def make_PCO2_vs_Month_plots(sitesDir, siteList=None, siteFile=None, classFile=''):
sitePanelDict, sitePhreeqcDict = loadSiteListData(processedSitesDir=sitesDir, loadPhreeqc=True)
if classFile != '':
class_xls = read_excel(classFile, 'Sheet1',index_col=1, names=['name','site', 'recharge', 'age'])
site_class = []
plotsDir = os.path.join(sitesDir, 'plots/')
for site in list(sitePanelDict.keys()):
if classFile !='':
#read in classification for this site
this_class = class_xls['recharge'][site]
this_color=class_colors[this_class]
site_class.append(this_class)
else:
this_color='black'
print(("Making plot for: "+site))
#Read out data into shorter variable names
T_C = sitePanelDict[site].data['Temperature, water']
CO2 = sitePhreeqcDict[site].CO2_Molality
good_values = get_good_indicies([T_C,CO2])
T_C = T_C[good_values]
CO2 = CO2[good_values]
#Calculate PCO2
T_K = CtoK(T_C)
K_H = calc_K_H(T_K)
PCO2 = CO2/K_H
sample_month = PCO2.index.month
figure()
#semilogy(sample_month, PCO2, 'o', color=this_color)
plot(sample_month, PCO2, 'o', color=this_color)
xlabel('Month')
ylabel('PCO2 (atm)')
savefig(os.path.join(plotsDir,site+'-PCO2_vs_Month.pdf'))
def make_PCO2_vs_Month_plots(sitesDir, siteList=None, siteFile=None, classFile=''):
sitePanelDict, sitePhreeqcDict = loadSiteListData(processedSitesDir=sitesDir, loadPhreeqc=True)
if classFile != '':
class_xls = read_excel(classFile, 'Sheet1',index_col=1, names=['name','site', 'recharge', 'age'])
site_class = []
plotsDir = os.path.join(sitesDir, 'plots/')
for site in sitePanelDict.keys():
if classFile !='':
#read in classification for this site
this_class = class_xls['recharge'][site]
this_color=class_colors[this_class]
site_class.append(this_class)
else:
this_color='black'
print("Making plot for: "+site)
#Read out data into shorter variable names
T_C = sitePanelDict[site].data['Temperature, water']
CO2 = sitePhreeqcDict[site].CO2_Molality
good_values = get_good_indicies([T_C,CO2])
T_C = T_C[good_values]
CO2 = CO2[good_values]
#Calculate PCO2
T_K = CtoK(T_C)
K_H = calc_K_H(T_K)
PCO2 = CO2/K_H
sample_month = PCO2.index.month
figure()
#semilogy(sample_month, PCO2, 'o', color=this_color)
plot(sample_month, PCO2, 'o', color=this_color)
xlabel('Month')
ylabel('PCO2 (atm)')
savefig(os.path.join(plotsDir,site+'-PCO2_vs_Month.pdf'))
def make_saturation_ratio_vs_Q_plots(sitesDir,
siteList=None,
siteFile=None,
makeHistograms=True):
sitePanelDict, sitePhreeqcDict = loadSiteListData(
processedSitesDir=sitesDir, loadPhreeqc=True)
rs = []
ps = []
slopes = []
plotsDir = os.path.join(sitesDir, 'plots/')
for site in sitePanelDict.keys():
print("Making plot for: " + site)
#Read out data into shorter variable names
Q = sitePanelDict[site].data['Stream flow, mean. daily']
T_C = sitePanelDict[site].data['Temperature, water']
CO2 = sitePhreeqcDict[site].CO2_Molality
Ca = sitePhreeqcDict[site]['Ca']
#Filter out nan values
