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_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_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_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 make_saturation_index_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']
SI = sitePhreeqcDict[site].SI_Calcite
#Filter out nan values
good_values = get_good_indicies([Q, SI])
Q = Q[good_values]
SI = SI[good_values]
check_plots_dir(sitesDir)
#Make Discharge vs. Saturation Index Plot
figure()
semilogx(Q, SI, 'o')
xlabel('Discharge (cfs)')
ylabel('$SI_{calcite}$')
savefig(os.path.join(plotsDir, site + '-Q_vs_SI.pdf'))
#Calculate correlation coefficient and regression
slope, intercept, r, p, stderr = linregress(log10(Q), SI)
rs.append(r)
ps.append(p)
slopes.append(slope)
if makeHistograms:
figure()
hist(SI, normed=True)
xlabel('$SI_{calcite}$')
ylabel('Frequency')
savefig(os.path.join(plotsDir, site + '-SIHist.pdf'))
#Make histogram of pearson r values
figure()
hist(rs, normed=True)
xlabel('Pearson R')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'PearsonR_Q_SIRatio.pdf'))
def make_saturation_index_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']
SI = sitePhreeqcDict[site].SI_Calcite
#Filter out nan values
good_values = get_good_indicies([Q,SI])
Q = Q[good_values]
SI = SI[good_values]
check_plots_dir(sitesDir)
#Make Discharge vs. Saturation Index Plot
figure()
semilogx(Q, SI, 'o')
xlabel('Discharge (cfs)')
ylabel('$SI_{calcite}$')
savefig(os.path.join(plotsDir,site+'-Q_vs_SI.pdf'))
#Calculate correlation coefficient and regression
slope,intercept,r,p,stderr = linregress(log10(Q), SI)
rs.append(r)
ps.append(p)
slopes.append(slope)
if makeHistograms:
figure()
hist(SI, normed=True)
xlabel('$SI_{calcite}$')
ylabel('Frequency')
savefig(os.path.join(plotsDir,site+'-SIHist.pdf'))
#Make histogram of pearson r values
figure()
hist(rs,normed=True)
xlabel('Pearson R')
ylabel('Frequency')
savefig(os.path.join(plotsDir,'PearsonR_Q_SIRatio.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 make_pwp_vs_Q_plots(sitesDir, siteList=None, siteFile=None, makeHistograms=True, classFile='', bracket_charge_balance=False,plotloglog=False, nbins=10):
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)
df = DataFrame({'Q':Q, 'pwp':pwp_rates})
df = df.dropna()
Qposids = df.Q>0
df = df[Qposids]
#Calculate fitting function parameters
fit_converged = True
if df.Q.values.size>5:
try:
opt_params, pcov = curve_fit(pwp_Q_fit, df['Q'], df['pwp'], p0=[1.,1.,1.])
k = opt_params[0]
a = opt_params[1]
beta = opt_params[2]
except RuntimeError as e:
fit_converged = False
print(e)
else:
fit_converged = False
#Make Discharge vs. Saturation Ratio Plot
figure()
if plotloglog:
if bracket_charge_balance:
loglog(df.Q, df.pwp, '.', ms=0, color=this_color)
else:
loglog(df.Q, df.pwp, '.', color=this_color)
else:
if bracket_charge_balance:
semilogx(df.Q, df.pwp, '.', color=this_color, ms=0)
else:
semilogx(df.Q, df.pwp, '.', color=this_color)
#plot fitting function
if fit_converged:
Q_fit = linspace(df.Q.min(), df.Q.max(), 1000)
pwp_fit = pwp_Q_fit(Q_fit, k, a, beta)
plot(Q_fit, pwp_fit, '-k')
xlabel('Discharge (cfs)')
ylabel('PWP Dissolution Rate (mm/yr)')
if bracket_charge_balance:
#Load charge bracket data
df_alk = read_csv(os.path.join(sitesDir, site, site+'-PHREEQC-Alk.csv'), parse_dates = True, index_col=0)
df_ca = read_csv(os.path.join(sitesDir, site, site+'-PHREEQC-Ca.csv'), parse_dates = True, index_col=0)
