def project_locations(data, zone, letter):
data.locations = data.get_locs(mode='latlong')
for ii in range(len(data.locations)):
easting, northing = utils.project((data.locations[ii, 1],
data.locations[ii, 0]),
zone=zone, letter=letter)[2:]
data.locations[ii, 1], data.locations[ii, 0] = easting, northing
return data
def transform_locations(dataset, UTM):
dataset.raw_data.locations = dataset.raw_data.get_locs(mode='latlong')
UTM_number = int(UTM[:2])
UTM_letter = UTM[-1]
for ii in range(len(dataset.raw_data.locations)):
easting, northing = project(
(dataset.raw_data.locations[ii, 1], dataset.raw_data.locations[ii,
0]),
zone=UTM_number,
letter=UTM_letter)[2:]
dataset.raw_data.locations[ii, 1], dataset.raw_data.locations[
ii, 0] = easting, northing
import e_colours.colourmaps as cm
from mpl_toolkits.axes_grid1 import make_axes_locatable
cmap = cm.jet_plus(64)
# listfile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Regions\dbr15\j2\allsites.lst'
# listfile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Regions\MetalEarth\j2\allbb.lst')
# datafile = r'C:\Users\eric\Documents\MATLAB\MATLAB\Inversion\Regions\MetalEarth\j2\allbb.data')
# datafile = 'C:/Users/eric/Documents/MATLAB/MATLAB/Inversion/Regions/MetalEarth/swayze/swz_cull1/swz_cull1f_Z.dat'
datafile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/j2/cull_allSuperior.data'
listfile = 'C:/Users/eric/phd/ownCloud/data/Regions/MetalEarth/j2/culled_allSuperior.lst'
data = WSDS.Data(datafile=datafile, listfile=listfile)
raw = WSDS.RawData(listfile=listfile)
raw.locations = raw.get_locs(mode='latlong')
for ii in range(len(raw.locations)):
lon, lat = utils.project((raw.locations[ii, 1], raw.locations[ii, 0]), zone=16, letter='U')[2:]
raw.locations[ii, 1], raw.locations[ii, 0] = lon, lat
data.locations = raw.locations
save_path = 'C:/Users/eric/phd/ownCloud/Documents/Seminars/Seminar 3/Figures/Pseudosections/culled/'
# rmsites = [site for site in data.site_names if site[0] == 'e' or site[0] == 'd']
# data.remove_sites(rmsites)
# data.sort_sites(order='west-east')
rho = {site.name: utils.compute_rho(site)[0] for site in data.sites.values()}
pha = {site.name: utils.compute_phase(site)[0] for site in data.sites.values()}
rho_lim = [0, 5]
n_interp = 250
period = 14
padding = 50000
# bost = {site.name: utils.compute_bost1D(site)[0] for site in data.sites.values()}
# depths = {site.name: utils.compute_bost1D(site)[1] for site in data.sites.values()}
radius = 20
label_offset = -4.5
file_path = local_path + '/phd/ownCloud/Documents/ME_Transects/Dryden_paper/RoughFigures/'
file_name = 'pt_pseudosection_phi2'
file_types = ['.pdf', '.png'] #, '.ps', '.png')
dpi = 600
linear_xaxis = True
# cmap = cm.jet_plus_r(64)
cmap = cm.jet(64)
# cmap = cm.bwr(64)
data.locations = data.get_locs(mode='latlong')
main_transect.locations = main_transect.get_locs(mode='latlong')
for ii, site in enumerate(data.site_names):
easting, northing = utils.project(
(data.locations[ii, 1], data.locations[ii, 0]), zone=16,
letter='U')[2:]
data.locations[ii, 1], data.locations[ii, 0] = easting, northing
data.sites[site].locations['X'], data.sites[site].locations[
'Y'] = northing, easting
for ii, site in enumerate(main_transect.site_names):
easting, northing = utils.project(
(main_transect.locations[ii, 1], main_transect.locations[ii, 0]),
zone=16,
letter='U')[2:]
main_transect.locations[ii,
1], main_transect.locations[ii,
0] = easting, northing
main_transect.sites[site].locations['X'], main_transect.sites[
site].locations['Y'] = northing, easting
main_transect.spatial_units = 'km'
dist = euclidean(
(ME_data.locations[ii, 1], ME_data.locations[ii, 0]),
(ME_data.locations[jj, 1], ME_data.locations[jj, 0]))
if dist < cutoff_distance and site1 in all_sites_ME and (site1 !=
site2):
if site2 in all_sites_ME:
all_sites_ME.remove(site2)
rm_sites = [
site for site in ME_data.site_names if site not in all_sites_ME
]
ME_data.remove_sites(sites=rm_sites)
ME_raw.remove_sites(sites=rm_sites)
ME_raw.locations = ME_raw.get_locs(mode='latlong')
for ii in range(len(ME_raw.locations)):
lon, lat = utils.project(
(ME_raw.locations[ii, 1], ME_raw.locations[ii, 0]),
zone=16,
letter='U')[2:]
ME_raw.locations[ii, 1], ME_raw.locations[ii, 0] = lon, lat
ME_data.locations = ME_raw.locations
all_sites = deepcopy(all_data.site_names)
# Remove redunantly close points
for ii, site1 in enumerate(all_data.site_names):
for jj, site2 in enumerate(all_data.site_names):
dist = euclidean(
(all_data.locations[ii, 1], all_data.locations[ii, 0]),
(all_data.locations[jj, 1], all_data.locations[jj, 0]))
if dist < cutoff_distance and site1 in all_sites and (site1 !=
site2):
if site2 in all_sites and not (site2 in all_sites_ME):
all_sites.remove(site2)
save_path = 'C:/Users/eric/phd/ownCloud/Documents/Seminars/Seminar 3/Figures/Pseudosections/subprovinces/botCBar/'
raw = WSDS.RawData(list_file)
data = WSDS.Data(datafile=datafile, listfile=list_file)
use_periods = data.periods[0:1]
raw.locations = raw.get_locs(mode='latlong')
# transform = ccrs.PlateCarree()
# We want the data plotted in UTM, and we will convert them to UTM before plotting
# transform = ccrs.UTM(zone=16)
transform = ccrs.TransverseMercator(central_longitude=-85,
central_latitude=49,
false_northing=5430000,
false_easting=645000)
for ii in range(len(raw.locations)):
easting, northing = utils.project(
(raw.locations[ii, 1], raw.locations[ii, 0]), zone=16, letter='U')[2:]
raw.locations[ii, 1], raw.locations[ii, 0] = easting, northing
shp = shapereader.Reader(shp_file_base)
# Note I use ccrs.PlateCarree() here because that is the projection the shapefile is in
# I.E., latlong, not UTM. cartopy will take care of converting them as long as these are
# all defined properly.
# plt.plot(raw.locations[:, 1], raw.locations[:, 0], 'k.', transform=transform)
# plt.show()
data.locations = raw.locations
rho = {site.name: utils.compute_rho(site)[0] for site in data.sites.values()}
pha = {site.name: utils.compute_phase(site)[0] for site in data.sites.values()}
for idx, period in enumerate(use_periods):
loc_x = []
loc_y = []