29): (11, [339]),\n",
- " (0, 28): (11, [338]),\n",
- " (5, 28): (11, [337]),\n",
- " (5, 29): (11, [336]),\n",
- " (5, 30): (11, [335]),\n",
- " (5, 31): (11, [334]),\n",
- " (5, 32): (11, [333]),\n",
- " (5, 33): (11, [332]),\n",
- " (5, 34): (11, [331]),\n",
- " (5, 35): (11, [330]),\n",
- " (5, 36): (11, [329]),\n",
- " (5, 37): (11, [328]),\n",
- " (5, 38): (11, [327]),\n",
- " (5, 39): (11, [326]),\n",
- " (3, 0): (12, [381]),\n",
- " (3, 1): (12, [380]),\n",
- " (3, 2): (12, [379]),\n",
- " (3, 3): (12, [378]),\n",
- " (3, 4): (12, [377]),\n",
- " (3, 5): (12, [376]),\n",
- " (3, 6): (12, [375]),\n",
- " (3, 7): (12, [374]),\n",
- " (3, 8): (12, [373]),\n",
- " (3, 9): (12, [372]),\n",
- " (3, 10): (12, [371]),\n",
- " (3, 11): (12, [370]),\n",
- " (3, 12): (12, [369]),\n",
- " (3, 13): (12, [368]),\n",
- " (3, 14): (12, [367]),\n",
- " (3, 15): (12, [366]),\n",
- " (3, 16): (12, [365]),\n",
- " (3, 17): (12, [364]),\n",
- " (3, 18): (12, [363]),\n",
- " (3, 19): (12, [362]),\n",
- " (3, 20): (12, [361]),\n",
- " (4, 20): (12, [360]),\n",
- " (4, 19): (12, [359]),\n",
- " (4, 18): (12, [358]),\n",
- " (4, 17): (12, [357]),\n",
- " (4, 16): (12, [356]),\n",
- " (4, 15): (12, [355]),\n",
- " (4, 14): (12, [354]),\n",
- " (4, 13): (12, [353]),\n",
- " (4, 12): (12, [352]),\n",
- " (4, 11): (12, [351]),\n",
- " (4, 10): (12, [350]),\n",
- " (4, 9): (12, [349]),\n",
- " (4, 39): (13, [414]),\n",
- " (4, 38): (13, [413]),\n",
- " (4, 37): (13, [412]),\n",
- " (4, 36): (13, [411]),\n",
- " (4, 35): (13, [410]),\n",
- " (4, 34): (13, [409]),\n",
- " (4, 33): (13, [408]),\n",
- " (4, 32): (13, [407]),\n",
- " (4, 31): (13, [406]),\n",
- " (4, 30): (13, [405]),\n",
- " (4, 29): (13, [404]),\n",
- " (4, 28): (13, [403]),\n",
- " (4, 27): (13, [402]),\n",
- " (4, 26): (13, [401]),\n",
- " (4, 25): (13, [400]),\n",
- " (4, 24): (13, [399]),\n",
- " (4, 23): (13, [398]),\n",
- " (4, 22): (13, [397]),\n",
- " (4, 21): (13, [396]),\n",
- " (3, 21): (13, [395]),\n",
- " (3, 22): (13, [394]),\n",
- " (3, 23): (13, [393]),\n",
- " (3, 24): (13, [392]),\n",
- " (3, 25): (13, [391]),\n",
- " (3, 26): (13, [390]),\n",
- " (3, 27): (13, [389]),\n",
- " (3, 28): (13, [388]),\n",
- " (3, 29): (13, [387]),\n",
- " (3, 30): (13, [386]),\n",
- " (3, 31): (13, [385]),\n",
- " (3, 32): (13, [384]),\n",
- " (3, 33): (13, [383]),\n",
- " (3, 34): (13, [382])}"
- ]
- },
- "execution_count": 62,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s=vh_vb.__dict__\n",
- "scafs=s[\"_scaf\"]\n",
- "staps=s[\"_stap\"]\n",
- "staps"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "cecd1c9f",
- "metadata": {},
- "outputs": [],
- "source": [
- "class vstrands (object):\n",
- "\n",
- " def __init__(self):\n",
- " self.vhelices = []\n",
- "\n",
- " def add_vhelix(self, toadd):\n",
- " self.vhelices.append(toadd)\n",
- "\n",
- " def bbox(self):\n",
- " rows = []\n",
- " cols = []\n",
- " lens = []\n",
- " for h in self.vhelices:\n",
- " rows.append(h.row)\n",
- " cols.append(h.col)\n",
- " lens.append(len(h.stap))\n",
- "\n",
- " dr = DIST_SQUARE * (max(rows) - min(rows) + 2)\n",
- " dc = DIST_SQUARE * (max(cols) - min(cols) + 2)\n",
- " dl = 0.34 * (max(lens) + 2)\n",
- " \n",
- " return 2 * max([dr, dc, dl]) * BOX_FACTOR\n",
- " \n",
- " def __str__(self):\n",
- " a = '{\\n\"vstrands\":[\\n'\n",
- " if len(self.vhelices) > 0:\n",
- " for h in self.vhelices:\n",
- " a = a + str(h) + ','\n",
- " a = a[0:len(a) - 1]\n",
- " a = a + '}\\n'\n",
- " return a\n",
- "class vhelix (object):\n",
- "\n",
- " def __init__(self):\n",
- " self.stapLoop = []\n",
- " self.scafLoop = []\n",
- " self.skip = []\n",
- " self.loop = []\n",
- " self.stap_colors = []\n",
- " self.row = 0\n",
- " self.col = 0\n",
- " self.num = 0\n",
- " self.stap = []\n",
- " self.scaf = []\n",
- " self.cad_index = -1\n",
- " self.skiploop_bases = 0\n",
- "\n",
- " def get_length(self):\n",
- " return max (len(self.scaf), len(self.stap))\n",
- "\n",
- " len = property (get_length)\n",
- "\n",
- " def add_square(self, toadd, which):\n",
- " if which == 'stap':\n",
- " self.stap.append(toadd)\n",
- " elif which == 'scaf':\n",
- " self.scaf.append (toadd)\n",
- " else:\n",
- " base.Logger.log(\"Cannot add square that is not scaf or stap. Dying now\", base.Logger.CRITICAL)\n",
- " sys.exit(1)\n",
- " \n",
- " def __str__(self):\n",
- " a = '{\\n'\n",
- "\n",
- " a = a + '\"stapLoop\":['\n",
- " if len(self.stapLoop) > 0:\n",
- " for i in self.stapLoop:\n",
- " a = a + str(i) + ','\n",
- " a = a[0:len(a) - 1] # remove last comma\n",
- " a = a + '],\\n'\n",
- " a = a + '\"skip\":['\n",
- " if len(self.skip) > 0:\n",
- " for e in self.skip:\n",
- " a = a + str(e) + ','\n",
- " a = a[0:len(a) - 1] # remove last comma\n",
- " a = a + '],\\n'\n",
- " \n",
- " a = a + '\"loop\":['\n",
- " if len(self.loop) > 0:\n",
- " for e in self.loop:\n",
- " a = a + str(e) + ','\n",
- " a = a[0:len(a) - 1] # remove last comma\n",
- " a = a + '],\\n'\n",
- " \n",
- " a = a + '\"stap_colors\":['\n",
- " if len (self.stap_colors) > 0:\n",
- " for e in self.stap_colors:\n",
- " a = a + str(e) + ','\n",
- " a = a[0:len(a) - 1] # remove last comma\n",
- " a = a + '],\\n'\n",
- "\n",
