def plot_net_rate(resS,resE,col,min_age,max_age,plot_title,n_bins):
#computes and plots net RATES
marginal_rates_list = resS[6]-resE[6]
mean_rates= np.mean(marginal_rates_list,axis=0)
min_rates,max_rates=[],[]
for i in range(n_bins):
hpd = util.calcHPD(marginal_rates_list[:,i],0.95)
min_rates += [hpd[0]]
max_rates += [hpd[1]]
out_str = "\n#Net Diversification Rate"
out_str += util.print_R_vec("\ntime",resS[0]-min_age)
minXaxis,maxXaxis= max_age-min_age,min_age-min_age
#right now I don't have support for log, but I think this is less likely to be needed for net rates
out_str += util.print_R_vec("\nnet_rate",mean_rates[::-1])
out_str += util.print_R_vec("\nnet_minHPD",np.array(min_rates[::-1]))
out_str += util.print_R_vec("\nnet_maxHPD",np.array(max_rates[::-1]))
out_str += "\nplot(time,time,type = 'n', ylim = c(%s, %s), xlim = c(%s,%s), ylab = 'Net Rate', xlab = 'Time',lwd=2, main='%s', col= '%s' )" \
% (min(0,1.1*np.nanmin(min_rates)),1.1*np.nanmax(max_rates),minXaxis,maxXaxis,plot_title,col)
out_str += "\npolygon(c(time, rev(time)), c(net_maxHPD, rev(net_minHPD)), col = alpha('%s',0.3), border = NA)" % (col)
out_str += "\nlines(time,net_rate, col = '%s', lwd=2)" % (col)
out_str += "\nabline(h=0,lty=2)\n"
return out_str
def plot_net_rate(resS, resE, col, min_age, max_age, plot_title, n_bins):
#computes and plots net RATES
marginal_rates_list = resS[6] - resE[6]
mean_rates = np.mean(marginal_rates_list, axis=0)
min_rates, max_rates = [], []
for i in range(n_bins):
hpd = util.calcHPD(marginal_rates_list[:, i], 0.95)
min_rates += [hpd[0]]
max_rates += [hpd[1]]
out_str = "\n#Net Diversification Rate"
out_str += util.print_R_vec("\ntime", resS[0] - min_age)
minXaxis, maxXaxis = max_age - min_age, min_age - min_age
#right now I don't have support for log, but I think this is less likely to be needed for net rates
out_str += util.print_R_vec("\nnet_rate", mean_rates[::-1])
out_str += util.print_R_vec("\nnet_minHPD", np.array(min_rates[::-1]))
out_str += util.print_R_vec("\nnet_maxHPD", np.array(max_rates[::-1]))
out_str += "\nplot(time,time,type = 'n', ylim = c(%s, %s), xlim = c(%s,%s), ylab = 'Net Rate', xlab = 'Time',lwd=2, main='%s', col= '%s' )" \
% (min(0,1.1*np.nanmin(min_rates)),1.1*np.nanmax(max_rates),minXaxis,maxXaxis,plot_title,col)
out_str += "\npolygon(c(time, rev(time)), c(net_maxHPD, rev(net_minHPD)), col = alpha('%s',0.3), border = NA)" % (
col)
out_str += "\nlines(time,net_rate, col = '%s', lwd=2)" % (col)
out_str += "\nabline(h=0,lty=2)\n"
return out_str
def plot_net_rate(resS, resE, col, min_age, max_age, plot_title, n_bins):
#computes and plots net RATES
resS_marginal_rate = resS[6]
resE_marginal_rate = resE[6]
# in case they have different number of samples
max_indx = np.min(
[resS_marginal_rate.shape[0], resE_marginal_rate.shape[0]])
