import sys

import test_plot

reload(test_plot)

import numpy as np

import os

import time

from matplotlib import pyplot as plt

sys.path.insert(0,"/home/kasey/PyVC/")

from pyvc import vcplots

from pyvc import vcanalysis

from pyvc import vcutils

from pyvc import *

# Need to remove a cached file for Arial fonts to be used

if os.path.isfile('/home/kasey/.matplotlib/fontList.cache'):

os.remove('/home/kasey/.matplotlib/fontList.cache')

#=============================================================================

#M0 = 4.0*pow(10,20)

#print test_plot.mag_from_moment(M0)

# DATA

DTTF = [1000.0,1000.0,1000.0] # depth to top of fault (Okubo's style)

_DIP = [np.pi/2.0,np.pi/3.0,np.pi/6.0]

#_DIP = [np.pi/5.0,np.pi/10.0,np.pi/4.0]

_L = [10000.0,10000.0,10000.0]

_W = [10000.0,10000.0,10000.0]

_C = [DTTF[i] + _W[i]*np.sin(_DIP[i]) for i in range(len(_L))] #meters

_US = [5.0,0.0,0.0]

_UD = [0.0,-5.0,5.0]

_UT = [0.0,0.0,0.0]

_LAMBDA = 3.2e10

_MU = 3.0e10

_Xmin,_Xmax = [-20000.0,-20000.0,-20000.0],[30000.0,30000.0,30000.0]

_Nx = 300.0

_Ymin,_Ymax = [-35000.0,-35000.0,-35000.0],[35000.0,35000.0,35000.0]

_Ny = 300.0

## SWITCHES

#*******************

SAVE = True

_CLIMITS = True

_suffix = 'compare'

field_type = 'gravity'

_HIST = False

_SHOW = False

_NO_LABELS = True

NUM_TICKS = 7

FRAME_FONT = 14

TICK_FONT = 14

FREE_AIR = False

XTICKS = False

#*******************

#for k in range(3):

#test_plot.cbar_plot(_Xmin[k],_Xmax[k],_Nx,_Ymin[k],_Ymax[k],_Ny,_C[k],_DIP[k],_L[k],_W[k],_US[k],_UD[k],_UT[k],_LAMBDA,_MU,save=SAVE,CLIMITS=_CLIMITS,SUFFIX=_suffix,HIST=_HIST,SHOW=_SHOW,NOLABELS=_NO_LABELS,frame_font=FRAME_FONT,tick_font=TICK_FONT,num_ticks=NUM_TICKS,field_type=field_type,x_ticks=XTICKS,CBAR='bottom')

"""

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

## ANIMATION SWITCHES & KNOBS

#******************************

#center_evnum = 91382 #pick a large event to be the centerpiece

#sim_file = '../VCModels/ALLCAL2_1-7-11_no-creep/ALLCAL2_1-7-11_no-creep_dyn-0-5_st-5.h5'

#sim_file = 'ALLCAL2_3_4_14_southern_SAF_no-creep_dyn-0-5_st-20_10000yr.h5'

#FIELD = 'displacement'

#LENGTH = 10.0

#FPS = 5.0

#FADE = 1.0

#PLOT = True

#MIN_MAG_MARK = 6.5

duration = 100.0 # in years, make it small for test

start_year = 50.0

DT = 0.1

sim_time_range = {'type':'year', 'filter':(start_year,start_year+duration)}

SOUTH_SAF_SECS = {'filter':(13,14,15,16,17,18)}

sim_file = 'ALLCAL2_1-7-11_no-creep_dyn-05_st-20.h5'

#sim_file = 'ALLCAL2_3_4_14_southern_SAF_no-creep_dyn-0-5_st-20_10000yr.h5'

#output_directory = 'time_series_test/'

#evnum = 55

#out_file = output_directory+'dg_field_'+str(evnum)+'.png'

FIELD = 'gravity'

#CUTOFF = 1000.0

CUTOFF = None

LENGTH = 50.0

FPS = 20.0

MAG_FILTER = "<= 7.0"

TEJON = {'filter':(14,15)}

SF = {'filter':(1,2,3,4,5,6,7,8,9,10)}

LP = {'filter':(7,8,9,10)}

NR = {'filter':[52]}

LA = {'filter':(148,30)}

SECS = [TEJON,SF,NR,LA]