- " a = a + '\"row\":' + str(self.row) + ',\\n'\n",
- " a = a + '\"col\":' + str(self.col) + ',\\n'\n",
- " a = a + '\"num\":' + str(self.num) + ',\\n'\n",
- " \n",
- " a = a + '\"scafLoop\":['\n",
- " if len(self.scafLoop) > 0:\n",
- " for i in self.scafLoop:\n",
- " a = a + str(i) + ','\n",
- " a = a[0:len(a) - 1] # remove last comma\n",
- " a = a + '],\\n'\n",
- " \n",
- " a = a + '\"stap\":['\n",
- " if len(self.stap) > 0:\n",
- " for i in self.stap:\n",
- " a = a + str(i) + ','\n",
- " a = a[0:len(a) - 1] # remove last comma\n",
- " a = a + '],\\n'\n",
- " \n",
- " a = a + '\"scaf\":['\n",
- " if len(self.scaf) > 0:\n",
- " for i in self.scaf:\n",
- " a = a + str(i) + ','\n",
- " a = a[0:len(a) - 1] # remove last comma\n",
- " a = a + ']\\n}'\n",
- " return a\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "b4af37c1",
- "metadata": {},
- "outputs": [],
- "source": [
- "L=[]\n",
- "for i in df[\"vstrands\"]:\n",
- " L.append(i)\n",
- "\n",
- "cadsys = vstrands()\n",
- "vh = vhelix()\n",
- "for s in L:\n",
- " \n",
- " vh.stap = [ i for i in s[\"scaf\"]]\n",
- " vh.scaf = [i for i in s[\"stap\"]]\n",
- " vh.skiploop_bases = len(s[\"skip\"]) + sum(s[\"loop\"]) - sum(s[\"skip\"])\n",
- " cadsys.add_vhelix(vh)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "2fa89abc",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['__class__',\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",
- " '__reduce__',\n",
- " '__reduce_ex__',\n",
- " '__repr__',\n",
- " '__setattr__',\n",
- " '__sizeof__',\n",
- " '__str__',\n",
- " '__subclasshook__',\n",
- " '__weakref__',\n",
- " 'add_square',\n",
- " 'cad_index',\n",
- " 'col',\n",
- " 'get_length',\n",
- " 'len',\n",
- " 'loop',\n",
- " 'num',\n",
- " 'row',\n",
- " 'scaf',\n",
- " 'scafLoop',\n",
- " 'skip',\n",
- " 'skiploop_bases',\n",
- " 'stap',\n",
- " 'stapLoop',\n",
- " 'stap_colors']"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s0=cadsys.vhelices[0]\n",
- "dir(s0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "6e620d24",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[[-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, 5, 10],\n",
- " [5, 9, 5, 11],\n",
- " [5, 10, 5, 12],\n",
- " [5, 11, 5, 13],\n",
- " [5, 12, 5, 14],\n",
- " [5, 13, 5, 15],\n",
- " [5, 14, 5, 16],\n",
- " [5, 15, 5, 17],\n",
- " [5, 16, 5, 18],\n",
- " [5, 17, 5, 19],\n",
- " [5, 18, 5, 20],\n",
- " [5, 19, 5, 21],\n",
- " [5, 20, 5, 22],\n",
- " [5, 21, 5, 23],\n",
- " [5, 22, 5, 24],\n",
- " [5, 23, 5, 25],\n",
- " [5, 24, 5, 26],\n",
- " [5, 25, 5, 27],\n",
- " [5, 26, 0, 27],\n",
- " [0, 28, 5, 29],\n",
- " [5, 28, 5, 30],\n",
- " [5, 29, 5, 31],\n",
- " [5, 30, 5, 32],\n",
- " [5, 31, 5, 33],\n",
- " [5, 32, 5, 34],\n",
- " [5, 33, 5, 35],\n",
- " [5, 34, 5, 36],\n",
- " [5, 35, 5, 37],\n",
- " [5, 36, 5, 38],\n",
- " [5, 37, 5, 39],\n",
- " [5, 38, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1]]"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s0.scaf"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fef6094a",
- "metadata": {},
- "outputs": [],
- "source": [
- "for s in s0.scaf:\n",
- " if s[0]==-1 and s[1]==-1:\n",
- " pass\n",
- " elif s[2]==len(s0.scaf) and abs(s[3])==1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "be089a18",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[-1, -1, -1, -1]"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s0.scaf[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "6f75d365",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['__class__',\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",
- " '__reduce__',\n",
- " '__reduce_ex__',\n",
- " '__repr__',\n",
- " '__setattr__',\n",
- " '__sizeof__',\n",
- " '__str__',\n",
- " '__subclasshook__',\n",
- " '__weakref__',\n",
- " 'add_vhelix',\n",
- " 'bbox',\n",
- " 'vhelices']"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "dir(cadsys)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "id": "8dfadf61",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[[5, 1, -1, -1],\n",
- " [5, 2, 5, 0],\n",
- " [5, 3, 5, 1],\n",
- " [-1, -1, 5, 2],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1],\n",
- " [5, 10, 4, 9],\n",
- " [5, 11, 5, 9],\n",
- " [5, 12, 5, 10],\n",
- " [5, 13, 5, 11],\n",
- " [5, 14, 5, 12],\n",
- " [5, 15, 5, 13],\n",
- " [5, 16, 5, 14],\n",
- " [5, 17, 5, 15],\n",
- " [5, 18, 5, 16],\n",
- " [5, 19, 5, 17],\n",
- " [5, 20, 5, 18],\n",
- " [5, 21, 5, 19],\n",
- " [5, 22, 5, 20],\n",
- " [-1, -1, 5, 21],\n",
- " [5, 24, -1, -1],\n",
- " [5, 25, 5, 23],\n",
- " [5, 26, 5, 24],\n",
- " [5, 27, 5, 25],\n",
- " [5, 28, 5, 26],\n",
- " [5, 29, 5, 27],\n",
- " [5, 30, 5, 28],\n",
- " [5, 31, 5, 29],\n",
- " [5, 32, 5, 30],\n",
- " [5, 33, 5, 31],\n",
- " [5, 34, 5, 32],\n",
- " [5, 35, 5, 33],\n",
- " [5, 36, 5, 34],\n",
- " [5, 37, 5, 35],\n",
- " [5, 38, 5, 36],\n",
- " [5, 39, 5, 37],\n",
- " [4, 39, 5, 38],\n",
- " [-1, -1, -1, -1],\n",
- " [-1, -1, -1, -1]]"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "vh.stap"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "ba097c82",
- "metadata": {},
- "outputs": [
- {
- "ename": "ModuleNotFoundError",