marginal_rates_list = resS_marginal_rate[
0:max_indx, :] - resE_marginal_rate[0:max_indx, :]
mean_rates = np.mean(marginal_rates_list, axis=0)
min_rates, max_rates = [], []
time_ax = []
for i in range(n_bins):
hpd = util.calcHPD(marginal_rates_list[:, i], 0.95)
min_rates += [hpd[0]]
max_rates += [hpd[1]]
times = abs(resS[0])
indx = np.intersect1d((times >= min_age).nonzero()[0],
(times <= max_age).nonzero()[0])
times = times[indx]
out_str = "\n#Net Diversification Rate"
out_str += util.print_R_vec("\ntime", -times)
minXaxis, maxXaxis = min_age, max_age
mean_rates = np.array(mean_rates)[::-1]
min_rates = np.array(min_rates)[::-1]
max_rates = np.array(max_rates)[::-1]
mean_rates = mean_rates[indx]
min_rates = min_rates[indx]
max_rates = max_rates[indx]
out_str += util.print_R_vec("\nnet_rate", mean_rates)
out_str += util.print_R_vec("\nnet_minHPD", np.array(min_rates))
out_str += util.print_R_vec("\nnet_maxHPD", np.array(max_rates))
out_str += "\nplot(time,time,type = 'n', ylim = c(%s, %s), xlim = c(%s,%s), ylab = 'Net Rate', xlab = 'Time',lwd=2, main='%s', col= '%s' )" \
% (min(0,1.1*np.nanmin(min_rates)),1.1*np.nanmax(max_rates),-maxXaxis,-minXaxis,plot_title,col)
out_str += "\npolygon(c(time, rev(time)), c(net_maxHPD, rev(net_minHPD)), col = alpha('%s',0.3), border = NA)" % (
col)
out_str += "\nlines(time,net_rate, col = '%s', lwd=2)" % (col)
out_str += "\nabline(h=0,lty=2)\n"
return out_str
def r_plot_code(res, wd, name_file, alpha=0.5, plot_title="RTT plot"):
data = "library(scales)\ntrans=%s" % (alpha)
if platform.system() == "Windows" or platform.system() == "Microsoft":
wd_forward = os.path.abspath(wd).replace('\\', '/')
data += "\n\npdf(file='%s/%s_RTT.pdf',width=0.6*9, height=0.6*14)\npar(mfrow=c(2,1))" % (
wd_forward, name_file) # 9
else:
data += "\n\npdf(file='%s/%s_RTT.pdf',width=0.6*9, height=0.6*14)\npar(mfrow=c(2,1))" % (
wd, name_file) # 9
data += util.print_R_vec('\nage', -res[:, 0])
data += util.print_R_vec('\nL_mean', res[:, 1])
data += util.print_R_vec('\nL_hpd_m', res[:, 2])
data += util.print_R_vec('\nL_hpd_M', res[:, 3])
data += util.print_R_vec('\nM_mean', res[:, 4])
data += util.print_R_vec('\nM_hpd_m', res[:, 5])
data += util.print_R_vec('\nM_hpd_M', res[:, 6])
max_x_axis, min_x_axis = -max(res[:, 0]), -min(res[:, 0])
data += "\nplot(age,age,type = 'n', ylim = c(0, %s), xlim = c(%s,%s), ylab = 'Speciation rate', xlab = 'Ma',main='%s' )" \
% (1.1*max(res[:,3]),max_x_axis,min_x_axis,plot_title)
data += """\nlines(rev(age), rev(L_mean), col = "#4c4cec", lwd=3)"""
data += """\npolygon(c(age, rev(age)), c(L_hpd_M, rev(L_hpd_m)), col = alpha("#4c4cec",trans), border = NA)"""
data += "\nplot(age,age,type = 'n', ylim = c(0, %s), xlim = c(%s,%s), ylab = 'Extinction rate', xlab = 'Ma' )" \
% (1.1*max(res[:,6]),max_x_axis,min_x_axis)
