In [2]:
%pylab inline
!pip install xlrd
!pip install xlwt

Populating the interactive namespace from numpy and matplotlib
Requirement already satisfied: xlrd in /usr/local/lib/python2.7/dist-packages
Requirement already satisfied: xlwt in /usr/local/lib/python2.7/dist-packages
In [50]:
import tellurium as te
import pandas as pd
from scipy.stats import linregress
In [51]:
U2019_5= te.loadAntimonyModel("Models/U2019.5.txt")
U2020_5= te.loadAntimonyModel("Models/U2020.5.txt")
In [52]:
U2019_5_res = U2019_5.simulate(0,24*15,1000)
U2020_5_res = U2020_5.simulate(0,24*15,1000)
In [88]:
shift = 10*24
plot(U2020_5_res["time"][760:820]-shift,U2020_5_res["[cL]"][760:820],color="red")
plot(U2020_5_res["time"]-shift,U2020_5_res["[cL]"][:])
#xlim(0,72)
#plot(U2020_5_res["time"],U2019_5_res["[cL]"])
Out[88]:
[<matplotlib.lines.Line2D at 0x409c327390>]
In [80]:
print min(U2019_5_res["[cL]"][650:720])
print max(U2019_5_res["[cL]"][650:720])

10040.62835182498
103818.07298034037
In [85]:
print min(U2019_5_res["[cL]"][760:820])
print max(U2019_5_res["[cL]"][760:820])

7825.687965505705
53061.625697218435
In [81]:
print min(U2020_5_res["[cL]"][650:720])
print max(U2020_5_res["[cL]"][650:720])

8415.358763569713
114585.12636174941
In [87]:
print min(U2020_5_res["[cL]"][760:820])
print max(U2020_5_res["[cL]"][760:820])

6325.883059550391
37379.63740060429
In [93]:
shift = 10*24
plot(U2019_5_res["time"][820:920]-shift,U2019_5_res["[cP7]"][820:920],color="red")
plot(U2019_5_res["time"]-shift,U2019_5_res["[cP7]"][:])
#xlim(0,72)
#plot(U2020_5_res["time"],U2019_5_res["[cL]"])
Out[93]:
[<matplotlib.lines.Line2D at 0x409ce63690>]
In [100]:
print min(U2019_5_res["[cP7]"][820:920])
print max(U2019_5_res["[cP7]"][820:920])

5558.317651212382
28623.343065986814
In [98]:
shift = 10*24
plot(U2020_5_res["time"][820:920]-shift,U2020_5_res["[cP7]"][820:920],color="red")
plot(U2020_5_res["time"]-shift,U2020_5_res["[cP7]"][:])
#xlim(0,72)
#plot(U2020_5_res["time"],U2019_5_res["[cL]"])
Out[98]:
[<matplotlib.lines.Line2D at 0x409cf57650>]
In [99]:
print min(U2020_5_res["[cP7]"][820:920])
print max(U2020_5_res["[cP7]"][820:920])

2829.261532220374
13885.098818535123
In [69]:
shift = 10*24
plot(U2020_5_res["time"][700:800]-shift,U2019_5_res["[cLUX]"][700:800],color="red")
plot(U2020_5_res["time"]-shift,U2019_5_res["[cLUX]"][:])
#xlim(0,72)
#plot(U2020_5_res["time"],U2019_5_res["[cL]"])
Out[69]:
[<matplotlib.lines.Line2D at 0x408bc1c390>]
In [68]:
print min(U2019_5_res["[cLUX]"][700:800])
print max(U2019_5_res["[cLUX]"][700:800])

15499.643829423081
191158.26056710974
In [8]:
print min(U2020_5_res["[cLUX]"][800:900])
print max(U2020_5_res["[cLUX]"][800:900])

10261.509493796471
138629.58009718126
In [9]:
U2019_ELF3_total = U2019_5_res["[cE3c]"]+U2019_5_res["[cE3n]"]+U2019_5_res["[cEC]"]+U2019_5_res["[cEGn]"]+U2019_5_res["[cEGc]"]+U2019_5_res["[cE34]"]
U2020_ELF3_total = U2020_5_res["[cE3c]"]+U2020_5_res["[cE3n]"]+U2020_5_res["[cEC]"]+U2020_5_res["[cEGn]"]+U2020_5_res["[cEGc]"]+U2020_5_res["[cE34]"]
plot(U2019_5_res["time"]-24*11,U2019_ELF3_total, "-", lw=2, color="black", label="U2019.5")
plot(U2020_5_res["time"]-24*11,U2020_ELF3_total, "--",lw=2,color="black", label="U2020.5")
Out[9]:
[<matplotlib.lines.Line2D at 0x407a0ec290>]
In [66]:
print min(U2019_ELF3_total[800:900])
print max(U2019_ELF3_total[800:900])

7212.979254934358
28035.566589194652
In [10]:
plot(U2019_ELF3_total[500:600])
Out[10]:
[<matplotlib.lines.Line2D at 0x407a6a5d50>]
In [11]:
print min(U2019_ELF3_total[500:600])
print max(U2019_ELF3_total[500:600])

2783.992818880389
19813.546397036705
In [67]:
print min(U2020_ELF3_total[800:900])
print max(U2020_ELF3_total[800:900])

2827.1231407604446
14705.237855872014
In [12]:
print min(U2020_ELF3_total[500:600])
print max(U2020_ELF3_total[500:600])