SHOW = 20

"""

#el_mc_evnum = 96646

# -------------------- Forecasting ---------------

sim_file = 'ALLCAL2_1-7-11_no-creep_dyn-05_st-20.h5'

#sim_file = 'events_two_fault_100k.h5'

BAJA = {'filter':(16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 56, 57, 69, 70, 73, 83, 84, 92, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 123, 124, 125, 126, 149)}

NORCAL = {'filter':(1,2,3,4,5,6,7,8,9,10,36,38,39,40,41,42,43,44,45,46,47,48,49,50,51,138,139,140,141,142,167,168,169,170,171,172,181)}

sec_list = list(np.arange(181)+1)

sec_list.remove(11)

sec_list.remove(12)

sec_list.remove(19)

ALLCAL = {'filter':sec_list}

NAPA = {'filter':(50,44,45,169,170,171)}

###############

baja = True

norcal = False

napa = False

loma_napa = False

allcal = False

WEIBULL = True

MAGS = ">= 7.0"

name = "AGU"

duration= 50000.0

#t_and_t0 = (3.0,4.0)

t_and_t0 = None

###############

if baja:

start_year = 17831.6 #Baja

TAG = 'baja_'+str(int(duration/1000.0))+'k_'+name+'_M'+MAGS.split()[-1]

SECS = BAJA

DT = 30.0

#years_since = 3.0

years_since = 4.0

if MAGS.split()[-1] == "7.5":

BETA = 1.2544

TAU = 129.5127

elif MAGS.split()[-1] == "7.0":

BETA = 1.1018

TAU = 23.3027

elif allcal:

start_year = 10000.0

TAG = 'allcal_'+str(int(duration/1000.0))+'k_'+name+'_M'+MAGS.split()[-1]

SECS = ALLCAL

DT = 30.0

years_since = 62.0

if MAGS.split()[-1] == "7.5":

BETA = 1.2004

TAU = 62.2997

elif MAGS.split()[-1] == "7.0":

BETA = 1.0331

TAU = 10.3647

elif norcal:

TAG = 'norcal_'+str(int(duration/1000.0))+'k_'+name+'_M'+MAGS.split()[-1]

SECS = NORCAL

DT = 30.0

#years_since = 0.0

if MAGS.split()[-1] == "7.5":

BETA = 1.2966

TAU = 110.3578

start_year = 2380.8

years_since = 108.0

elif MAGS.split()[-1] == "7.0":

BETA = 1.0973

TAU = 20.5765

start_year = 3115.5

years_since = 25.0

elif MAGS.split()[-1] == "7.3":

BETA = 1.1007

TAU = 41.3967

start_year = 2380.8

years_since = 108.0

elif MAGS.split()[-1] == "7.4":

BETA = 1.1299

TAU = 63.8740

start_year = 2380.8

years_since = 108.0

elif napa:

start_year = 7716.0

TAG = 'napa_'+str(int(duration/1000.0))+'k_'+name+'_M'+MAGS.split()[-1]

SECS = NAPA

DT = 30.0

years_since = 0.0

if MAGS.split()[-1] == "6.0":

BETA = 0.9688

TAU = 33.3166

elif MAGS.split()[-1] == "6.5":

BETA = 1.0755

TAU = 46.0693

elif loma_napa:

start_year = 10138.8

TAG = 'loma_napa_'+str(int(duration/1000.0))+'k_'+name+'_M'+MAGS.split()[-1]

SECS = NAPA

DT = 30.0

years_since = 0.0

if MAGS.split()[-1] == "6.0":

BETA = 0.9784

TAU = 33.2155

"""

Napa

=======================================

899 earthquakes

---------------------------------------

avg interval max interval

33.28 216.20

---------------------------------------

For t0 = 0.00 years

25% waiting time: 10.00 years

50% waiting time: 22.00 years

75% waiting time: 49.00 years

=======================================

"""

"""

Loma -> Napa

908 earthquakes

---------------------------------------

avg interval max interval

32.97 216.20

---------------------------------------

For t0 = 0.00 years

25% waiting time: 10.00 years

50% waiting time: 22.00 years

75% waiting time: 49.00 years

"""

"""

