From 1afeeb3a2865e72ab90222cefc5cb9822210e710 Mon Sep 17 00:00:00 2001
From: pinyili2 <pinyili2@illinois.edu>
Date: Wed, 7 Aug 2024 16:36:04 -0500
Subject: [PATCH] add segmentmodel_from_cadnano
---
mrdna/readers/.gitignore | 1 -
.../.ipynb_checkpoints/test-checkpoint.ipynb | 1479 ++++++-
mrdna/readers/.nfs0000000000093fec000043e9 | Bin 16384 -> 0 bytes
mrdna/readers/__init__.py | 5 +
mrdna/readers/list.pdb | 8 -
mrdna/readers/list.psf | 39 -
mrdna/readers/segmentmodel_from_cadnano.py | 31 +-
mrdna/readers/test.ipynb | 902 ++--
mrdna/readers/test.json | 1 -
mrdna/readers/{ => test}/Na_liu.json | 0
mrdna/readers/{ => test}/rest_scaf_col.json | 0
mrdna/readers/test/test.ipynb | 3735 +++++++++++++++++
mrdna/readers/{ => test}/test.json.oxdna | 0
mrdna/readers/{ => test}/test.json.top | 0
mrdna/readers/{ => test}/test.seq.json | 0
mrdna/readers/{ => test}/test.virt2nuc | Bin
mrdna/readers/{ => test}/test2.ipynb | 0
mrdna/readers/{ => test}/test3.ipynb | 0
18 files changed, 5678 insertions(+), 523 deletions(-)
delete mode 100644 mrdna/readers/.nfs0000000000093fec000043e9
delete mode 100644 mrdna/readers/list.pdb
delete mode 100644 mrdna/readers/list.psf
delete mode 100644 mrdna/readers/test.json
rename mrdna/readers/{ => test}/Na_liu.json (100%)
rename mrdna/readers/{ => test}/rest_scaf_col.json (100%)
create mode 100644 mrdna/readers/test/test.ipynb
rename mrdna/readers/{ => test}/test.json.oxdna (100%)
rename mrdna/readers/{ => test}/test.json.top (100%)
rename mrdna/readers/{ => test}/test.seq.json (100%)
rename mrdna/readers/{ => test}/test.virt2nuc (100%)
rename mrdna/readers/{ => test}/test2.ipynb (100%)
rename mrdna/readers/{ => test}/test3.ipynb (100%)
diff --git a/mrdna/readers/.gitignore b/mrdna/readers/.gitignore
index 6db9d75..01b1df4 100644
--- a/mrdna/readers/.gitignore
+++ b/mrdna/readers/.gitignore
@@ -1,3 +1,2 @@
-libs
*.swp
.ipynb*
diff --git a/mrdna/readers/.ipynb_checkpoints/test-checkpoint.ipynb b/mrdna/readers/.ipynb_checkpoints/test-checkpoint.ipynb
index e061577..f708f52 100644
--- a/mrdna/readers/.ipynb_checkpoints/test-checkpoint.ipynb
+++ b/mrdna/readers/.ipynb_checkpoints/test-checkpoint.ipynb
@@ -2,8 +2,8 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
- "id": "b7b6b57a",
+ "execution_count": 342,
+ "id": "8abe7172",
"metadata": {
"scrolled": true
},
@@ -11,37 +11,1052 @@
"source": [
"import pandas as pd\n",
"import pickle\n",
- "import numpy as np\n",
- "df=pd.read_json(\"test.json\")\n",
- "d=list(df[\"vstrands\"])"
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 343,
+ "id": "a6c10574",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import cadnano\n",
+ "from cadnano.document import Document\n",
+ "from mrdna.arbdmodel.coords import readArbdCoords, readAvgArbdCoords, rotationAboutAxis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 553,
+ "id": "1322927e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_prop_table(json_file):\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",
+ " vhi=vslist.index[vhi]\n",
+ " vhj=vslist.index[vhj]\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(helix_id, indices) for helix_id, indices in zip(nt_prop[\"vh\"],nt_prop[\"zid\"])]\n",
+ " nt_prop[\"orientation\"]=[get_helix_angle(part, helix_id, indices) for helix_id,indices 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",
+ " index2=list(zip(vhzid,nt_prop[\"is_scaf\"]))\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",
+ " ## Todo: sequence \n",
+ "\n",
+ "\n",
+ " return nt_prop\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 555,
+ "id": "c787cb64",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found cadnano version 2 file\n"
+ ]
+ }
+ ],
+ "source": [
+ "nt_prop=gen_prop_table(\"test/Na_liu.json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 560,
+ "id": "927f76f3",
+ "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": 562,
+ "id": "2093c036",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "vh 0\n",
+ "zid 21\n",
+ "is_scaf True\n",
+ "r [0.0, 15.75, 7.140000000000001]\n",
+ "bp 7560\n",
+ "stack -1\n",
+ "threeprime 13\n",
+ "seq -1\n",
+ "orientation [[-4.440892098500626e-16, 1.0, 0.0], [-1.0, -4...\n",
+ "Name: 12, dtype: object"
+ ]
+ },
+ "execution_count": 562,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nt_prop.loc[12]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 468,
+ "id": "51b62714",
+ "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": "66e9eacd",
+ "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": "ddecdaef",
+ "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": "ea39cbd0",
+ "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": "5008fa72",
+ "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": "7409a9a8",
+ "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": "7e3b3abc",
+ "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": "33c599fb",
+ "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": "c781d982",
+ "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": "7efc3ee8",
+ "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": "0b3eaca4",
+ "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": "99e544cd",
+ "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": "264bc035",
+ "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": "4f596a68",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df=pd.DataFrame(data=d)\n",
+ "df=df.set_index(\"num\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ce978dbf",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "e10006d7",
+ "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": 358,
+ "id": "ed9e6b6f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found cadnano version 2 file\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "NucleicAcidPart_-1_3904"
+ ]
+ },
+ "execution_count": 358,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p,f=read_json_file(\"test.json\")\n",
+ "p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 441,
+ "id": "8ec446ce",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
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+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>row</th>\n",
+ " <th>col</th>\n",
+ " <th>scaf</th>\n",
+ " <th>stap</th>\n",
+ " <th>loop</th>\n",
+ " <th>skip</th>\n",
+ " <th>scafLoop</th>\n",
+ " <th>stapLoop</th>\n",
+ " <th>stap_colors</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>num</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>12</td>\n",
+ " <td>16</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ...</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [0, 3, -1...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[23, 13369809], [38, 12060012]]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>12</td>\n",
+ " <td>15</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ...</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[3, 1501302]]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>13</td>\n",
+ " <td>15</td>\n",
+ " <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",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[34, 8947848]]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>13</td>\n",
+ " <td>16</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [3, 3, 2,...</td>\n",
+ " <td>[[-1, -1, 3, 1], [3, 0, 3, 2], [3, 1, 3, 3], [...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[0, 13369344]]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>13</td>\n",
+ " <td>17</td>\n",
+ " <td>[[-1, -1, 4, 1], [4, 0, 4, 2], [4, 1, 4, 3], [...</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[39, 8947848]]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>12</td>\n",
+ " <td>17</td>\n",
+ " <td>[[5, 1, -1, -1], [5, 2, 5, 0], [5, 3, 5, 1], [...</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[9, 0]]</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "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": "263415de",
+ "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": "6e58a7e7",
+ "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": "3ee34e17",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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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])"
+ ]
+ },
+ "execution_count": 442,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.array(nt_prop[\"seq\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 302,