data += """\nlines(rev(age), rev(M_mean), col = "#e34a33", lwd=3)"""
data += """\npolygon(c(age, rev(age)), c(M_hpd_M, rev(M_hpd_m)), col = alpha("#e34a33",trans), border = NA)"""
data += "\nn <- dev.off()"
return data
def r_plot_code(res,wd,name_file,alpha=0.5,plot_title="RTT plot"):
data = "library(scales)\ntrans=%s" % (alpha)
if platform.system() == "Windows" or platform.system() == "Microsoft":
wd_forward = os.path.abspath(wd).replace('\\', '/')
data+= "\n\npdf(file='%s/%s_RTT.pdf',width=0.6*9, height=0.6*14)\npar(mfrow=c(2,1))" % (wd_forward,name_file) # 9
else:
data+= "\n\npdf(file='%s/%s_RTT.pdf',width=0.6*9, height=0.6*14)\npar(mfrow=c(2,1))" % (wd,name_file) # 9
data += util.print_R_vec('\nage', -res[:,0])
data += util.print_R_vec('\nL_mean', res[:,1])
data += util.print_R_vec('\nL_hpd_m',res[:,2])
data += util.print_R_vec('\nL_hpd_M',res[:,3])
data += util.print_R_vec('\nM_mean', res[:,4])
data += util.print_R_vec('\nM_hpd_m',res[:,5])
data += util.print_R_vec('\nM_hpd_M',res[:,6])
max_x_axis,min_x_axis = -max(res[:,0]),-min(res[:,0])
data += "\nplot(age,age,type = 'n', ylim = c(0, %s), xlim = c(%s,%s), ylab = 'Speciation rate', xlab = 'Ma',main='%s' )" \
% (1.1*max(res[:,3]),max_x_axis,min_x_axis,plot_title)
data += """\nlines(rev(age), rev(L_mean), col = "#4c4cec", lwd=3)"""
data += """\npolygon(c(age, rev(age)), c(L_hpd_M, rev(L_hpd_m)), col = alpha("#4c4cec",trans), border = NA)"""
data += "\nplot(age,age,type = 'n', ylim = c(0, %s), xlim = c(%s,%s), ylab = 'Extinction rate', xlab = 'Ma' )" \
% (1.1*max(res[:,6]),max_x_axis,min_x_axis)
data += """\nlines(rev(age), rev(M_mean), col = "#e34a33", lwd=3)"""
data += """\npolygon(c(age, rev(age)), c(M_hpd_M, rev(M_hpd_m)), col = alpha("#e34a33",trans), border = NA)"""
data += "\nn <- dev.off()"
return data
def get_r_plot(res,col,parameter,min_age,max_age,plot_title,plot_log,run_simulation=1, plot_shifts=1, line_wd=2):
times = res[0]
rates = res[1][::-1]
rates_m = res[2][::-1]
rates_M = res[3][::-1]
shifts = res[4]
indx = np.intersect1d((times>=min_age).nonzero()[0], (times<=max_age).nonzero()[0])
times = times[indx]
#print indx,times, rates
rates = rates[indx]
rates_m = rates_m[indx]
rates_M = rates_M[indx]
try:
shifts= shifts[shifts>min_age]
shifts= shifts[shifts<max_age]
except: plot_shifts=0
out_str = "\n"
out_str += util.print_R_vec("\ntime",-times)
out_str += util.print_R_vec("\nrate",rates)
out_str += util.print_R_vec("\nminHPD",rates_m)
out_str += util.print_R_vec("\nmaxHPD",rates_M)
if plot_log==0:
out_str += "\nplot(time,time,type = 'n', ylim = c(%s, %s), xlim = c(%s,%s), ylab = '%s', xlab = 'Time',main='%s' )" \
% (0,1.1*np.nanmax(rates_M),-max_age,-min_age,parameter,plot_title)
out_str += "\npolygon(c(time, rev(time)), c(maxHPD, rev(minHPD)), col = alpha('%s',0.3), border = NA)" % (col)
out_str += "\nlines(time,rate, col = '%s', lwd=%s)" % (col, line_wd)
