def getErosionProfile(plan, profile):
x = np.linspace(0, L, xRes)
m, b = np.polyfit(x, convert(exposure(plan, profile)), 1)
return m * x + b #might need to round to a certain number of sigNIFICANT digits -
# DO NOT USE np.around IT CUTS OFF AFTER GIVEN NUMBER OF DECIMALS
## Plan = con, div, uni
## Profile = convex,parallel, concave
#def getErosionProfile(plan, profile):
# return convert(exposure(plan, profile))
def pbpkModel(avector, time, BW, VFC):
vols = volumes(BW, VFC)
flow = flows(BW)
exps = exposure(BW)
# Create your models here.
# Permeability surface area coefficients
perm = {
'fat': perms['fat'] * (vols['fat'] - vols['fatblood']),
'muscle': perms['muscle'] * (vols['muscle'] - vols['muscleblood']),
'slow': perms['slow'] * (vols['slow'] - vols['slowblood'])
}
maxi = min(len(avector), len(compartments))
blood = {}
tissue = {}
A = {compartments[i]: max(avector[i], 0) for i in range(maxi)}
d = {
'arterial': 0,
'liver': 0,
'kidney': 0,
'urine': 0,
'muscleblood': 0, 'muscle': 0,
'fatblood': 0, 'fat': 0,
'rich': 0,
'slowblood': 0, 'slow': 0
}
###########################################################################
# Concentration of the chemical in vein compartment
rich = ('liver', 'kidney', 'rich')
poor = ('fat', 'muscle', 'slow')
bloodOut = 0;
for r in rich:
blood[r] = richBloodCalc(A[r], vols[r], flow[r], parts[r])
bloodOut += blood[r].out
for p in poor:
pb = p + 'blood'
blood[p] = poorBloodCalc(vols[p], flow[p], vols['blood'], A[pb], A[p])
bloodOut += blood[p].out
#########################################################################
# OTC in blood compartment
# con' of chemical in the vein
CV = bloodOut / flow['total']
CA = A['arterial'] / vols['blood'] # con' in artery = amount in artery / volume of blood
RA = flow['total'] * (CV - CA) # rate of change in amount in tissue of blood
d['arterial'] = RA
###########################################################################
# OTC in liver compartment
tissue['liver'] = richTissueCalc(A['liver'], vols['liver'], flow['liver'], blood['liver'].conc, CA)
d['liver'] = tissue['liver'].rate # amount of chemical in liver
CL = tissue['liver'].conc # con' of chem in liver
###########################################################################
# OTC in kidney compartment
# Urinary excretion of OTC
Rurine = exps['Kurine'] * blood['kidney'].conc
d['urine'] = Rurine
# kidney
tissue['kidney'] = richTissueCalc(A['kidney'], vols['kidney'], flow['kidney'], blood['kidney'].conc, CA, -Rurine)
d['kidney'] = tissue['kidney'].rate
CK = tissue['kidney'].conc # con' of chem in kidney
###########################################################################
# OTC in RPT of body compartment
tissue['rich'] = richTissueCalc(A['rich'], vols['rich'], flow['rich'], blood['rich'].conc, CA)
d['rich'] = tissue['rich'].rate
CR = tissue['rich'].conc
###########################################################################
# OTC in muscle compartment
tns = ('muscle', 'fat', 'slow')
for tn in tns:
tb = tn + 'blood'
tissue[tn] = poorTissueCalc(vols[tn], flow[tn], A[tb], A[tn], blood[tn], CA, perm[tn], parts[tn])
d[tb] = tissue[tn].brate # amount of chemical in muscle
d[tn] = tissue[tn].rate
###########################################################################
# Mass balance
# Qbal = flows(BW)['total'] - sum(flows.values())
Tmass = A['arterial'] + A['liver'] + A['kidney'] + A['urine'] + A['rich'] + A['muscle'] + A['muscleblood'] + \
A['fat'] + A['fatblood'] + A['slow'] + A['slowblood']
#if Tmass > BW + 1:
# raise ValueError(sum(A.values()), A, sum(d.values()), d)
for key in A:
if A[key] < 0:
raise ValueError(key, A[key], d[key])
ld = list(d.values())
return ld