- "evalue": "No module named 'mrdna'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmrdna\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mreaders\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcadnano_segments\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcadnano\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdocument\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Document\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcadnano\u001b[39;00m\n",
- "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'mrdna'"
- ]
- }
- ],
- "source": [
- "\n",
- "\n",
- "from mrdna.readers.cadnano_segments import *\n",
- "from cadnano.document import Document\n",
- "import cadnano"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1c4d4fa9",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "d4ff1f83",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found cadnano version 2 file\n"
- ]
- }
- ],
- "source": [
- "json_data=read_json_file(\"test.json\")\n",
- "part=decode_cadnano_part(json_data)\n",
- "model=cadnano_part(part)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "6f1dab46",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found cadnano version 2 file\n"
- ]
- }
- ],
- "source": [
- "doc=Document()\n",
- "cadnano.fileio.v2decode.decode(doc, json_data)\n",
- "parts = [p for p in doc.getParts()]\n",
- "part=parts[0]\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "615964fe",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Oligo_(0.1[38])_1328\t23\t'None\n",
- "Oligo_(2.1[34])_9584\t35\t'None\n",
- "Oligo_(1.1[36])_7488\t188\t'None\n",
- "Oligo_(4.1[39])_4384\t33\t'None\n",
- "Oligo_(5.0[9])_0240\t23\t'None\n",
- "Oligo_(1.0[3])_8256\t37\t'None\n",
- "Oligo_(3.0[0])_3296\t33\t'None\n",
- "Oligo_(0.1[23])_9088\t21\t'None\n",
- "VH0\n",
- "\t <fwd_StrandSet(0)> \t [(5, 36)] \n",
- "\t\t\t\t ['#0066cc']\n",
- "\t <rev_StrandSet(0)> \t [(2, 20), (21, 23), (24, 27), (28, 38)] \n",
- "\t\t\t\t ['#cc0000', '#b8056c', '#f74308', '#1700de']\n",
- "VH1\n",
- "\t <fwd_StrandSet(1)> \t [(3, 20), (21, 38)] \n",
- "\t\t\t\t ['#cc0000', '#b8056c']\n",
- "\t <rev_StrandSet(1)> \t [(5, 18), (19, 36)] \n",
- "\t\t\t\t ['#0066cc', '#0066cc']\n",
- "VH2\n",
- "\t <fwd_StrandSet(2)> \t [(2, 18), (19, 32)] \n",
- "\t\t\t\t ['#0066cc', '#0066cc']\n",
- "\t <rev_StrandSet(2)> \t [(0, 34)] \n",
- "\t\t\t\t ['#888888']\n",
- "VH3\n",
- "\t <fwd_StrandSet(3)> \t [(0, 20), (21, 34)] \n",
- "\t\t\t\t ['#cc0000', '#888888']\n",
- "\t <rev_StrandSet(3)> \t [(2, 15), (16, 32)] \n",
- "\t\t\t\t ['#0066cc', '#0066cc']\n",
- "VH4\n",
- "\t <fwd_StrandSet(4)> \t [(9, 15), (16, 39)] \n",
- "\t\t\t\t ['#0066cc', '#0066cc']\n",
- "\t <rev_StrandSet(4)> \t [(9, 20), (21, 39)] \n",
- "\t\t\t\t ['#cc0000', '#888888']\n",
- "VH5\n",
- "\t <fwd_StrandSet(5)> \t [(9, 27), (28, 39)] \n",
- "\t\t\t\t ['#f74308', '#1700de']\n",
- "\t <rev_StrandSet(5)> \t [(9, 39)] \n",
- "\t\t\t\t ['#0066cc']\n"
- ]
- }
- ],
- "source": [
- "part.__dict__.keys()\n",
- "\n",
- "oligos = part.oligos()\n",
- "for oligo in oligos:\n",
- " print(\"{0}\\t{1}\\t\\'{2}\".format(oligo,\n",
- " oligo.length(),\n",
- " oligo.sequence()))\n",
- "\n",
- "vhs = list(part.getIdNums()) # convert set to list\n",
- "for vh_id in vhs: # display first 3 vhs\n",
- " fwd_ss, rev_ss = part.getStrandSets(vh_id)\n",
- " print('VH{0}'.format(vh_id))\n",
- " print('\\t', fwd_ss, '\\t', [s.idxs() for s in fwd_ss.strands()], '\\n\\t\\t\\t\\t',\n",
- " [s.getColor() for s in fwd_ss.strands()])\n",
- " print('\\t', rev_ss, '\\t', [s.idxs() for s in rev_ss.strands()], '\\n\\t\\t\\t\\t',\n",
- " [s.getColor() for s in rev_ss.strands()])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "a71b0639",
- "metadata": {},
- "outputs": [],
- "source": [
- "strands5 = [o.strand5p() for o in part.oligos()]\n",
- "strands3 = [o.strand3p() for o in part.oligos()]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "2bb83a1e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['__class__',\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",
- " '__reduce__',\n",
- " '__reduce_ex__',\n",
- " '__repr__',\n",
- " '__setattr__',\n",
- " '__sizeof__',\n",
- " '__slots__',\n",
- " '__str__',\n",
- " '__subclasshook__',\n",
- " '__weakref__',\n",
- " '_decrementLength',\n",
- " '_incrementLength',\n",
- " '_is_circular',\n",
- " '_parent',\n",
- " '_part',\n",
- " '_props',\n",
- " '_setColor',\n",
- " '_setLength',\n",
- " '_setLoop',\n",
- " '_setProperty',\n",
- " '_signals',\n",
- " '_strand5p',\n",
- " '_strandMergeUpdate',\n",
- " '_strandSplitUpdate',\n",
- " 'addToPart',\n",
- " 'applyAbstractSequences',\n",
- " 'applyColor',\n",
- " 'applySequence',\n",
- " 'applySequenceCMD',\n",
- " 'clearAbstractSequences',\n",
- " 'connect',\n",
- " 'deleteLater',\n",
- " 'destroy',\n",
- " 'disconnect',\n",
- " 'displayAbstractSequences',\n",
- " 'dump',\n",
- " 'editable_properties',\n",
- " 'getAbsolutePositionAtLength',\n",
- " 'getColor',\n",
- " 'getModelProperties',\n",
- " 'getName',\n",
- " 'getNumberOfBasesToEachXover',\n",
- " 'getOutlineProperties',\n",
- " 'getProperty',\n",
- " 'getStrandLengths',\n",
- " 'isCircular',\n",
- " 'length',\n",
- " 'locString',\n",
- " 'oligoPropertyChangedSignal',\n",
- " 'oligoRemovedSignal',\n",