+ "id": "72245714",
+ "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": "c3891b4a",
+ "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": "b3fcb6ac",
+ "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": 7,
- "id": "d7794e7b",
+ "execution_count": 430,
+ "id": "2565f375",
+ "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"
+ }
+ ],
+ "source": [
+ "stackid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 431,
+ "id": "ecf2414e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "232"
+ ]
+ },
+ "execution_count": 431,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nt_prop[\"stack\"][233]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 433,
+ "id": "5bc1a2b8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "353"
+ ]
+ },
+ "execution_count": 433,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nt_prop[\"stack\"][354]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 167,
+ "id": "5f500be3",
"metadata": {},
"outputs": [],
"source": [
- "import cadnano\n",
- "from cadnano.document import Document\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",
+ "nts=scaf_id+stap_id"
]
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "a18ffc26",
+ "execution_count": 360,
+ "id": "d7f0b909",
"metadata": {},
"outputs": [],
"source": [
- "df=pd.DataFrame(data=d)\n",
- "df=df.set_index(\"num\")"
+ "nt_prop[\"orientation\"]=[get_helix_angle(p,i,j) for i,j in zip(nt_prop[\"vh\"],nt_prop[\"zid\"])]\n"
]
},
{
"cell_type": "code",
- "execution_count": 5,
- "id": "22e5fd1e",
+ "execution_count": 190,
+ "id": "686640b1",
"metadata": {},
"outputs": [
{
@@ -212,215 +1227,146 @@
"5 [[9, 0]] "
]
},
- "execution_count": 5,
+ "execution_count": 190,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "6ddb3f62",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found cadnano version 2 file\n"
- ]
- }
- ],
- "source": [
- "doc = Document()\n",
- "def read_json_file(filename):\n",
- " import json\n",
- " import re\n",
- "\n",
- " try:\n",
- " with open(filename) as ch:\n",
- " 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",
- " data = json.loads(content)\n",
- " return data\n",
- "f=read_json_file(\"test.json\")\n",
- "cadnano.fileio.v2decode.decode(doc, f)\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "9a6e2f17",
- "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"
+ "vslist"
]
},
{
"cell_type": "code",
- "execution_count": 121,
- "id": "0253e593",
+ "execution_count": 200,
+ "id": "ff964cef",
"metadata": {},
"outputs": [],
"source": [
- "def read_json_file(filename):\n",
- " import json\n",
- " import re\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",
+ "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 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_df\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": 44,
- "id": "2929c2ed",
+ "execution_count": 201,
+ "id": "97bce4fa",
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "[42, 42, 42, 42, 42, 42]"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
+ "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": [
- "bpnum=[len(df[\"scaf\"][i]) for i in df.index]\n",
- "bpnum"
+ "mrdna_model_from_cadnano(\"test.json\")"
]
},
{
"cell_type": "code",
- "execution_count": 122,
- "id": "20eab478",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found cadnano version 2 file\n"
- ]
- }
- ],
+ "execution_count": 146,
+ "id": "30246519",
+ "metadata": {},
+ "outputs": [],
"source": [
- "f,b=read_json_file(\"test.json\")\n"
+ "a,b=np.where(np.array(nts)==1)"
]
},
{
"cell_type": "code",
- "execution_count": 52,
- "id": "4ab55565",
+ "execution_count": 148,
+ "id": "2b287b99",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "Int64Index([0, 1, 2, 3, 4, 5], dtype='int64', name='num')"
+ "array([ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
+ " 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 39, 40,\n",
+ " 41, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
+ " 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 39,\n",
+ " 40, 41, 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, 29, 30, 31, 32, 39,\n",
+ " 40, 41, 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, 29, 30, 31, 32, 37,\n",
+ " 38, 39, 40, 41, 0, 1, 2, 3, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
+ " 35, 36, 37, 38, 39, 0, 1, 2, 3, 9, 10, 11, 12, 13, 14, 15, 16,\n",
+ " 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": 52,
+ "execution_count": 148,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "b.index"
+ "nt_prop=pd.DataFrame(index)"
]
},
{
"cell_type": "code",
- "execution_count": 83,
- "id": "c55fdc19",
+ "execution_count": 137,
+ "id": "781327b0",
"metadata": {},
"outputs": [],
"source": [
- "def ntcount(scafs):\n",
+ "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 np.sum(n)\n",
+ " return n\n",
"\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91188f0a",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 84,
- "id": "3552d3d6",
+ "id": "a4317b6b",
"metadata": {},
"outputs": [],
"source": [
@@ -430,23 +1376,134 @@
},
{
"cell_type": "code",
- "execution_count": 123,
- "id": "58b92427",
+ "execution_count": 156,
+ "id": "7154c666",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>r</th>\n",
+ " <th>bp</th>\n",
+ " <th>stack</th>\n",
+ " <th>threeprime</th>\n",
+ " <th>seq</th>\n",
+ " <th>orientation</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>vh</th>\n",
+ " <th>zid</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>3</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <th>1</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <th>2</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>8</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 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"
+ ]
+ },
+ "execution_count": 156,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"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"
+ "d.set_index([\"vh\",\"zid\"],inplace=True)\n",
+ "d"
]
},
{
"cell_type": "code",
- "execution_count": 126,
- "id": "aaf6cb45",
+ "execution_count": 157,
+ "id": "f82be5d8",
"metadata": {},
"outputs": [
{
@@ -549,19 +1606,21 @@
"4 1 8 NaN NaN NaN NaN NaN NaN"
]
},
- "execution_count": 126,
+ "execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "d.reset_index()"
+ "\n",
+ "d=d.reset_index()\n",
+ "d"
]
},
{
"cell_type": "code",
"execution_count": 128,
- "id": "6daf8f3b",
+ "id": "4053cca5",
"metadata": {},
"outputs": [
{
@@ -583,7 +1642,7 @@
{
"cell_type": "code",
"execution_count": 127,
- "id": "126916f8",
+ "id": "2a012588",
"metadata": {},
"outputs": [
{
@@ -703,7 +1762,7 @@
{
"cell_type": "code",
"execution_count": 100,
- "id": "18f368b0",
+ "id": "acfa6f0c",
"metadata": {},
"outputs": [
{
@@ -728,7 +1787,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "998eed5e",
+ "id": "7e82631e",
"metadata": {},
"outputs": [
{
@@ -749,7 +1808,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "id": "6e62da4d",
+ "id": "c69e710a",
"metadata": {},
"outputs": [
{
@@ -771,7 +1830,7 @@
{
"cell_type": "code",
"execution_count": 198,
- "id": "b786e74c",
+ "id": "c93f7b4b",
"metadata": {},
"outputs": [
{
@@ -793,7 +1852,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "e36b19a3",
+ "id": "f44d5593",
"metadata": {},
"outputs": [
{
@@ -814,7 +1873,7 @@
{
"cell_type": "code",
"execution_count": 173,
- "id": "46b64c39",
+ "id": "da99c2f2",
"metadata": {},
"outputs": [],
"source": [
@@ -850,7 +1909,7 @@
{
"cell_type": "code",
"execution_count": 177,
- "id": "4d59fd91",
+ "id": "bf41fbbe",
"metadata": {},
"outputs": [],
"source": [
@@ -886,7 +1945,7 @@
{
"cell_type": "code",
"execution_count": 187,
- "id": "d49175db",
+ "id": "becc8ec8",
"metadata": {},
"outputs": [
{
@@ -1113,7 +2172,7 @@
{
"cell_type": "code",
"execution_count": 142,
- "id": "a033b838",
+ "id": "65d304e7",
"metadata": {},
"outputs": [],
"source": [
@@ -1150,7 +2209,7 @@
{
"cell_type": "code",
"execution_count": 117,
- "id": "2f0d79a6",
+ "id": "1102332d",
"metadata": {},
"outputs": [],
"source": [
@@ -1171,7 +2230,7 @@
{
"cell_type": "code",
"execution_count": 116,
- "id": "7874ae4e",
+ "id": "9349147e",
"metadata": {},
"outputs": [
{
@@ -1383,7 +2442,7 @@
{
"cell_type": "code",
"execution_count": 157,
- "id": "ba99db6a",
+ "id": "23c69dfb",
"metadata": {},
