datablock = Data[s]['datablock']

pars=copy.deepcopy(Data[s]['pars']) # assignments to pars are assiging to Data[s]['pars']

# get MagIC mothod codes:

#pars['magic_method_codes']="LP-PI-TRM" # thellier Method

import SPD

import SPD.spd as spd

Pint_pars = spd.PintPars(Data, str(s), tmin, tmax, 'magic', preferences['show_statistics_on_gui'])

Pint_pars.reqd_stats() # calculate only statistics indicated in preferences

if not Pint_pars.pars:

print "Could not get any parameters for {}".format(Pint_pars)

return 0

#Pint_pars.calculate_all_statistics() # calculate every statistic available

#print "-D- Debag"

#print Pint_pars.keys()

pars.update(Pint_pars.pars) #

t_Arai=Data[s]['t_Arai']

x_Arai=Data[s]['x_Arai']

y_Arai=Data[s]['y_Arai']

x_tail_check=Data[s]['x_tail_check']

y_tail_check=Data[s]['y_tail_check']

zijdblock=Data[s]['zijdblock']

z_temperatures=Data[s]['z_temp']

#print tmin,tmax,z_temperatures

# check tmin

if tmin not in t_Arai or tmin not in z_temperatures:

return(pars)

# check tmax

if tmax not in t_Arai or tmin not in z_temperatures:

return(pars)

start=t_Arai.index(tmin)

end=t_Arai.index(tmax)

zstart=z_temperatures.index(tmin)

zend=z_temperatures.index(tmax)

zdata_segment=Data[s]['zdata'][zstart:zend+1]

# replacing PCA for zdata and for ptrms here

## removed a bunch of Ron's commented out old code

#lj

#-------------------------------------------------

# York regresssion (York, 1967) following Coe (1978)

# calculate f,fvds,

# modified from pmag.py

#-------------------------------------------------

x_Arai_segment= x_Arai[start:end+1]

y_Arai_segment= y_Arai[start:end+1]

# replace thellier_gui code for york regression here

pars["specimen_int"]=-1*pars['lab_dc_field']*pars["specimen_b"]

# replace thellier_gui code for ptrm checks, DRAT etc. here

# also tail checks and SCAT

#-------------------------------------------------

# Add missing parts of code from old get_PI

#-------------------------------------------------

if MICROWAVE==True:

LP_code="LP-PI-M"

else:

LP_code="LP-PI-TRM"

count_IZ= Data[s]['steps_Arai'].count('IZ')

count_ZI= Data[s]['steps_Arai'].count('ZI')

if count_IZ >1 and count_ZI >1:

pars['magic_method_codes']=LP_code+":"+"LP-PI-BT-IZZI"

elif count_IZ <1 and count_ZI >1:

pars['magic_method_codes']=LP_code+":"+"LP-PI-ZI"

elif count_IZ >1 and count_ZI <1:

pars['magic_method_codes']=LP_code+":"+"LP-PI-IZ"

else:

pars['magic_method_codes']=LP_code

if 'ptrm_checks_temperatures' in Data[s].keys() and len(Data[s]['ptrm_checks_temperatures'])>0:

if MICROWAVE==True:

pars['magic_method_codes']+=":LP-PI-ALT-PMRM"

else:

pars['magic_method_codes']+=":LP-PI-ALT-PTRM"

if 'tail_check_temperatures' in Data[s].keys() and len(Data[s]['tail_check_temperatures'])>0:

pars['magic_method_codes']+=":LP-PI-BT-MD"

if 'additivity_check_temperatures' in Data[s].keys() and len(Data[s]['additivity_check_temperatures'])>0:

pars['magic_method_codes']+=":LP-PI-BT"

#-------------------------------------------------

# Calculate anistropy correction factor

#-------------------------------------------------

if "AniSpec" in Data[s].keys():

pars["AC_WARNING"]=""