- " 'oligoSelectedChangedSignal',\n",
- " 'oligoSequenceAddedSignal',\n",
- " 'oligoSequenceClearedSignal',\n",
- " 'parent',\n",
- " 'part',\n",
- " 'refreshLength',\n",
- " 'remove',\n",
- " 'removeFromPart',\n",
- " 'sequence',\n",
- " 'sequenceExport',\n",
- " 'setParent',\n",
- " 'setPart',\n",
- " 'setProperty',\n",
- " 'setStrand5p',\n",
- " 'shallowCopy',\n",
- " 'shouldHighlight',\n",
- " 'signals',\n",
- " 'splitAtAbsoluteLengths',\n",
- " 'strand3p',\n",
- " 'strand5p',\n",
- " 'undoStack']"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "L=[o for o in part.oligos()]\n",
- "dir(L[2])\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "86f5c21e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "defaultdict(dict, {0: {}, 1: {}, 2: {}, 3: {}, 4: {}, 5: {}})"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "part.insertions()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "f3fae511",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'name': 'NaPart1',\n",
- " 'color': '#0066cc',\n",
- " 'is_visible': True,\n",
- " 'active_phos': None,\n",
- " 'crossover_span_angle': 45,\n",
- " 'max_vhelix_length': 42,\n",
- " 'neighbor_active_angle': '',\n",
- " 'grid_type': <GridEnum.HONEYCOMB: 2>,\n",
- " 'virtual_helix_order': [0, 1, 2, 3, 4, 5],\n",
- " 'is_lattice': True,\n",
- " <GridEnum.HONEYCOMB: 2>: <GridEnum.NONE: 0>}"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "part.getModelProperties()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "07f3352b",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " _\n",
- " _____ ___ _| |___ ___\n",
- "| | _| . | | .'|\n",
- "|_|_|_|_| |___|_|_|__,| v1.0a.dev74 \n",
- "it/its\n",
- "\n"
- ]
- }
- ],
- "source": [
- "import pdb\n",
- "import numpy as np\n",
- "import os,sys\n",
- "import scipy\n",
- "\n",
- "from mrdna import logger, devlogger\n",
- "from mrdna.segmentmodel import SegmentModel, SingleStrandedSegment, DoubleStrandedSegment\n",
- "from mrdna.arbdmodel.coords import quaternion_from_matrix, rotationAboutAxis, quaternion_slerp\n",
- "from mrdna import get_resource_path\n",
- "\n",
- "ref_stack_position = np.array((-2.41851735, -0.259761333, 3.39999978))\n",
- "\n",
- "def _three_prime_list_to_five_prime(three_prime):\n",
- " five_prime = -np.ones(three_prime.shape, dtype=int)\n",
- " has_three_prime = np.where(three_prime >= 0)[0]\n",
- " five_prime[three_prime[has_three_prime]] = has_three_prime\n",
- " return five_prime \n",
- "def _primes_list_to_strands(three_prime, five_prime):\n",
- " five_prime_ends = np.where(five_prime < 0)[0]\n",
- " strands = []\n",
- " strand_is_circular = []\n",
- " \n",
- " idx_to_strand = -np.ones(three_prime.shape, dtype=int)\n",
- "\n",
- " def build_strand(nt_idx, conditional):\n",
- " strand = [nt_idx]\n",
- " idx_to_strand[nt_idx] = len(strands)\n",
- " while conditional(nt_idx):\n",
- " nt_idx = three_prime[nt_idx]\n",
- " strand.append(nt_idx)\n",
- " idx_to_strand[nt_idx] = len(strands)\n",
- " strands.append( np.array(strand, dtype=int) )\n",
- "\n",
- " for nt_idx in five_prime_ends:\n",
- " build_strand(nt_idx,\n",
- " lambda nt: three_prime[nt] >= 0)\n",
- " strand_is_circular.append(False)\n",
- "\n",
- " while True:\n",
- " ## print(\"WARNING: working on circular strand {}\".format(len(strands)))\n",
- " ids = np.where(idx_to_strand < 0)[0]\n",
- " if len(ids) == 0: break\n",
- " build_strand(ids[0],\n",
- " lambda nt: three_prime[nt] >= 0 and \\\n",
- " idx_to_strand[three_prime[nt]] < 0)\n",
- " strand_is_circular.append(True)\n",
- "\n",
- " return strands, strand_is_circular\n",
- "\n",
- "def find_stacks(centers, transforms):\n",
- "\n",
- " ## Find orientation and center of each nucleotide\n",
- " expected_stack_positions = []\n",
- " for R,c in zip(transforms,centers):\n",
- " expected_stack_positions.append( c + ref_stack_position.dot(R) )\n",
- "\n",
- " expected_stack_positions = np.array(expected_stack_positions, dtype=np.float32)\n",
- "\n",
- " dists = scipy.spatial.distance_matrix(expected_stack_positions, centers)\n",
- " dists = dists + 5*np.eye(len(dists))\n",
- " idx1, idx2 = np.where(dists < 3.5)\n",
- "\n",
- " ## Convert distances to stacks\n",
- " stacks_above = -np.ones(len(centers), dtype=int)\n",
- " _z = np.array((0,0,1))\n",
- " for i in np.unique(idx1):\n",
- " js = idx2[ idx1 == i ]\n",
- " with np.errstate(divide='ignore',invalid='ignore'):\n",
- " angles = [np.arccos( transforms[j].T.dot( transforms[i].dot(_z) ).dot( _z ) ) for j in js]\n",
- " angles = np.array( angles )\n",
- " tmp = np.argmin(dists[i][js] + 1.0*angles)\n",
- " j = js[tmp]\n",
- " stacks_above[i] = j\n",
- "\n",
- " return stacks_above\n",
- "\n",
- "def basepairs_and_stacks_to_helixmap(basepairs,stacks_above):\n",
- "\n",
- " helixmap = -np.ones(basepairs.shape, dtype=int)\n",
- " helixrank = -np.ones(basepairs.shape)\n",
- " is_fwd = np.ones(basepairs.shape, dtype=int)\n",
- " \n",
- " ## Remove stacks with nts lacking a basepairs\n",
- " nobp = np.where(basepairs < 0)[0]\n",
- " stacks_above[nobp] = -1\n",
- " stacks_with_nobp = np.in1d(stacks_above, nobp)\n",
- " stacks_above[stacks_with_nobp] = -1\n",
- "\n",
- " end_ids = np.where( (stacks_above < 0)*(basepairs >= 0) )[0]\n",
- "\n",