NorCal M>7.0

1520 earthquakes

---------------------------------------

avg interval max interval

19.71 190.42

---------------------------------------

For t0 = 11.00 years

25% waiting time: 6.00 years

50% waiting time: 13.00 years

75% waiting time: 24.00 years

NorCal M>7.5, 50000 years

482 earthquakes

---------------------------------------

avg interval max interval

98.36 507.60

---------------------------------------

For t0 = 108.00 years

25% waiting time: 27.00 years

50% waiting time: 55.00 years

75% waiting time: 94.00 years

Baja M>7.0, 50000 years

1454 earthquakes

---------------------------------------

avg interval max interval

22.11 164.61

---------------------------------------

For t0 = 4.00 years

25% waiting time: 7.00 years

50% waiting time: 16.00 years

75% waiting time: 29.00 years

"""

EV_RANGE = {'type':'year', 'filter':(start_year,start_year+duration)}

vcplots.forecast_plots(sim_file, event_graph_file=None, event_sequence_graph_file=None, event_range=EV_RANGE, section_filter=SECS, magnitude_filter=MAGS, padding=0.08, fixed_dt=DT,weibull=WEIBULL,fname_tag=TAG,beta=BETA,tau=TAU,year_eval=years_since,P_t_t0_eval=t_and_t0)

#test_plot.fit_to_weibull(sim_file,BETA,TAU,event_range=EV_RANGE,magnitude_filter=MAGS,section_filter=SECS)

# ----------------- End Forecasting ---------------

# ----------------- Plotting event fields ---------------

"""

#plot_ids = [193054,9940,96646]

#plot_ids = [60019,231185,193054]

#tags = ["Tejon_paper_20mugal","SF_paper_20mugal","Northridge_paper_20mugal"]

#plot_ids = [53043,53344]

#tags = ["Loma_Prieta_candidate","Napa_candidate"]

plot_ids = [231185,96646]

tags = ["SF_AGU","ElMC_AGU"]

CUTOFF = None

FIELD = 'gravity'

LAT_LON = False

FRINGES = True

HIRES = True

for k in range(len(plot_ids)):

TAG = tags[k]

evid= plot_ids[k]

vcplots.plot_event_field(sim_file, evid, 'local/', field_type=FIELD, padding=0.08, cutoff=CUTOFF,tag=TAG,lat_lon=LAT_LON,fringes=FRINGES,hi_res=HIRES)

#vcanalysis.eq_info(sim_file,plot_ids)

# ----------------- END Plotting event fields -----------

"""

# num year magnitude slip [m] rupt.len [km]

# 96646 17831.57 7.28 0.82 138.43

# 231185 41752.61 7.88 2.20 711.67

"""

for k in range(len(TAGS)):

TAG = TAGS[k]

EV_RANGE = {'type':'year', 'filter':(start_year,start_year+(k+1)*10000)}

vcplots.forecast_plots(sim_file, event_graph_file=None, event_sequence_graph_file=None, event_range=EV_RANGE, section_filter=SECS, magnitude_filter=MAGS, padding=0.08, fixed_dt=30.0,fname_tag=TAG)

#vcplots.plot_forecast(sim_file, event_graph_file=None, event_sequence_graph_file=None, event_range=EV_RANGE, section_filter=NORCAL, magnitude_filter=MAGS, padding=0.08, fixed_dt=30.0,output_file="local/"+TAG+"_forecast.png")

"""

#SF_ids = [9940,71369,53163,69060,82904]

#for evid in SF_ids:

# TAG = 'sf_search'

# vcplots.plot_event_field(sim_file, evid, 'local', field_type='gravity', padding=0.08, cutoff=CUTOFF,tag=TAG)

#vcanalysis.event_sections(sim_file,el_mc_evnum)

#vcanalysis.cum_prob(sim_file, output_file, event_range=EV_RANGE, section_filter=BAJA, magnitude_filter=MAG_FILTER,plot_type=PLOT_TYPE)

# num year mag slip [m] rupt.len [km]

# 96646 17831.57 7.28 0.82 138.43

#vcplots.plot_event_field(sim_file, evnum, 'local', field_type='displacement', padding=0.08, cutoff=CUTOFF,tag=TAG)

#----------------- EQ hunting --------------------

"""