"outputs": [
{
@@ -1407,7 +2466,7 @@
{
"cell_type": "code",
"execution_count": 152,
- "id": "c26f006c",
+ "id": "c6e2518a",
"metadata": {},
"outputs": [
{
@@ -1428,7 +2487,7 @@
{
"cell_type": "code",
"execution_count": 62,
- "id": "108484f8",
+ "id": "bf85089c",
"metadata": {},
"outputs": [
{
@@ -1656,7 +2715,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "3c44e8e6",
+ "id": "3fd57867",
"metadata": {},
"outputs": [],
"source": [
@@ -1782,7 +2841,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "2b15ebca",
+ "id": "014caa04",
"metadata": {},
"outputs": [],
"source": [
@@ -1803,7 +2862,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "b115cc56",
+ "id": "95719bec",
"metadata": {},
"outputs": [
{
@@ -1865,7 +2924,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "3ef5fda5",
+ "id": "f4034eb9",
"metadata": {},
"outputs": [
{
@@ -1927,7 +2986,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "6e4cec2f",
+ "id": "b9adb4fe",
"metadata": {},
"outputs": [],
"source": [
@@ -1940,7 +2999,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "c194aaf1",
+ "id": "ca67b08f",
"metadata": {},
"outputs": [
{
@@ -1961,7 +3020,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "fd8812c1",
+ "id": "86a7a627",
"metadata": {},
"outputs": [
{
@@ -2010,7 +3069,7 @@
{
"cell_type": "code",
"execution_count": 36,
- "id": "eb8c3d36",
+ "id": "63614edc",
"metadata": {},
"outputs": [
{
@@ -2072,7 +3131,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "fc31059a",
+ "id": "177fe2f1",
"metadata": {},
"outputs": [
{
@@ -2098,7 +3157,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "87e1b359",
+ "id": "0c4a7b82",
"metadata": {},
"outputs": [],
"source": []
@@ -2106,7 +3165,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "8ed06a4e",
+ "id": "82b5073c",
"metadata": {},
"outputs": [
{
@@ -2126,7 +3185,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "dc56eb01",
+ "id": "684cdf08",
"metadata": {},
"outputs": [
{
@@ -2147,7 +3206,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "e24820fe",
+ "id": "95b9f60b",
"metadata": {},
"outputs": [
{
@@ -2217,7 +3276,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "id": "adabfec6",
+ "id": "da04d8df",
"metadata": {},
"outputs": [],
"source": [
@@ -2228,7 +3287,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "id": "83617f96",
+ "id": "fc9fa817",
"metadata": {},
"outputs": [
{
@@ -2337,7 +3396,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "a9e7ae70",
+ "id": "d030eea1",
"metadata": {},
"outputs": [
{
@@ -2358,7 +3417,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "062ccc4d",
+ "id": "6586e97f",
"metadata": {},
"outputs": [
{
@@ -2389,7 +3448,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "225701b3",
+ "id": "f087752e",
"metadata": {},
"outputs": [
{
@@ -2886,7 +3945,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "eb0dfa9d",
+ "id": "d548b882",
"metadata": {},
"outputs": [
{
@@ -2920,7 +3979,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "2ea725af",
+ "id": "bd65fa30",
"metadata": {},
"outputs": [
{
@@ -2940,7 +3999,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "e7ecad72",
+ "id": "96fe4106",
"metadata": {},
"outputs": [],
"source": [
@@ -2950,7 +4009,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "921ab252",
+ "id": "70b6c9a6",
"metadata": {},
"outputs": [
{
@@ -2971,7 +4030,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "5e3ddee4",
+ "id": "fc17c5e7",
"metadata": {},
"outputs": [
{
@@ -2998,7 +4057,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "16b9a0f0",
+ "id": "ba6c81e7",
"metadata": {},
"outputs": [],
"source": []
diff --git a/mrdna/readers/.nfs0000000000093fec000043e9 b/mrdna/readers/.nfs0000000000093fec000043e9
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diff --git a/mrdna/readers/__init__.py b/mrdna/readers/__init__.py
index 7e0e4db..2778b41 100644
--- a/mrdna/readers/__init__.py
+++ b/mrdna/readers/__init__.py
@@ -7,6 +7,11 @@ def read_cadnano(json_file, sequence=None, fill_sequence='T', **model_parameters
data = read_json_file(json_file)
return model_from_cadnano_json(data, sequence, fill_sequence, **model_parameters)
+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_vhelix(maya_file, **model_parameters):
from .polygon_mesh import parse_maya_file, convert_maya_bases_to_segment_model
diff --git a/mrdna/readers/list.pdb b/mrdna/readers/list.pdb
deleted file mode 100644
index a986d3d..0000000
--- a/mrdna/readers/list.pdb
+++ /dev/null
@@ -1,8 +0,0 @@
-CRYST1 5000.000 5000.000 5000.000 90.00 90.00 90.00 P 1 1
-ATOM 1 DNA DNA A 1 0.50000 0.00000 8.50000 0.00 0.00 0 0
-ATOM 2 DNA DNA A 2 0.50000 0.00000 15.3000 0.00 0.00 0 1
-ATOM 3 DNA DNA A 3 0.50000 0.00000 15.3000 0.00 0.00 0 1
-ATOM 4 NAS NAS A 4 0.00000 0.00000-1.70000 0.00 0.00 0 2
-ATOM 5 NAS NAS A 5 0.00000 0.00000 1.70000 0.00 0.00 0 2
-ATOM 6 NAS NAS A 6 0.00000 0.00000 5.10000 0.00 0.00 0 2
-ATOM 7 NAS NAS A 7 0.00000 0.00000 20.4000 0.00 0.00 0 3
diff --git a/mrdna/readers/list.psf b/mrdna/readers/list.psf
deleted file mode 100644
index 828fb57..0000000
--- a/mrdna/readers/list.psf
+++ /dev/null
@@ -1,39 +0,0 @@
-PSF NAMD
-
- 0 !NTITLE
-
- 7 !NATOM
- 1 0 1 DNA DNA D000 0.000000 225.0000 0
- 2 1 2 DNA DNA D001 0.000000 300.0000 0
- 3 1 3 DNA DNA D002 0.000000 375.0000 0
- 4 2 4 NAS NAS S000 0.000000 150.0000 0
- 5 2 5 NAS NAS S000 0.000000 150.0000 0
- 6 2 6 NAS NAS S001 0.000000 75.0000 0
- 7 3 7 NAS NAS S000 0.000000 150.0000 0
-
- 7 !NBOND
- 1 6 4 5 5 6 4 7
- 1 3 2 3 2 7
-
- 3 !NTHETA
- 4 5 6 5 4 7 1 3 2
-
- 0 !NPHI
-
- 0 !NIMPHI
-
-
- 0 !NDON: donors
-
-
-
- 0 !NACC: acceptors
-
-
-
- 0 !NNB
-
- 0 0 0 0 0 0 0
-
- 1 0 !NGRP
-
diff --git a/mrdna/readers/segmentmodel_from_cadnano.py b/mrdna/readers/segmentmodel_from_cadnano.py
index c642396..9bccb0c 100644
--- a/mrdna/readers/segmentmodel_from_cadnano.py
+++ b/mrdna/readers/segmentmodel_from_cadnano.py
@@ -6,10 +6,16 @@ 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 cadnano
+from cadnano.document import Document
+
## Only testing on cadnano2.5
## TODO: separate SegmentModel from ArbdModel so multiple parts can be combined
@@ -34,10 +40,6 @@ def get_lattice(part):
def read_json_file(filename):
- import json
- import re
- import cadnano
- from cadnano.document import Document
try:
with open(filename) as ch:
@@ -138,11 +140,13 @@ def gen_prop_table(json_file):
tot_id=scaf_id+stap_id
vhi,zidi=np.where(np.array(scaf_id)==1)
vhj,zidj=np.where(np.array(stap_id)==1)
+ vhi=vslist.index[vhi]
+ vhj=vslist.index[vhj]
nt_prop["vh"]=list(vhi)+list(vhj)
nt_prop["zid"]=list(zidi)+list(zidj)
vhzid=list(zip(nt_prop["vh"],nt_prop["zid"]))
- nt_prop["r"]=[part.getCoordinate(i,j) for i,j in zip(nt_prop["vh"],nt_prop["zid"])]
- nt_prop["orientation"]=[get_helix_angle(part, helix_id, indices) for i,j in zip(nt_prop["vh"],nt_prop["zid"])]
+ nt_prop["r"]=[part.getCoordinate(helix_id, indices) for helix_id, indices in zip(nt_prop["vh"],nt_prop["zid"])]
+ nt_prop["orientation"]=[get_helix_angle(part, helix_id, indices) for helix_id,indices in zip(nt_prop["vh"],nt_prop["zid"])]
nt_prop=nt_prop.fillna(-1)
for i in range(int(len(vhzid)/2)):
try:
@@ -152,6 +156,7 @@ def gen_prop_table(json_file):
except:
pass
tprime_list=-np.ones(len(nt_prop.index),dtype=int)
+ index2=list(zip(vhzid,nt_prop["is_scaf"]))
for i in range(len(nt_prop.index)):
((m,n),p)=list(zip(vhzid,nt_prop["is_scaf"]))[i]
if p==True:
@@ -169,13 +174,17 @@ def gen_prop_table(json_file):
(n,)=np.where(nt_prop["threeprime"]==-1)
stackid=nt_prop["bp"][[list(nt_prop["threeprime"]).index(i) for i in n]]
nt_prop["stack"][stackid.index[np.where(np.array(stackid)!=-1)]]=nt_prop["threeprime"][stackid.index[np.where(np.array(stackid)!=-1)]]
+ ## Todo: sequence
return nt_prop
-def mrdna_model_from_cadnano(json_file,**model_parameters):
+def mrdna_model_from_cadnano(json_file,seq=None,**model_parameters):
nt_prop=gen_prop_table(json_file)
- model = model_from_basepair_stack_3prime( nt_prop["r"], nt_prop["bp"], stack, three_prime, seq, orientation, **model_parameters )
-
-if __name__ == '__main__':
-
+ if seq is None:
+ if nt_prop["seq"][0]==-1:
+ seq=None
+ else:
+ seq=nt_prop["seq"]
+ model = model_from_basepair_stack_3prime( nt_prop["r"], nt_prop["bp"], nt_prop["stack"], nt_prop["threeprime"], seq,nt_prop["orientation"], **model_parameters )
+
diff --git a/mrdna/readers/test.ipynb b/mrdna/readers/test.ipynb