- " hid = 0\n",
- " for end in end_ids:\n",
- " if helixmap[end] >= 0:\n",
- " continue\n",
- " rank = 0\n",
- " nt = basepairs[end]\n",
- " bp = basepairs[nt]\n",
- " assert( bp == end )\n",
- " if helixmap[nt] >= 0 or helixmap[bp] >= 0:\n",
- " logger.warning(f'Ill-formed helix: problematic basepair or stacking data near nucleotide {nt} or {bp}... skipping')\n",
- " continue\n",
- " # assert(helixmap[nt] == -1)\n",
- " # assert(helixmap[bp] == -1)\n",
- " helixmap[nt] = helixmap[bp] = hid\n",
- " helixrank[nt] = helixrank[bp] = rank\n",
- " is_fwd[bp] = 0\n",
- " rank +=1\n",
- "\n",
- " _tmp = [(nt,bp)]\n",
- " \n",
- " while stacks_above[nt] >= 0:\n",
- " nt = stacks_above[nt]\n",
- " if basepairs[nt] < 0: break\n",
- " bp = basepairs[nt]\n",
- " if helixmap[nt] >= 0 or helixmap[bp] >= 0:\n",
- " logger.warning(f'Ill-formed helix: problematic basepair or stacking data near nucleotide {nt} or {bp}... skipping')\n",
- " break\n",
- " helixmap[nt] = helixmap[bp] = hid\n",
- " helixrank[nt] = helixrank[bp] = rank\n",
- " is_fwd[bp] = 0\n",
- " _tmp.append((nt,bp))\n",
- " rank +=1\n",
- "\n",
- " hid += 1\n",
- "\n",
- " ## Create \"helix\" for each circular segment\n",
- " intrahelical = []\n",
- " processed = set()\n",
- " unclaimed_bases = np.where( (basepairs >= 0)*(helixmap == -1) )[0]\n",
- " for nt0 in unclaimed_bases:\n",
- " if nt0 in processed: continue\n",
- "\n",
- " nt = nt0\n",
- " all_nts = [nt]\n",
- "\n",
- " rank = 0\n",
- " nt = nt0\n",
- " bp = basepairs[nt]\n",
- " if helixmap[nt] >= 0 or helixmap[bp] >= 0:\n",
- " logger.warning(f'Ill-formed cylic helix: problematic basepair or stacking data near nucleotide {nt} or {bp}... skipping')\n",
- " continue\n",
- " helixmap[nt] = helixmap[bp] = hid\n",
- " helixrank[nt] = helixrank[bp] = rank\n",
- " is_fwd[bp] = 0\n",
- " rank +=1\n",
- " processed.add(nt)\n",
- " processed.add(bp)\n",
- "\n",
- " counter = 0\n",
- " while stacks_above[nt] >= 0:\n",
- " lastnt = nt\n",
- " nt = stacks_above[nt]\n",
- " bp = basepairs[nt]\n",
- " if nt == nt0 or nt == basepairs[nt0]:\n",
- " intrahelical.append((lastnt,nt0))\n",
- " break\n",
- " \n",
- " assert( bp >= 0 )\n",
- " if helixmap[nt] >= 0 or helixmap[bp] >= 0:\n",
- " logger.warning(f'Ill-formed cyclic helix: problematic basepair or stacking data near nucleotide {nt} or {bp}... skipping')\n",
- " break\n",
- " \n",
- " helixmap[nt] = helixmap[bp] = hid\n",
- " helixrank[nt] = helixrank[bp] = rank\n",
- " is_fwd[bp] = 0\n",
- " processed.add(nt)\n",
- " processed.add(bp)\n",
- " rank +=1\n",
- " hid += 1\n",
- "\n",
- " return helixmap, helixrank, is_fwd, intrahelical\n",
- "\n",
- "\n",
- "def set_splines(seg, coordinate, hid, hmap, hrank, fwd, basepair, orientation=None):\n",
- " maxrank = np.max( hrank[hmap==hid] )\n",
- " if maxrank == 0:\n",
- " ids = np.where((hmap == hid))[0]\n",
- " pos = np.mean( [coordinate[r,:] for r in ids ], axis=0 )\n",
- " coords = [pos,pos]\n",
- " contours = [0,1]\n",
- " if orientation is not None:\n",
- " ids = np.where((hmap == hid) * fwd)[0]\n",
- " assert( len(ids) == 1 )\n",
- " q = quaternion_from_matrix( orientation[ids[0]] )\n",
- " quats = [q, q]\n",
- " coords[-1] = pos + orientation[ids[0]].dot(np.array((0,0,1)))\n",
- "\n",
- " else:\n",
- " coords,contours,quats = [[],[],[]]\n",
- " last_q = None\n",
- " for rank in range(int(maxrank)+1):\n",
- " ids = np.where((hmap == hid) * (hrank == rank))[0]\n",
- " \n",
- " coords.append(np.mean( [coordinate[r,:] for r in ids ], axis=0 ))\n",
- " contours.append( float(rank+0.5)/(maxrank+1) )\n",
- " if orientation is not None:\n",
- " ids = np.where((hmap == hid) * (hrank == rank) * fwd)[0]\n",
- " assert(len(ids) == 1)\n",
- " q = quaternion_from_matrix( orientation[ids[0]] )\n",
- "\n",
- " if last_q is not None and last_q.dot(q) < 0:\n",
- " q = -q\n",
- "\n",
- " ## Average quaterion with reverse direction\n",
- " bp = basepair[ids[0]]\n",
- " if bp >= 0:\n",
- " bp_o = orientation[bp].dot(rotationAboutAxis(np.array((1,0,0)),180))\n",
- " q2 = quaternion_from_matrix( bp_o )\n",
- " if q.dot(q2) < 0:\n",
- " q2 = -q2\n",
- "\n",
- " ## probably good enough, but slerp is better: q = (q + q2)*0.5\n",
- " q = quaternion_slerp(q,q2,0.5)\n",
- "\n",
- " quats.append(q)\n",
- " last_q = q\n",
- "\n",
- " coords = np.array(coords)\n",
- " seg.set_splines(contours,coords)\n",
- " if orientation is not None:\n",
- " quats = np.array(quats)\n",
- " seg.set_orientation_splines(contours,quats)\n",
- "\n",
- " seg.start_position = coords[0,:]\n",
- " seg.end_position = coords[-1,:]\n",
- "\n",
- "\n",
- "def model_from_basepair_stack_3prime(coordinate, basepair, stack, three_prime,\n",
- " sequence=None, orientation=None,\n",
- " max_basepairs_per_bead = 5,\n",
- " max_nucleotides_per_bead = 5,\n",
- " local_twist = False,\n",
- " dimensions=(5000,5000,5000),\n",
- " **model_parameters):\n",
- " \"\"\" \n",
- " Creates a SegmentModel object from lists of each nucleotide's\n",
- " basepair, its stack (on 3' side) and its 3'-connected nucleotide\n",
- "\n",
- " The first argument should be an N-by-3 numpy array containing the\n",
- " coordinate of each nucleotide, where N is the number of\n",