Napa_only_events = [168097, 6478, 197127, 123267, 267261, 186946, 39569, 251833, 247140, 8148, 176612, 103919, 171869, 18164, 146039, 50036, 107992, 59996]

Napa_only_years = [30491.240338917687, 1747.5899074185204, 35693.370627909986, 22529.086708134993, 48139.093874213759, 33883.849702347077, 7715.4004976180586, 45410.922680991636, 44567.554570983062, 2060.4421738168362, 32027.627820965194, 19116.453701417951, 31190.642924825341, 3882.1632034263889, 26577.658460504372, 9565.6155153407308, 19849.030083585647, 11339.761605784435]

Loma_events = [52095,194106,227982,233975,240651,138797,215928,10894,251303,53043,125265]

Loma_years = [9901.2323535322394, 35168.138523346599, 41203.639369679993, 42270.534455852081, 43429.681499054794, 25254.743279807106, 39080.608740562078, 2566.2828671653174, 45326.887758374956, 10084.091735158539, 22872.440635772586]

Loma = {'filter':(9,181)}

Napa = {'filter':[50]}

mendo = {'filter':[1,159,160,161,162,163,164,165]}

sf_bay = {'filter':[5,6,7,42,43,36,140]}

MAGS = "<= 7.1"

index = 8

#start_year = Loma_years[index]

#end_year = start_year + 75.0

start_year = 50.0

end_year = 19900.0

MIN_MAG = 6.8

#with VCSimData() as sim_data:

# sim_data.open_file(sim_file)

# events = VCEvents(sim_data)

# for evid in Loma_events:

# year = events.get_event_year(evid)

# Loma_years.append(year)

#print "\nLoma eivd-year = {}-{}\n".format(Loma_events[index],start_year)

these_ev = vcanalysis.detailed_sim_info(sim_file,show=20,section_filter=Loma,magnitude_filter=MAGS,event_range={'type':'year', 'filter':(start_year,end_year)},return_evnums=True,min_mag=MIN_MAG)

#print "\n",these_ev

for evnum in these_ev:

vcanalysis.event_sections(sim_file,evnum)

print "\n"

#vcanalysis.event_sections(sim_file,53043)

#vcanalysis.event_sections(sim_file,53344)

# Forecast after Napa only (not after Napa and Loma)

#num year magnitude slip [m] rupt.len [km]

#39569 7715.40 6.06 0.23 24.04

# Forecast after Loma Prieta then Napa

# num year magnitude slip [m] rupt.len [km]

# 53043 10084.09 6.83 0.44 57.34 (Loma Prieta like)

# 53344 10138.01 5.08 0.05 3.00 (Napa like)

"""

#------------------- EQ hunting --------------------

"""

bad_evid = 191433

### Event numbers selected as 10 events with largest magnitude <= 7.5 on southern SAF

EVIDS_30 = [107556,183553,76977,33928,37557,252358,233381,225632,274396,77121,76324,138170,83894,171713,253346,4699,170663,35068,51659,67814,187742,143456,8877,9081,6348,269889,276933,150988,27209,197279]

EVIDS_10 = [107556,183553,76977,33928,37557,252358,233381,225632,274396,77121]

early = [76977,33928,37557,77121,107556]

late = [183553,252358,233381,225632,274396]

#extra = [188313,184777,13688,207677]

"""

#event_range={'type':'year','filter':(2555,2655)}

#output_directory = 'animation_test_v/'

#vcplots.event_field_animation(sim_file, output_directory, event_range,

# field_type='gravity', fringes=True, padding=0.08, cutoff=None,

# animation_target_length=LENGTH/12.0, animation_fps = FPS, fade_seconds = 1.0,

# min_mag_marker = 6.5, force_plot=True)

#vcplots.event_field_evolution(sim_file, output_directory, sim_time_range,

# field_type='gravity', fringes=True, padding=0.08, cutoff=CUTOFF,

# animation_target_length=LENGTH, animation_fps = FPS,

# min_mag_marker = 5.5, start_year = start_year, duration=duration,section_filter=SOUTH_SAF_SECS,

# force_plot=True)

"""

output_directory = 'post_5_eq_fields/'

BUFFER = 5.0

CUTOFF = 1000.0

PRE1 = False

PRE2 = True

BACK1 = False

BACK2 = False

EQ1 = False

EQ2 = False

SECS = (SOUTH_SAF_SECS,SOUTH_SAF_SECS)

field1dir = 'post_5_fields/'

field2dir = 'pre_5_fields/'

out_dir = 'local/'