pwp_alk = pwp_to_mm_yr(calc_site_pwp(sitePanelDict[site], df_alk))
pwp_ca = pwp_to_mm_yr(calc_site_pwp(sitePanelDict[site], df_ca))
delta_alk = df.pwp - pwp_alk
delta_ca = df.pwp - pwp_ca
delta_upper = zeros(delta_ca.size)
delta_lower = zeros(delta_ca.size)
for i, delta in enumerate(delta_ca):
if delta<0:
delta_upper[i] = abs(delta_ca[i])
delta_lower[i] = abs(delta_alk[i])
elif delta>0:
delta_lower[i] = abs(delta_ca[i])
delta_upper[i] = abs(delta_alk[i])
df['pwp_upper'] = delta_upper[Qposids.values]
df['pwp_lower'] = delta_lower[Qposids.values]
df['Alkalinity_balance_error'] = df_alk['Percent error']
not_converged = df['Alkalinity_balance_error']>5.0
df.pwp[not_converged]=nan
df = df.dropna()
df = df[df.Q>0] #USGS database has some negative values? -999999
errorbar(df.Q, df.pwp, yerr=[df.pwp_upper, df.pwp_lower], fmt='o', color=this_color)
tight_layout()
savefig(os.path.join(plotsDir,site+'-Q_vs_PWP.pdf'))
#Calculate correlation coefficient and regression
slope,intercept,r,p,stderr = linregress(log10(df.Q), df.pwp)
rs.append(r)
if isnan(r):
return df
ps.append(p)
[this_spr, this_spp] = spearmanr(log10(df.Q), df.pwp)
spr.append(this_spr)
spp.append(this_spp)
slopes.append(slope)
if makeHistograms:
figure()
hist(df.pwp, 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()
arr_rs = array(rs)
arr_ps = array(ps)
arr_spr = array(spr)
arr_spp = array(spp)
arr_site_class = array(site_class)
goodids = logical_and(~isnan(arr_rs), ~isnan(arr_ps))
arr_rs = arr_rs[goodids]
arr_ps = arr_ps[goodids]
arr_site_class = arr_site_class[goodids]
if arr_rs[arr_site_class==1].shape[0]>1:
arr1_rs_sig = arr_rs[logical_and(arr_site_class==1, arr_ps<0.05)]
print("Class 1: number of sig ps = ",arr1_rs_sig.size, "number insig = ", arr_rs[arr_site_class==1].size - arr1_rs_sig.size)
hist(arr1_rs_sig, nbins, color=class_colors[1], alpha=0.5)
if arr_rs[arr_site_class==2].shape[0]>1:
arr2_rs_sig = arr_rs[logical_and(arr_site_class==2, arr_ps<0.05)]
print("Class 2: number of sig ps = ",arr2_rs_sig.size, "number insig = ", arr_rs[arr_site_class==2].size - arr2_rs_sig.size)
hist(arr2_rs_sig, nbins, color=class_colors[2], alpha=0.5)
if arr_rs[arr_site_class==3].shape[0]>1:
arr3_rs_sig = arr_rs[logical_and(arr_site_class==3, arr_ps<0.05)]
print("Class 3: number of sig ps = ",arr3_rs_sig.size, "number insig = ", arr_rs[arr_site_class==3].size - arr3_rs_sig.size)
hist(arr3_rs_sig, nbins, color=class_colors[3], alpha=0.5)
xlabel('Pearson R')
ylabel('Frequency')
savefig(os.path.join(plotsDir,'PearsonR_Q_PWP.pdf'))
#Make histogram of pearson p values
figure()
if arr_ps[arr_site_class==1].shape[0]>1:
hist(arr_ps[arr_site_class==1], nbins, color=class_colors[1], alpha=0.5)
if arr_ps[arr_site_class==2].shape[0]>1:
hist(arr_ps[arr_site_class==2], nbins, color=class_colors[2], alpha=0.5)
if arr_ps[arr_site_class==3].shape[0]>1:
hist(arr_ps[arr_site_class==3], nbins, color=class_colors[3], alpha=0.5)
xlabel('Pearson p')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'PearsonP_Q_PWP.pdf'))
#Make histogram of spearman r values
arr_spr = array(spr)
arr_spp = array(spp)
arr_site_class = array(site_class)
goodids = logical_and(~isnan(arr_spr), ~isnan(arr_spp))
arr_spr = arr_spr[goodids]
arr_spp = arr_spp[goodids]
arr_site_class = arr_site_class[goodids]
figure()
if arr_spr[arr_site_class==1].shape[0]>1:
arr1_spr_sig = arr_spr[logical_and(arr_site_class==1, arr_spp<0.05)]