- " nucleotides. The following three arguments should be integer lists\n",
- " where the i-th element corresponds to the i-th nucleotide; the\n",
- " list element should the integer index of the corresponding\n",
- " basepaired / stacked / phosphodiester-bonded nucleotide. If there\n",
- " is no such nucleotide, the value should be -1.\n",
- "\n",
- " Args:\n",
- " basepair: List of each nucleotide's basepair's index\n",
- " stack: List containing index of the nucleotide stacked on the 3' of each nucleotide\n",
- " three_prime: List of each nucleotide's the 3' end of each nucleotide\n",
- "\n",
- " Returns:\n",
- " SegmentModel\n",
- " \"\"\"\n",
- "\n",
- " \"\"\" Validate Input \"\"\"\n",
- " inputs = (basepair,three_prime)\n",
- " try:\n",
- " basepair,three_prime = [np.array(a,dtype=int) for a in inputs]\n",
- " except:\n",
- " raise TypeError(\"One or more of the input lists could not be converted into a numpy array\")\n",
- " inputs = (basepair,three_prime)\n",
- " coordinate = np.array(coordinate)\n",
- "\n",
- " if np.any( [len(a.shape) > 1 for a in inputs] ):\n",
- " raise ValueError(\"One or more of the input lists has the wrong dimensionality\")\n",
- "\n",
- " if len(coordinate.shape) != 2:\n",
- " raise ValueError(\"Coordinate array has the wrong dimensionality\")\n",
- "\n",
- " inputs = (coordinate,basepair,three_prime)\n",
- " if not np.all(np.diff([len(a) for a in inputs]) == 0):\n",
- " raise ValueError(\"Inputs are not the same length\")\n",
- " \n",
- " num_nt = len(basepair)\n",
- " if sequence is not None and len(sequence) != num_nt:\n",
- " raise ValueError(\"The 'sequence' parameter is the wrong length {} != {}\".format(len(sequence),num_nt))\n",
- "\n",
- " if orientation is not None:\n",
- " orientation = np.array(orientation)\n",
- " if len(orientation.shape) != 3:\n",
- " raise ValueError(\"The 'orientation' array has the wrong dimensionality (should be Nx3x3)\")\n",
- " if orientation.shape != (num_nt,3,3):\n",
- " raise ValueError(\"The 'orientation' array is not properly formatted\")\n",
- "\n",
- " if stack is None:\n",
- " if orientation is not None:\n",
- " stack = find_stacks(coordinate, orientation)\n",
- " else:\n",
- " ## Guess stacking based on 3' connectivity\n",
- " stack = np.array(three_prime,dtype=int) # Assume nts on 3' ends are stacked\n",
- " _stack_below = _three_prime_list_to_five_prime(stack)\n",
- " _has_bp = (basepair >= 0)\n",
- " _nostack = np.where( (stack == -1)*_has_bp )[0]\n",
- " _has_stack_below = _stack_below[basepair[_nostack]] >= 0\n",
- " _nostack2 = _nostack[_has_stack_below]\n",
- " stack[_nostack2] = basepair[_stack_below[basepair[_nostack2]]]\n",
- "\n",
- " else:\n",
- " try:\n",
- " stack = np.array(stack,dtype=int)\n",
- " except:\n",
- " raise TypeError(\"The 'stack' array could not be converted into a numpy integer array\")\n",
- "\n",
- " if len(stack.shape) != 1:\n",
- " raise ValueError(\"The 'stack' array has the wrong dimensionality\")\n",
- "\n",
- " if len(stack) != num_nt:\n",
- " raise ValueError(\"The length of the 'stack' array does not match other inputs\")\n",
- "\n",
- " bps = basepair # alias\n",
- "\n",
- " \"\"\" Fix stacks: require that the stack of a bp of a base's stack is its bp \"\"\"\n",
- " _has_bp = (bps >= 0)\n",
- " _has_stack = (stack >= 0)\n",
- " _stack_has_basepair = (bps[stack] >= 0) * _has_stack\n",
- " stack = np.where( (stack[bps[stack]] == bps) * _has_bp * _has_stack * _has_bp,\n",
- " stack, -np.ones(len(stack),dtype=int) )\n",
- "\n",
- " five_prime = _three_prime_list_to_five_prime(three_prime)\n",
- "\n",
- " \"\"\" Build map of dsDNA helices and strands \"\"\"\n",
- " hmap,hrank,fwd,intrahelical = basepairs_and_stacks_to_helixmap(bps,stack)\n",
- " double_stranded_helices = np.unique(hmap[hmap >= 0]) \n",
- " strands, strand_is_circular = _primes_list_to_strands(three_prime, five_prime)\n",
- "\n",
- " \"\"\" Add ssDNA to hmap \"\"\"\n",
- " if len(double_stranded_helices) > 0:\n",
- " hid = double_stranded_helices[-1]+1\n",
- " else:\n",
- " hid = 0\n",
- " ss_residues = hmap < 0\n",
- " #\n",
- " if np.any(bps[ss_residues] != -1):\n",
- " logger.warning(f'{np.sum(bps[ss_residues] != -1)} ssDNA nucleotides appear to have basepairs... ignoring')\n",
- " \n",
- " for s,c in zip(strands, strand_is_circular):\n",
- " strand_segment_ends = [i for i in np.where( np.diff(hmap[s]) != 0 )[0]] + [len(s)-1]\n",
- " seg_start = 0\n",
- " for i in strand_segment_ends:\n",
- " if hmap[s[i]] < 0:\n",
- " ## Found single-stranded segment\n",
- " ids = s[seg_start:i+1]\n",
- " assert( np.all(hmap[ids] == -1) )\n",
- " hmap[ids] = hid\n",
- " hrank[ids] = np.arange(i+1-seg_start)\n",
- " hid+=1\n",
- " seg_start = i+1\n",
- "\n",
- " if len(double_stranded_helices) > 0:\n",
- " single_stranded_helices = np.arange(double_stranded_helices[-1]+1,hid)\n",
- " else:\n",
- " single_stranded_helices = np.arange(hid)\n",
- "\n",
- " ## Create double-stranded segments\n",
- " doubleSegments = []\n",
- " for hid in double_stranded_helices:\n",
- " seg = DoubleStrandedSegment(name=str(hid),\n",
- " num_bp = np.sum(hmap==hid)//2)\n",
- " set_splines(seg, coordinate, hid, hmap, hrank, fwd, basepair, orientation)\n",
- "\n",
- " assert(hid == len(doubleSegments))\n",
- " doubleSegments.append(seg)\n",
- "\n",
- " ## Create single-stranded segments\n",
- " singleSegments = []\n",
- " for hid in single_stranded_helices:\n",
- " seg = SingleStrandedSegment(name=str(hid),\n",
- " num_nt = np.sum(hmap==hid))\n",
- " set_splines(seg, coordinate, hid, hmap, hrank, fwd, basepair, orientation)\n",
- "\n",
- " assert(hid == len(doubleSegments) + len(singleSegments))\n",
- " singleSegments.append(seg)\n",
- "\n",
- " ## Find crossovers and 5prime/3prime ends\n",
- " crossovers,prime5,prime3 = [[],[],[]]\n",
- " for s,c in zip(strands,strand_is_circular):\n",
- " tmp = np.where(np.diff(hmap[s]) != 0)[0]\n",
- " for i in tmp:\n",
- " crossovers.append( (s[i],s[i+1]) )\n",
- " if c:\n",
- " if hmap[s[-1]] != hmap[s[0]]:\n",
- " crossovers.append( (s[-1],s[0]) )\n",
- " else:\n",
- " prime5.append(s[0])\n",
- " prime3.append(s[-1])\n",
- "\n",
- " ## Add connections\n",
- " allSegments = doubleSegments+singleSegments\n",
- "\n",
- " for r1,r2 in crossovers:\n",
- " seg1,seg2 = [allSegments[hmap[i]] for i in (r1,r2)]\n",
- " nt1,nt2 = [hrank[i] for i in (r1,r2)]\n",
- " f1,f2 = [fwd[i] for i in (r1,r2)]\n",
- "\n",
- " ## Handle connections at the ends\n",
- " is_terminal1 = (nt1,f1) in ((0,0),(seg1.num_nt-1,1))\n",
- " is_terminal2 = (nt2,f2) in ((0,1),(seg2.num_nt-1,0))\n",
- "\n",
- " print(seg1,seg2, r1, r2, is_terminal1, is_terminal2)\n",
- " if is_terminal1 or is_terminal2:\n",
- " \"\"\" Ensure that we don't have three-way dsDNA junctions \"\"\"\n",
- " if is_terminal1 and (bps[r1] >= 0) and (five_prime[bps[r1]] >= 0) and (three_prime[r1] >= 0):\n",
- " if (bps[five_prime[bps[r1]]] >= 0) and (bps[three_prime[r1]] >= 0):\n",
- " # is_terminal1 = (three_prime[r1] == bps[five_prime[bps[r1]]])\n",
- " is_terminal1 = hmap[five_prime[bps[r1]]] == hmap[three_prime[r1]]\n",
- " if is_terminal2 and (bps[r2] >= 0) and (three_prime[bps[r2]] >= 0) and (five_prime[r2] >= 0):\n",
- " if (bps[three_prime[bps[r2]]] >= 0) and (bps[five_prime[r2]] >= 0):\n",
- " # is_terminal2 = (five_prime[r2] == bps[three_prime[bps[r2]]])\n",
- " is_terminal2 = hmap[three_prime[bps[r2]]] == hmap[five_prime[r2]]\n",
- " \n",
- " \"\"\" Place connection \"\"\"\n",
- " if is_terminal1 and is_terminal2:\n",
- " end1 = seg1.end3 if f1 else seg1.start3\n",
- " end2 = seg2.start5 if f2 else seg2.end5\n",
- " seg1._connect_ends( end1, end2, type_='intrahelical')\n",
- " else:\n",
- " seg1.add_crossover(nt1,seg2,nt2,[f1,f2],type_=\"terminal_crossover\")\n",
- " else:\n",
- " seg1.add_crossover(nt1,seg2,nt2,[f1,f2])\n",
- "\n",
- " ## Add 5prime/3prime ends\n",
- " for r in prime5:\n",
- " seg = allSegments[hmap[r]]\n",
- " seg.add_5prime(hrank[r],fwd[r])\n",
- " for r in prime3:\n",
- " seg = allSegments[hmap[r]]\n",
- " seg.add_3prime(hrank[r],fwd[r])\n",
- "\n",
- " ## Add intrahelical connections to circular helical sections\n",
- " for nt0,nt1 in intrahelical:\n",
- " seg = allSegments[hmap[nt0]]\n",
- " assert( seg is allSegments[hmap[nt1]] )\n",
- " if three_prime[nt0] >= 0:\n",
- " if hmap[nt0] == hmap[three_prime[nt0]]:\n",
- " seg.connect_end3(seg.start5)\n",
- "\n",
- " bp0,bp1 = [bps[nt] for nt in (nt0,nt1)]\n",
- " if three_prime[bp1] >= 0:\n",
- " if hmap[bp1] == hmap[three_prime[bp1]]:\n",
- " seg.connect_start3(seg.end5)\n",
- "\n",
- " ## Assign sequence\n",
- " if sequence is not None:\n",
- " for hid in range(len(allSegments)):\n",
- " resids = np.where( (hmap==hid)*(fwd==1) )[0]\n",
- " s = allSegments[hid]\n",
- " s.sequence = [sequence[r] for r in sorted(resids,key=lambda x: hrank[x])]\n",
- "\n",
- "\n",
- " ## Build model\n",
- " model = SegmentModel( allSegments,\n",
- " max_basepairs_per_bead = max_basepairs_per_bead,\n",
- " max_nucleotides_per_bead = max_nucleotides_per_bead,\n",
- " local_twist = local_twist,\n",
- " dimensions = dimensions,\n",
- " **model_parameters )\n",
- "\n",
- "\n",
- " model._reader_list_coordinates = coordinate\n",
- " model._reader_list_basepair = basepair\n",
- " model._reader_list_stack = stack\n",
- " model._reader_list_three_prime = three_prime\n",
- " model._reader_list_five_prime = five_prime\n",
- " model._reader_list_sequence = sequence\n",
- " model._reader_list_orientation = orientation\n",
- " model._reader_list_hmap = hmap\n",
- " model._reader_list_fwd = fwd\n",
- " model._reader_list_hrank = hrank\n",
- "\n",
- " if sequence is None:\n",
- " for s in model.segments:\n",
- " s.randomize_unset_sequence()\n",
- "\n",
- " return model\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "61be89c1",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<DoubleStrandedSegment'> 1[1]> <DoubleStrandedSegment'> 0[1]> 5 3 True True\n",
- "<SingleStrandedSegment'> 2[2]> <DoubleStrandedSegment'> 0[1]> 1 2 True True\n",
- "<DoubleStrandedSegment'> 0[1]> <DoubleStrandedSegment'> 1[1]> 2 4 True True\n",
- "<DoubleStrandedSegment'> 1[1]> <SingleStrandedSegment'> 3[1]> 4 6 True True\n",
- "<SingleStrandedSegment'> 3[1]> <SingleStrandedSegment'> 2[2]> 6 0 True True\n"
- ]
- }
- ],
- "source": [
- "coordinate = [(0,0,3.4*i) for i in range(7)]\n",
- "three_prime = [ 1, 2, 4,-1, 6, 3, 0]\n",
- "basepair = [-1,-1, 3, 2, 5, 4,-1]\n",
- "stack = [-1,-1, -1,-1,-1, -1,-1]\n",