#good_values = ~(Q+T_C+CO2+Ca).isnull()#Addition will create Nans if any of the series contains a NaN
good_values = get_good_indicies([Q, T_C, CO2, Ca])
Q = Q[good_values]
T_C = T_C[good_values]
CO2 = CO2[good_values]
Ca = Ca[good_values]
#Calculate PCO2
T_K = CtoK(T_C)
K_H = calc_K_H(T_K)
PCO2 = CO2 / K_H
#Calculate Equilibrium Ca concentration
CaEq = concCaEqFromPCO2(PCO2, T_C=T_C)
#Check for plots directory and make it if one doesn't exist
check_plots_dir(sitesDir)
#Make Discharge vs. Saturation Ratio Plot
figure()
loglog(Q, Ca / CaEq, 'o')
xlabel('Discharge (cfs)')
ylabel('Saturation Ratio $[Ca]/[Ca]_{eq}$')
savefig(os.path.join(plotsDir, site + '-Q_vs_SatRatio.pdf'))
#Calculate correlation coefficient and regression
slope, intercept, r, p, stderr = linregress(log10(Q), Ca / CaEq)
rs.append(r)
ps.append(p)
slopes.append(slope)
if makeHistograms:
figure()
hist(Ca / CaEq, normed=True)
xlabel('Saturation Ratio $[Ca]/[Ca]_{eq}$')
ylabel('Frequency')
savefig(os.path.join(plotsDir, site + '-SatRatioHist.pdf'))
#Make histogram of pearson r values
figure()
hist(rs, normed=True)
xlabel('Pearson R')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'PearsonR_Q_SatRatio.pdf'))
def calc_site_pwp(sitedf, sitephreeqc, returnPCO2=False):
have_data = False
if ('Stream flow, mean. daily' in sitedf.columns) and (
'Temperature, water' in sitedf.columns) and (
'CO2_Molality' in sitephreeqc.columns) and (
'Ca+2_Activity' in sitephreeqc.columns) and (
'CO2_Activity' in sitephreeqc.columns) and (
'H+_Activity' in sitephreeqc.columns) and (
'HCO3-_Activity' in sitephreeqc.columns):
have_data = True
if have_data:
#create dataframe subset
subdf = DataFrame({
'Q': sitedf['Stream flow, mean. daily'],
'T_C': sitedf['Temperature, water'],
'CO2': sitephreeqc.CO2_Molality,
'a_Ca': sitephreeqc['Ca+2_Activity'],
'a_H2CO3s': sitephreeqc['CO2_Activity'],
'a_H': sitephreeqc['H+_Activity'],
'a_HCO3': sitephreeqc['HCO3-_Activity']
})
#Clear out NaN values
subdf = subdf.dropna()
if subdf.size > 0:
#Average any duplicate indicies
g = subdf.groupby(level=0) #group by duplicate indicies
subdf = g.mean() #average duplicate indicies
#Calculate PCO2
T_K = CtoK(subdf.T_C)
K_H = calc_K_H(T_K)
PCO2 = subdf.CO2 / K_H
pwp_rates = pwpRateTheory(a_Ca=subdf.a_Ca,
a_H2CO3s=subdf.a_H2CO3s,
a_H=subdf.a_H,
a_HCO3=subdf.a_HCO3,
T_K=T_K,
PCO2=PCO2)
if returnPCO2:
return [pwp_rates, PCO2]
else:
return pwp_rates
else:
if returnPCO2:
return [None, None]
else:
return None
#Data were missing
else:
if returnPCO2:
return [None, None]
else:
return None
def make_saturation_ratio_vs_Q_plots(sitesDir, siteList=None, siteFile=None, makeHistograms=True):
sitePanelDict, sitePhreeqcDict = loadSiteListData(processedSitesDir=sitesDir, loadPhreeqc=True)
rs = []
ps = []
slopes = []
plotsDir = os.path.join(sitesDir, 'plots/')
for site in sitePanelDict.keys():
print("Making plot for: "+site)
#Read out data into shorter variable names