print("Class 1: number of sig ps = ",arr1_spr_sig.size, "number insig = ", arr_spr[arr_site_class==1].size - arr1_spr_sig.size)
hist(arr1_spr_sig, nbins, color=class_colors[1], alpha=0.5)
if arr_spr[arr_site_class==2].shape[0]>1:
arr2_spr_sig = arr_spr[logical_and(arr_site_class==2, arr_spp<0.05)]
print("Class 2: number of sig ps = ",arr2_spr_sig.size, "number insig = ", arr_spr[arr_site_class==2].size - arr2_spr_sig.size)
hist(arr2_spr_sig, nbins, color=class_colors[2], alpha=0.5)
if arr_spr[arr_site_class==3].shape[0]>1:
arr3_spr_sig = arr_spr[logical_and(arr_site_class==3, arr_spp<0.05)]
print("Class 3: number of sig ps = ",arr3_spr_sig.size, "number insig = ", arr_spr[arr_site_class==3].size - arr3_spr_sig.size)
hist(arr3_spr_sig, nbins, color=class_colors[3], alpha=0.5)
xlabel('Spearman R')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'SpearmanR_Q_PWP.pdf'))
#Make histogram of spearman p values
figure()
if arr_spp[arr_site_class==1].shape[0]>1:
hist(arr_spp[arr_site_class==1], nbins, color=class_colors[1], alpha=0.5)
if arr_spp[arr_site_class==2].shape[0]>1:
hist(arr_spp[arr_site_class==2], nbins, color=class_colors[2], alpha=0.5)
if arr_spp[arr_site_class==3].shape[0]>1:
hist(arr_spp[arr_site_class==3], nbins, color=class_colors[3], alpha=0.5)
xlabel('Spearman p')
ylabel('Frequency')
savefig(os.path.join(plotsDir, 'SpearmanP_Q_PWP.pdf'))
def make_pwp_vs_PCO2_plots(sitesDir, siteList=None, siteFile=None, makeHistograms=True, classFile='', bracket_charge_balance=False,plotloglog=False, nbins=10):
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,PCO2 = calc_site_pwp(sitePanelDict[site], sitePhreeqcDict[site], returnPCO2=True)
pwp_rates = pwp_to_mm_yr(pwp_rates)
df = DataFrame({'PCO2':PCO2, 'pwp':pwp_rates})
df = df.dropna()
#Make PWP vs. PCO2 Plot
figure()
if plotloglog:
if bracket_charge_balance:
loglog(df.PCO2, df.pwp, '.', ms=0, color=this_color)
else:
loglog(df.PCO2, df.pwp, '.', color=this_color)
else:
if bracket_charge_balance:
semilogx(df.PCO2, df.pwp, '.', color=this_color, ms=0)
else:
semilogx(df.PCO2, df.pwp, '.', color=this_color)
xlabel('PCO2 (atm)')
ylabel('PWP Dissolution Rate (mm/yr)')
if bracket_charge_balance:
#Load charge bracket data
df_alk = read_csv(os.path.join(sitesDir, site, site+'-PHREEQC-Alk.csv'), parse_dates = True, index_col=0)
df_ca = read_csv(os.path.join(sitesDir, site, site+'-PHREEQC-Ca.csv'), parse_dates = True, index_col=0)
pwp_alk = pwp_to_mm_yr(calc_site_pwp(sitePanelDict[site], df_alk))
pwp_ca = pwp_to_mm_yr(calc_site_pwp(sitePanelDict[site], df_ca))
delta_alk = df.pwp - pwp_alk
delta_ca = df.pwp - pwp_ca
delta_upper = zeros(delta_ca.size)
delta_lower = zeros(delta_ca.size)
for i, delta in enumerate(delta_ca):
if delta<0:
delta_upper[i] = abs(delta_ca[i])
delta_lower[i] = abs(delta_alk[i])
elif delta>0:
delta_lower[i] = abs(delta_ca[i])
delta_upper[i] = abs(delta_alk[i])
df['pwp_upper'] = delta_upper
df['pwp_lower'] = delta_lower
df['Alkalinity_balance_error'] = df_alk['Percent error']
not_converged = df['Alkalinity_balance_error']>5.0
df.pwp[not_converged]=nan
df = df.dropna()
errorbar(df.PCO2, df.pwp, yerr=[df.pwp_upper, df.pwp_lower], fmt='o', color=this_color)
savefig(os.path.join(plotsDir,site+'-PCO2_vs_PWP.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 processSites(sitesDir,
PHREEQC_PATH,
DATABASE_FILE,
phreeqcDict=None,
regEx='USGS-*',
bracket_charge_balance=False,
process_regular=True):
"""
Processes all data from site directories in PHREEQC.
