In [1]:
from astropy.convolution import *
import sys
import os
sys.path.append(os.path.abspath('..'))
%matplotlib notebook
# utils=os.path.abspath('..')
from utils import *
import time
from KBs import *
global log
log = []
In [2]:
(HTML(tog))
Out[2]:
In [2]:
# convolve_int=lambda a,fir,method:np.around(convolve(a,fir,method)).astype(np.int);
def csv2dat(fname):
import csv
import numpy as np
global log
dat=[];
with open(fname, 'r') as csvfile:
spamreader = csv.reader(csvfile, delimiter='\t')
for row in (spamreader):
dat+=[row];
log = log + ['Used \''+fname+'\' as input '];
return (np.array(dat));
# if not 'input_rulestr' in locals():
# input_rulestr='000000000060031c61c67f86a0';
# input_rulestr
# CA_sys
In [9]:
def measure_temperature(sys0=None,hdist=None,*args,**kwargs):
# varargin = measure_temperature.varargin
# nargin = measure_temperature.nargin
sysX=copy.copy(sys0)
jmax=sysX.N;
avi=sysX.rdf()
siz=avi.shape
siz=(sysX.hmax,)+siz;
tmp=np.zeros(siz)
smtmp=np.zeros(siz)
avc=avi
i=0
fir=np.reshape(2 ** (np.arange(0,9)),[1,3,3])
trans=6
mtp=0
stp=0
while i+1 < sysX.hmax:
i=i + 1
avcnew=(sysX.adv(avc,i))
cavc=convolve_int(avc,fir,'wrap').astype(np.int);
cavcnew=convolve_int(avcnew,fir,'wrap').astype(np.int);
idx=np.ravel_multi_index((cavc,cavcnew),[2**9,2**9]);
tmp[i,:,:,:]=np.expand_dims(hdist.flat[idx],0)
if i >= trans:
smtmpnow=np.mean(tmp[i - trans:i,:,:,:],axis=0)
smtmp[i - trans,:,:,:]=smtmpnow
if i >= trans + 10:
mtp=np.mean(smtmpnow.flat)
stpmat=((smtmp[i - trans,:,:,:] - smtmp[i - trans - trans,:,:,:]))
a=np.mean(np.abs(stpmat.flat))
b=abs(np.mean(stpmat.flat))
stp=a - b
stp1=np.mean(avcnew.flat)
stp1=min(stp1,1 - stp1)
avc=avcnew;
# im1=[avc(1,:,:)];
if mtp < 0.02 and i > 20:
break
fam_alias=sys0.familyname+'_'+sys0.alias;
# /home/shouldsee/Documents/repos/CA_tfmat/custom_function/measure_temperature.m:55
# s=sprintf('%s\\t%s\\t%d\\t%f\\t%f\\t%f\\n',fam_alias,num2str(sys0.od),i,mtp,stp,stp1)
s='{}\t{}\t{:d}\t{:f}\t{:f}\t{:f}\n'.format(fam_alias,sysX.rulestr,i,mtp,stp,stp1)
# /home/shouldsee/Documents/repos/CA_tfmat/custom_function/measure_temperature.m:56
return s
# if __name__ == '__main__':
# pass
In [10]:
def profile(input_list, log = []):
# global log
output_data=[];
repeat=2;
# input_list=[input_rulestr];
ipt_list=input_list*repeat;
# for i in range(5):
l_ipt=len(input_list)
log += ['Log of the process:'];
logs='Starting to profile {:d} rules at {:d} replicates,\n totaling {:d} instances'.format(l_ipt,repeat,l_ipt*repeat);
log += [logs];
# print('Starting to profile {:d} rules at {:d} replicates,\n totaling {:d} instances'.format(l_ipt,repeat,l_ipt*repeat))
for num,rulestr in enumerate(ipt_list):
ca1=CA_sys(familyname,rulestr,[400,100,400]);
ca1.rulestr2alias();
s=measure_temperature(ca1,hdist);
output_data+=[s];
# print('{:d} of {:d}'.format(num,len(ipt_list)))
logs =('{:d} of {:d} '.format(num,len(ipt_list)));