516.8382553578571
11319.804185799872
In [14]:
calibration = pd.read_excel('../protein_cuantitation/calibration_long_time_series_.xlsx', header=None)
figure(figsize=(7,5))
xticks(fontsize=16)
yticks(fontsize=16)
errorbar(array([1e2,1e3,1e4,1e5,1e6]),log((mean(calibration)-mean(calibration)[0])[1:]),
         yerr = std(log((mean(calibration)-mean(calibration)[0])[1:]))/sqrt(24),
         fmt='s-', color="black", lw=2)

ylabel('ln(RLU)', fontsize=22)
xlabel('#molecules/cell', fontsize=22)
xscale("log")
#xlim(0,8)
#ylim(0,3)

reg = linregress(log10(array([1e2,1e3,1e4,1e5,1e6])),log((mean(calibration)-mean(calibration)[0])[1:]))
y=reg.slope*log10(array([1e2,1e3,1e4,1e5,1e6]))+reg.intercept
plot(array([1e2,1e3,1e4,1e5,1e6]),y, color="red", lw=2)

tight_layout()
#savefig('Calibration_NL.png', format='png', dpi=600)
#savefig('Calibration_NL.pdf', format='pdf', dpi=600)
#savefig('Calibration_NL.svg', format='svg', dpi=600)
In [15]:
data = pd.read_excel('../protein_cuantitation/lhy1.xlsx', sheet_name='series', header=None)
In [16]:
abs_unit = 10**((log(data)-reg.intercept)/reg.slope)
#abs_unit.to_excel('protein_abs_units_timeseries.xls')
In [17]:
def pmsToEscel(params, path):
    from xlwt import Workbook
    book=Workbook()
    p_export = book.add_sheet('params')
    p_export.write(0,0,'IDs')
    p_export.write(0,1,'Value')
    i = 1;
    for p in params.keys():
        if 'gm' in p:
            continue
        else:
            p_export.write(i,0, p)
            p_export.write(i,1, params.getByKey(p))
            i+=1
    book.save(path+'.xls')

def data_formater(data,genotypes,genes ,nuclei_number_gFW, displacement,var_log=False):
    '''
    Formating of data for sloppycell starting from an excel file
    This formating assumes that it is being fed with a dictionary of panda Data frames

    '''

    network_data={}

    for network in data.keys():
        if network not in genotypes.keys():
            continue

        gene_dict = {}
        for gene in genes.keys():
            try:
                if gene in genotypes[network]:
                    continue
            except:
                pass
            time_dict={}
            for zt in range(0,len(data[network][genes[gene]]),2):

                if not (np.isnan(data[network][genes[gene]][zt]) or np.isnan(data[network][genes[gene]][zt+1])):
                    if var_log == True:
                        points  = np.log(np.array([data[network][genes[gene]][zt],
                                                   data[network][genes[gene]][zt+1]])/nuclei_number_gFW)
                        data_mean=np.mean(points)
                        data_var=np.std(points)
                    else:
                        points  = np.array([data[network][genes[gene]][zt],data[network][genes[gene]][zt+1]])/nuclei_number_gFW
                        data_mean=np.mean(points)
                        data_var=np.std(points)
                else:
                    if np.isnan(data[network][genes[gene]][zt+1]) and not np.isnan(data[network][genes[gene]][zt]):
                        if var_log == True:
                            points  = np.log(np.array([data[network][genes[gene]][zt]])/nuclei_number_gFW)
                            data_mean=np.mean(points)
                            data_var = 0.6
                        else:
                            points  = np.array([data[network][genes[gene]][zt]])/nuclei_number_gFW
                            data_mean=np.mean(points)
                            data_var = 0.6
                    elif np.isnan(data[network][genes[gene]][zt]) and not np.isnan(data[network][genes[gene]][zt+1]):
                        if var_log == True:
                            points  = np.log(np.array([data[network][genes[gene]][zt+1]])/nuclei_number_gFW)
                            data_mean=np.mean(points)
                            data_var = 0.6
                        else:
                            points  = np.array([data[network][genes[gene]][zt+1]])/nuclei_number_gFW
                            data_mean=np.mean(points)
                            data_var = 0.6
                            



                time_dict[zt+displacement] = (data_mean,data_var)
            if var_log == True:
                gene_dict['log_'+gene]=time_dict
            else:
                gene_dict[gene]=time_dict
        network_data[network]=gene_dict
    return network_data


def KeyedList_from_Excel(path):
    
    params_excel = read_excel(path)
    excel_list = KeyedList([])
    optimisable = {}
    constraints = {}
    for idp,p in enumerate(params_excel.IDs):
        excel_list.extend(KeyedList([(p,params_excel.Value.iloc[idp])]))
        if not params_excel.fixed.iloc[idp]:
            optimisable[p] = True
            if params_excel.dist_const.iloc[idp]:
                optimisable[p] = (params_excel.mean_log.iloc[idp],params_excel.std_log.iloc[idp])
        else:
            optimisable[p] = False
    return excel_list, optimisable


def circadian_model_loader(path, genotype, entrainment,sf=False, params_from_excel=False,optimisable=False
                           ,var_scale=False, light='light'):
    days_entre = entrainment['days_entre']
    days_LL = entrainment['days_LL']
    days_DD = entrainment['days_DD']
    