Parameters
----------
sitesDir : str
The directory that contains the site directories to be processed.
PHREEQC_PATH : str
The path to the phreeqc executable.
DATABASE_FILE : str
The path to the phreeqc database file to be used.
phreeqcDict : dict
a dictionary with WQX characteristics as keys and phreeqc chemical names as entries. By default, processPanel will use the built in translation dict, default_phreeqc_to_WQX_translation.
regEx : str (optional)
A regular expression to be used to locate site directories. (default = 'USGS-')
bracket_charge_balance : bool
If set to True, then charge balance will be bracketed by forcing balance on Ca and Alkalinity alternately. Default = False.
process_regular : bool
If set to True, then PHREEQC will be run without any charge balance forcing. This can be set to False if the calculations without forced balance are already done and you only want to do the charge balance runs. Default = True.
Returns
-------
None
"""
sitesDict = loadSiteListData(regEx=regEx, processedSitesDir=sitesDir)
if process_regular:
#Run PHREEQC on sites without forced charge balance
for site, site_panel in sitesDict.items():
print(("Processing " + site + " in PHREEQC"))
sitedf = processPanel(site_panel,
os.path.join(sitesDir, site),
PHREEQC_PATH=PHREEQC_PATH,
DATABASE_FILE=DATABASE_FILE,
phreeqcDict=phreeqcDict)
phreeqc_site_file = os.path.join(sitesDir, site,
site + '-PHREEQC.pkl')
try:
pickle.dump(sitedf, open(phreeqc_site_file, 'wb'))
sitedf.to_csv(phreeqc_site_file[:-3] + 'csv')
except IOError:
print('Problem writing out PHREEQC data file.')
if bracket_charge_balance:
#Run PHREEQC using bracketed charge balance for sites
for site, site_panel in sitesDict.items():
#Force balance on Calcium
phreeqc_df_ca = processPanel(site_panel,
os.path.join(sitesDir, site),
PHREEQC_PATH,
DATABASE_FILE,
force_balance='Ca')
phreeqc_site_file_ca = os.path.join(sitesDir, site,
site + '-PHREEQC-Ca.pkl')
try:
pickle.dump(phreeqc_df_ca, open(phreeqc_site_file_ca, 'wb'))
phreeqc_df_ca.to_csv(phreeqc_site_file_ca[:-3] + 'csv')
except IOError:
print('Problem writing out PHREEQC Ca data file.')
#Force balance on Alkalinity
phreeqc_df_alk = processPanel(site_panel,
os.path.join(sitesDir, site),
PHREEQC_PATH,
DATABASE_FILE,
force_balance='Alk')
phreeqc_site_file_alk = os.path.join(sitesDir, site,
site + '-PHREEQC-Alk.pkl')
try:
pickle.dump(phreeqc_df_alk, open(phreeqc_site_file_alk, 'wb'))
phreeqc_df_alk.to_csv(phreeqc_site_file_alk[:-3] + 'csv')
except IOError:
print('Problem writing out PHREEQC Alk data file.')
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_Q_plots(sitesDir, siteList=None, siteFile=None, makeHistograms=True, classFile='', bracket_charge_balance=False,plotloglog=False, nbins=10):
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)
df = DataFrame({'Q':Q, 'pwp':pwp_rates})
df = df.dropna()
Qposids = df.Q>0
df = df[Qposids]
#Calculate fitting function parameters
fit_converged = True
if df.Q.values.size>5:
try:
opt_params, pcov = curve_fit(pwp_Q_fit, df['Q'], df['pwp'], p0=[1.,1.,1.])