else:
out_str += "\nplot(time,time,type = 'n', ylim = c(%s, %s), xlim = c(%s,%s), ylab = 'Log10 %s', xlab = 'Time',main='%s' )" \
% (np.nanmin(np.log10(0.9*rates_m)),np.nanmax(np.log10(1.1*rates_M)),-max_age,-min_age,parameter,plot_title)
out_str += "\npolygon(c(time, rev(time)), c(log10(maxHPD), rev(log10(minHPD))), col = alpha('%s',0.3), border = NA)" % (col)
out_str += "\nlines(time,log10(rate), col = '%s', lwd=%s)" % (col, line_wd)
if plot_shifts:
# add barplot rate shifts
bins_histogram = np.linspace(0,max_age,len(res[0]))
if len(shifts)>1: # rate shift sampled at least once
h = np.histogram(shifts,bins =bins_histogram) #,density=1)
else:
h = [np.zeros(len(bins_histogram)-1),bins_histogram]
a = h[1]
mids = (a-a[1]/2.)[1:]
out_str += util.print_R_vec("\nmids",-mids)
out_str += util.print_R_vec("\ncounts",h[0]/float(res[5]))
out_str += "\nplot(mids,counts,type = 'h', xlim = c(%s,%s), ylim=c(0,%s), ylab = 'Frequency of rate shift', xlab = 'Time',lwd=5,col='%s')" \
% (-max_age,-min_age,max(max(h[0]/float(res[5])),0.2),col)
# get BFs
if run_simulation==1:
BFs = get_prior_shift(min_age,max_age,bins_histogram)
out_str += "\nbf2 = %s\nbf6 = %s" % (BFs[1],BFs[2])
out_str += "\nabline(h=bf2, lty=2)"
out_str += "\nabline(h=bf6, lty=2)"
return out_str
def get_r_plot(res,
col,
parameter,
min_age,
max_age,
plot_title,
plot_log,
run_simulation=1):
out_str = "\n"
out_str += util.print_R_vec("\ntime", -res[0])
out_str += util.print_R_vec("\nrate", res[1][::-1])
out_str += util.print_R_vec("\nminHPD", res[2][::-1])
out_str += util.print_R_vec("\nmaxHPD", res[3][::-1])
if plot_log == 0:
out_str += "\nplot(time,time,type = 'n', ylim = c(%s, %s), xlim = c(%s,%s), ylab = '%s', xlab = 'Time',main='%s' )" \
% (0,1.1*np.nanmax(res[3]),-max_age,-min_age,parameter,plot_title)
out_str += "\npolygon(c(time, rev(time)), c(maxHPD, rev(minHPD)), col = alpha('%s',0.3), border = NA)" % (
col)
out_str += "\nlines(time,rate, col = '%s', lwd=2)" % (col)
else:
out_str += "\nplot(time,time,type = 'n', ylim = c(%s, %s), xlim = c(%s,%s), ylab = 'Log10 %s', xlab = 'Time',main='%s' )" \
% (np.nanmin(np.log10(0.9*res[2])),np.nanmax(np.log10(1.1*res[3])),-max_age,-min_age,parameter,plot_title)
out_str += "\npolygon(c(time, rev(time)), c(log10(maxHPD), rev(log10(minHPD))), col = alpha('%s',0.3), border = NA)" % (
col)
out_str += "\nlines(time,log10(rate), col = '%s', lwd=2)" % (col)
# add barplot rate shifts
bins_histogram = np.linspace(0, max_age, len(res[0]))
if len(res[4]) > 1: # rate shift sampled at least once
h = np.histogram(res[4], bins=bins_histogram) #,density=1)
else:
h = [np.zeros(len(bins_histogram) - 1), bins_histogram]
a = h[1]
mids = (a - a[1] / 2.)[1:]
out_str += util.print_R_vec("\nmids", -mids)
out_str += util.print_R_vec("\ncounts", h[0] / float(res[5]))