log += [logs];
temp_data=[];
# sample_data=[]
for line in output_data:
temp_data+=[line.rstrip('\n').split('\t')];
sample_data=np.array(temp_data)
# print('data is succesfully generated at {:d} replicates'.format(repeat))
logs=('data is succesfully generated at {:d} replicates'.format(repeat))
log += [logs];
# print('\n Detail of the input:')
logs='\n Detail of the input:';
log+=[logs];
for k,v in ca1.__dict__.items():
if not callable(v):
# print(k+str(v).ljust(-10))
# print("{:5} {:<15} {:<10}".format('',k, str(v)))
logs=("{:5} {:<15} {:<10}".format('',k, str(v)));
log+=[logs];
return( [sample_data,log]);
In [4]:
# #test
# ca1=CA_sys('2dntca',input_rulestr,[600,100,400]);
# ca1.rulestr2alias();
# s=measure_temperature(ca1,hdist);
# output_data=[];
from os import environ
# global log
# log=[];
if 'query' not in locals():
query = environ.get('query');
print query
if not query==None:
query = environ['query'];
if query[-4:]=='.csv':
query_type = 'csv';
else:
query_type = 'rulestr';
# familyname, input_rulestr = query.split('_');
else:
# familynam
query='2dntca_000000000060031c61c67f86a0';
query_type = 'rulestr'
# familyname, input_rulestr = query.split('_');
log+=['fail to fetch query, using default rule, B3/S23 \n' +
'example query:'+query];
## Using B3/S23 as example
if query_type == 'csv':
# sample_dat, log =
sample_data=csv2dat('../calc_temp_data/'+query);
print('data loaded')
if query_type == 'rulestr':
familyname, input_rulestr = query.split('_');
sample_data,log = profile([input_rulestr],log)
log+=['data generated for '+query];
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input_rulestr = '000000000060031c61c67f86a0'
sample_data,log = profile([input_rulestr],log)
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input_list = ['2dntca_000000000060031c61c67f86a0']
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sample_data
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In [16]:
from astropy.convolution import convolve
convolve_int=lambda a,fir,method:np.around(convolve(a,fir,method)).astype(np.int);
In [17]:
input_list = ['000000000060031c61c67f86a0']
# global log
output_data=[];
repeat=2;
# input_list=[input_rulestr];
ipt_list=input_list*repeat;
# for i in range(5):
l_ipt=len(input_list)
log += ['Log of the process:'];
logs='Starting to profile {:d} rules at {:d} replicates,\n totaling {:d} instances'.format(l_ipt,repeat,l_ipt*repeat);
log += [logs];
# print('Starting to profile {:d} rules at {:d} replicates,\n totaling {:d} instances'.format(l_ipt,repeat,l_ipt*repeat))
for num,rulestr in enumerate(ipt_list):
ca1=CA_sys(familyname,rulestr,[400,100,400]);
ca1.rulestr2alias();
s=measure_temperature(ca1,hdist);
output_data+=[s];
# print('{:d} of {:d}'.format(num,len(ipt_list)))
logs =('{:d} of {:d} '.format(num,len(ipt_list)));