index 217f357..85e11c4 100644
--- a/mrdna/readers/test.ipynb
+++ b/mrdna/readers/test.ipynb
@@ -3,7 +3,7 @@
{
"cell_type": "code",
"execution_count": 342,
- "id": "767fe31e",
+ "id": "090f69c6",
"metadata": {
"scrolled": true
},
@@ -11,28 +11,517 @@
"source": [
"import pandas as pd\n",
"import pickle\n",
- "import numpy as np\n",
- "\n",
- "df=pd.read_json(\"test.json\")\n",
- "d=list(df[\"vstrands\"])"
+ "import numpy as np"
]
},
{
"cell_type": "code",
- "execution_count": 343,
- "id": "52f3059d",
+ "execution_count": 2,
+ "id": "a99e1791",
"metadata": {},
"outputs": [],
+ "source": [
+ "from cadnano.views.pathview import pathstyles"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0b58d7e9",
+ "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": 553,
+ "id": "6033edc9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_prop_table(json_file):\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",
+ " vhi=vslist.index[vhi]\n",
+ " vhj=vslist.index[vhj]\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(helix_id, indices) for helix_id, indices in zip(nt_prop[\"vh\"],nt_prop[\"zid\"])]\n",
+ " nt_prop[\"orientation\"]=[get_helix_angle(part, helix_id, indices) for helix_id,indices 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",
+ " index2=list(zip(vhzid,nt_prop[\"is_scaf\"]))\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",
+ " ## Todo: sequence \n",
+ "\n",
+ "\n",
+ " return nt_prop\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 555,
+ "id": "8e48e7d1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found cadnano version 2 file\n"
+ ]
+ }
+ ],
+ "source": [
+ "nt_prop=gen_prop_table(\"test/Na_liu.json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 560,
+ "id": "4da2c91e",
+ "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": 562,
+ "id": "da512048",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "vh 0\n",
+ "zid 21\n",
+ "is_scaf True\n",
+ "r [0.0, 15.75, 7.140000000000001]\n",
+ "bp 7560\n",
+ "stack -1\n",
+ "threeprime 13\n",
+ "seq -1\n",
+ "orientation [[-4.440892098500626e-16, 1.0, 0.0], [-1.0, -4...\n",
+ "Name: 12, dtype: object"
+ ]
+ },
+ "execution_count": 562,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nt_prop.loc[12]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 468,
+ "id": "95fb0515",
+ "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": "dcc227ca",
+ "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": "08d33dfb",
+ "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": "c2347336",
+ "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": "795140b5",
+ "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": "f98c2927",
+ "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": "56385511",
+ "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": "8565bdb9",
+ "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": "ffa55e8a",
+ "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": "43a9cef8",
+ "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": "5d92876d",
+ "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": "6f5d2675",
+ "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": "15fa1dec",
+ "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": "2fb0f9e3",
+ "id": "abd8b729",
"metadata": {},
"outputs": [],
"source": [
@@ -42,8 +531,67 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "id": "312a3b3c",
+ "execution_count": null,
+ "id": "86dc559b",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "b7a36513",
+ "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": 358,
+ "id": "c590e405",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found cadnano version 2 file\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "NucleicAcidPart_-1_3904"
+ ]
+ },
+ "execution_count": 358,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p,f=read_json_file(\"test.json\")\n",
+ "p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 441,
+ "id": "28e8fc5f",
"metadata": {},
"outputs": [
{
@@ -214,164 +762,19 @@
"5 [[9, 0]] "
]
},
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "df00a798",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found cadnano version 2 file\n"
- ]
- }
- ],
- "source": [
- "doc = Document()\n",
- "def read_json_file(filename):\n",
- " import json\n",
- " import re\n",
- "\n",
- " try:\n",
- " with open(filename) as ch:\n",
- " 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",
- " data = json.loads(content)\n",
- " return data\n",
- "f=read_json_file(\"test.json\")\n",
- "cadnano.fileio.v2decode.decode(doc, f)\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "ab971d26",
- "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": 195,
- "id": "1a5439a1",
- "metadata": {},
- "outputs": [],
- "source": [
- "def read_json_file(filename):\n",
- " import json\n",
- " import re\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_df\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 358,
- "id": "254f8faf",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found cadnano version 2 file\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "NucleicAcidPart_-1_3904"
- ]
- },
- "execution_count": 358,
+ "execution_count": 441,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "p,f=read_json_file(\"test.json\")\n",
- "p"
+ "f"
]
},
{
"cell_type": "code",
"execution_count": 199,
- "id": "054ffa7d",
+ "id": "75798a1d",
"metadata": {},
"outputs": [
{
@@ -392,7 +795,7 @@
{
"cell_type": "code",
"execution_count": 434,
- "id": "5b268b86",
+ "id": "e90bd308",
"metadata": {},
"outputs": [],
"source": [
@@ -459,60 +862,53 @@
},
{
"cell_type": "code",
- "execution_count": 437,
- "id": "67d740a5",
+ "execution_count": 442,
+ "id": "f1b789b7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225,\n",
- " 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238,\n",
- " 239, 240, 241, 242, 243, 244, -1, -1, -1, 249, 250, 251, 252,\n",
- " 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265,\n",
- " 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278,\n",
- " 279, 280, -1, -1, -1, 285, 286, 287, 288, 289, 290, 291, 292,\n",
- " 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305,\n",
- " 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, -1, -1, -1,\n",
- " 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332,\n",
- " 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345,\n",
- " 346, 347, 348, 349, 350, -1, -1, -1, -1, -1, -1, -1, -1,\n",
- " -1, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n",
- " 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377,\n",
- " 378, 379, 380, 381, 382, 383, -1, -1, -1, -1, 384, 385, 386,\n",
- " 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399,\n",
- " 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, -1,\n",
- " -1, -1, -1, -1, -1, 0, 1, 2, 3, 4, 5, 6, 7,\n",
- " 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
- " 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, -1, -1,\n",
- " -1, -1, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n",
- " 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,\n",
- " 59, 60, 61, 62, 63, 64, 65, 66, -1, -1, -1, -1, 70,\n",
- " 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,\n",
- " 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,\n",
- " 97, 98, 99, 100, -1, -1, -1, -1, 104, 105, 106, 107, 108,\n",
- " 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,\n",
- " 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,\n",
- " -1, -1, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,\n",
- " 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
- " 168, 169, 170, 171, 172, 173, 174, 179, 180, 181, 182, 183, 184,\n",
- " 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197,\n",
- " 198, 199, 200, 201, 202, 203, 204, 205, 206, -1, -1, -1])"
+ "array([-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, -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, -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": 437,
+ "execution_count": 442,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "np.array(nt_prop[\"bp\"])"
+ "np.array(nt_prop[\"seq\"])"
]
},
{
"cell_type": "code",
"execution_count": 302,
- "id": "8df80d92",
+ "id": "15c2f5a3",
"metadata": {},
"outputs": [],
"source": [
@@ -536,7 +932,7 @@
{
"cell_type": "code",
"execution_count": 368,
- "id": "1ba36ce9",