TAGS = ['tejon_100mgal','northridge_100mgal','sf_100mgal']

PLOT_EVIDS = [60019,193054,231185]

"""

"""

center_evnum = 109382

with VCSimData() as sim_data:

sim_data.open_file(sim_file)

events = VCEvents(sim_data)

center_evyear = events.get_event_year(center_evnum)

start_year = round(center_evyear)-duration/2.0

end_year = round(center_evyear)+duration/2.0

event_range={'type':'year','filter':(start_year,end_year)}

event_range=None

pre = 'local/web_sim_full_'

output_files = [pre+'magn_rup_area.png',pre+'avg_slip_rup_len.png',pre+'freq_mag.png',pre+'recurr_intervals.png']

MAG_FILTER = None

vcplots.magnitude_rupture_area(sim_file,output_files[0],event_range=event_range,magnitude_filter=MAG_FILTER)

vcplots.average_slip_surface_rupture_length(sim_file,output_files[1],event_range=event_range,magnitude_filter=MAG_FILTER)

vcplots.frequency_magnitude(sim_file,output_files[2],event_range=event_range,magnitude_filter=MAG_FILTER)

#vcplots.plot_recurrence_intervals(sim_file,output_file=output_files[3],event_range=event_range,magnitude_filter=MAG_FILTER)

"""

"""

for k in range(len(PLOT_EVIDS)):

evnum = PLOT_EVIDS[k]

TAG = TAGS[k]

vcplots.plot_event_field(sim_file, evnum, out_dir, field_type='gravity', padding=0.08, cutoff=CUTOFF,tag=TAG)

#print '\n'+TAG

#vcanalysis.event_sections(sim_file,evnum)

"""

"""

search_evnums = vcanalysis.detailed_sim_info(sim_file,show=SHOW,section_filter=LA,magnitude_filter=MAGS[-1],return_evnums=True)

for evnum in search_evnums:

vcanalysis.event_sections(sim_file,evnum)

"""

#BUFFER = 3.0

#output_directory = 'pre_3_fields/'

#vcplots.compute_composite_fields(sim_file,output_directory,EVIDS_10,field_type='gravity',pre=True,buffer=BUFFER,section_filter=SOUTH_SAF_SECS,cutoff=CUTOFF)

#vcplots.plot_backslip(sim_file, 1.0, section_filter=SOUTH_SAF_SECS)

#vcplots.diff_composite_fields(sim_file, (EVIDS_10,EVIDS_10),field1dir,field2dir,

# field_type='gravity', fringes=True, padding=0.08, cutoff=None,

# pre=(PRE1,PRE2),buffer=(BUFFER,BUFFER),section_filter=SOUTH_SAF_SECS,

# backslip_only=(BACK1,BACK2),eq_slip_only=(EQ1,EQ2),tag=TAG)

#evnums = [193054,231185,60019]

#lat_lon = {'Los Angeles':(34.045536,-118.259297),'San Francisco':(37.773984,-122.418202),'Sacramento':(38.577646,-121.489948),'San Luis Obispo':(35.286790,-120.660601),'Santa Clarita':(34.388459,-118.539607),'Simi Valley':(34.266523,-118.780306),'Fort Bragg':(39.442104,-123.793432),'San Jose':(37.339686,-121.895108),'Bakersfield':(35.373937,-119.018800),'Fresno':(36.750055,-119.767782)}

#tags = ['Northridge','San Francisco','Fort Tejon']

#sites = [['Santa Clarita','Simi Valley','Los Angeles'],['Fort Bragg','San Francisco','San Jose'],['Los Angeles','Bakersfield','San Luis Obispo']]

"""

for k in range(len(evnums)):

evnum = evnums[k]

tag = tags[k]

cities= sites[k]

with VCSimData() as sim_data:

sim_data.open_file(sim_file)

geometry = VCGeometry(sim_data)

#events = VCEvents(sim_data)

min_lat = geometry.min_lat

max_lat = geometry.max_lat

min_lon = geometry.min_lon

max_lon = geometry.max_lon

base_lat = geometry.base_lat

base_lon = geometry.base_lon

#slip_rates = geometry.get_slip_rates()