log += [logs];
temp_data=[];
# sample_data=[]
for line in output_data:
temp_data+=[line.rstrip('\n').split('\t')];
sample_data=np.array(temp_data)
# print('data is succesfully generated at {:d} replicates'.format(repeat))
logs=('data is succesfully generated at {:d} replicates'.format(repeat))
log += [logs];
# print('\n Detail of the input:')
logs='\n Detail of the input:';
log+=[logs];
for k,v in ca1.__dict__.items():
if not callable(v):
# print(k+str(v).ljust(-10))
# print("{:5} {:<15} {:<10}".format('',k, str(v)))
logs=("{:5} {:<15} {:<10}".format('',k, str(v)));
log+=[logs];
print sample_data
# return( [sample_data,log]);
In [40]:
from astropy.convolution import convolve
convolve_int=lambda a,fir,method:np.around(convolve(a,fir,method)).astype(np.int);
def make_adv(rulestr):
ruleprj=np.array(
list(hex2bin(rulestr,102)[::-1]),
np.int);
# ruleprj=np.array(list(hex2bin(rulestr,102)[::-1]));
fir=(2**np.arange(0,9)).reshape([1,3,3]);
pj=rca2ntca;
def adv(a,horizon):
return ruleprj[pj[convolve_int(a,fir,'wrap').astype(np.int)]]
# adv=lambda a, horizon: ruleprj[pj[convolve_int(a,fir,'wrap').astype(np.int)]]
return adv
tadv = make_adv('000000000060031c61c67f86a0')
In [42]:
ruleprj
In [1]:
# %load_ext autoreload
# %autoreload
import KBs
kb = KBs.kb_2dntca()
kb.alias2rulestr("B2ce4-cint67e8/S14567")
Out[1]:
In [55]:
import scipy.ndimage
convolve_int=lambda a,fir,method:scipy.ndimage.filters.convolve(a,fir,mode = method)
# from astropy.convolution import convolve
# convolve_int=lambda a,fir,method:np.around(convolve(a,fir,method)).astype(np.int);
avc=sysX.rdf().astype(np.int)
convolve_int(avc[0:1,:,:],(2**np.arange(0,9)).reshape([1,3,3]),'wrap').astype(np.int)
Out[55]:
In [41]:
%matplotlib notebook
avc=sysX.rdf()
avc=(tadv(avc,i))
# avc
plt.imshow(avc[0,:,:])
Out[41]:
In [ ]:
import numpy as np
from matplotlib import pyplot as plt
# def main():
# plt.axis([-50,50,0,10000])
# plt.ion()
# plt.show()
# x = np.arange(-50, 51)
# for pow in range(1,5): # plot x^1, x^2, ..., x^4
# y = [Xi**pow for Xi in x]
# plt.plot(x, y)
# plt.draw()
# plt.pause(0.001)
# input("Press [enter] to continue.")
plt.axis([-50,50,0,10000])
plt.ion()
plt.show()
sysX = ca1
if __name__ == '__main__':
# main()
i = 0
while i+1 < sysX.hmax:
i=i + 1
avc=(sysX.adv(avc,i))
plt.imshow(avc[0,:,:])
# plt.show()
plt.draw()
# plt.pause(0.001)
In [1]:
# hdist
In [19]:
# def measure_temperature(sys0=None,hdist=None,*args,**kwargs):
sys0 = ca1
hdist = hdist
if 1:
# varargin = measure_temperature.varargin
# nargin = measure_temperature.nargin
sysX=copy.copy(sys0)
jmax=sysX.N;
avi=sysX.rdf()
siz=avi.shape
siz=(sysX.hmax,)+siz;
tmp=np.zeros(siz)
smtmp=np.zeros(siz)
avc=avi
i=0
fir=np.reshape(2 ** (np.arange(0,9)),[1,3,3])
trans=6
mtp=0
stp=0
while i+1 < sysX.hmax:
i=i + 1
avcnew=(sysX.adv(avc,i))
cavc=convolve_int(avc,fir,'wrap').astype(np.int);