+ "id": "2103280f",
"metadata": {},
"outputs": [],
"source": [
@@ -569,7 +965,7 @@
{
"cell_type": "code",
"execution_count": 429,
- "id": "c275fa92",
+ "id": "db2d23a1",
"metadata": {},
"outputs": [],
"source": [
@@ -584,7 +980,7 @@
{
"cell_type": "code",
"execution_count": 430,
- "id": "05d1934e",
+ "id": "7a63c8bc",
"metadata": {},
"outputs": [
{
@@ -619,7 +1015,7 @@
{
"cell_type": "code",
"execution_count": 431,
- "id": "7d0daba0",
+ "id": "bdf5fe8f",
"metadata": {},
"outputs": [
{
@@ -640,7 +1036,7 @@
{
"cell_type": "code",
"execution_count": 433,
- "id": "214ff037",
+ "id": "6f25d6f3",
"metadata": {},
"outputs": [
{
@@ -661,7 +1057,7 @@
{
"cell_type": "code",
"execution_count": 167,
- "id": "eb07d74e",
+ "id": "75d426b5",
"metadata": {},
"outputs": [],
"source": [
@@ -673,7 +1069,7 @@
{
"cell_type": "code",
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- "id": "63302624",
+ "id": "f7ebdd30",
"metadata": {},
"outputs": [],
"source": [
@@ -683,7 +1079,7 @@
{
"cell_type": "code",
"execution_count": 190,
- "id": "bc0af039",
+ "id": "eead924e",
"metadata": {},
"outputs": [
{
@@ -866,7 +1262,7 @@
{
"cell_type": "code",
"execution_count": 200,
- "id": "fd6b27af",
+ "id": "71493bd1",
"metadata": {},
"outputs": [],
"source": [
@@ -902,7 +1298,7 @@
{
"cell_type": "code",
"execution_count": 201,
- "id": "1130c056",
+ "id": "3622ebc9",
"metadata": {},
"outputs": [
{
@@ -925,7 +1321,7 @@
{
"cell_type": "code",
"execution_count": 146,
- "id": "7a77a10c",
+ "id": "74c4ae5a",
"metadata": {},
"outputs": [],
"source": [
@@ -935,7 +1331,7 @@
{
"cell_type": "code",
"execution_count": 148,
- "id": "07bed4f1",
+ "id": "e6dc1404",
"metadata": {},
"outputs": [
{
@@ -968,7 +1364,7 @@
{
"cell_type": "code",
"execution_count": 137,
- "id": "efe96d4a",
+ "id": "59fac010",
"metadata": {},
"outputs": [],
"source": [
@@ -985,7 +1381,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "9ac7ac46",
+ "id": "d42bb41e",
"metadata": {},
"outputs": [],
"source": []
@@ -993,7 +1389,7 @@
{
"cell_type": "code",
"execution_count": 84,
- "id": "a28688b9",
+ "id": "c73af530",
"metadata": {},
"outputs": [],
"source": [
@@ -1004,7 +1400,7 @@
{
"cell_type": "code",
"execution_count": 156,
- "id": "7583686f",
+ "id": "0b16d613",
"metadata": {},
"outputs": [
{
@@ -1130,7 +1526,7 @@
{
"cell_type": "code",
"execution_count": 157,
- "id": "1b71d3f6",
+ "id": "90f57ab5",
"metadata": {},
"outputs": [
{
@@ -1247,7 +1643,7 @@
{
"cell_type": "code",
"execution_count": 128,
- "id": "65e31a02",
+ "id": "fac146fd",
"metadata": {},
"outputs": [
{
@@ -1269,7 +1665,7 @@
{
"cell_type": "code",
"execution_count": 127,
- "id": "9e9fe8c1",
+ "id": "baf83a53",
"metadata": {},
"outputs": [
{
@@ -1389,7 +1785,7 @@
{
"cell_type": "code",
"execution_count": 100,
- "id": "7a82b9f7",
+ "id": "42d9c84a",
"metadata": {},
"outputs": [
{
@@ -1414,7 +1810,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "759146bc",
+ "id": "9cbfe156",
"metadata": {},
"outputs": [
{
@@ -1435,7 +1831,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "id": "817cf65c",
+ "id": "5c918abe",
"metadata": {},
"outputs": [
{
@@ -1457,7 +1853,7 @@
{
"cell_type": "code",
"execution_count": 198,
- "id": "683deaa8",
+ "id": "eae94886",
"metadata": {},
"outputs": [
{
@@ -1479,7 +1875,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "cf734789",
+ "id": "8fc2335a",
"metadata": {},
"outputs": [
{
@@ -1500,7 +1896,7 @@
{
"cell_type": "code",
"execution_count": 173,
- "id": "7561d936",
+ "id": "715d6b94",
"metadata": {},
"outputs": [],
"source": [
@@ -1536,7 +1932,7 @@
{
"cell_type": "code",
"execution_count": 177,
- "id": "2fd676d8",
+ "id": "327e0a5e",
"metadata": {},
"outputs": [],
"source": [
@@ -1572,7 +1968,7 @@
{
"cell_type": "code",
"execution_count": 187,
- "id": "6b288cdc",
+ "id": "f31089a3",
"metadata": {},
"outputs": [
{
@@ -1799,7 +2195,7 @@
{
"cell_type": "code",
"execution_count": 142,
- "id": "c516286a",
+ "id": "fede9c99",
"metadata": {},
"outputs": [],
"source": [
@@ -1836,7 +2232,7 @@
{
"cell_type": "code",
"execution_count": 117,
- "id": "461e5389",
+ "id": "b0272fe0",
"metadata": {},
"outputs": [],
"source": [
@@ -1857,7 +2253,7 @@
{
"cell_type": "code",
"execution_count": 116,
- "id": "1cfe41f9",
+ "id": "ad669f8a",
"metadata": {},
"outputs": [
{
@@ -2069,7 +2465,7 @@
{
"cell_type": "code",
"execution_count": 157,
- "id": "b1e9c75c",
+ "id": "95284143",
"metadata": {},
"outputs": [
{
@@ -2093,7 +2489,7 @@
{
"cell_type": "code",
"execution_count": 152,
- "id": "91717641",
+ "id": "1e445750",
"metadata": {},
"outputs": [
{
@@ -2114,7 +2510,7 @@
{
"cell_type": "code",
"execution_count": 62,
- "id": "c2735ea2",
+ "id": "9768df8d",
"metadata": {},
"outputs": [
{
@@ -2342,7 +2738,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "21ebd651",
+ "id": "583c116a",
"metadata": {},
"outputs": [],
"source": [
@@ -2468,7 +2864,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "29d8b2a3",
+ "id": "1f10069b",
"metadata": {},
"outputs": [],
"source": [
@@ -2489,7 +2885,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "ac70d73d",
+ "id": "465ae2a5",
"metadata": {},
"outputs": [
{
@@ -2551,7 +2947,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "b531515d",
+ "id": "81d0004d",
"metadata": {},
"outputs": [
{
@@ -2613,7 +3009,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "f11056d9",
+ "id": "91dc0c7f",
"metadata": {},
"outputs": [],
"source": [
@@ -2626,7 +3022,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "7c724714",
+ "id": "e884018f",
"metadata": {},
"outputs": [
{
@@ -2647,7 +3043,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "44e3bf14",
+ "id": "eef6f08c",
"metadata": {},
"outputs": [
{
@@ -2696,7 +3092,7 @@
{
"cell_type": "code",
"execution_count": 36,
- "id": "e0b4e485",
+ "id": "6a4b7695",
"metadata": {},
"outputs": [
{
@@ -2758,7 +3154,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "97c05d36",
+ "id": "30cde48b",
"metadata": {},
"outputs": [
{
@@ -2784,7 +3180,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "5818a167",
+ "id": "f05ebf5f",
"metadata": {},
"outputs": [],
"source": []
@@ -2792,7 +3188,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "005b032e",
+ "id": "2bd820af",
"metadata": {},
"outputs": [
{
@@ -2812,7 +3208,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "95c7c24d",
+ "id": "394c44c5",
"metadata": {},
"outputs": [
{
@@ -2833,7 +3229,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "7bec19b6",
+ "id": "12c3ce83",
"metadata": {},
"outputs": [
{
@@ -2903,7 +3299,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "id": "289d2009",
+ "id": "11a55f6c",
"metadata": {},
"outputs": [],
"source": [
@@ -2914,7 +3310,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "id": "8ac5cdf9",
+ "id": "07e5d147",
"metadata": {},
"outputs": [
{
@@ -3023,7 +3419,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "f39f61c8",
+ "id": "123e2f5e",
"metadata": {},
"outputs": [
{
@@ -3044,7 +3440,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "a4e2fc7b",
+ "id": "670a0bc5",
"metadata": {},
"outputs": [
{
@@ -3075,7 +3471,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "3a00cb97",
+ "id": "bcb8f7cc",
"metadata": {},
"outputs": [
{
@@ -3572,7 +3968,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "8ba9d3ad",
+ "id": "1b177c60",
"metadata": {},
"outputs": [
{
@@ -3606,7 +4002,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "9dcaba3d",
+ "id": "677c2d67",
"metadata": {},
"outputs": [
{
@@ -3626,7 +4022,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "5a768b47",
+ "id": "7e7ba0e1",
"metadata": {},
"outputs": [],
"source": [
@@ -3636,7 +4032,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "d64d0055",
+ "id": "52014792",
"metadata": {},
"outputs": [
{
@@ -3657,7 +4053,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "c7e397f4",
+ "id": "8b1b263b",
"metadata": {},
"outputs": [
{
@@ -3684,7 +4080,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "24d5d7e8",
+ "id": "abb6ad51",
"metadata": {},
"outputs": [],
"source": []
diff --git a/mrdna/readers/test.json b/mrdna/readers/test.json
deleted file mode 100644
index 5332be9..0000000
--- a/mrdna/readers/test.json
+++ /dev/null
@@ -1 +0,0 @@