#event_element_slips = {evid:events.get_event_element_slips(evid) for evid in event_data['event_number']}

EF = vcutils.VCGravityField(min_lat, max_lat, min_lon, max_lon, base_lat, base_lon, padding=0.08)

output_directory = 'local/'

field_values_directory = '{}field_values/'.format(output_directory)

PRE = '{}{}_'.format(field_values_directory, evnum)

field_values_loaded = EF.load_field_values(PRE)

print '\n{} - {}:'.format(tag,evnum)

for city in cities:

lat,lon = lat_lon[city]

print city+'\t{:>6.3f}'.format(EF.get_field_value(lat,lon))

#print lat,lon

"""

"""

# event_element_slips = dictionary indexed by event_id with entries being dictionaries of slips indexed by block_id

# slip_time_series = dictionary indexed by block_id with entries being arrays of absolute slip at each time step

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

def get_slip_time_series(events,slip_rates,event_range,section_filter=None):

import pickle

# Convert slip rates from meters/second to meters/(decimal year)

CONVERSION = 3.15576*pow(10,7)

DT = 0.1 # 0.1yr evaluates field every 36.5 days

with VCSimData() as sim_data:

sim_data.open_file(sim_file)

geometry = VCGeometry(sim_data)

events = VCEvents(sim_data)

slip_rates = geometry.get_slip_rates(section_filter=section_filter)

if section_filter is not None:

events_in_range = events.get_event_data(['event_number', 'event_year','event_magnitude'],

event_range = event_range,section_filter=section_filter)

else:

events_in_range = events.get_event_data(['event_number', 'event_year','event_magnitude'],

event_range = event_range)

event_element_slips = {evid:events.get_event_element_slips(evid) for evid in events_in_range['event_number']}

#Initialize blocks with 0.0 slip at time t=0.0

slip_time_series = {block_id:[0.0] for block_id in slip_rates.keys()}

#Initialize time steps to evaluate slip

time_values = np.arange(start_year+DT,start_year+duration+DT,DT)

for k in range(len(time_values)):

if k>0:

# current time in simulation

right_now = time_values[k]

# back slip all elements by subtracting the slip_rate*dt

for block_id in slip_time_series.keys():

last_slip = slip_time_series[block_id][k-1]

this_slip = slip_rates[block_id]*CONVERSION*DT

slip_time_series[block_id].append(last_slip-this_slip)

# check if any elements slip as part of simulated event in the window of simulation time

# between (current time - DT, current time), add event slips to the slip at current time

# for elements involved

for evid in events_in_range['event_number']:

if right_now-DT < events_in_range['event_year'][evid] <= right_now:

for block_id in event_element_slips[evid].keys():

slip_time_series[block_id][k] += event_element_slips[evid][block_id]

out = open(outfile,'wb')

pickle.dump(slip_time_series,out)

out.close()

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

"""

"""

for event in events_in_range['event_number']:

print "\nevent_id : %i\t year : %.5f\t magnitude : %.4f\t"%(event,events_in_range['event_year'][event],events_in_range['event_magnitude'][event])

print "involved elements: "+str(sorted(event_element_slips[event].keys()))

"""

"""

for block_id in slip_time_series.keys():

this_series = slip_time_series[block_id]

this_filename = output_directory+'element_'+str(block_id)+'_slips.png'

plt.clf()

plt.cla()

plt.plot(time_values,this_series,label="element "+str(block_id))

plt.axhline(y=0,xmin=0.0,xmax=time_values[-1],ls='--',color='k',lw=2)

plt.xlabel('simulated time [decimal years]')

plt.ylabel('accumulated slip on element [meters]')

plt.legend()

plt.savefig(this_filename,dpi=200)

print "written to: "+this_filename

"""

#events = VCEvents(sim_data)

#center_evyear = events.get_event_year(center_evnum)

#start_year = round(center_evyear)-duration/2.0

#end_year = round(center_evyear)+duration/2.0

#

#start_year = 0.0

#end_year = 100.0

#event_range={'type':'year','filter':(start_year,end_year)}

#vcplots.event_field_animation(sim_file, output_directory, event_range,

# field_type=FIELD, fringes=True, padding=0.08, cutoff=None,

# animation_target_length=LENGTH, animation_fps = FPS, fade_seconds = FADE,

# min_mag_marker = MIN_MAG_MARK, force_plot=PLOT)

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

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

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

"""