# if both aarm and atrm tensor axist, try first the aarm. if it fails use the atrm.

if 'AARM' in Data[s]["AniSpec"].keys() and 'ATRM' in Data[s]["AniSpec"].keys():

TYPES=['AARM','ATRM']

else:

TYPES=Data[s]["AniSpec"].keys()

for TYPE in TYPES:

red_flag=False

S_matrix=zeros((3,3),'f')

S_matrix[0,0]=Data[s]['AniSpec'][TYPE]['anisotropy_s1']

S_matrix[1,1]=Data[s]['AniSpec'][TYPE]['anisotropy_s2']

S_matrix[2,2]=Data[s]['AniSpec'][TYPE]['anisotropy_s3']

S_matrix[0,1]=Data[s]['AniSpec'][TYPE]['anisotropy_s4']

S_matrix[1,0]=Data[s]['AniSpec'][TYPE]['anisotropy_s4']

S_matrix[1,2]=Data[s]['AniSpec'][TYPE]['anisotropy_s5']

S_matrix[2,1]=Data[s]['AniSpec'][TYPE]['anisotropy_s5']

S_matrix[0,2]=Data[s]['AniSpec'][TYPE]['anisotropy_s6']

S_matrix[2,0]=Data[s]['AniSpec'][TYPE]['anisotropy_s6']

#Data[s]['AniSpec']['anisotropy_type']=Data[s]['AniSpec']['anisotropy_type']

Data[s]['AniSpec'][TYPE]['anisotropy_n']=int(float(Data[s]['AniSpec'][TYPE]['anisotropy_n']))

this_specimen_f_type=Data[s]['AniSpec'][TYPE]['anisotropy_type']+"_"+"%i"%(int(Data[s]['AniSpec'][TYPE]['anisotropy_n']))

Ftest_crit={}

Ftest_crit['ATRM_6']= 3.1059

Ftest_crit['AARM_6']= 3.1059

Ftest_crit['AARM_9']= 2.6848

Ftest_crit['AARM_15']= 2.4558

# threshold value for Ftest:

if 'AniSpec' in Data[s].keys() and TYPE in Data[s]['AniSpec'].keys()\

and 'anisotropy_sigma' in Data[s]['AniSpec'][TYPE].keys() \

and Data[s]['AniSpec'][TYPE]['anisotropy_sigma']!="":

# Calculate Ftest. If Ftest exceeds threshold value: set anistropy tensor to identity matrix

sigma=float(Data[s]['AniSpec'][TYPE]['anisotropy_sigma'])

nf = 3*int(Data[s]['AniSpec'][TYPE]['anisotropy_n'])-6

F=calculate_ftest(S_matrix,sigma,nf)

#print s,"F",F

Data[s]['AniSpec'][TYPE]['ftest']=F

#print "s,sigma,nf,F,Ftest_crit[this_specimen_f_type]"

#print s,sigma,nf,F,Ftest_crit[this_specimen_f_type]

if acceptance_criteria['anisotropy_ftest_flag']['value'] in ['g','1',1,True,'TRUE','True'] :

Ftest_threshold=Ftest_crit[this_specimen_f_type]

if Data[s]['AniSpec'][TYPE]['ftest'] < Ftest_crit[this_specimen_f_type]:

S_matrix=identity(3,'f')

pars["AC_WARNING"]=pars["AC_WARNING"]+"%s tensor fails F-test; "%(TYPE)

red_flag=True

else:

Data[s]['AniSpec'][TYPE]['anisotropy_sigma']=""

Data[s]['AniSpec'][TYPE]['ftest']=99999

if 'anisotropy_alt' in Data[s]['AniSpec'][TYPE].keys() and Data[s]['AniSpec'][TYPE]['anisotropy_alt']!="":

if acceptance_criteria['anisotropy_alt']['value'] != -999 and \

(float(Data[s]['AniSpec'][TYPE]['anisotropy_alt']) > float(acceptance_criteria['anisotropy_alt']['value'])):

S_matrix=identity(3,'f')

pars["AC_WARNING"]=pars["AC_WARNING"]+"%s tensor fails alteration check: %.1f > %.1f; "%(TYPE,float(Data[s]['AniSpec'][TYPE]['anisotropy_alt']),float(acceptance_criteria['anisotropy_alt']['value']))

red_flag=True

else:

Data[s]['AniSpec'][TYPE]['anisotropy_alt']=""

Data[s]['AniSpec'][TYPE]['S_matrix']=S_matrix

#--------------------------

# if AARM passes all, use the AARM.

# if ATRM fail alteration use the AARM

# if both fail F-test: use AARM

if len(TYPES)>1:

if "ATRM tensor fails alteration check" in pars["AC_WARNING"]:

TYPE='AARM'

elif "ATRM tensor fails F-test" in pars["AC_WARNING"]:

TYPE='AARM'

else:

TYPE=='AARM'

S_matrix= Data[s]['AniSpec'][TYPE]['S_matrix']

#---------------------------

TRM_anc_unit=array(pars['specimen_PCA_v1'])/sqrt(pars['specimen_PCA_v1'][0]**2+pars['specimen_PCA_v1'][1]**2+pars['specimen_PCA_v1'][2]**2)

B_lab_unit=pmag.dir2cart([ Data[s]['Thellier_dc_field_phi'], Data[s]['Thellier_dc_field_theta'],1])

#B_lab_unit=array([0,0,-1])

Anisotropy_correction_factor=linalg.norm(dot(inv(S_matrix),TRM_anc_unit.transpose()))*norm(dot(S_matrix,B_lab_unit))

pars["Anisotropy_correction_factor"]=Anisotropy_correction_factor

pars["AC_specimen_int"]= pars["Anisotropy_correction_factor"] * float(pars["specimen_int"])

pars["AC_anisotropy_type"]=Data[s]['AniSpec'][TYPE]["anisotropy_type"]

pars["specimen_int_uT"]=float(pars["AC_specimen_int"])*1e6

if TYPE=='AARM':

if ":LP-AN-ARM" not in pars['magic_method_codes']:

pars['magic_method_codes']+=":LP-AN-ARM:AE-H:DA-AC-AARM"

pars['specimen_correction']='c'

pars['specimen_int_corr_anisotropy']=Anisotropy_correction_factor

if TYPE=='ATRM':

if ":LP-AN-TRM" not in pars['magic_method_codes']:

pars['magic_method_codes']+=":LP-AN-TRM:AE-H:DA-AC-ATRM"

pars['specimen_correction']='c'

pars['specimen_int_corr_anisotropy']=Anisotropy_correction_factor

else:

pars["Anisotropy_correction_factor"]=1.0

pars["specimen_int_uT"]=float(pars["specimen_int"])*1e6

pars["AC_WARNING"]="No anistropy correction"

pars['specimen_correction']='u'

pars["specimen_int_corr_anisotropy"]=pars["Anisotropy_correction_factor"]

#-------------------------------------------------

# NLT and anisotropy correction together in one equation

# See Shaar et al (2010), Equation (3)

#-------------------------------------------------

if 'NLT_parameters' in Data[s].keys():

alpha=Data[s]['NLT_parameters']['tanh_parameters'][0][0]

beta=Data[s]['NLT_parameters']['tanh_parameters'][0][1]

b=float(pars["specimen_b"])

Fa=pars["Anisotropy_correction_factor"]

if ((abs(b)*Fa)/alpha) <1.0:

Banc_NLT=math.atanh( ((abs(b)*Fa)/alpha) ) / beta

pars["NLTC_specimen_int"]=Banc_NLT

pars["specimen_int_uT"]=Banc_NLT*1e6

if "AC_specimen_int" in pars.keys():

pars["NLT_specimen_correction_factor"]=Banc_NLT/float(pars["AC_specimen_int"])

else:

pars["NLT_specimen_correction_factor"]=Banc_NLT/float(pars["specimen_int"])

if ":LP-TRM" not in pars['magic_method_codes']:

pars['magic_method_codes']+=":LP-TRM:DA-NL"

pars['specimen_correction']='c'

else:

GUI_log.write ("-W- WARNING: problematic NLT mesurements for specimens %s. Cant do NLT calculation. check data\n"%s)

pars["NLT_specimen_correction_factor"]=-1

else:

pars["NLT_specimen_correction_factor"]=-1

#-------------------------------------------------

# Calculate the final result with cooling rate correction

#-------------------------------------------------

pars["specimen_int_corr_cooling_rate"]=-999

if 'cooling_rate_data' in Data[s].keys():

if 'CR_correction_factor' in Data[s]['cooling_rate_data'].keys():

if Data[s]['cooling_rate_data']['CR_correction_factor'] != -1 and Data[s]['cooling_rate_data']['CR_correction_factor'] !=-999:

pars["specimen_int_corr_cooling_rate"]=Data[s]['cooling_rate_data']['CR_correction_factor']

pars['specimen_correction']='c'

pars["specimen_int_uT"]=pars["specimen_int_uT"]*pars["specimen_int_corr_cooling_rate"]

if ":DA-CR" not in pars['magic_method_codes']:

pars['magic_method_codes']+=":DA-CR"

if 'CR_correction_factor_flag' in Data[s]['cooling_rate_data'].keys():

if Data[s]['cooling_rate_data']['CR_correction_factor_flag']=="calculated":

pars['CR_flag']="calculated"

else:

pars['CR_flag']=""

if 'CR_correction_factor_flag' in Data[s]['cooling_rate_data'].keys() \

and Data[s]['cooling_rate_data']['CR_correction_factor_flag']!="calculated":

pars["CR_WARNING"]="inferred cooling rate correction"

else:

pars["CR_WARNING"]="no cooling rate correction"

def combine_dictionaries(d1, d2):

"""

combines dict1 and dict2 into a new dict.

if dict1 and dict2 share a key, the value from dict1 is used

"""

for key, value in d2.iteritems():

if key not in d1.keys():

d1[key] = value

return d1

Data[s]['pars'] = pars

#print pars.keys()

chibar=(s[0][0]+s[1][1]+s[2][2])/3.

t=array(linalg.eigvals(s))

F=0.4*(t[0]**2+t[1]**2+t[2]**2 - 3*chibar**2)/(float(sigma)**2)

'''

# Check if specimen pass Acceptance criteria

'''

#if 'pars' not in self.Data[specimen].kes():

# return

pars['specimen_fail_criteria']=[]

for crit in acceptance_criteria.keys():

if crit not in pars.keys():

continue

if acceptance_criteria[crit]['value']==-999:

continue

if acceptance_criteria[crit]['category']!='IE-SPEC':

continue

cutoff_value=acceptance_criteria[crit]['value']

if crit=='specimen_scat':

if pars["specimen_scat"] in ["Fail",'b',0,'0','FALSE',"False",False]:

pars['specimen_fail_criteria'].append('specimen_scat')

elif crit=='specimen_k' or crit=='specimen_k_prime':

if abs(pars[crit])>cutoff_value:

pars['specimen_fail_criteria'].append(crit)

# high threshold value:

elif acceptance_criteria[crit]['threshold_type']=="high":

if pars[crit]>cutoff_value:

pars['specimen_fail_criteria'].append(crit)

elif acceptance_criteria[crit]['threshold_type']=="low":

if pars[crit]<cutoff_value:

pars['specimen_fail_criteria'].append(crit)

site=""

sites=Data_hierarchy['sites'].keys()

for S in sites:

if sample in Data_hierarchy['sites'][S]:

site=S

break