Q = sitePanelDict[site].data['Stream flow, mean. daily']
T_C = sitePanelDict[site].data['Temperature, water']
CO2 = sitePhreeqcDict[site].CO2_Molality
Ca = sitePhreeqcDict[site]['Ca']
#Filter out nan values
#good_values = ~(Q+T_C+CO2+Ca).isnull()#Addition will create Nans if any of the series contains a NaN
good_values = get_good_indicies([Q,T_C,CO2,Ca])
Q = Q[good_values]
T_C = T_C[good_values]
CO2 = CO2[good_values]
Ca = Ca[good_values]
#Calculate PCO2
T_K = CtoK(T_C)
K_H = calc_K_H(T_K)
PCO2 = CO2/K_H
#Calculate Equilibrium Ca concentration
CaEq = concCaEqFromPCO2(PCO2, T_C=T_C)
#Check for plots directory and make it if one doesn't exist
check_plots_dir(sitesDir)
#Make Discharge vs. Saturation Ratio Plot
figure()
loglog(Q, Ca/CaEq, 'o')
xlabel('Discharge (cfs)')
ylabel('Saturation Ratio $[Ca]/[Ca]_{eq}$')
savefig(os.path.join(plotsDir,site+'-Q_vs_SatRatio.pdf'))
#Calculate correlation coefficient and regression
slope,intercept,r,p,stderr = linregress(log10(Q), Ca/CaEq)
rs.append(r)
ps.append(p)
slopes.append(slope)
if makeHistograms:
figure()
hist(Ca/CaEq, normed=True)
xlabel('Saturation Ratio $[Ca]/[Ca]_{eq}$')
ylabel('Frequency')
savefig(os.path.join(plotsDir,site+'-SatRatioHist.pdf'))
#Make histogram of pearson r values
figure()
hist(rs,normed=True)
xlabel('Pearson R')
ylabel('Frequency')
savefig(os.path.join(plotsDir,'PearsonR_Q_SatRatio.pdf'))
def make_PCO2_vs_Q_plots(sitesDir,
siteList=None,
siteFile=None,
classFile='',
makeHistograms=True):
sitePanelDict, sitePhreeqcDict = loadSiteListData(
processedSitesDir=sitesDir, loadPhreeqc=True)
if classFile != '':
class_xls = read_excel(classFile,
'Sheet1',
index_col=1,
names=['name', 'site', 'recharge', 'age'])
rs = []
ps = []
site_class = []
slopes = []
plotsDir = os.path.join(sitesDir, 'plots/')
for site in sitePanelDict.keys():
if classFile != '':
#read in classification for this site
this_class = class_xls['recharge'][site]
this_color = class_colors[this_class]
site_class.append(this_class)
else:
this_color = 'black'
print("Making plot for: " + site)
#Read out data into shorter variable names
Q = sitePanelDict[site].data['Stream flow, mean. daily']
T_C = sitePanelDict[site].data['Temperature, water']
CO2 = sitePhreeqcDict[site].CO2_Molality
Ca = sitePhreeqcDict[site]['Ca']
#Filter out nan values
#good_values = ~(Q+T_C+CO2+Ca).isnull()#Addition will create Nans if any of the series contains a NaN
good_values = get_good_indicies([Q, T_C, CO2, Ca])
Q = Q[good_values]
T_C = T_C[good_values]
CO2 = CO2[good_values]
Ca = Ca[good_values]
#Calculate PCO2
T_K = CtoK(T_C)
K_H = calc_K_H(T_K)
PCO2 = CO2 / K_H
figure()
loglog(Q, PCO2, 'o', color=this_color)
xlabel('Discharge (cfs)')
ylabel('PCO2 (atm)')
savefig(os.path.join(plotsDir, site + '-PCO2_vs_Q.pdf'))
def calc_site_pwp(sitedf, sitephreeqc, returnPCO2=False):
have_data = False