    
    
    networks = {}
    for genotype in genotypes.keys():
        if 'pp' not in genotype:
            print 'Compiling network: ', genotype
            networks[genotype] = IO.from_SBML_file(path+genotype[:-3]+'.xml', genotype , duplicate_rxn_params=True)
            original_parameters = networks[genotype].GetParameters()
        else:
            print 'Compiling network: ', genotype
            networks[genotype] = IO.from_SBML_file(path+'col_0.xml',genotype ,duplicate_rxn_params=True)

        if sf: ##In case we are setting sf previosly fitted
            networks[genotype].set_var_ics(sf)
        #if params_from_excel: ## in case we are setting parameters previously fitter
            #networks[genotype].set_var_ics(params_from_excel)
        if params_from_excel:
            for parameter in networks[genotype].GetParameters().keys():
                if optimisable[parameter]:
                    networks[genotype].set_var_ics(KeyedList([(parameter, params_from_excel.getByKey(parameter))]))
                    networks[genotype].set_var_optimizable(parameter,True)
                else: 
                    networks[genotype].set_var_ics(KeyedList([(parameter, params_from_excel.getByKey(parameter))]))
                    networks[genotype].set_var_optimizable(parameter,False)


        ## If the data was log transformed the varaibles to be used in the cost need to be also log transfromed
        if var_scale == True:
            print 'Log transforming variables'
            for gene in genes.keys():
                networks[genotype].add_species('log_'+gene,'compartment_')
                networks[genotype].add_assignment_rule('log_'+gene,'log('+gene+')')


        if '_LL' in genotype:

            flag=1
            for i in range(0,24*(days_LL)+12,12):
                if flag:
                    networks[genotype].add_event('on_'+str(i), 'gt(time,'+str(i)+')', {light: 1.})
                    flag = 0
                else:
                    networks[genotype].add_event('off_'+str(i), 'gt(time,'+str(i)+')', {light: 0.})
                    flag = 1
            networks[genotype].compile()


        elif '_DD' in genotype:

            flag=1
            for i in range(0,24*(days_DD),12):
                if flag:
                    networks[genotype].add_event('on_'+str(i), 'gt(time,'+str(i)+')', {light: 1.})
                    flag = 0
                else:
                    networks[genotype].add_event('off_'+str(i), 'gt(time,'+str(i)+')', {light: 0.})
                    flag = 1

            networks[genotype].compile()

        elif 'pp' in genotype:
            flag=1
            day_length = int(genotype[2:].split('-')[0])
            for i in range(0,24*(days_entre)+12,12):
                if flag:
                    networks[genotype].add_event('on_'+str(i), 'gt(time,'+str(i)+')', {light: 1.})
                    flag = 0
                else:
                    networks[genotype].add_event('off_'+str(i-(12-day_length)), 'gt(time,'+str(i-(12-day_length))+')', {light: 0.})
                    flag = 1
            networks[genotype].compile()


        print "Network "+genotype+" loaded"
    print '===>Networks ready'
    return networks, original_parameters

#colours = ['red','blue','black','green','orange','cyan']
#colour_pallet ={}
#for idx,genotype in enumerate(genotypes.keys()):
#    colour_pallet[genotype] = colours[idx]
    
def SloppyCellData_to_numpy(data,genotype,gene):
    x =[]
    y =[]
    yerr =[]
    
    for i in sorted(data[genotype][gene].keys()):
        #print i
        x.append(i)
        y.append(data[genotype][gene][i][0])
        yerr.append(data[genotype][gene][i][1])
    
    return array(x),array(y),array(yerr)
In [21]:
book = pd.ExcelFile('data/PhotoPeriodTiMetEdited.xlsx')
book.sheet_names

data = {}
for network in book.sheet_names:
    data[network] =book.parse(network)
genes = {'cL_m':'lhy','cP9_m':'prr9','cP7_m':'prr7','cP5_m':'prr5','cC_m':'cca1',
         'cT_m':'toc1','cLUX_m':'lux','cG_m':'gi','cE3_m':'elf3','cE4_m':'elf4'}



genotypes = {}
genotypes['col_0_LL'] = []
genotypes['prr79_LL'] = ['cP9_m','cP7_m']
genotypes['lhycca1_LL'] = ['cL_m']
genotypes['toc1_LL'] = ['cT_m']
genotypes['gi_LL'] = ['cG_m']
entrainment={}
entrainment['days_entre'] = 10
entrainment['days_LL'] = 11
entrainment['days_DD'] = 11
light = 'light'


##Experiment section

nuclei_number_gFW = 25.0e6
uncertainty = 0.93
displacement=24*10
rna_data =data_formater(data, genotypes,genes,nuclei_number_gFW, displacement, var_log=False)
In [22]:
genes = ['LHY','CCA1','PRR7','TOC1','LUX','Col-0','empty']
genes_colors = {'CCA1':'crimson','LHY':'gold','PRR7':'indigo','TOC1':'#87CEFA','LUX':'plum','empty':'black','Col-0':'red'}
In [23]:
figure(figsize=(12,7))
errorbar(array(range(-24,24*2+12,2)),mean(abs_unit.iloc[0:6,:], axis=0),barsabove=False,
         yerr=std(abs_unit.iloc[0:6,:], axis=0)/sqrt(2), fmt='s-', lw=2.5, label='LHY-NL', color=genes_colors["LHY"])
errorbar(array(range(-24,24*2+12,2)),mean(abs_unit.iloc[6:12,:], axis=0),barsabove=False, 
         yerr=std(abs_unit.iloc[6:12,:], axis=0)/sqrt(2), fmt='s-', lw=2.5, label='CCA1-NL', color=genes_colors["CCA1"])