# IPython log file import exposure exposure() exposure.exposure() import cloud jid = cloud.call(exposure) jid = cloud.call(exposure.exposure) print jid cloud.status() cloud.status(jid) cloud.result(jid) exposure.exposure() exposure.exposure(1024) exposure.exposure(512) exposure.exposure(1024) args = 1024 N = 100 zip([args]*N) jids = cloud.map(exposure.exposure, *zip(*([args]*N))) zip(args*N) zip([args]*N) jids = cloud.map(exposure.exposure, zip(([args]*N))) jids.status() jids cloud.status(jids) cloud.result(jids) jids = cloud.map(exposure.exposure, *zip(([args]*N))) cloud.status(jids) cloud.result(jids) jids = cloud.map(exposure.exposure, *zip(*([args]*N)))
#plt.plot(x, y, color='k')
#prettify()
#plt.yticks([])
#plt.xticks([])
#plt.annotate('2', xy=(0,plotYMax), verticalalignment='top', fontsize=fontLarge)
#
#plt.subplot(3,3,3)
#y = convert(exposure('div', 'concave'))
#plt.plot(x, y, color='k')
#prettify()
#plt.yticks([])
#plt.xticks([])
#plt.annotate('3', xy=(0,plotYMax), verticalalignment='top', fontsize=fontLarge)
plt.subplot(1, 2, 1)
y = convert(exposure('con', 'planar'))
plt.plot(x, y, color='k')
prettify()
plt.xticks([])
#plt.annotate('4', xy=(0,plotYMax), verticalalignment='top', fontsize=fontLarge)
plt.ylabel('Chemical Denudation (mm)', fontsize=fontSmall)
prettify()
#plt.subplot(3,3,5)
#y = convert(exposure('uni', 'planar'))
#plt.plot(x, y, color='k')
#prettify()
#plt.yticks([])
#plt.xticks([])
#plt.annotate('5', xy=(0,plotYMax), verticalalignment='top', fontsize=fontLarge)
x = np.linspace(0, L, L)
def prettify():
plt.xlim(plotXMin, plotXMax)
plt.ylim(plotYMin, plotYMax)
plt.yticks([0, 1.5 * 3600 * 24, 3 * 3600 * 24], [0, 1.5, 3],
fontsize=fontSmall)
plt.xticks([0, 50, 100], [0, 50, 100], fontsize=fontSmall)
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.subplot(3, 3, 1)
plt.plot(x, exposure('con', 'concave'), color='k')
prettify()
plt.xticks([])
plt.annotate('1',
xy=(0, plotYMax),
fontsize=fontLarge,
verticalalignment='top')
plt.subplot(3, 3, 2)
plt.plot(x, exposure('uni', 'concave'), color='k')
prettify()
plt.yticks([])
plt.xticks([])
plt.annotate('2',
xy=(0, plotYMax),
fontsize=fontLarge,
import matplotlib.pyplot as plt
from plotWaterTable import plotWaterTable
from plotWaterTablePlanar import plotWaterTablePlanar
from parameters import *
from exposure import exposure
from getDenudationRate import getDenudationRate
plt.figure(figsize=figDimSmall)
x = np.linspace(0,L,xRes)
#convert from 1 storm long exposure time to annual denudation (in meters)
timeLength = 1 #year
def convert(exp):
return stormsPerYear*(exp + Tr)*getDenudationRate()*timeLength
plt.subplot(3,1,1)
yCon = convert(exposure('con','convex'))
plt.plot(x,yCon,color='k', linestyle='--', label='Actual')
m,b = np.polyfit(x,yCon,1)
plt.plot(x,m*x+b, color='k', linestyle='-', label='Linear Fit')
print('Convergent: m = ' + str(m) + ' and b = ' + str(b))
plt.annotate('Convergent', xy=(100,0), fontsize=fontSmall,
horizontalalignment='right', verticalalignment='bottom')
plt.xticks([])
plt.legend(frameon=False, ncol=2, fontsize=fontXSmall)
plt.subplot(3,1,2)
yUni = convert(exposure('uni','convex'))
plt.plot(x,yUni,color='k', linestyle='--')
m,b = np.polyfit(x,yUni,1)
plt.plot(x,m*x+b, color='k', linestyle='-')
print('Uniform: m = ' + str(m) + ' and b = ' + str(b))
def pbpk(BW, VFC):
# PBPK output
out = odeint(pbpkModel, list(state.values()), time, args=(BW, VFC))
return out
veces = np.linspace(1, 10, 10)
colors = ('#ffff00', '#00ffff', '#ff00ff', '#0000ff', '#ff0000', '#00ff00',
'#000000', '#ff7f00', '#ff007f', '#7fff00', '#00ff7f')
for i in veces:
vars = variableInput.randomize()
BW = vars['BW']
VFC = vars['VFC']
exps = exposure(BW)
state = {
'arterial': exps['IV'],
'liver': exps['ORAL'],
'kidney': 0,
'urine': 0,
'muscleblood': 0,
'muscle': exps['IM'],
'fatblood': 0,
'fat': 0,
'rich': 0,
'slowblood': 0,
'slow': 0
}
output = pbpk(BW, VFC)
putout = transpose(output)