def create_model(n_euler_rotations, use_tpw):
if n_euler_rotations < 0:
raise Exception(
"Number of plate Euler rotations must be greater than or equal to zero"
)
if use_tpw != False and use_tpw != True:
raise Exception("Must enter 'true' or 'false' for whether to use TPW")
if n_euler_rotations == 0 and use_tpw == False:
raise Exception(
"Must use either TPW or plate Euler rotations, or both")
print("Fitting Keweenawan APW track with"\
+("out TPW and " if use_tpw == False else " TPW and ")\
+str(n_euler_rotations)+" Euler rotation"\
+ ("" if n_euler_rotations == 1 else "s") )
data = pd.read_csv("pole_means.csv")
# Give unnamed column an appropriate name
data.rename(columns={'Unnamed: 0': 'Name',\
'Unnamed: 14': 'GaussianOrUniform'},\
inplace=True)
data = data[data.Name !=
'Osler_N'] #Huge error, does not contribute much to the model
data = data[data.PoleName !=
'Abitibi'] # Standstill at the beginning, not realistic to fit
data = data[data.PoleName !=
'Haliburton'] #Much younger, far away pole, difficutlt to fit
data.sort_values('AgeNominal', ascending=False, inplace=True)
poles = []
pole_names = []
pole_colors = []
for i, row in data.iterrows():
pole_lat = row['PLat']
pole_lon = row['PLon'] - lon_shift
a95 = row['A95']
age = row['AgeNominal']
if row['GaussianOrUniform'] == 'gaussian':
sigma_age = row['Gaussian_2sigma'] / 2.
elif row['GaussianOrUniform'] == 'uniform':
sigma_age = (row['AgeLower'], row['AgeUpper'])
else:
raise Exception("Unrecognized age error type")
pole = mcplates.PaleomagneticPole(pole_lon,
pole_lat,
angular_error=a95,
age=age,
sigma_age=sigma_age)
poles.append(pole)
pole_names.append(row['PoleName'])
pole_colors.append(row['color'])
tpw_str = 'true' if use_tpw else 'false'
prefix = 'keweenawan_' + str(n_euler_rotations) + '_' + tpw_str
path = mcplates.APWPath(prefix, poles, n_euler_rotations)
tpw_rate_scale = 2.5 if use_tpw else None
path.create_model(site_lon_lat=(slon, slat), watson_concentration=0.0,\
rate_scale=2.5, tpw_rate_scale=tpw_rate_scale)
return path, poles, pole_names, pole_colors
#Make a dummy APW path to create the synthetic data
dummy_pole_position_fn = mcplates.APWPath.generate_pole_position_fn(
n_euler_poles, start_age)
pole_list = []
for a in ages:
lon_lat = dummy_pole_position_fn(hidden_start_pole, a, 0.0, 0.0,
hidden_euler_pole, hidden_euler_rate)
pole_list.append(
mcplates.PaleomagneticPole(lon_lat[0],
lon_lat[1],
angular_error=10.,
age=a,
sigma_age=0.01))
path = mcplates.APWPath(dbname, pole_list, n_euler_poles)
path.create_model(watson_concentration=0.0, rate_scale=2.5)
def plot_result():
fig = plt.figure(figsize=(8, 4))
ax = fig.add_subplot(1, 2, 1, projection=ccrs.Orthographic(0., 15.))
ax.gridlines()
ax.set_global()
colors = itertools.cycle([cmap_red, cmap_green])
direction_samples = path.euler_directions()
for directions in direction_samples:
mcplates.plot.plot_distribution(ax,
directions[:, 0],
# Add pole with low error for present day pole
poles.append(
mcplates.PaleomagneticPole(0.,
90.,
angular_error=1.,
age=0.,
sigma_age=0.01))
# Reference position on the Australian continent
slat = -25.3 # Uluru lat
slon = 131.0 - lon_shift # Uluru lon
uluru = mcplates.PlateCentroid(slon, slat)
prefix = 'australia_' + str(n_euler_rotations) + '_' + sys.argv[2]
path = mcplates.APWPath(prefix, poles, n_euler_rotations)
tpw_rate_scale = 2.5 if use_tpw else None
path.create_model(site_lon_lat=(slon, slat), watson_concentration=0.0,\
rate_scale=2.5, tpw_rate_scale=tpw_rate_scale)
def plot_synthetic_paths(ax=None, title=''):
if ax is None:
if proj_type == 'M':
myax = plt.axes(projection=ccrs.Mollweide(200. - lon_shift))
elif proj_type == 'O':
myax = plt.axes(
projection=ccrs.Orthographic(200. - lon_shift, 30.))
else:
myax = ax
import mcplates
dbname = 'pickles/apw.pickle'
# Generate a synthetic data set
ages =[0.,10.,20.,30.,40]
sigma_ages = [2., 2., 2., 2., [38.,43.]]
lon_lats = [ [300., -20.], [340.,0.], [0.,30.], [20., 0.], [60.,-20.]]
poles = []
for a, s, ll in zip(ages, sigma_ages, lon_lats):
pole = mcplates.PaleomagneticPole( ll[0], ll[1], angular_error=10., age=a, sigma_age=s)
poles.append(pole)
path = mcplates.APWPath( 'apw', poles, 2 )
path.create_model()
def plot_result():
ax = plt.axes(projection = ccrs.Orthographic(0.,-30.))
#ax = plt.axes(projection = ccrs.Mollweide(0.))
ax.gridlines()
ax.set_global()
direction_samples = path.euler_directions()
for directions in direction_samples:
mcplates.plot.plot_distribution( ax, directions[:,0], directions[:,1])
pathlons, pathlats = path.compute_synthetic_paths(n=100)
for pathlon,pathlat in zip(pathlons,pathlats):