yticks(fontsize=16)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))
#ylim(1e4,5e6)
yscale('log')
legend(loc='upper right', fontsize=16)
xlabel('Time in constant light (h)', fontsize=22)
ylabel('log(#molecules/cell)', fontsize=22)
xticks(range(0,23*3,6),fontsize=22)
#savefig('ProteinTimeseries.pdf', format='pdf', dpi=300)
ylim(80,3e5)
xlim(-24,24*3+4)

time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cL_m')
errorbar(time_rna-24*11,mean_rna,std_rna, label='LHY trnascript', marker='o', lw=2.5, ls='-', color=genes_colors["LHY"])
time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cC_m')
errorbar(time_rna-24*11,mean_rna,std_rna, label='CCA1 transcript', marker='o', lw=2.5, ls='-', color=genes_colors["CCA1"])
ylim(0.1,1e6)
#yscale('log')

yticks(fontsize=16)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))

#yscale('log')
legend(loc='lower right', fontsize=16)
xlabel('Time in constant light (h)', fontsize=22)
ylabel('#molecules/cell', fontsize=22)
xticks(range(0,23*3+4,24),fontsize=22)
yticks(fontsize=22)
#savefig('ProteinTimeseries.pdf', format='pdf', dpi=300)
axvspan(-12,0,color='gray')
axvspan(12,24,color='gray', alpha=0.4)
axvspan(12+24,24*2,color='gray', alpha=0.4)
#ylim(0,3e5)
xlim(-24,24*3)
tight_layout()
#savefig('lhycca1_transcript_protein.png', format='png', dpi=600)
#savefig('lhycca1_transcript_protein.pdf', format='pdf', dpi=600)
#savefig('lhycca1_transcript_protein.svg', format='svg', dpi=600)
In [24]:
figure(figsize=(15,5),dpi=300)
#figure(dpi=600)
subplot(132)
time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cL_m')
errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0),mean_rna, xerr=std(abs_unit.iloc[12:18,:-6], axis=0)/sqrt(2),
         yerr=std_rna, marker='s', lw=2, ls='-', color='orange')

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0:13+6],mean_rna[0:13+6], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0:13+6]/sqrt(2),
         yerr=std_rna[0:13+6], marker='s', lw=2, ls='-', color='k')

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='b', markersize=20)

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='g', markersize=20)
errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[12],mean_rna[12], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[12]/sqrt(2),
         yerr=std_rna[12], marker='o', lw=2, ls='-', color='r', markersize=20)


errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[9],mean_rna[9], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[9]/sqrt(2),
         yerr=std_rna[9], marker='o', lw=2, ls='-', color='b', markersize=20)

yticks(fontsize=22)
xticks(fontsize=22)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))
yscale('log')
xscale('log')
ylim(1e-1,1e4)
xlim(1e3,1e5)
xlabel('TOC1-NL #molecules/cell', fontsize=22)
ylabel('$LHY$ TiMet #molecules/cell', fontsize=22)
tight_layout()

subplot(133)

time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cC_m')
errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0),mean_rna, xerr=std(abs_unit.iloc[12:18,:-6], axis=0)/sqrt(2),
         yerr=std_rna, marker='s', lw=2, ls='-', color='orange', label='LL')

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0:13+6],mean_rna[0:13+6], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0:13+6]/sqrt(2),
         yerr=std_rna[0:13+6], marker='s', lw=2, ls='-', color='k', label='LD')

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='b', markersize=20)

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='g', markersize=20)
errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[12],mean_rna[12], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[12]/sqrt(2),
         yerr=std_rna[12], marker='o', lw=2, ls='-', color='r', markersize=20)

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[9],mean_rna[9], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[9]/sqrt(2),
         yerr=std_rna[9], marker='o', lw=2, ls='-', color='b', markersize=20)

yscale('log')
xscale('log')
ylim(1e-1,1e4)
xlim(1e3,1e5)
xlabel('TOC1-NL #molecules/cell', fontsize=22)
ylabel('$CCA1$ TiMet #molecules/cell', fontsize=22)
legend(loc='upper right', fontsize=14)

yticks(fontsize=22)
xticks(fontsize=22)
tight_layout()

subplot(131)
plot(U2019_5_res["[cT]"][200:775],U2019_5_res["[cL_m]"][200:775], color="black", label="U2019.5 LD")
plot(U2019_5_res["[cT]"][775:],U2019_5_res["[cL_m]"][775:], color="orange", label="U2019.5 LL")
plot(U2020_5_res["[cT]"][200:768],U2020_5_res["[cL_m]"][200:768], "--",color="black", label="U2020.5 LD", alpha=0.5)
plot(U2020_5_res["[cT]"][768:],U2020_5_res["[cL_m]"][768:], "--",color="orange", label="U2020.5 LL", alpha=0.5)
xticks(fontsize=22)
yticks(fontsize=22)
yscale("log")
xscale("log")
xlim(1e3,1e5)
ylim(1e-1,1e4)
ylabel("$cLm$ #molecules/cell", fontsize=22)
xlabel("cT #molecules/cell", fontsize=22)
legend(loc="upper right", fontsize=14)
tight_layout()
#savefig('TOC1p_vs_CCA1_LHY_t_U2019_U2020_models_phase_plot.png', format='png', dpi=300)
#savefig('TOC1p_vs_CCA1_LHY_t_U2019_U2020_models_phase_plot.pdf', format='pdf', dpi=300)
#savefig('TOC1p_vs_CCA1_LHY_t_U2019_U2020_models_phase_plot.svg', format='svg', dpi=300)
In [25]:
figure(figsize=(12,7))
errorbar(array(range(-24,24*2+12,2)),mean(abs_unit.iloc[12:18,:], axis=0),barsabove=False,
         yerr=std(abs_unit.iloc[12:18,:], axis=0)/sqrt(2), fmt='s-', lw=2.5, label='TOC1-NL', color=genes_colors["TOC1"])
errorbar(array(range(-24,24*2+12,2)),mean(abs_unit.iloc[18:24,:], axis=0), 
         yerr=std(abs_unit.iloc[18:24,:], axis=0)/sqrt(2), fmt='s-', lw=2.5, label='PRR7-NL', color=genes_colors["PRR7"])