if ('Stream flow, mean. daily' in sitedf.columns) and ( 'Temperature, water' in sitedf.columns) and ('CO2_Molality' in sitephreeqc.columns) and ('Ca+2_Activity' in sitephreeqc.columns) and ('CO2_Activity' in sitephreeqc.columns) and ('H+_Activity' in sitephreeqc.columns) and ('HCO3-_Activity' in sitephreeqc.columns):
have_data = True
if have_data:
#create dataframe subset
subdf = DataFrame({
'Q':sitedf['Stream flow, mean. daily'],
'T_C':sitedf['Temperature, water'],
'CO2':sitephreeqc.CO2_Molality,
'a_Ca':sitephreeqc['Ca+2_Activity'],
'a_H2CO3s':sitephreeqc['CO2_Activity'],
'a_H':sitephreeqc['H+_Activity'],
'a_HCO3':sitephreeqc['HCO3-_Activity']
})
#Clear out NaN values
subdf = subdf.dropna()
if subdf.size>0:
#Average any duplicate indicies
g = subdf.groupby(level=0)#group by duplicate indicies
subdf = g.mean()#average duplicate indicies
#Calculate PCO2
T_K = CtoK(subdf.T_C)
K_H = calc_K_H(T_K)
PCO2 = subdf.CO2/K_H
pwp_rates = pwpRateTheory(a_Ca=subdf.a_Ca, a_H2CO3s=subdf.a_H2CO3s, a_H=subdf.a_H, a_HCO3=subdf.a_HCO3, T_K=T_K, PCO2=PCO2)
if returnPCO2:
return [pwp_rates, PCO2]
else:
return pwp_rates
else:
if returnPCO2:
return [None,None]
else:
return None
#Data were missing
else:
if returnPCO2:
return [None,None]
else:
return None
def make_PCO2_vs_Q_plots(sitesDir, siteList=None, siteFile=None, classFile='', makeHistograms=True):
sitePanelDict, sitePhreeqcDict = loadSiteListData(processedSitesDir=sitesDir, loadPhreeqc=True)
if classFile != '':
class_xls = read_excel(classFile, 'Sheet1',index_col=1, names=['name','site', 'recharge', 'age'])
rs = []
ps = []
site_class = []
slopes = []
plotsDir = os.path.join(sitesDir, 'plots/')
for site in sitePanelDict.keys():
if classFile !='':
#read in classification for this site
this_class = class_xls['recharge'][site]
this_color=class_colors[this_class]
site_class.append(this_class)
else:
this_color='black'
print("Making plot for: "+site)
#Read out data into shorter variable names
Q = sitePanelDict[site].data['Stream flow, mean. daily']
T_C = sitePanelDict[site].data['Temperature, water']
CO2 = sitePhreeqcDict[site].CO2_Molality
Ca = sitePhreeqcDict[site]['Ca']
#Filter out nan values
#good_values = ~(Q+T_C+CO2+Ca).isnull()#Addition will create Nans if any of the series contains a NaN
good_values = get_good_indicies([Q,T_C,CO2,Ca])
Q = Q[good_values]
T_C = T_C[good_values]
CO2 = CO2[good_values]
Ca = Ca[good_values]
#Calculate PCO2
T_K = CtoK(T_C)
K_H = calc_K_H(T_K)
PCO2 = CO2/K_H
figure()
loglog(Q, PCO2, 'o', color=this_color)
xlabel('Discharge (cfs)')
ylabel('PCO2 (atm)')
savefig(os.path.join(plotsDir,site+'-PCO2_vs_Q.pdf'))
def make_pwp_vs_T_plots(sitesDir, siteList=None, siteFile=None, makeHistograms=True, classFile=''):
sitePanelDict, sitePhreeqcDict = loadSiteListData(processedSitesDir=sitesDir, loadPhreeqc=True)
if classFile != '':
class_xls = read_excel(classFile, 'Sheet1',index_col=1, names=['name','site', 'recharge', 'age'])
rs = []
ps = []
slopes = []
plotsDir = os.path.join(sitesDir, 'plots/')
for site in list(sitePanelDict.keys()):
if classFile !='':
#read in classification for this site
this_class = class_xls['recharge'][site]
this_color=class_colors[this_class]
else:
this_color='black'
print(("Making plot for: "+site))
#Read out data into shorter variable names
Q = sitePanelDict[site].data['Stream flow, mean. daily']
T_C = sitePanelDict[site].data['Temperature, water']
CO2 = sitePhreeqcDict[site].CO2_Molality
Ca = sitePhreeqcDict[site]['Ca']
a_Ca = sitePhreeqcDict[site]['Ca+2_Activity']
a_H2CO3s = sitePhreeqcDict[site]['CO2_Activity']
a_HCO3 = sitePhreeqcDict[site]['HCO3-_Activity']
a_H = sitePhreeqcDict[site]['H+_Activity']
#Filter out nan values
#good_values = ~(Q+T_C+CO2+Ca).isnull()#Addition will create Nans if any of the series contains a NaN
good_values = get_good_indicies([Q,T_C,CO2,Ca,a_Ca,a_H2CO3s,a_H, a_HCO3])
Q = Q[good_values]
T_C = T_C[good_values]
CO2 = CO2[good_values]
Ca = Ca[good_values]
a_Ca = a_Ca[good_values]
a_H = a_H[good_values]
a_H2CO3s = a_H2CO3s[good_values]
a_HCO3 = a_HCO3[good_values]
#Calculate PCO2
T_K = CtoK(T_C)
K_H = calc_K_H(T_K)
PCO2 = CO2/K_H
#Calculate PWP rates
pwp_rates = []
for i, value in enumerate(Q):
# sol = solutionFromCaPCO2(Ca[i], PCO2[i], T_C = T_C[i])
# this_pwp = pwpFromSolution(sol)
this_pwp = pwpRateTheory(a_Ca=a_Ca[i], a_H2CO3s=a_H2CO3s[i], a_H=a_H[i], a_HCO3=a_HCO3[i], T_K=T_K[i], PCO2=PCO2[i])
this_rate_mm_yr = pwp_to_mm_yr(this_pwp)
pwp_rates.append(this_rate_mm_yr)
#Check for plots directory and make it if one doesn't exist
check_plots_dir(sitesDir)
#Make Discharge vs. Saturation Ratio Plot
figure()
plot(T_C, pwp_rates, 'o', color=this_color)
xlabel('Temperature ($^\circ C$)')
ylabel('PWP Dissolution Rate (mm/yr)')
savefig(os.path.join(plotsDir,site+'-T_vs_PWP.pdf'))
#Calculate correlation coefficient and regression
slope,intercept,r,p,stderr = linregress(T_C, pwp_rates)
rs.append(r)
ps.append(p)
slopes.append(slope)
if makeHistograms:
figure()
hist(pwp_rates, normed=True, color=this_color)
xlabel('PWP Rate mm/yr')
ylabel('Frequency')
savefig(os.path.join(plotsDir,site+'-PWPHist.pdf'))
#Make histogram of pearson r values
figure()
hist(rs,normed=True)
xlabel('Pearson R')
ylabel('Frequency')
savefig(os.path.join(plotsDir,'PearsonR_T_PWP.pdf'))
def make_pwp_vs_saturation_ratio_plots(sitesDir, siteList=None, siteFile=None, classFile='', plotloglog=True):
sitePanelDict, sitePhreeqcDict = loadSiteListData(processedSitesDir=sitesDir, loadPhreeqc=True)
if classFile != '':
class_xls = read_excel(classFile, 'Sheet1',index_col=1, names=['name','site', 'recharge', 'age'])
site_class = []
rs =[]
ps = []
spp = []
spr = []
slopes= []
plotsDir = os.path.join(sitesDir, 'plots/')
#Check for plots directory and make it if one doesn't exist
check_plots_dir(sitesDir)
for site in list(sitePanelDict.keys()):
if classFile !='':
#read in classification for this site