out_str += "\nplot(mids,counts,type = 'h', xlim = c(%s,%s), ylim=c(0,%s), ylab = 'Frequency of rate shift', xlab = 'Time',lwd=5,col='%s')" \
% (-max_age,-min_age,max(max(h[0]/float(res[5])),0.2),col)
# get BFs
if run_simulation == 1:
BFs = get_prior_shift(min_age, max_age, bins_histogram)
out_str += "\nbf2 = %s\nbf6 = %s" % (BFs[1], BFs[2])
out_str += "\nabline(h=bf2, lty=2)"
out_str += "\nabline(h=bf6, lty=2)"
return out_str
hpd_array_L[:, i] = calcHPD(marginal_L[:, i])
hpd_array_M[:, i] = calcHPD(marginal_M[:, i])
print "done"
# write R file
print "\ngenerating R file...",
out = "%s/%s_RTT.r" % (output_wd, name_file)
newfile = open(out, "wb")
if platform.system() == "Windows" or platform.system() == "Microsoft":
wd_forward = os.path.abspath(output_wd).replace('\\', '/')
r_script = "\n\npdf(file='%s/%s_RTT.pdf',width=0.6*20, height=0.6*20)\nlibrary(scales)\n" % (
wd_forward, name_file)
else:
r_script = "\n\npdf(file='%s/%s_RTT.pdf',width=0.6*20, height=0.6*20)\nlibrary(scales)\n" % (
output_wd, name_file)
r_script += print_R_vec("\n\nt", age_vec)
r_script += "\ntime = -t"
r_script += print_R_vec("\nspeciation", l_vec)
r_script += print_R_vec("\nextinction", m_vec)
r_script += print_R_vec('\nL_hpd_m', hpd_array_L[0, :])
r_script += print_R_vec('\nL_hpd_M', hpd_array_L[1, :])
r_script += print_R_vec('\nM_hpd_m', hpd_array_M[0, :])
r_script += print_R_vec('\nM_hpd_M', hpd_array_M[1, :])
r_script += """
par(mfrow=c(2,1))
plot(speciation ~ time,type="l",col="#4c4cec", lwd=3,main="Speciation rates", ylim = c(0,max(c(L_hpd_M,M_hpd_M))),xlab="Time",ylab="speciation rate",xlim=c(min(time),0))
polygon(c(time, rev(time)), c(L_hpd_M, rev(L_hpd_m)), col = alpha("#4c4cec",0.3), border = NA)
plot(extinction ~ time,type="l",col="#e34a33", lwd=3,main="Extinction rates", ylim = c(0,max(c(L_hpd_M,M_hpd_M))),xlab="Time",ylab="extinction",xlim=c(min(time),0))
for i in range(np.shape(marginal_L)[1]):
l_vec[i] = np.mean(marginal_L[:,i])
m_vec[i] = np.mean(marginal_M[:,i])
hpd_array_L[:,i] = calcHPD(marginal_L[:,i])
hpd_array_M[:,i] = calcHPD(marginal_M[:,i])
print "done"
# write R file
print "\ngenerating R file...",
out="%s/%s_RTT.r" % (output_wd,name_file)
newfile = open(out, "wb")
if platform.system() == "Windows" or platform.system() == "Microsoft":
wd_forward = os.path.abspath(output_wd).replace('\\', '/')
r_script= "\n\npdf(file='%s/%s_RTT.pdf',width=0.6*20, height=0.6*20)\nlibrary(scales)\n" % (wd_forward,name_file)
else: r_script= "\n\npdf(file='%s/%s_RTT.pdf',width=0.6*20, height=0.6*20)\nlibrary(scales)\n" % (output_wd,name_file)
r_script += print_R_vec("\n\nt", age_vec)
r_script += "\ntime = -t"
r_script += print_R_vec("\nspeciation",l_vec)
r_script += print_R_vec("\nextinction",m_vec)
r_script += print_R_vec('\nL_hpd_m',hpd_array_L[0,:])
r_script += print_R_vec('\nL_hpd_M',hpd_array_L[1,:])