yticks(fontsize=16)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))
#ylim(1e4,5e6)
yscale('log')
legend(loc='upper right', fontsize=16)
xlabel('Time in constant light (h)', fontsize=22)
ylabel('log(#molecules/cell)', fontsize=22)
xticks(range(0,23*3,6),fontsize=22)
#savefig('ProteinTimeseries.pdf', format='pdf', dpi=300)
ylim(80,3e5)
xlim(-24,24*3+4)

time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cP7_m')
errorbar(time_rna-24*11,mean_rna,std_rna, label='PRR7 transcript', marker='o', lw=2.5, color=genes_colors["PRR7"])
time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cT_m')
errorbar(time_rna-24*11,mean_rna,std_rna, label='TOC1 transcript', marker='o', lw=2.5, color=genes_colors["TOC1"])
ylim(0.1,1e6)
#yscale('log')

yticks(fontsize=16)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))

#yscale('log')
legend(loc='lower right', fontsize=16)
xlabel('Time in constant light (h)', fontsize=22)
ylabel('#molecules/cell', fontsize=22)
xticks(range(0,23*3+4,24),fontsize=22)
yticks(fontsize=22)
#savefig('ProteinTimeseries.pdf', format='pdf', dpi=300)
axvspan(-12,0,color='gray')
axvspan(12,24,color='gray', alpha=0.4)
axvspan(12+24,24*2,color='gray', alpha=0.4)
#ylim(0,3e5)
xlim(-24,24*3+6)
#savefig('prr7toc1_transcript.png', format='png', dpi=300)
tight_layout()
#savefig('prr7toc1_transcript_protein.png', format='png', dpi=600)
#savefig('prr7toc1_transcript_protein.pdf', format='pdf', dpi=600)
#savefig('prr7toc1_transcript_protein.svg', format='svg', dpi=600)
In [26]:
figure(figsize=(15,5),dpi=300)
subplot(132)
time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cL_m')
errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0),mean_rna, xerr=std(abs_unit.iloc[18:24,:-6], axis=0)/sqrt(2),
         yerr=std_rna, marker='s', lw=2, ls='-', color='orange')

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0:13],mean_rna[0:13], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0:13]/sqrt(2),
         yerr=std_rna[0:13], marker='s', lw=2, ls='-', color='k')

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='b', markersize=20)

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='g', markersize=20)
errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[12],mean_rna[12], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[12]/sqrt(2),
         yerr=std_rna[12], marker='o', lw=2, ls='-', color='r', markersize=20)


errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[9],mean_rna[9], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[9]/sqrt(2),
         yerr=std_rna[9], marker='o', lw=2, ls='-', color='b', markersize=20)

yticks(fontsize=22)
xticks(fontsize=22)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))

yscale('log')
xscale('log')
xlim(1e2,1e6)
ylim(1e-1,1e4)
xlabel('PRR7-NL #molecules/cell', fontsize=22)
ylabel('$LHY$ TiMet #Molecules/cell', fontsize=22)
#savefig('ProteinTimeseries.pdf', format='pdf', dpi=300)
tight_layout()
subplot(133)
time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cC_m')
errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0),mean_rna, xerr=std(abs_unit.iloc[12:18,:-6], axis=0)/sqrt(2),
         yerr=std_rna, marker='s', lw=2, ls='-', color='orange', label="LL")

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0:13],mean_rna[0:13], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0:13]/sqrt(2),
         yerr=std_rna[0:13], marker='s', lw=2, ls='-', color='k', label="LD")

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='b', markersize=20)

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='g', markersize=20)
errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[12],mean_rna[12], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[12]/sqrt(2),
         yerr=std_rna[12], marker='o', lw=2, ls='-', color='r', markersize=20)

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[9],mean_rna[9], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[9]/sqrt(2),
         yerr=std_rna[9], marker='o', lw=2, ls='-', color='b', markersize=20)


legend(loc='upper right', fontsize=14)

yticks(fontsize=22)
xticks(fontsize=22)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))

yscale('log')
xscale('log')
xlim(1e2,1e6)
ylim(1e-1,1e4)
xlabel('PRR7-NL #molcules/cell', fontsize=22)
ylabel('$CCA1$ TiMet #molecules/cell', fontsize=22)
tight_layout()

subplot(131)

plot(U2019_5_res["[cP7]"][200:775],U2019_5_res["[cL_m]"][200:775], color="black", label="U2019.5 LD")
plot(U2019_5_res["[cP7]"][775:],U2019_5_res["[cL_m]"][775:], color="orange", label="U2019.5 LL")
plot(U2020_5_res["[cP7]"][200:768],U2020_5_res["[cL_m]"][200:768], "--",color="black", label="U2020.5 LD", alpha=0.5)
plot(U2020_5_res["[cP7]"][768:],U2020_5_res["[cL_m]"][768:], "--",color="orange", label="U2020.5 LL", alpha=0.5)
xticks(fontsize=22)
yticks(fontsize=22)

yscale('log')
xscale('log')
xlim(1e2,1e6)
ylim(1e-1,1e4)
xlabel('cP7 #molecules/cell', fontsize=22)
ylabel('$cLm$ #molecules/cell', fontsize=22)
legend(loc='upper right', fontsize=12)
tight_layout()