this_class = class_xls['recharge'][site]
this_color=class_colors[this_class]
site_class.append(this_class)
else:
this_color='black'
print(("Making plot for: "+site))
Q = sitePanelDict[site].data['Stream flow, mean. daily']
pwp_rates = calc_site_pwp(sitePanelDict[site], sitePhreeqcDict[site])
pwp_rates = pwp_to_mm_yr(pwp_rates)
T_C = sitePanelDict[site].data['Temperature, water']
T_K = CtoK(T_C)
K_H = calc_K_H(T_K)
CO2 = sitePhreeqcDict[site].CO2_Molality
PCO2 = CO2/K_H
CaEq = concCaEqFromPCO2(PCO2, T_C=T_C)
sat_ratio = sitePhreeqcDict[site].Ca/CaEq
df = DataFrame({'Q':Q, 'pwp':pwp_rates, 'sat_ratio':sat_ratio})
df = df.dropna()
#Make PWP vs. Saturation Ratio Plot
figure()
if plotloglog:
loglog(df.pwp, df.sat_ratio, '.', color=this_color)
else:
semilogx(df.pwp, df.sat_ratio, '.', color=this_color)
xlabel('PWP Dissolution Rate (mm/yr)')
ylabel('Saturation ratio')
savefig(os.path.join(plotsDir,site+'-Sat_ratio_vs_PWP.pdf'))
#Calculate correlation coefficient and regression
slope,intercept,r,p,stderr = linregress(df.pwp, df.sat_ratio)
rs.append(r)
ps.append(p)
[this_spr, this_spp] = spearmanr(df.pwp, df.sat_ratio)
spr.append(this_spr)
spp.append(this_spp)
slopes.append(slope)
#Make histogram of pearson r values
figure()
arr_rs = array(rs)
print("size arr_rs=", arr_rs.size)
arr_ps = array(ps)
arr_spr = array(spr)
arr_spp = array(spp)
arr_site_class = array(site_class)
hist(arr_rs[arr_site_class==1],color=class_colors[1], alpha=0.5)
hist(arr_rs[arr_site_class==2],color=class_colors[2], alpha=0.5)
hist(arr_rs[arr_site_class==3],color=class_colors[3], alpha=0.5)
xlabel('Pearson R')
ylabel('Frequency')
savefig(os.path.join(plotsDir,'PearsonR_PWP_SatRatio.pdf'))
#Make histogram of pearson p values
figure()
hist(arr_ps[arr_site_class==1],color=class_colors[1], alpha=0.5)
hist(arr_ps[arr_site_class==2],color=class_colors[2], alpha=0.5)
hist(arr_ps[arr_site_class==3],color=class_colors[3], alpha=0.5)
xlabel('Pearson p')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'PearsonP_PWP_SatRatio.pdf'))
#Make histogram of spearman r values
figure()
hist(arr_spr[arr_site_class==1],color=class_colors[1], alpha=0.5)
hist(arr_spr[arr_site_class==2],color=class_colors[2], alpha=0.5)
hist(arr_spr[arr_site_class==3],color=class_colors[3], alpha=0.5)
xlabel('Spearman R')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'SpearmanR_PWP_SatRatio.pdf'))
#Make histogram of spearman p values
figure()
hist(arr_spp[arr_site_class==1],color=class_colors[1], alpha=0.5)
hist(arr_spp[arr_site_class==2],color=class_colors[2], alpha=0.5)
hist(arr_spp[arr_site_class==3],color=class_colors[3], alpha=0.5)
xlabel('Spearman p')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'SpearmanP_PWP_SatRatio.pdf'))
def make_pwp_vs_T_plots(sitesDir, siteList=None, siteFile=None, makeHistograms=True, classFile=''):
sitePanelDict, sitePhreeqcDict = loadSiteListData(processedSitesDir=sitesDir, loadPhreeqc=True)