cavcnew=convolve_int(avcnew,fir,'wrap').astype(np.int);
idx=np.ravel_multi_index((cavc,cavcnew),[2**9,2**9]);
tmp[i,:,:,:]=np.expand_dims(hdist.flat[idx],0)
if i >= trans:
smtmpnow=np.mean(tmp[i - trans:i,:,:,:],axis=0)
smtmp[i - trans,:,:,:]=smtmpnow
if i >= trans + 10:
mtp=np.mean(smtmpnow.flat)
stpmat=((smtmp[i - trans,:,:,:] - smtmp[i - trans - trans,:,:,:]))
a=np.mean(np.abs(stpmat.flat))
b=abs(np.mean(stpmat.flat))
stp=a - b
stp1=np.mean(avcnew.flat)
stp1=min(stp1,1 - stp1)
avc=avcnew;
# im1=[avc(1,:,:)];
if mtp < 0.02 and i > 20:
break
fam_alias=sys0.familyname+'_'+sys0.alias;
# /home/shouldsee/Documents/repos/CA_tfmat/custom_function/measure_temperature.m:55
# s=sprintf('%s\\t%s\\t%d\\t%f\\t%f\\t%f\\n',fam_alias,num2str(sys0.od),i,mtp,stp,stp1)
s='{}\t{}\t{:d}\t{:f}\t{:f}\t{:f}\n'.format(fam_alias,sysX.rulestr,i,mtp,stp,stp1)
# /home/shouldsee/Documents/repos/CA_tfmat/custom_function/measure_temperature.m:56
# return s
# if __name__ == '__main__':
# pass
In [6]:
# import numpy as np
# # TEST sample_data
# sample_data=np.array([['2dntca_b3ianjrecqyks2ac3i2e3a2k3nj2i3re2n3cqyk',
# '000000000060031c61c67f86a0', '99', '0.148772', '0.087523',
# '0.089563'],
# ['2dntca_b3ianjrecqyks2ac3i2e3a2k3nj2i3re2n3cqyk',
# '000000000060031c61c67f86a0', '99', '0.152794', '0.086693',
# '0.091644'],
# ['2dntca_b3ianjrecqyks2ac3i2e3a2k3nj2i3re2n3cqyk',
# '000000000060031c61c67f86a0', '99', '0.148733', '0.089657',
# '0.089387'],
# ['2dntca_b3ianjrecqyks2ac3i2e3a2k3nj2i3re2n3cqyk',
# '000000000060031c61c67f86a0', '99', '0.150105', '0.092000',
# '0.090981'],
# ['2dntca_b3ianjrecqyks2ac3i2e3a2k3nj2i3re2n3cqyk',
# '000000000060031c61c67f86a0', '99', '0.153150', '0.089772',
# '0.090938']],
# dtype='<U46')
In [7]:
viewer='''<script src="lv-plugin.js"></script>\n
<meta name="LifeViewer" content="viewer textarea 60 hide">\n
<div class="viewer" id="viewer"><textarea id="textarea">bob!</textarea><br><canvas id="cv1" width="480" height="480"></canvas></div>\n''';
# HTML(viewer)
In [8]:
%%html
<script>
String.prototype.formatUnicorn = String.prototype.formatUnicorn ||
function () {
"use strict";
var str = this.toString();
if (arguments.length) {
var t = typeof arguments[0];
var key;
var args = ("string" === t || "number" === t) ?
Array.prototype.slice.call(arguments)
: arguments[0];
for (key in args) {
str = str.replace(new RegExp("\\{" + key + "\\}", "gi"), args[key]);
}
}
return str;
};
var s= new String("");
var soup=new String("3bo4b11o2b2ob2o2bobo$obobo2bo3bobo3bo2bob3ob2o$2b3obobob6o3b5o2bo2bo$bob2o3b2ob2ob3o4bo5bob2o$2o3b3o3bob4ob2o4bobob2o$5bo4bo4b2obo3b3o2bo$o2bob3obob4o4b3obob2obobo$3bo4bob5o5b3ob2o2b2o$2b3obob2ob4o2bo3bobo3bo$bo4bob2ob6obo3bo4bob2o$5b4obo3bo4bo5bo2bo$ob2obobo2bob2o2bobo2bo2b2o4bo$obo2b2ob2o2b2o3bo2b2obobob4o$6ob2ob2o3b2ob4obo3bob2o$3ob3obo2b3ob4ob4o5bo$obobo7bo3b2o6b3o3bo$bob3o6bob2ob2o3b4obob2o$o2b2o2bo2bo2bobob7ob2o3bo$2obo2bo2b5ob2obo3b2o4b2o$b2o2bob5obo2b5o2bob4o$2o2b4ob2ob4obo3bob3obo2bo$b2o3bo2bo3b6o2bo3bo2bo$bobo3b2ob2o3b3obo3bo5bo$o4b8obobobobob2ob2ob2o$3obo3b4obo3bob3obobo$o3b2obobob2o2b2o5bob2o$6bo5bob2o3b2obo2b2obo$7bo2b2obo2bobobo3bobo2b2o$2o4b7o4bo2b2ob2o3bo$o2bo3bobo3b4o2bo3b2obobo!");