#savefig('PRR7p_vs_CCA1_LHY_t_U2019_U2020_models_phase_plot.png', format='png', dpi=300)
#savefig('PRR7p_vs_CCA1_LHY_t_U2019_U2020_models_phase_plot.pdf', format='pdf', dpi=300)
#savefig('PRR7p_vs_CCA1_LHY_t_U2019_U2020_models_phase_plot.svg', format='svg', dpi=300)
In [27]:
figure(figsize=(15,15), dpi=300)
subplot(221)
time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cL_m')
errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0),mean_rna, xerr=std(abs_unit.iloc[12:18,:-6], axis=0)/sqrt(2),
         yerr=std_rna, marker='s', lw=2, ls='-', color='orange')

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0:13+6],mean_rna[0:13+6], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0:13+6]/sqrt(2),
         yerr=std_rna[0:13+6], marker='s', lw=2, ls='-', color='k')

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='b', markersize=20)

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='g', markersize=20)
errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[12],mean_rna[12], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[12]/sqrt(2),
         yerr=std_rna[12], marker='o', lw=2, ls='-', color='r', markersize=20)


errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[9],mean_rna[9], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[9]/sqrt(2),
         yerr=std_rna[9], marker='o', lw=2, ls='-', color='b', markersize=20)

yticks(fontsize=22)
xticks(fontsize=22)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))
yscale('log')
xscale('log')
ylim(1e-1,1e4)
xlim(1e3,5e5)
xlabel('TOC1-NL #molecules/cell', fontsize=22)
ylabel('$LHY$ TiMet #molecules/cell', fontsize=22)
tight_layout()

subplot(222)

time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cC_m')
errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0),mean_rna, xerr=std(abs_unit.iloc[12:18,:-6], axis=0)/sqrt(2),
         yerr=std_rna, marker='s', lw=2, ls='-', color='orange', label='LL')

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0:13+6],mean_rna[0:13+6], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0:13+6]/sqrt(2),
         yerr=std_rna[0:13+6], marker='s', lw=2, ls='-', color='k', label='LD')

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='b', markersize=20)

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='g', markersize=20)
errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[12],mean_rna[12], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[12]/sqrt(2),
         yerr=std_rna[12], marker='o', lw=2, ls='-', color='r', markersize=20)

errorbar(mean(abs_unit.iloc[12:18,:-6], axis=0)[9],mean_rna[9], xerr=std(abs_unit.iloc[12:18,:-6], axis=0)[9]/sqrt(2),
         yerr=std_rna[9], marker='o', lw=2, ls='-', color='b', markersize=20)

yscale('log')
xscale('log')
ylim(1e-1,1e4)
xlim(1e3,5e5)
xlabel('TOC1-NL #molecules/cell', fontsize=22)
ylabel('$CCA1$ TiMet #molecules/cell', fontsize=22)
legend(loc='upper right', fontsize=14)

yticks(fontsize=22)
xticks(fontsize=22)
tight_layout()

subplot(223)
time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cL_m')
errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0),mean_rna, xerr=std(abs_unit.iloc[18:24,:-6], axis=0)/sqrt(2),
         yerr=std_rna, marker='s', lw=2, ls='-', color='orange')

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0:13],mean_rna[0:13], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0:13]/sqrt(2),
         yerr=std_rna[0:13], marker='s', lw=2, ls='-', color='k')

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='b', markersize=20)

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='g', markersize=20)
errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[12],mean_rna[12], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[12]/sqrt(2),
         yerr=std_rna[12], marker='o', lw=2, ls='-', color='r', markersize=20)


errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[9],mean_rna[9], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[9]/sqrt(2),
         yerr=std_rna[9], marker='o', lw=2, ls='-', color='b', markersize=20)

yticks(fontsize=22)
xticks(fontsize=22)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))

yscale('log')
xscale('log')
xlim(1e3,5e5)
ylim(1e-1,1e4)
xlabel('PRR7-NL #molecules/cell', fontsize=22)
ylabel('$LHY$ TiMet #molecules/cell', fontsize=22)
#savefig('ProteinTimeseries.pdf', format='pdf', dpi=300)
tight_layout()
subplot(224)
time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cC_m')
errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0),mean_rna, xerr=std(abs_unit.iloc[12:18,:-6], axis=0)/sqrt(2),
         yerr=std_rna, marker='s', lw=2, ls='-', color='orange', label="LL")

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0:13],mean_rna[0:13], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0:13]/sqrt(2),
         yerr=std_rna[0:13], marker='s', lw=2, ls='-', color='k', label="LD")

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='b', markersize=20)

