sys.path.insert(0, '/data/ap4909/30.10.2013-hot_cold_geochem/mantle_temp_cals/calculate_p_t_using_lee/aux_files')
import pet_read_in_with_labels
from scipy.optimize import fsolve
samps_markers={}
h2o_colours={0:"r",4:'g',8:'b'}
volcano_names=["Ata","Tofua","Niuatoputapu","Tafahi","Kao","Late"]
island_symbols={"Agrigan":".","Almagan":"v","Anatahan":"8","Asuncion":"s","Guguan":"D","Pagan":'p',"Sarigan":"*","Uracas":"o"}#symbols for each island
h2o_vals=np.linspace(0,8,10)
for sub_zone in["Southern_Marianas"]:#
samps1=pet_read_in_with_labels.pet_read_in("/data/ap4909/30.10.2013-hot_cold_geochem/get_lat_lon_data/sz_with_lat_lon/individual_files/individual_files_with_feo/%s_new.xls"%(sub_zone),1,sub_zone)[0]
mineral_props={"Fo=92":{"ol":0.65,"opx":0.3,"cpx":0.05},"Fo=91":{"ol":0.65,"opx":0.25,"cpx":0.1},"Fo=90":{"ol":0.65,"opx":0.2,"cpx":0.15}} #mineral proportions for fo90,fo91,fo92 mantle
for samps,symbol,colour,label in zip([samps1],["s"],["r"],["My samples"]):
for comp in [0.9]:
for key,value in samps.iteritems():
print key
P_vals=[]
mf_vals=[]
for h2o in h2o_vals:
ti_corrected=value.get_ti_corrected(0.9,h2o,0.25)
comp_frac_1 = np.interp(comp, mg_num_pressures[2], mf_pressures[2])
comp_frac_2 = np.interp(comp, mg_num_pressures[3], mf_pressures[3])
comp_frac_list = [comp_frac_1, comp_frac_2]
comp_frac = np.interp(P, [2, 3], comp_frac_list)
elif pre_P > 3:
comp_frac = np.interp(comp, mg_num_pressures[3], mf_pressures[3])
cpx_mode = value.mineral_mode(
comp_frac, pre_P, "cpx") #getting modal abundance of cpx in source
return cpx_mode
for sub_zone in ["Southern_Marianas"]: #
samps1 = pet_read_in_with_labels.pet_read_in(
"/data/ap4909/30.10.2013-hot_cold_geochem/get_lat_lon_data/sz_with_lat_lon/individual_files/individual_files_with_feo/%s_new.xls"
% (sub_zone), 1, sub_zone)[0]
kelley_samps = pet_read_in_with_labels.pet_read_in(
"/data/ap4909/30.10.2013-hot_cold_geochem/sample_testing/southern_marianas/kelley/kelley_appendix_4_formatted.xls",
1, sub_zone)[0]
mineral_props = {
"Fo=92": {
"ol": 0.65,
"opx": 0.3,
"cpx": 0.05
},
"Fo=91": {
"ol": 0.65,
"opx": 0.25,
"cpx": 0.1
ti_source.append(batch_modal_melting(f,0.09)*0.09)
ti_mg_num=[]
for tim in ti_melt_fraction:
ti_mg_num.append(np.interp(tim,mf,mg_nums))
print ti_mg_num
for zone in ["Southern_Marianas"]:#["Southern_Marianas","South_Sandwich","West_Aleutians","Izu","South_Lesser_Antilles","Tonga","Central_Aleutians","Kermadec","New_Britain","New_Zealand","North_Vanuatu"]:
sub_zone="%s"%zone
print sub_zone
labels=[]
index=1
samps1=pet_read_in_with_labels.pet_read_in("/data/ap4909/30.10.2013-hot_cold_geochem/get_lat_lon_data/sz_with_lat_lon/individual_files/individual_files_with_feo/%s_new.xls"%(sub_zone),1,sub_zone)[0]
one_samp={}
kelley_samps=pet_read_in_with_labels.pet_read_in("/data/ap4909/30.10.2013-hot_cold_geochem/sample_testing/southern_marianas/kelley/kelley_appendix_4_formatted.xls",0,sub_zone)[0]
sample_best_P_vals=[]
sample_best_f=[]
sample_best_ti_source=[]
sample_best_source_mg=[]
for samps,symbol in zip([kelley_samps,samps1],["d","s"]):
#for samps,symbol in zip([samps1],["s"]):
for key,value in samps.iteritems():
if value.pts["MGO(WT%)"]>6:# and key not in exclude_samps:
katz_mg_res_fit=[]
ti_mg_res_fit=[]
x0 = fsolve(mf_root_finding, 0, (cond[1], cond[0], h2o_bulk))[0]
return x0
volcano_TP = {
"Agrigan": [1330, 1.87],
"Guguan": [1356, 1.9],
"Pagan": [1346, 1.61],
"Sarigan": [1260, 1.14]
}
volcano_colours = {"Agrigan": "b", "Guguan": "r", "Pagan": "g", "Sarigan": "m"}
for sub_zone in ["Southern_Marianas"]: #
#samps1=pet_read_in_with_labels.pet_read_in("/data/ap4909/30.10.2013-hot_cold_geochem/get_lat_lon_data/sz_with_lat_lon/individual_files/individual_files_with_feo/%s_new.xls"%(sub_zone),1,sub_zone)[0]
kelley_samps = pet_read_in_with_labels.pet_read_in(
"/data/ap4909/30.10.2013-hot_cold_geochem/sample_testing/southern_marianas/kelley/kelley_appendix_4_formatted.xls",
0, sub_zone)[0]
mineral_props = {
"Fo=92": {
"ol": 0.65,
"opx": 0.3,
"cpx": 0.05
},
"Fo=91": {
"ol": 0.65,
"opx": 0.25,
"cpx": 0.1
},
"Fo=90": {
"ol": 0.65,
b=132.899012
c=1159.66061#value from kelley 2010
x=-136.88
y=332.01
Dh2o=0.012
return h2o_bulk-(Dh2o*(1-Ff)+Ff)*((T-(a*P**2+b*P+c)-(x*math.log(P)+y)*F)/-60)**1.85
x0=fsolve(mf_root_finding,0,(cond[1],cond[0],h2o_bulk))[0]
return x0
volcano_TP={"Agrigan":[1330,1.87],"Guguan":[1356,1.9],"Pagan":[1346,1.61],"Sarigan":[1260,1.14]}
volcano_colours={"Agrigan":"b","Guguan":"r","Pagan":"g","Sarigan":"m"}
for sub_zone in["Southern_Marianas"]:#
#samps1=pet_read_in_with_labels.pet_read_in("/data/ap4909/30.10.2013-hot_cold_geochem/get_lat_lon_data/sz_with_lat_lon/individual_files/individual_files_with_feo/%s_new.xls"%(sub_zone),1,sub_zone)[0]
kelley_samps=pet_read_in_with_labels.pet_read_in("/data/ap4909/30.10.2013-hot_cold_geochem/sample_testing/southern_marianas/kelley/kelley_appendix_4_formatted.xls",0,sub_zone)[0]
mineral_props={"Fo=92":{"ol":0.65,"opx":0.3,"cpx":0.05},"Fo=91":{"ol":0.65,"opx":0.25,"cpx":0.1},"Fo=90":{"ol":0.65,"opx":0.2,"cpx":0.15}} #mineral proportions for fo90,fo91,fo92 mantle
ax=plt.gca()
#ax2=ax.twinx()
latitudes=[]
p_vals=[]
mf_vals=[]
for samps,symbol in zip([kelley_samps],["d"]):
#for samps,symbol in zip([samps1],["s"]):
for h2o in [4]:
samp_errors={} #stores lowest for each sample
done=0 #indicating whether first composition has been run through
sub_zone="%s"%zone
#Pressure function
d_a=[ 0., 3000., 15000., 24400., 71000., 171000., 220000.,271000., 371000., 400000., 471000., 571000., 670000., 771000., 871000., 971000., 1071000., 1171000., 1271000., 1371000., 1471000.,1571000., 1671000., 1771000., 1871000., 1971000., 2071000., 2171000.,2271000., 2371000., 2471000., 2571000., 2671000., 2771000., 2871000.,2891000.] #PREM depths
p_a=[ 0.00000000e+00, 0.00000000e+00, 3.00000000e+08, 6.00000000e+08, 2.20000000e+09, 5.50000000e+09 , 7.10000000e+09, 8.90000000e+09, 1.23000000e+10, 1.34000000e+10, 1.60000000e+10, 1.99000000e+10, 2.38000000e+10, 2.83000000e+10, 3.28000000e+10, 3.73000000e+10, 4.19000000e+10 , 4.65000000e+10, 5.12000000e+10, 5.59000000e+10, 6.07000000e+10, 6.55000000e+10,7.04000000e+10, 7.54000000e+10, 8.04000000e+10, 8.55000000e+10, 9.06000000e+10, 9.58000000e+10, 1.01100000e+11, 1.06400000e+11, 1.11900000e+11, 1.17400000e+11, 1.23000000e+11, 1.28800000e+11 , 1.34600000e+11, 1.35800000e+11] #PREM pressures
def prem_pressure(height):
for prem_depth in range(1,len(d_a)): #loops through depths, starting at index one
if -height<=d_a[prem_depth]: #checks in which depth interval the height is
Pressure=((p_a[prem_depth]-p_a[prem_depth-1])/(d_a[prem_depth]-d_a[prem_depth-1]))*((-height)-d_a[prem_depth-1])+p_a[prem_depth-1]
break
return Pressure
labels=[]
index=1
samps=pet_read_in_with_labels.pet_read_in("pt_files/%s_calculate_pt.csv"%(sub_zone))
#samps=pet_read_in_with_labels.pet_read_in("testing/test1.csv")
mineral_props={"Fo=92":{"ol":0.65,"opx":0.3,"cpx":0.05},"Fo=91":{"ol":0.65,"opx":0.25,"cpx":0.1},"Fo=90":{"ol":0.65,"opx":0.2,"cpx":0.15}} #mineral proportions for fo90,fo91,fo92 mantle
mant_vals={"Fo=92":{"Y":3.129,"La":0.134,"Ti":650.0},"Fo=91":{"Y":3.328,"La":0.192,"Ti":716.3},"Fo=90":{"Y":3.548,"La":0.253,"Ti":792.0}}#mantle concentrations for DDMM, Ave DMM and enriched DMM from workman and Hart 2005
for comp in mant_vals.keys():
for key,value in samps.iteritems():
ti_est=[]# list to store ti melt fraction estimates at different h2os
Y_est=[]# list to store Y melt fraction estimates at different h2os
katz_est=[]# list to store major element melt fraction estimates at different h2os
pt_sample={}
#Plotting F estimates from element concs