#sim_file = '../VCModels/ALLCAL2_1-7-11_no-creep/ALLCAL2_1-7-11_no-creep_dyn-0-5_st-5.h5'

sim_file = '/home/kasey/Okubo-test/ALLCAL2_1-7-11_no-creep_dyn-05_st-20.h5'

evnum = 109382 # Mag = 8.0

#evnum = 1455 # Mag = 7.83

#evnum = 48467 # Mag = 7.99

#evnum = 49261 # Mag = 7.63

#evnum = 54526 # Mag = 7.73

#evnum = 54358 # Mag = 7.79

out_file = '/home/kasey/Okubo-test/dg_plots/dg_'+str(evnum)+'_match.png'

FRINGES = True

FIELD = 'gravity'

LAT_RANGE = (30.65,42.85)

LON_RANGE = (-125.52,-113.24)

#if FIELD == 'gravity':

# FIELD_VALUE_DIR = 'animation_test_g'

#elif FIELD == 'displacement':

# FIELD_VALUE_DIR = 'animation_test_d'

#elif FIELD == 'potential':

# FIELD_VALUE_DIR = 'animation_test_v'

vcplots.plot_event_field(sim_file, evnum, output_file=out_file, field_type=FIELD,

fringes=FRINGES, padding=0.08, cutoff=None, save_file_prefix=None,

custom_lat_range=LAT_RANGE,custom_lon_range=LON_RANGE)

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

"""

"""

for k in range(len(_C)):

test_plot.plot_for_cutoff(_Xmin[k],_Xmax[k],_Nx,_Ymin[k],_Ymax[k],_Ny,_C[k],

_DIP[k],_L[k],_W[k],_US[k],_UD[k],_UT[k],

_LAMBDA,_MU,save=SAVE,SHOW=_SHOW,_CLIMS=_CLIMITS)

"""

"""

#=============================================================================

for k in range(3):

# Need to remove a cached file for Arial fonts to be used

if os.path.isfile('/home/kasey/.matplotlib/fontList.cache'):

os.remove('/home/kasey/.matplotlib/fontList.cache')

test_plot.cbar_plot(_Xmin[k],_Xmax[k],_Nx,_Ymin[k],_Ymax[k],_Ny,_C[k],

_DIP[k],_L[k],_W[k],_US[k],_UD[k],_UT[k],

_LAMBDA,_MU,save=SAVE,DG2=_DG2,DG=_DG,DZ=_DZ,

DH=_DH,DIFFZ=_DIFFZ,DIFFG=_DIFFG,CLIMITS=_CLIMITS,

SUFFIX=_suffix,HIST=_HIST,SHOW=_SHOW)

#111111111111111111111111111111111111111111111

center_evnum = 109382 #pick a large event to be the centerpiece

duration = 100 # in years, make it small for test

sim_file = 'ALLCAL2_1-7-11_no-creep_dyn-05_st-20.h5'

with VCSimData() as sim_data:

sim_data.open_file(sim_file)

events = VCEvents(sim_data)

center_evyear = events.get_event_year(center_evnum)

start_year = round(center_evyear)-duration/2.0

end_year = round(center_evyear)+duration/2.0

#print 'start year: {},end year: {},duration: {}'.format(start_year,end_year,end_year-start_year)

start_time = time.time()

output_dir = '../Dropbox/UCD/Stat_Mech_219B/'

tags = ['mag_area','slip_rupture_length','frequency_mag','recurrence']

output_files= []

for k in range(len(tags)):

fname = output_dir+tags[k]+'_'+str(duration)+'_ALLCAL2_nocreep.png'

output_files.append(fname)

event_range={'type':'year','filter':(start_year,end_year)}

vcplots.magnitude_rupture_area(sim_file,output_files[0],event_range=event_range)

vcplots.average_slip_surface_rupture_length(sim_file,output_files[1],event_range=event_range)

vcplots.frequency_magnitude(sim_file,output_files[2],event_range=event_range)

#vcplots.plot_recurrence_intervals(sim_file,output_file=output_files[3],event_range=event_range)

print "Total time - {} seconds".format(time.time()-start_time)

"""