if classFile != '':
class_xls = read_excel(classFile, 'Sheet1',index_col=1, names=['name','site', 'recharge', 'age'])
rs = []
ps = []
slopes = []
plotsDir = os.path.join(sitesDir, 'plots/')
for site in sitePanelDict.keys():
if classFile !='':
#read in classification for this site
this_class = class_xls['recharge'][site]
this_color=class_colors[this_class]
else:
this_color='black'
print("Making plot for: "+site)
#Read out data into shorter variable names
Q = sitePanelDict[site].data['Stream flow, mean. daily']
T_C = sitePanelDict[site].data['Temperature, water']
CO2 = sitePhreeqcDict[site].CO2_Molality
Ca = sitePhreeqcDict[site]['Ca']
a_Ca = sitePhreeqcDict[site]['Ca+2_Activity']
a_H2CO3s = sitePhreeqcDict[site]['CO2_Activity']
a_HCO3 = sitePhreeqcDict[site]['HCO3-_Activity']
a_H = sitePhreeqcDict[site]['H+_Activity']
#Filter out nan values
#good_values = ~(Q+T_C+CO2+Ca).isnull()#Addition will create Nans if any of the series contains a NaN
good_values = get_good_indicies([Q,T_C,CO2,Ca,a_Ca,a_H2CO3s,a_H, a_HCO3])
Q = Q[good_values]
T_C = T_C[good_values]
CO2 = CO2[good_values]
Ca = Ca[good_values]
a_Ca = a_Ca[good_values]
a_H = a_H[good_values]
a_H2CO3s = a_H2CO3s[good_values]
a_HCO3 = a_HCO3[good_values]
#Calculate PCO2
T_K = CtoK(T_C)
K_H = calc_K_H(T_K)
PCO2 = CO2/K_H
#Calculate PWP rates
pwp_rates = []
for i, value in enumerate(Q):
# sol = solutionFromCaPCO2(Ca[i], PCO2[i], T_C = T_C[i])
# this_pwp = pwpFromSolution(sol)
this_pwp = pwpRateTheory(a_Ca=a_Ca[i], a_H2CO3s=a_H2CO3s[i], a_H=a_H[i], a_HCO3=a_HCO3[i], T_K=T_K[i], PCO2=PCO2[i])
this_rate_mm_yr = pwp_to_mm_yr(this_pwp)
pwp_rates.append(this_rate_mm_yr)
#Check for plots directory and make it if one doesn't exist
check_plots_dir(sitesDir)
#Make Discharge vs. Saturation Ratio Plot
figure()
plot(T_C, pwp_rates, 'o', color=this_color)
xlabel('Temperature ($^\circ C$)')
ylabel('PWP Dissolution Rate (mm/yr)')
savefig(os.path.join(plotsDir,site+'-T_vs_PWP.pdf'))
#Calculate correlation coefficient and regression
slope,intercept,r,p,stderr = linregress(T_C, pwp_rates)
rs.append(r)
ps.append(p)
slopes.append(slope)
if makeHistograms:
figure()
hist(pwp_rates, normed=True, color=this_color)
xlabel('PWP Rate mm/yr')
ylabel('Frequency')
savefig(os.path.join(plotsDir,site+'-PWPHist.pdf'))
#Make histogram of pearson r values
figure()
hist(rs,normed=True)
xlabel('Pearson R')
ylabel('Frequency')
savefig(os.path.join(plotsDir,'PearsonR_T_PWP.pdf'))
def make_pwp_vs_saturation_ratio_plots(sitesDir, siteList=None, siteFile=None, classFile='', plotloglog=True):
sitePanelDict, sitePhreeqcDict = loadSiteListData(processedSitesDir=sitesDir, loadPhreeqc=True)
if classFile != '':
class_xls = read_excel(classFile, 'Sheet1',index_col=1, names=['name','site', 'recharge', 'age'])
site_class = []
rs =[]
ps = []
spp = []
spr = []
slopes= []
plotsDir = os.path.join(sitesDir, 'plots/')