k = opt_params[0]
a = opt_params[1]
beta = opt_params[2]
except RuntimeError, e:
fit_converged = False
print e
else:
fit_converged = False
#Make Discharge vs. Saturation Ratio Plot
figure()
if plotloglog:
if bracket_charge_balance:
loglog(df.Q, df.pwp, '.', ms=0, color=this_color)
else:
loglog(df.Q, df.pwp, '.', color=this_color)
else:
if bracket_charge_balance:
semilogx(df.Q, df.pwp, '.', color=this_color, ms=0)
else:
semilogx(df.Q, df.pwp, '.', color=this_color)
#plot fitting function
if fit_converged:
Q_fit = linspace(df.Q.min(), df.Q.max(), 1000)
pwp_fit = pwp_Q_fit(Q_fit, k, a, beta)
plot(Q_fit, pwp_fit, '-k')
xlabel('Discharge (cfs)')
ylabel('PWP Dissolution Rate (mm/yr)')
if bracket_charge_balance:
#Load charge bracket data
df_alk = read_csv(os.path.join(sitesDir, site, site+'-PHREEQC-Alk.csv'), parse_dates = True, index_col=0)
df_ca = read_csv(os.path.join(sitesDir, site, site+'-PHREEQC-Ca.csv'), parse_dates = True, index_col=0)
pwp_alk = pwp_to_mm_yr(calc_site_pwp(sitePanelDict[site], df_alk))
pwp_ca = pwp_to_mm_yr(calc_site_pwp(sitePanelDict[site], df_ca))
delta_alk = df.pwp - pwp_alk
delta_ca = df.pwp - pwp_ca
delta_upper = zeros(delta_ca.size)
delta_lower = zeros(delta_ca.size)
for i, delta in enumerate(delta_ca):
if delta<0:
delta_upper[i] = abs(delta_ca[i])
delta_lower[i] = abs(delta_alk[i])
elif delta>0:
delta_lower[i] = abs(delta_ca[i])
delta_upper[i] = abs(delta_alk[i])
df['pwp_upper'] = delta_upper[Qposids.values]
df['pwp_lower'] = delta_lower[Qposids.values]
df['Alkalinity_balance_error'] = df_alk['Percent error']
not_converged = df['Alkalinity_balance_error']>5.0
df.pwp[not_converged]=nan
df = df.dropna()
df = df[df.Q>0] #USGS database has some negative values? -999999
errorbar(df.Q, df.pwp, yerr=[df.pwp_upper, df.pwp_lower], fmt='o', color=this_color)
tight_layout()
savefig(os.path.join(plotsDir,site+'-Q_vs_PWP.pdf'))
#Calculate correlation coefficient and regression
slope,intercept,r,p,stderr = linregress(log10(df.Q), df.pwp)
rs.append(r)
if isnan(r):
return df
ps.append(p)
[this_spr, this_spp] = spearmanr(log10(df.Q), df.pwp)
spr.append(this_spr)
spp.append(this_spp)
slopes.append(slope)
if makeHistograms:
figure()
hist(df.pwp, normed=True, color=this_color)
xlabel('PWP Rate mm/yr')
ylabel('Frequency')
savefig(os.path.join(plotsDir,site+'-PWPHist.pdf'))
def make_pwp_vs_PCO2_plots(sitesDir, siteList=None, siteFile=None, makeHistograms=True, classFile='', bracket_charge_balance=False,plotloglog=False, nbins=10):
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,PCO2 = calc_site_pwp(sitePanelDict[site], sitePhreeqcDict[site], returnPCO2=True)
pwp_rates = pwp_to_mm_yr(pwp_rates)
df = DataFrame({'PCO2':PCO2, 'pwp':pwp_rates})
df = df.dropna()
#Make PWP vs. PCO2 Plot
figure()
if plotloglog:
if bracket_charge_balance:
loglog(df.PCO2, df.pwp, '.', ms=0, color=this_color)
else:
loglog(df.PCO2, df.pwp, '.', color=this_color)
else:
if bracket_charge_balance:
semilogx(df.PCO2, df.pwp, '.', color=this_color, ms=0)
else:
semilogx(df.PCO2, df.pwp, '.', color=this_color)
xlabel('PCO2 (atm)')
ylabel('PWP Dissolution Rate (mm/yr)')
if bracket_charge_balance:
#Load charge bracket data
df_alk = read_csv(os.path.join(sitesDir, site, site+'-PHREEQC-Alk.csv'), parse_dates = True, index_col=0)
df_ca = read_csv(os.path.join(sitesDir, site, site+'-PHREEQC-Ca.csv'), parse_dates = True, index_col=0)
pwp_alk = pwp_to_mm_yr(calc_site_pwp(sitePanelDict[site], df_alk))
pwp_ca = pwp_to_mm_yr(calc_site_pwp(sitePanelDict[site], df_ca))