-{"name":"test.json","vstrands":[{"row":12,"col":16,"num":0,"scaf":[[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[1,5,0,6],[0,5,0,7],[0,6,0,8],[0,7,0,9],[0,8,0,10],[0,9,0,11],[0,10,0,12],[0,11,0,13],[0,12,0,14],[0,13,0,15],[0,14,0,16],[0,15,0,17],[0,16,0,18],[0,17,0,19],[0,18,0,20],[0,19,0,21],[0,20,0,22],[0,21,0,23],[0,22,0,24],[0,23,0,25],[0,24,0,26],[0,25,0,27],[0,26,0,28],[0,27,0,29],[0,28,0,30],[0,29,0,31],[0,30,0,32],[0,31,0,33],[0,32,0,34],[0,33,0,35],[0,34,0,36],[0,35,1,36],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,0,40],[0,39,0,41],[0,40,-1,-1]],"stap":[[-1,-1,-1,-1],[-1,-1,-1,-1],[0,3,-1,-1],[0,4,0,2],[0,5,0,3],[0,6,0,4],[0,7,0,5],[0,8,0,6],[0,9,0,7],[0,10,0,8],[0,11,0,9],[0,12,0,10],[0,13,0,11],[0,14,0,12],[0,15,0,13],[0,16,0,14],[0,17,0,15],[0,18,0,16],[0,19,0,17],[0,20,0,18],[1,20,0,19],[0,22,1,21],[0,23,0,21],[-1,-1,0,22],[0,25,-1,-1],[0,26,0,24],[0,27,0,25],[5,27,0,26],[0,29,5,28],[0,30,0,28],[0,31,0,29],[0,32,0,30],[0,33,0,31],[0,34,0,32],[0,35,0,33],[0,36,0,34],[0,37,0,35],[0,38,0,36],[-1,-1,0,37],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1]],"loop":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"skip":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"scafLoop":[],"stapLoop":[],"stap_colors":[[23,13369809],[38,12060012]]},{"row":12,"col":15,"num":1,"scaf":[[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[1,6,0,5],[1,7,1,5],[1,8,1,6],[1,9,1,7],[1,10,1,8],[1,11,1,9],[1,12,1,10],[1,13,1,11],[1,14,1,12],[1,15,1,13],[1,16,1,14],[1,17,1,15],[1,18,1,16],[2,18,1,17],[1,20,2,19],[1,21,1,19],[1,22,1,20],[1,23,1,21],[1,24,1,22],[1,25,1,23],[1,26,1,24],[1,27,1,25],[1,28,1,26],[1,29,1,27],[1,30,1,28],[1,31,1,29],[1,32,1,30],[1,33,1,31],[1,34,1,32],[1,35,1,33],[1,36,1,34],[0,36,1,35],[-1,-1,-1,-1],[-1,-1,-1,-1],[1,40,-1,-1],[1,41,1,39],[-1,-1,1,40]],"stap":[[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,1,4],[1,3,1,5],[1,4,1,6],[1,5,1,7],[1,6,1,8],[1,7,1,9],[1,8,1,10],[1,9,1,11],[1,10,1,12],[1,11,1,13],[1,12,1,14],[1,13,1,15],[1,14,1,16],[1,15,1,17],[1,16,1,18],[1,17,1,19],[1,18,1,20],[1,19,0,20],[0,21,1,22],[1,21,1,23],[1,22,1,24],[1,23,1,25],[1,24,1,26],[1,25,1,27],[1,26,1,28],[1,27,1,29],[1,28,1,30],[1,29,1,31],[1,30,1,32],[1,31,1,33],[1,32,1,34],[1,33,1,35],[1,34,1,36],[1,35,1,37],[1,36,1,38],[1,37,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1]],"loop":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"skip":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"scafLoop":[],"stapLoop":[],"stap_colors":[[3,1501302]]},{"row":13,"col":15,"num":2,"scaf":[[-1,-1,-1,-1],[-1,-1,-1,-1],[3,2,2,3],[2,2,2,4],[2,3,2,5],[2,4,2,6],[2,5,2,7],[2,6,2,8],[2,7,2,9],[2,8,2,10],[2,9,2,11],[2,10,2,12],[2,11,2,13],[2,12,2,14],[2,13,2,15],[2,14,2,16],[2,15,2,17],[2,16,2,18],[2,17,1,18],[1,19,2,20],[2,19,2,21],[2,20,2,22],[2,21,2,23],[2,22,2,24],[2,23,2,25],[2,24,2,26],[2,25,2,27],[2,26,2,28],[2,27,2,29],[2,28,2,30],[2,29,2,31],[2,30,2,32],[2,31,3,32],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,2,40],[2,39,2,41],[2,40,-1,-1]],"stap":[[2,1,-1,-1],[2,2,2,0],[2,3,2,1],[2,4,2,2],[2,5,2,3],[2,6,2,4],[2,7,2,5],[2,8,2,6],[2,9,2,7],[2,10,2,8],[2,11,2,9],[2,12,2,10],[2,13,2,11],[2,14,2,12],[2,15,2,13],[2,16,2,14],[2,17,2,15],[2,18,2,16],[2,19,2,17],[2,20,2,18],[2,21,2,19],[2,22,2,20],[2,23,2,21],[2,24,2,22],[2,25,2,23],[2,26,2,24],[2,27,2,25],[2,28,2,26],[2,29,2,27],[2,30,2,28],[2,31,2,29],[2,32,2,30],[2,33,2,31],[2,34,2,32],[-1,-1,2,33],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1]],"loop":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"skip":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"scafLoop":[],"stapLoop":[],"stap_colors":[[34,8947848]]},{"row":13,"col":16,"num":3,"scaf":[[-1,-1,-1,-1],[-1,-1,-1,-1],[3,3,2,2],[3,4,3,2],[3,5,3,3],[3,6,3,4],[3,7,3,5],[3,8,3,6],[3,9,3,7],[3,10,3,8],[3,11,3,9],[3,12,3,10],[3,13,3,11],[3,14,3,12],[3,15,3,13],[4,15,3,14],[3,17,4,16],[3,18,3,16],[3,19,3,17],[3,20,3,18],[3,21,3,19],[3,22,3,20],[3,23,3,21],[3,24,3,22],[3,25,3,23],[3,26,3,24],[3,27,3,25],[3,28,3,26],[3,29,3,27],[3,30,3,28],[3,31,3,29],[3,32,3,30],[2,32,3,31],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[3,38,-1,-1],[3,39,3,37],[3,40,3,38],[3,41,3,39],[-1,-1,3,40]],"stap":[[-1,-1,3,1],[3,0,3,2],[3,1,3,3],[3,2,3,4],[3,3,3,5],[3,4,3,6],[3,5,3,7],[3,6,3,8],[3,7,3,9],[3,8,3,10],[3,9,3,11],[3,10,3,12],[3,11,3,13],[3,12,3,14],[3,13,3,15],[3,14,3,16],[3,15,3,17],[3,16,3,18],[3,17,3,19],[3,18,3,20],[3,19,4,20],[4,21,3,22],[3,21,3,23],[3,22,3,24],[3,23,3,25],[3,24,3,26],[3,25,3,27],[3,26,3,28],[3,27,3,29],[3,28,3,30],[3,29,3,31],[3,30,3,32],[3,31,3,33],[3,32,3,34],[3,33,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1]],"loop":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"skip":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"scafLoop":[],"stapLoop":[],"stap_colors":[[0,13369344]]},{"row":13,"col":17,"num":4,"scaf":[[-1,-1,4,1],[4,0,4,2],[4,1,4,3],[4,2,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[5,9,4,10],[4,9,4,11],[4,10,4,12],[4,11,4,13],[4,12,4,14],[4,13,4,15],[4,14,3,15],[3,16,4,17],[4,16,4,18],[4,17,4,19],[4,18,4,20],[4,19,4,21],[4,20,4,22],[4,21,4,23],[4,22,4,24],[4,23,4,25],[4,24,4,26],[4,25,4,27],[4,26,4,28],[4,27,4,29],[4,28,4,30],[4,29,4,31],[4,30,4,32],[4,31,4,33],[4,32,4,34],[4,33,4,35],[4,34,4,36],[4,35,4,37],[4,36,4,38],[4,37,4,39],[4,38,5,39],[-1,-1,-1,-1],[-1,-1,-1,-1]],"stap":[[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[4,10,-1,-1],[4,11,4,9],[4,12,4,10],[4,13,4,11],[4,14,4,12],[4,15,4,13],[4,16,4,14],[4,17,4,15],[4,18,4,16],[4,19,4,17],[4,20,4,18],[3,20,4,19],[4,22,3,21],[4,23,4,21],[4,24,4,22],[4,25,4,23],[4,26,4,24],[4,27,4,25],[4,28,4,26],[4,29,4,27],[4,30,4,28],[4,31,4,29],[4,32,4,30],[4,33,4,31],[4,34,4,32],[4,35,4,33],[4,36,4,34],[4,37,4,35],[4,38,4,36],[4,39,4,37],[-1,-1,4,38],[-1,-1,-1,-1],[-1,-1,-1,-1]],"loop":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"skip":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"scafLoop":[],"stapLoop":[],"stap_colors":[[39,8947848]]},{"row":12,"col":17,"num":5,"scaf":[[5,1,-1,-1],[5,2,5,0],[5,3,5,1],[-1,-1,5,2],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[5,10,4,9],[5,11,5,9],[5,12,5,10],[5,13,5,11],[5,14,5,12],[5,15,5,13],[5,16,5,14],[5,17,5,15],[5,18,5,16],[5,19,5,17],[5,20,5,18],[5,21,5,19],[5,22,5,20],[-1,-1,5,21],[5,24,-1,-1],[5,25,5,23],[5,26,5,24],[5,27,5,25],[5,28,5,26],[5,29,5,27],[5,30,5,28],[5,31,5,29],[5,32,5,30],[5,33,5,31],[5,34,5,32],[5,35,5,33],[5,36,5,34],[5,37,5,35],[5,38,5,36],[5,39,5,37],[4,39,5,38],[-1,-1,-1,-1],[-1,-1,-1,-1]],"stap":[[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1],[-1,-1,5,10],[5,9,5,11],[5,10,5,12],[5,11,5,13],[5,12,5,14],[5,13,5,15],[5,14,5,16],[5,15,5,17],[5,16,5,18],[5,17,5,19],[5,18,5,20],[5,19,5,21],[5,20,5,22],[5,21,5,23],[5,22,5,24],[5,23,5,25],[5,24,5,26],[5,25,5,27],[5,26,0,27],[0,28,5,29],[5,28,5,30],[5,29,5,31],[5,30,5,32],[5,31,5,33],[5,32,5,34],[5,33,5,35],[5,34,5,36],[5,35,5,37],[5,36,5,38],[5,37,5,39],[5,38,-1,-1],[-1,-1,-1,-1],[-1,-1,-1,-1]],"loop":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"skip":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"scafLoop":[],"stapLoop":[],"stap_colors":[[9,0]]}]}
\ No newline at end of file
diff --git a/mrdna/readers/Na_liu.json b/mrdna/readers/test/Na_liu.json
similarity index 100%
rename from mrdna/readers/Na_liu.json
rename to mrdna/readers/test/Na_liu.json
diff --git a/mrdna/readers/rest_scaf_col.json b/mrdna/readers/test/rest_scaf_col.json
similarity index 100%
rename from mrdna/readers/rest_scaf_col.json
rename to mrdna/readers/test/rest_scaf_col.json
diff --git a/mrdna/readers/test/test.ipynb b/mrdna/readers/test/test.ipynb
new file mode 100644
index 0000000..f9f7eeb
--- /dev/null
+++ b/mrdna/readers/test/test.ipynb
@@ -0,0 +1,3735 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 342,
+ "id": "1955acef",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import pickle\n",
+ "import numpy as np\n",
+ "\n",
+ "df=pd.read_json(\"test.json\")\n",
+ "d=list(df[\"vstrands\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 343,
+ "id": "1a6b8cb2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import cadnano\n",
+ "from cadnano.document import Document\n",
+ "from mrdna.arbdmodel.coords import readArbdCoords, readAvgArbdCoords, rotationAboutAxis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c7d2f43e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df=pd.DataFrame(data=d)\n",
+ "df=df.set_index(\"num\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "62f9b7f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>row</th>\n",
+ " <th>col</th>\n",
+ " <th>scaf</th>\n",
+ " <th>stap</th>\n",
+ " <th>loop</th>\n",
+ " <th>skip</th>\n",
+ " <th>scafLoop</th>\n",
+ " <th>stapLoop</th>\n",
+ " <th>stap_colors</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>num</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>12</td>\n",
+ " <td>16</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ...</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [0, 3, -1...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[23, 13369809], [38, 12060012]]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>12</td>\n",
+ " <td>15</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ...</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[3, 1501302]]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>13</td>\n",
+ " <td>15</td>\n",
+ " <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",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[34, 8947848]]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>13</td>\n",
+ " <td>16</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [3, 3, 2,...</td>\n",
+ " <td>[[-1, -1, 3, 1], [3, 0, 3, 2], [3, 1, 3, 3], [...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[0, 13369344]]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>13</td>\n",
+ " <td>17</td>\n",
+ " <td>[[-1, -1, 4, 1], [4, 0, 4, 2], [4, 1, 4, 3], [...</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[39, 8947848]]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>12</td>\n",
+ " <td>17</td>\n",
+ " <td>[[5, 1, -1, -1], [5, 2, 5, 0], [5, 3, 5, 1], [...</td>\n",
+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+ " <td>[]</td>\n",
+ " <td>[]</td>\n",
+ " <td>[[9, 0]]</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "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": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "b317d21a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found cadnano version 2 file\n"
+ ]
+ }
+ ],
+ "source": [
+ "doc = Document()\n",
+ "def read_json_file(filename):\n",
+ " import json\n",
+ " import re\n",
+ "\n",
+ " try:\n",
+ " with open(filename) as ch:\n",
+ " 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",
+ " data = json.loads(content)\n",
+ " return data\n",
+ "f=read_json_file(\"test.json\")\n",
+ "cadnano.fileio.v2decode.decode(doc, f)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "dc7eb261",
+ "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": 195,
+ "id": "1bf753c6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def read_json_file(filename):\n",
+ " import json\n",
+ " import re\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_df\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 358,
+ "id": "9b534b7c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found cadnano version 2 file\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "NucleicAcidPart_-1_3904"
+ ]
+ },
+ "execution_count": 358,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p,f=read_json_file(\"test.json\")\n",
+ "p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 439,
+ "id": "cbb83c93",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NucleicAcidPart_-1_3904"
+ ]
+ },
+ "execution_count": 439,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 199,
+ "id": "3bb28a94",
+ "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": "be894ade",
+ "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": 440,
+ "id": "9a290811",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
+ " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
+ " 27, 28, 29, 30, 31, 66, 33, 34, -1, 0, 35, 36, 37,\n",
+ " 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 87, 49, 50,\n",
+ " 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,\n",
+ " 64, 65, -1, 67, 68, 71, 72, 73, 74, 75, 76, 77, 78,\n",
+ " 79, 80, 81, 82, 83, 84, 85, 86, 48, 88, 89, 90, 91,\n",
+ " 92, 93, 94, 95, 96, 97, 98, 99, 100, 134, 102, 103, -1,\n",
+ " 70, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,\n",
+ " 116, 151, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,\n",
+ " 129, 130, 131, 132, 133, -1, 135, 136, 137, 138, 141, 142, 143,\n",
+ " -1, 145, 146, 147, 148, 149, 150, 117, 152, 153, 154, 155, 156,\n",
+ " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n",
+ " 170, 171, 172, 173, 174, 209, -1, 175, 176, 177, 144, 179, 180,\n",
+ " 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, -1, 193,\n",
+ " 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,\n",
+ " 207, 208, -1, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
+ " 220, 221, 222, 223, 224, 225, 226, 227, 265, 229, 230, -1, 232,\n",
+ " 233, 234, 403, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,\n",
+ " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n",
+ " 261, 262, 263, 264, 228, 266, 267, 268, 269, 270, 271, 272, 273,\n",
+ " 274, 275, 276, 277, 278, 279, 280, 281, 282, -1, -1, 283, 284,\n",
+ " 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297,\n",
+ " 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310,\n",
+ " 311, 312, 313, 314, 315, 316, 319, 320, 321, 322, 323, 324, 325,\n",
+ " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n",
+ " 364, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n",
+ " 352, -1, -1, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362,\n",
+ " 363, 339, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375,\n",
+ " 376, 377, 378, 379, 380, 381, 382, 385, 386, 387, 388, 389, 390,\n",
+ " 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 235,\n",
+ " 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, -1])"
+ ]
+ },
+ "execution_count": 440,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.array(nt_prop[\"threeprime\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 302,
+ "id": "551cc70f",
+ "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": "1d40286a",
+ "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": "f45dd87c",
+ "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": "ef29b662",
+ "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"
+ }
+ ],
+ "source": [
+ "stackid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 431,
+ "id": "6678f56c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "232"
+ ]
+ },
+ "execution_count": 431,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nt_prop[\"stack\"][233]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 433,
+ "id": "cb9dbf13",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "353"
+ ]
+ },
+ "execution_count": 433,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nt_prop[\"stack\"][354]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 167,
+ "id": "165d3cc6",
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 360,
+ "id": "b2856178",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nt_prop[\"orientation\"]=[get_helix_angle(p,i,j) for i,j in zip(nt_prop[\"vh\"],nt_prop[\"zid\"])]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 190,
+ "id": "07918f5c",
+ "metadata": {},
+ "outputs": [
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+ " <td>[]</td>\n",
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+ " <td>[[0, 13369344]]</td>\n",
+ " </tr>\n",
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+ " <td>17</td>\n",
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+ " <td>[[39, 8947848]]</td>\n",
+ " </tr>\n",
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+ " <td>[[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -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",
+ " <td>[]</td>\n",
+ " <td>[[9, 0]]</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "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": 190,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vslist"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 200,
+ "id": "86293e8a",
+ "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": "b398277c",
+ "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": "c0d9eb64",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a,b=np.where(np.array(nts)==1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "id": "ab563ec9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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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": "3c45aeac",