r_script += print_R_vec('\nM_hpd_m',hpd_array_M[0,:])
r_script += print_R_vec('\nM_hpd_M',hpd_array_M[1,:])
r_script += """
par(mfrow=c(2,1))
plot(speciation ~ time,type="l",col="#4c4cec", lwd=3,main="Speciation rates", ylim = c(0,max(c(L_hpd_M,M_hpd_M))),xlab="Time",ylab="speciation rate",xlim=c(min(time),0))
polygon(c(time, rev(time)), c(L_hpd_M, rev(L_hpd_m)), col = alpha("#4c4cec",0.3), border = NA)
def get_r_plot(res,
col,
parameter,
min_age,
max_age,
plot_title,
plot_log,
run_simulation=1,
plot_shifts=1,
line_wd=2):
times = res[0]
rates = res[1][::-1]
rates_m = res[2][::-1]
rates_M = res[3][::-1]
shifts = res[4]
indx = np.intersect1d((times >= min_age).nonzero()[0],
(times <= max_age).nonzero()[0])
times = times[indx]
#print indx,times, rates
rates = rates[indx]
rates_m = rates_m[indx]
rates_M = rates_M[indx]
try:
shifts = shifts[shifts > min_age]
shifts = shifts[shifts < max_age]
except:
plot_shifts = 0
out_str = "\n"
out_str += util.print_R_vec("\ntime", -times)
out_str += util.print_R_vec("\nrate", rates)
out_str += util.print_R_vec("\nminHPD", rates_m)
out_str += util.print_R_vec("\nmaxHPD", rates_M)
if plot_log == 0:
out_str += "\nplot(time,time,type = 'n', ylim = c(%s, %s), xlim = c(%s,%s), ylab = '%s', xlab = 'Time',main='%s' )" \
% (0,1.1*np.nanmax(rates_M),-max_age,-min_age,parameter,plot_title)
out_str += "\npolygon(c(time, rev(time)), c(maxHPD, rev(minHPD)), col = alpha('%s',0.3), border = NA)" % (
col)
out_str += "\nlines(time,rate, col = '%s', lwd=%s)" % (col, line_wd)
else:
out_str += "\nplot(time,time,type = 'n', ylim = c(%s, %s), xlim = c(%s,%s), ylab = 'Log10 %s', xlab = 'Time',main='%s' )" \
% (np.nanmin(np.log10(0.9*rates_m)),np.nanmax(np.log10(1.1*rates_M)),-max_age,-min_age,parameter,plot_title)
out_str += "\npolygon(c(time, rev(time)), c(log10(maxHPD), rev(log10(minHPD))), col = alpha('%s',0.3), border = NA)" % (
col)
out_str += "\nlines(time,log10(rate), col = '%s', lwd=%s)" % (col,
line_wd)
if plot_shifts:
# add barplot rate shifts
bins_histogram = np.linspace(0, max_age, len(res[0]))
if len(shifts) > 1: # rate shift sampled at least once
h = np.histogram(shifts, bins=bins_histogram) #,density=1)
else:
h = [np.zeros(len(bins_histogram) - 1), bins_histogram]
a = h[1]
mids = (a - a[1] / 2.)[1:]
out_str += util.print_R_vec("\nmids", -mids)
out_str += util.print_R_vec("\ncounts", h[0] / float(res[5]))
out_str += "\nplot(mids,counts,type = 'h', xlim = c(%s,%s), ylim=c(0,%s), ylab = 'Frequency of rate shift', xlab = 'Time',lwd=5,col='%s')" \
% (-max_age,-min_age,max(max(h[0]/float(res[5])),0.2),col)
# get BFs
if run_simulation == 1:
BFs = get_prior_shift(min_age, max_age, bins_histogram)
out_str += "\nbf2 = %s\nbf6 = %s" % (BFs[1], BFs[2])
out_str += "\nabline(h=bf2, lty=2)"
out_str += "\nabline(h=bf6, lty=2)"
return out_str