- "for i in [3,5]:\n",
- " coordinate[i] = (1,0,3.4*i)\n",
- "\n",
- "model = model_from_basepair_stack_3prime(coordinate, basepair, stack, three_prime,\n",
- " max_basepairs_per_bead=1,\n",
- " max_nucleotides_per_bead=1,\n",
- " local_twist=False)\n",
- "model.writePsf(\"list.psf\")\n",
- "model.writePdb(\"list.pdb\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "a00c445c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[<SegmentParticle DNA on <DoubleStrandedSegment'> 0[1]>[1.00]>], [<SegmentParticle DNA on <DoubleStrandedSegment'> 1[1]>[0.00]>, <SegmentParticle DNA on <DoubleStrandedSegment'> 1[1]>[1.00]>], [<SegmentParticle NAS on <SingleStrandedSegment'> 2[2]>[0.00]>, <SegmentParticle NAS on <SingleStrandedSegment'> 2[2]>[0.50]>, <SegmentParticle NAS on <SingleStrandedSegment'> 2[2]>[1.00]>], [<SegmentParticle NAS on <SingleStrandedSegment'> 3[1]>[0.50]>]]\n",
- "[[<Connection <Location 1.end3[0,on_fwd_strand]>--intrahelical--<Location 0.end5[0,on_fwd_strand]>]>, <Connection <Location 2.end3[1,on_fwd_strand]>--sscrossover--<Location 0.end5[0,on_rev_strand]>]>, <Connection <Location 0.end3[0,on_rev_strand]>--intrahelical--<Location 1.end5[0,on_rev_strand]>]>], [<Connection <Location 1.end3[0,on_fwd_strand]>--intrahelical--<Location 0.end5[0,on_fwd_strand]>]>, <Connection <Location 0.end3[0,on_rev_strand]>--intrahelical--<Location 1.end5[0,on_rev_strand]>]>, <Connection <Location 1.end3[0,on_rev_strand]>--intrahelical--<Location 3.end5[0,on_fwd_strand]>]>], [<Connection <Location 2.end3[1,on_fwd_strand]>--sscrossover--<Location 0.end5[0,on_rev_strand]>]>, <Connection <Location 3.end3[0,on_fwd_strand]>--intrahelical--<Location 2.end5[0,on_fwd_strand]>]>], [<Connection <Location 1.end3[0,on_rev_strand]>--intrahelical--<Location 3.end5[0,on_fwd_strand]>]>, <Connection <Location 3.end3[0,on_fwd_strand]>--intrahelical--<Location 2.end5[0,on_fwd_strand]>]>]]\n"
- ]
- }
- ],
- "source": [
- "print([i.children for i in model.children])\n",
- "print([i.connections for i in model.children])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "a648fae4",
- "metadata": {},
- "outputs": [],
- "source": [
- "s=model.children[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "9d100033",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'0'"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s.segname"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "66d3eaf4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[(0, 0, 0.0),\n",
- " (0, 0, 3.4),\n",
- " (0, 0, 6.8),\n",
- " (1, 0, 10.2),\n",
- " (0, 0, 13.6),\n",
- " (1, 0, 17.0),\n",
- " (0, 0, 20.4)]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "coordinate"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f60b0cdc",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/mrdna/readers/test/libs b/mrdna/readers/test/libs
deleted file mode 120000
index d4bda9b46951e0c8c2007f84bbc447f93995502d..0000000000000000000000000000000000000000
--- a/mrdna/readers/test/libs
+++ /dev/null
@@ -1 +0,0 @@
-../libs
\ No newline at end of file
diff --git a/mrdna/readers/test/test.json b/mrdna/readers/test/test.json
deleted file mode 100644
index 5332be92ca7e69748471d79a41ad39cf8bc1aa26..0000000000000000000000000000000000000000
--- a/mrdna/readers/test/test.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/mrdna/readers/test/test.json.oxdna b/mrdna/readers/test/test.json.oxdna
deleted file mode 100644
index 22b3082f579505ad14e9e718bb2433c980c3bdfd..0000000000000000000000000000000000000000
--- a/mrdna/readers/test/test.json.oxdna
+++ /dev/null
@@ -1,418 +0,0 @@
-t = 0
-b = 59.840000 59.840000 59.840000
-E = 0.000000 0.000000 0.000000
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diff --git a/mrdna/readers/test/test.json.top b/mrdna/readers/test/test.json.top
deleted file mode 100644
index ccbe1c947870d931f857608066554b7585c925e7..0000000000000000000000000000000000000000
--- a/mrdna/readers/test/test.json.top
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diff --git a/mrdna/readers/test/test_insert.json b/mrdna/readers/test/test_insert.json
index fec383704d9f16d0d379b680c13d0a229eac50cb..50dc3dc32e00de3024a261645984309863a278f3 100644
--- a/mrdna/readers/test/test_insert.json
+++ b/mrdna/readers/test/test_insert.json
@@ -1 +1 @@
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diff --git a/mrdna/readers/test/test_insert.json.oxdna b/mrdna/readers/test/test_insert.json.oxdna
index 382e21729e7ec34e838bd44b73a24be3b3054329..c86ba89b884930d7ab058b7c371608d5be41265b 100644
--- a/mrdna/readers/test/test_insert.json.oxdna
+++ b/mrdna/readers/test/test_insert.json.oxdna
@@ -1,66 +1,66 @@
t = 0
b = 145.600000 145.600000 145.600000
E = 0.000000 0.000000 0.000000
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diff --git a/mrdna/readers/test/test_insert.json.top b/mrdna/readers/test/test_insert.json.top
index eab249d4e5725f616666ba17f80633e79bd7a0bc..6b91c087e875b379cf29e41e0948857c9be6b496 100644
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diff --git a/mrdna/readers/test/virt2nuc b/mrdna/readers/test/test_insert.json.virt2nuc
similarity index 100%
rename from mrdna/readers/test/virt2nuc
rename to mrdna/readers/test/test_insert.json.virt2nuc