+ "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": "803b3c67",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "id": "adb6b347",
+ "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": "307e53ad",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>r</th>\n",
+ " <th>bp</th>\n",
+ " <th>stack</th>\n",
+ " <th>threeprime</th>\n",
+ " <th>seq</th>\n",
+ " <th>orientation</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>vh</th>\n",
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+ " </tbody>\n",
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+ "</div>"
+ ],
+ "text/plain": [
+ " 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"
+ ]
+ },
+ "execution_count": 156,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "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",
+ "d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 157,
+ "id": "d030974e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " <tr style=\"text-align: right;\">\n",
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+ " <td>NaN</td>\n",
+ " </tr>\n",
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+ " <th>2</th>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>1</td>\n",
+ " <td>8</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " vh zid r bp stack threeprime seq orientation\n",
+ "0 0 0 NaN NaN NaN NaN NaN NaN\n",
+ "1 1 3 NaN NaN NaN NaN NaN NaN\n",
+ "2 2 1 NaN NaN NaN NaN NaN NaN\n",
+ "3 3 2 NaN NaN NaN NaN NaN NaN\n",
+ "4 1 8 NaN NaN NaN NaN NaN NaN"
+ ]
+ },
+ "execution_count": 157,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "d=d.reset_index()\n",
+ "d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "id": "6ddb4784",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([0, 2]),)"
+ ]
+ },
+ "execution_count": 128,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s=[True,False,True,False,False]\n",
+ "np.where(np.array(s)==True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 127,
+ "id": "28e3acea",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
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+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>r</th>\n",
+ " <th>bp</th>\n",
+ " <th>stack</th>\n",
+ " <th>threeprime</th>\n",
+ " <th>seq</th>\n",
+ " <th>orientation</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>vh</th>\n",
+ " <th>zid</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
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+ " <tbody>\n",
+ " <tr>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
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+ " <th>3</th>\n",
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <th>1</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <th>2</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>8</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 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"
+ ]
+ },
+ "execution_count": 127,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "id": "545acf6d",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "\"None of [Int64Index([0, 0], dtype='int64')] are in the [columns]\"",
+ "output_type": "error",
+ "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]\""
+ ]
+ }
+ ],
+ "source": [
+ "d[[0,0]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "f6748d9c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[[23, 13369809], [38, 12060012]]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[\"vstrands\"][0][\"stap_colors\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "5005611f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "210"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vh_vb,pattern=pd.read_pickle(\"test.virt2nuc\")\n",
+ "len(vh_vb._scaf)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 198,
+ "id": "83fc4ec6",
+ "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\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "1cd359b5",
+ "metadata": {},
+ "outputs": [
+ {
+ "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"
+ }
+ ],
+ "source": [
+ "pattern"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 173,
+ "id": "078656d6",
+ "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": "914acd5d",
+ "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"
+ ]
+ },
+ {
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+ "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": "087e2625",
+ "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": "56387503",
+ "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": "c73234d5",
+ "metadata": {},
+ "outputs": [
+ {
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+ ]
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+ "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": "b37f7a4c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "[(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": "d11f5b9c",
+ "metadata": {},
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+ ]
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+ "execution_count": 152,
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+ "list(scafs.keys())[0]"
+ ]
+ },
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+ " (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": "efe70397",
+ "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": "646b1ae9",
+ "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": "7ead2ea3",
+ "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": "3095b830",
+ "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": "b749b541",
+ "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": "a5a89254",
+ "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": "04759ac6",
+ "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": "7c36faba",
+ "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": "9985773f",
+ "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": "134923d4",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "f7bd6aef",
+ "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": "9913320b",
+ "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": "46af2b4f",
+ "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": "6ae574e4",
+ "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": "4b56fb9d",
+ "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": "0c061135",
+ "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": "1d7952e2",
+ "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": "3a02aa96",
+ "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": "6ab2279a",
+ "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": "d7dbbbbf",
+ "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": "35968795",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s=model.children[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "7512de77",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'0'"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.segname"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "74944c42",
+ "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": "e1d9ef64",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.8.18"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mrdna/readers/test.json.oxdna b/mrdna/readers/test/test.json.oxdna
similarity index 100%
rename from mrdna/readers/test.json.oxdna
rename to mrdna/readers/test/test.json.oxdna
diff --git a/mrdna/readers/test.json.top b/mrdna/readers/test/test.json.top
similarity index 100%
rename from mrdna/readers/test.json.top
rename to mrdna/readers/test/test.json.top
diff --git a/mrdna/readers/test.seq.json b/mrdna/readers/test/test.seq.json
similarity index 100%
rename from mrdna/readers/test.seq.json
rename to mrdna/readers/test/test.seq.json
diff --git a/mrdna/readers/test.virt2nuc b/mrdna/readers/test/test.virt2nuc
similarity index 100%
rename from mrdna/readers/test.virt2nuc
rename to mrdna/readers/test/test.virt2nuc
diff --git a/mrdna/readers/test2.ipynb b/mrdna/readers/test/test2.ipynb
similarity index 100%
rename from mrdna/readers/test2.ipynb
rename to mrdna/readers/test/test2.ipynb
diff --git a/mrdna/readers/test3.ipynb b/mrdna/readers/test/test3.ipynb
similarity index 100%
rename from mrdna/readers/test3.ipynb
rename to mrdna/readers/test/test3.ipynb
--
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