delta_alk = df.pwp - pwp_alk
delta_ca = df.pwp - pwp_ca
delta_upper = zeros(delta_ca.size)
delta_lower = zeros(delta_ca.size)
for i, delta in enumerate(delta_ca):
if delta<0:
delta_upper[i] = abs(delta_ca[i])
delta_lower[i] = abs(delta_alk[i])
elif delta>0:
delta_lower[i] = abs(delta_ca[i])
delta_upper[i] = abs(delta_alk[i])
df['pwp_upper'] = delta_upper
df['pwp_lower'] = delta_lower
df['Alkalinity_balance_error'] = df_alk['Percent error']
not_converged = df['Alkalinity_balance_error']>5.0
df.pwp[not_converged]=nan
df = df.dropna()
errorbar(df.PCO2, df.pwp, yerr=[df.pwp_upper, df.pwp_lower], fmt='o', color=this_color)
savefig(os.path.join(plotsDir,site+'-PCO2_vs_PWP.pdf'))
def processSites(sitesDir, PHREEQC_PATH, DATABASE_FILE, phreeqcDict=None, regEx='USGS-*', bracket_charge_balance=False, process_regular=True):
"""
Processes all data from site directories in PHREEQC.
Parameters
----------
sitesDir : str
The directory that contains the site directories to be processed.
PHREEQC_PATH : str
The path to the phreeqc executable.
DATABASE_FILE : str
The path to the phreeqc database file to be used.
phreeqcDict : dict
a dictionary with WQX characteristics as keys and phreeqc chemical names as entries. By default, processPanel will use the built in translation dict, default_phreeqc_to_WQX_translation.
regEx : str (optional)
A regular expression to be used to locate site directories. (default = 'USGS-')
bracket_charge_balance : bool
If set to True, then charge balance will be bracketed by forcing balance on Ca and Alkalinity alternately. Default = False.
process_regular : bool
If set to True, then PHREEQC will be run without any charge balance forcing. This can be set to False if the calculations without forced balance are already done and you only want to do the charge balance runs. Default = True.
Returns
-------
None
"""
sitesDict = loadSiteListData(regEx=regEx, processedSitesDir = sitesDir)
if process_regular:
#Run PHREEQC on sites without forced charge balance
for site, site_panel in sitesDict.iteritems():
print("Processing "+site+" in PHREEQC")
sitedf = processPanel(site_panel, os.path.join(sitesDir,site), PHREEQC_PATH=PHREEQC_PATH, DATABASE_FILE=DATABASE_FILE, phreeqcDict=phreeqcDict)
phreeqc_site_file = os.path.join(sitesDir,site,site+'-PHREEQC.pkl')
try:
pickle.dump(sitedf, open(phreeqc_site_file, 'wb'))
sitedf.to_csv(phreeqc_site_file[:-3]+'csv')
except IOError:
print('Problem writing out PHREEQC data file.')
if bracket_charge_balance:
#Run PHREEQC using bracketed charge balance for sites
for site, site_panel in sitesDict.iteritems():
#Force balance on Calcium
phreeqc_df_ca = processPanel(site_panel, os.path.join(sitesDir,site), PHREEQC_PATH, DATABASE_FILE, force_balance='Ca')
phreeqc_site_file_ca = os.path.join(sitesDir,site,site+'-PHREEQC-Ca.pkl')
try:
pickle.dump(phreeqc_df_ca, open(phreeqc_site_file_ca, 'wb'))
phreeqc_df_ca.to_csv(phreeqc_site_file_ca[:-3]+'csv')
except IOError:
print('Problem writing out PHREEQC Ca data file.')
#Force balance on Alkalinity
phreeqc_df_alk = processPanel(site_panel, os.path.join(sitesDir,site), PHREEQC_PATH, DATABASE_FILE, force_balance='Alk')
phreeqc_site_file_alk = os.path.join(sitesDir,site,site+'-PHREEQC-Alk.pkl')
try:
pickle.dump(phreeqc_df_alk, open(phreeqc_site_file_alk, 'wb'))
phreeqc_df_alk.to_csv(phreeqc_site_file_alk[:-3]+'csv')
except IOError:
print('Problem writing out PHREEQC Alk data file.')
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'))