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[0],mean_rna[0], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[0]/sqrt(2),
         yerr=std_rna[0], marker='s', lw=2, ls='-', color='g', markersize=20)
errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[12],mean_rna[12], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[12]/sqrt(2),
         yerr=std_rna[12], marker='o', lw=2, ls='-', color='r', markersize=20)

errorbar(mean(abs_unit.iloc[18:24,:-6], axis=0)[9],mean_rna[9], xerr=std(abs_unit.iloc[18:24,:-6], axis=0)[9]/sqrt(2),
         yerr=std_rna[9], marker='o', lw=2, ls='-', color='b', markersize=20)


legend(loc='upper right', fontsize=14)

yticks(fontsize=22)
xticks(fontsize=22)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))

yscale('log')
xscale('log')
xlim(1e3,5e5)
ylim(1e-1,1e4)
xlabel('PRR7-NL #molcules/cell', fontsize=22)
ylabel('$CCA1$ TiMet #molecules/cell', fontsize=22)
tight_layout()
In [28]:
figure(figsize=(12,7))
errorbar(array(range(-24,24*2+12,2)),mean(abs_unit.iloc[24:30,:], axis=0),color=genes_colors["LUX"],barsabove=False,
         yerr=std(abs_unit.iloc[24:30,:], axis=0)/sqrt(2), fmt='s-', lw=2.5, label='LUX-NL')


yticks(fontsize=16)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))
#ylim(1e4,5e6)
#yscale('log')
legend(loc='upper right', fontsize=16)
xlabel('Time in constant light (h)', fontsize=22)
ylabel('log(#molecules/cell)', fontsize=22)
xticks(range(0,23*3,6),fontsize=22)
#savefig('ProteinTimeseries.pdf', format='pdf', dpi=300)

#ylim(0,1e3)
xlim(-24,24*3+4)
#savefig('lhycca1toc1prr7.png',format='png',dpi=300)


time_rna,mean_rna,std_rna = SloppyCellData_to_numpy(rna_data,'col_0_LL','cLUX_m')
errorbar(time_rna-24*11,mean_rna,std_rna, label='LUX transcript', marker='o', lw=2.5, color=genes_colors["LUX"])

#yscale('log')

yticks(fontsize=16)
#errorbar(range(0,24*3,2),mean(data.iloc[3:6,:], axis=0), yerr=std(data.iloc[3:6,:], axis=0))

yscale('log')
legend(loc='lower right', fontsize=16)
xlabel('Time in constant light (h)', fontsize=22)
ylabel('#molecules/cell', fontsize=22)
xticks(range(0,23*3+4,24),fontsize=22)
yticks(fontsize=22)
#savefig('ProteinTimeseries.pdf', format='pdf', dpi=300)
axvspan(-12,0,color='gray')
axvspan(12,24,color='gray', alpha=0.4)
axvspan(12+24,24*2,color='gray', alpha=0.4)
#ylim(0,3e5)
xlim(-24,24*3)
tight_layout()
#savefig('lux_transcript_protein.png', format='png', dpi=600)
#savefig('lux_transcript_protein.pdf', format='pdf', dpi=600)
#savefig('lux_transcript_protein.svg', format='svg', dpi=600)
#savefig('lux_transcript.png', format='png', dpi=300)
In [30]:
errorbar(array(range(-24+2*6,24*2+12,2)),mean(abs_unit.iloc[0:6,4:], axis=0),barsabove=False,
         yerr=std(abs_unit.iloc[0:6,4:], axis=0)/sqrt(2), fmt='s-', lw=2.5, label='LHY-NL', color=genes_colors["LHY"])
errorbar(array(range(-24+2*6,24*2+12,2)),mean(abs_unit.iloc[6:12,4:], axis=0),barsabove=False, 
         yerr=std(abs_unit.iloc[6:12,4:], axis=0)/sqrt(2), fmt='s-', lw=2.5, label='CCA1-NL', color=genes_colors["CCA1"])


ValueErrorTraceback (most recent call last)
<ipython-input-30-700ff09ba8d9> in <module>()
      1 errorbar(array(range(-24+2*6,24*2+12,2)),mean(abs_unit.iloc[0:6,4:], axis=0),barsabove=False,
----> 2          yerr=std(abs_unit.iloc[0:6,4:], axis=0)/sqrt(2), fmt='s-', lw=2.5, label='LHY-NL', color=genes_colors["LHY"])
      3 errorbar(array(range(-24+2*6,24*2+12,2)),mean(abs_unit.iloc[6:12,4:], axis=0),barsabove=False, 
      4          yerr=std(abs_unit.iloc[6:12,4:], axis=0)/sqrt(2), fmt='s-', lw=2.5, label='CCA1-NL', color=genes_colors["CCA1"])

/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.pyc in errorbar(x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, hold, data, **kwargs)
   2989                           xlolims=xlolims, xuplims=xuplims,
   2990                           errorevery=errorevery, capthick=capthick, data=data,
-> 2991                           **kwargs)
   2992     finally:
   2993         ax._hold = washold

/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.pyc in inner(ax, *args, **kwargs)
   1865                         "the Matplotlib list!)" % (label_namer, func.__name__),
   1866                         RuntimeWarning, stacklevel=2)
-> 1867             return func(ax, *args, **kwargs)
   1868 
   1869         inner.__doc__ = _add_data_doc(inner.__doc__,

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_axes.pyc in errorbar(self, x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, **kwargs)
   3199         if plot_line:
   3200             data_line = mlines.Line2D(x, y, **plot_line_style)
-> 3201             self.add_line(data_line)
   3202 
   3203         barcols = []

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc in add_line(self, line)
   1958             line.set_clip_path(self.patch)
   1959 
-> 1960         self._update_line_limits(line)
   1961         if not line.get_label():
   1962             line.set_label('_line%d' % len(self.lines))

