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_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_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_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_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 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'))