#Check for plots directory and make it if one doesn't exist
check_plots_dir(sitesDir)
for site in sitePanelDict.keys():
if classFile !='':
#read in classification for this site
this_class = class_xls['recharge'][site]
this_color=class_colors[this_class]
site_class.append(this_class)
else:
this_color='black'
print("Making plot for: "+site)
Q = sitePanelDict[site].data['Stream flow, mean. daily']
pwp_rates = calc_site_pwp(sitePanelDict[site], sitePhreeqcDict[site])
pwp_rates = pwp_to_mm_yr(pwp_rates)
T_C = sitePanelDict[site].data['Temperature, water']
T_K = CtoK(T_C)
K_H = calc_K_H(T_K)
CO2 = sitePhreeqcDict[site].CO2_Molality
PCO2 = CO2/K_H
CaEq = concCaEqFromPCO2(PCO2, T_C=T_C)
sat_ratio = sitePhreeqcDict[site].Ca/CaEq
df = DataFrame({'Q':Q, 'pwp':pwp_rates, 'sat_ratio':sat_ratio})
df = df.dropna()
#Make PWP vs. Saturation Ratio Plot
figure()
if plotloglog:
loglog(df.pwp, df.sat_ratio, '.', color=this_color)
else:
semilogx(df.pwp, df.sat_ratio, '.', color=this_color)
xlabel('PWP Dissolution Rate (mm/yr)')
ylabel('Saturation ratio')
savefig(os.path.join(plotsDir,site+'-Sat_ratio_vs_PWP.pdf'))
#Calculate correlation coefficient and regression
slope,intercept,r,p,stderr = linregress(df.pwp, df.sat_ratio)
rs.append(r)
ps.append(p)
[this_spr, this_spp] = spearmanr(df.pwp, df.sat_ratio)
spr.append(this_spr)
spp.append(this_spp)
slopes.append(slope)
#Make histogram of pearson r values
figure()
arr_rs = array(rs)
print "size arr_rs=", arr_rs.size
arr_ps = array(ps)
arr_spr = array(spr)
arr_spp = array(spp)
arr_site_class = array(site_class)
hist(arr_rs[arr_site_class==1],color=class_colors[1], alpha=0.5)
hist(arr_rs[arr_site_class==2],color=class_colors[2], alpha=0.5)
hist(arr_rs[arr_site_class==3],color=class_colors[3], alpha=0.5)
xlabel('Pearson R')
ylabel('Frequency')
savefig(os.path.join(plotsDir,'PearsonR_PWP_SatRatio.pdf'))
#Make histogram of pearson p values
figure()
hist(arr_ps[arr_site_class==1],color=class_colors[1], alpha=0.5)
hist(arr_ps[arr_site_class==2],color=class_colors[2], alpha=0.5)
hist(arr_ps[arr_site_class==3],color=class_colors[3], alpha=0.5)
xlabel('Pearson p')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'PearsonP_PWP_SatRatio.pdf'))
#Make histogram of spearman r values
figure()
hist(arr_spr[arr_site_class==1],color=class_colors[1], alpha=0.5)
hist(arr_spr[arr_site_class==2],color=class_colors[2], alpha=0.5)
hist(arr_spr[arr_site_class==3],color=class_colors[3], alpha=0.5)
xlabel('Spearman R')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'SpearmanR_PWP_SatRatio.pdf'))
#Make histogram of spearman p values
figure()
hist(arr_spp[arr_site_class==1],color=class_colors[1], alpha=0.5)
hist(arr_spp[arr_site_class==2],color=class_colors[2], alpha=0.5)
hist(arr_spp[arr_site_class==3],color=class_colors[3], alpha=0.5)
xlabel('Spearman p')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'SpearmanP_PWP_SatRatio.pdf'))