var rule="b3s23578";
var template="x = 300, y = 300, rule = {0} \n {1}";
var div_template="<textarea>{0}</textarea><br><canvas width=\"480\" height=\"480\" tabindex=\"1\"></canvas>";
//s=template.formatUnicorn("B3/S23","bbboooobbbboo!");
//var s="<textarea>x = 300, y = 300, rule = B3/S238 \nbobbbbbbooooooo!</textarea><br><canvas width=\"480\" height=\"480\"></canvas>";
//alert(s)
var s = template.formatUnicorn(rule,soup)
//document.getElementById("viewer").innerHTML = s;
</script>
In [9]:
In [ ]:
### Plotting sample_data
%matplotlib inline
# mpld3.enable_notebook()
from graphics import *
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#DDDDDD'
# ,projection='3d'
))
fig.set_size_inches([6,6])
ax.grid(color='white', linestyle='solid')
ax.set_ylim(-.1,0.38)
ax.set_xlim(0,1)
fig2,ax2=plt.subplots(subplot_kw=dict(axisbg='#DDDDDD'
,projection='3d'
))
fig2.set_size_inches([10,10])
# fig2.subplots_adjust(left=0.2, right=0.8, top=0.9, bottom=0.3)
ax2.grid(color='white', linestyle='solid')
ax2.set_ylim(-.1,0.38)
ax2.set_xlim(0,1)
fig,ax,fig2,ax2 = make_figure((fig,ax,fig2,ax2),sample_data)
fig.set_size_inches([7,7])
ax.set_ylim(-.1,0.38)
ax.set_xlim(0,1)
# display(HTML(viewer))
mpld3.display(fig)
# mpld3.display(fig2)
In [10]:
# len(KBs.henseldict)
# len(KBs.rca2ntca)
# KBs.henseldict[:51]
from KBs import *
import itertools
import collections
if 1:
OUT = ''
rulestr = '000000000060031c61c67f86a0'
r=hex2bin(rulestr,102);
r=r[::-1];
rule=[i for i,x in enumerate(r) if x=='1'];
# print r
lst = [henseldict[i] for i in rule]
lst.sort()
#### group by B/S
d = collections.OrderedDict((('b',{}),('s',{}))) ### set default
# d = {'b':{},'s':{}} ### set default
d.update(
{k:list(gp) for k,gp in itertools.groupby(lst, lambda x:x[0])}
)
for k,lst in d.iteritems():
d[k] = {k:list(gp) for k,gp in itertools.groupby(lst, lambda x:x[1])}
for bs, dd in d.iteritems():
OUT += bs
for k,lst in dd.iteritems():
OUT += k + ''.join( conf[-1] for conf in lst)
OUT = OUT.replace('_','')
alias = OUT
# return OUT
print OUT
# t
In [11]:
import numpy as np
sum(float(x)>0.3 for x in list(sample_data[:,3]))/sample_data.shape[0]
Out[11]:
In [12]:
# len(ntca_list)
# .shape
# print(tst_data)
print('\n'.join(log));
In [13]:
# from nbconvert import HTMLExporter
# import codecs
# import nbformat
# exporter = HTMLExporter()
# # execfile()
# output_notebook = nbformat.read('calc_temp.ipynb', as_version=4)
# output, resources = exporter.from_notebook_node(output_notebook)
# codecs.open('test.html', 'w', encoding='utf-8').write(output)