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc in _update_line_limits(self, line)
   1980         Figures out the data limit of the given line, updating self.dataLim.
   1981         """
-> 1982         path = line.get_path()
   1983         if path.vertices.size == 0:
   1984             return

/usr/local/lib/python2.7/dist-packages/matplotlib/lines.pyc in get_path(self)
    954         """
    955         if self._invalidy or self._invalidx:
--> 956             self.recache()
    957         return self._path
    958 

/usr/local/lib/python2.7/dist-packages/matplotlib/lines.pyc in recache(self, always)
    659             y = self._y
    660 
--> 661         self._xy = np.column_stack(np.broadcast_arrays(x, y)).astype(float)
    662         self._x, self._y = self._xy.T  # views
    663 

/usr/local/lib/python2.7/dist-packages/numpy/lib/stride_tricks.pyc in broadcast_arrays(*args, **kwargs)
    257     args = [np.array(_m, copy=False, subok=subok) for _m in args]
    258 
--> 259     shape = _broadcast_shape(*args)
    260 
    261     if all(array.shape == shape for array in args):

/usr/local/lib/python2.7/dist-packages/numpy/lib/stride_tricks.pyc in _broadcast_shape(*args)
    191     # use the old-iterator because np.nditer does not handle size 0 arrays
    192     # consistently
--> 193     b = np.broadcast(*args[:32])
    194     # unfortunately, it cannot handle 32 or more arguments directly
    195     for pos in range(32, len(args), 31):

ValueError: shape mismatch: objects cannot be broadcast to a single shape
In [31]:
print "CCA1max: ",max(mean(abs_unit.iloc[0:6,4:19], axis=0))
print "CCA1mean: ",min(mean(abs_unit.iloc[0:6,4:19], axis=0))
print "CCA1fold: ",max(mean(abs_unit.iloc[0:6,4:19], axis=0))/min(mean(abs_unit.iloc[0:6,4:19], axis=0))

CCA1max:  121118.180114
CCA1mean:  1431.22523781
CCA1fold:  84.6255200894
In [32]:
print "CCA1max: ",max(mean(abs_unit.iloc[0:6,17:30], axis=0))
print "CCA1mean: ",min(mean(abs_unit.iloc[0:6,17:30], axis=0))
print "CCA1fold: ",max(mean(abs_unit.iloc[0:6,17:30], axis=0))/min(mean(abs_unit.iloc[0:6,17:30], axis=0))

CCA1max:  93877.467013
CCA1mean:  2816.85625458
CCA1fold:  33.3270350094
In [33]:
print "LHYmax: ",max(mean(abs_unit.iloc[6:12,17:30], axis=0))
print "LHYmean: ",min(mean(abs_unit.iloc[6:12,17:30], axis=0))
print "LHYfold: ",max(mean(abs_unit.iloc[6:12,17:30], axis=0))/min(mean(abs_unit.iloc[6:12,17:30], axis=0))

LHYmax:  188712.684117
LHYmean:  3484.11514289
LHYfold:  54.1637335098
In [34]:
print "LHYmax: ",max(mean(abs_unit.iloc[6:12,4:19], axis=0))
print "LHYmean: ",min(mean(abs_unit.iloc[6:12,4:19], axis=0))
print "LHYfold: ",max(mean(abs_unit.iloc[6:12,4:19], axis=0))/min(mean(abs_unit.iloc[6:12,4:19], axis=0))

LHYmax:  182067.668868
LHYmean:  3164.05656918
LHYfold:  57.5424822177
In [35]:
print "TOC1max: ",max(mean(abs_unit.iloc[12:18,4:19], axis=0))
print "TOC1mean: ",min(mean(abs_unit.iloc[12:18,4:19], axis=0))
print "TOC1fold: ",max(mean(abs_unit.iloc[12:18,4:19], axis=0))/min(mean(abs_unit.iloc[12:18,4:19], axis=0))

TOC1max:  44482.9685328
TOC1mean:  3523.17385346
TOC1fold:  12.6258227334
In [36]:
print "TOC1max: ",max(mean(abs_unit.iloc[12:18,13:30], axis=0))
print "TOC1mean: ",min(mean(abs_unit.iloc[12:18,13:30], axis=0))
print "TOC1fold: ",max(mean(abs_unit.iloc[12:18,13:30], axis=0))/min(mean(abs_unit.iloc[12:18,13:30], axis=0))

TOC1max:  67995.3317238
TOC1mean:  3523.17385346
TOC1fold:  19.2994540014
In [45]:
print "PRR7max: ",max(mean(abs_unit.iloc[18:24,23:33], axis=0))
print "PRR7mean: ",min(mean(abs_unit.iloc[18:24,23:33], axis=0))
print "PRR7fold: ",max(mean(abs_unit.iloc[18:24,23:33], axis=0))/min(mean(abs_unit.iloc[18:24,23:33], axis=0))

PRR7max:  55881.0529525
PRR7mean:  3875.90709311
PRR7fold:  14.4175419095
In [46]:
print "PRR7max: ",max(mean(abs_unit.iloc[18:24,4:19], axis=0))
print "PRR7mean: ",min(mean(abs_unit.iloc[18:24,4:19], axis=0))
print "PRR7fold: ",max(mean(abs_unit.iloc[18:24,4:19], axis=0))/min(mean(abs_unit.iloc[18:24,4:19], axis=0))

PRR7max:  81208.7443356
PRR7mean:  4761.41009551
PRR7fold:  17.0556080461
In [42]:
len(mean(abs_unit.iloc[18:24,:], axis=0))
Out[42]:
42
In [ ]: