def plot_energy_error_heatmap(ax,
data_set,
struct_types=struct_types,
dyn_types=dyn_types,
bad_data_traj_list=[],
xbin_size=None,
ybin_size=None,
use_meV_y=False,
use_meV_x=False,
cmap=cm.get_cmap('plasma')):
scalex = 1
scaley = 1
if use_meV_x: scalex = 1000
if use_meV_y: scaley = 1000
def pick_bins(abin_size, A):
if abin_size is None:
abins = np.linspace(A.min(), A.max(), 100)
else:
abins = np.arange(A.min(), A.max(), abin_size)
return abins
#from matplotlib.ticker import MultipleLocator
#ax.yaxis.set_major_locator(MultipleLocator(base=30))
#ax.yaxis.set_minor_locator(MultipleLocator(base=5))
import copy
cmap_tweaked = copy.copy(cmap)
from matplotlib.colors import Colormap
Colormap.set_under(cmap_tweaked, color=(1, 1, 1, 0))
Colormap.set_over(cmap_tweaked, color=(1, 1, 1, 0))
fname = data_set[0]
data_name = data_set[1]
image_pairs = read_evaluation_data(filename=fname,
struct_types=struct_types,
dyn_types=dyn_types,
bad_data_traj_list=bad_data_traj_list)
cache_energy, data_energy = get_energy_lists(image_pairs)
energy_error = cache_energy - data_energy
error_rmse = np.sqrt(np.mean(energy_error**2)) * scaley
error_mae = np.mean(np.absolute(energy_error)) * scaley
X = data_energy * scalex
Y = energy_error * scaley
xbins = pick_bins(xbin_size, X)
ybins = pick_bins(ybin_size, Y)
ax.hist2d(X, Y, bins=(xbins, ybins), vmin=1, cmap=cmap_tweaked)
ax.set_title(data_name, fontsize=8)
ax.minorticks_on()
return error_rmse, error_mae
def plot_force_angle_heatmaps(axes,
data_sets,
struct_types=struct_types,
dyn_types=dyn_types,
struct_colors=struct_colors,
dyn_markers=dyn_markers,
bad_data_traj_list=[],
cmap=cm.get_cmap('plasma')):
deg = 180 / np.pi
bin_size = 2
my_bins = np.arange(0, 180 + bin_size / 2, bin_size)
ylims = (0, 180)
from matplotlib.ticker import MultipleLocator
import copy
cmap_tweaked = copy.copy(cmap)
from matplotlib.colors import Colormap
Colormap.set_under(cmap_tweaked, color=(1, 1, 1, 0))
Colormap.set_over(cmap_tweaked, color=(1, 1, 1, 0))
for di, data_set in enumerate(data_sets):
fname = data_set[0]
data_name = data_set[1]
#color = np.array(get_color(data_set[2]))
#lightness = data_set[3]
#zorder = zorders[di]
image_pairs = read_evaluation_data(filename=fname,
struct_types=struct_types,
dyn_types=dyn_types)
cache_forces, data_forces = get_force_list(image_pairs)
force_cosines_by_atom = compute_force_cosines_by_atom(
cache_forces, data_forces)
data_force_norms_by_atom = compute_force_norms_by_atom(data_forces)
X = collapse_sub_lists(data_force_norms_by_atom)
Y = deg * np.arccos(collapse_sub_lists(force_cosines_by_atom))
xbins = np.linspace(0, X.max(), 100)
axes[di].hist2d(X, Y, bins=(xbins, my_bins), vmin=1, cmap=cmap_tweaked)
#label = data_name + '\nMean: %.2f°\nRMS: %.2f°'%(mean_force_angles, rms_force_angles)
axes[di].set_title(data_name, fontsize=8)
axes[di].set_ylim(ylims)
#ax2.legend(fontsize = 8, handletextpad = 0.3, borderpad = 0.1)
axes[di].minorticks_on()
axes[di].yaxis.set_major_locator(MultipleLocator(base=30))
axes[di].yaxis.set_minor_locator(MultipleLocator(base=5))
if di == 0:
axes[di].set_ylabel('Force Angle, ' + formula_angle + ' (°)')
axes[di].set_xlabel('DFT Force (eV/Å)')
def plot_force_error_heatmap(ax,
data_set,
struct_types=struct_types,
dyn_types=dyn_types,
bad_data_traj_list=[],
xbin_size=None,
ybin_size=None,
use_meV_y=False,
use_meV_x=False,
by_atom=False,
cmap=cm.get_cmap('viridis')):
scalex = 1
scaley = 1
if use_meV_x: scalex = 1000
if use_meV_y: scaley = 1000
def pick_bins(abin_size, A):
if abin_size is None:
abins = np.linspace(A.min(), A.max(), 100)
else:
abins = np.arange(A.min(), A.max(), abin_size)
return abins
#from matplotlib.ticker import MultipleLocator
#ax.yaxis.set_major_locator(MultipleLocator(base=30))
#ax.yaxis.set_minor_locator(MultipleLocator(base=5))
import copy
cmap_tweaked = copy.copy(cmap)
from matplotlib.colors import Colormap
Colormap.set_under(cmap_tweaked, color=(1, 1, 1, 0))
Colormap.set_over(cmap_tweaked, color=(1, 1, 1, 0))
fname = data_set[0]
data_name = data_set[1]
image_pairs = read_evaluation_data(filename=fname,
struct_types=struct_types,
dyn_types=dyn_types)
cache_forces, data_forces = get_force_list(image_pairs)
force_error_list = compute_force_error_list(cache_forces, data_forces)
if by_atom:
atom_force_norms = compute_atom_force_norms(data_forces)
rms_force_error_by_atom = compute_rms_force_error_by_atom(
cache_forces, data_forces)
net_rms_force_error_by_atom = np.sqrt(
np.mean(collapse_sub_lists(rms_force_error_by_atom)**2))
X = scalex * atom_force_norms
Y = scaley * rms_force_error_by_atom
error_rmse = scaley * net_rms_force_error_by_atom
else:
image_force_norms = compute_force_norms_by_image(
data_forces) / np.sqrt(3)
rms_force_error_by_image = compute_rms_force_error_by_image(
cache_forces, data_forces) / np.sqrt(3)
net_rms_force_error_by_image = np.sqrt(
np.mean((rms_force_error_by_image)**2))
X = scalex * image_force_norms
Y = scaley * rms_force_error_by_image
error_rmse = scaley * net_rms_force_error_by_image
xbins = pick_bins(xbin_size, X)
ybins = pick_bins(ybin_size, Y)
ax.hist2d(X, Y, bins=(xbins, ybins), vmin=1, cmap=cmap_tweaked)
ax.set_title(data_name, fontsize=8)
ax.minorticks_on()
return error_rmse
def plot_force_angle_polar_heatmap(
ax,
data_set,
struct_types=struct_types,
dyn_types=dyn_types,
bad_data_traj_list=[],
theta_zero_location="S",
xbin_size=None,
ybin_size=None,
use_meV_y=False,
#use_meV_x = False,
by_atom=True,
cmap=cm.get_cmap('plasma')):
scalex = 1
scaley = 1
#if use_meV_x: scalex = 1000
if use_meV_y: scaley = 1000
deg = 180 / np.pi
def pick_bins(abin_size, A):
if abin_size is None:
abins = np.linspace(np.min(A), np.max(A), 100)
else:
abins = np.arange(np.min(A), np.max(A), abin_size)
return abins
if xbin_size is None:
xbin_size = 2
xbins = np.arange(0, 180, xbin_size)
from matplotlib.ticker import MultipleLocator
#ax.yaxis.set_major_locator(MultipleLocator(base=30))
#ax.yaxis.set_minor_locator(MultipleLocator(base=5))
import copy
cmap_tweaked = copy.copy(cmap)
from matplotlib.colors import Colormap
Colormap.set_under(cmap_tweaked, color=(1, 1, 1, 0))
Colormap.set_over(cmap_tweaked, color=(1, 1, 1, 0))
fname = data_set[0]
data_name = data_set[1]
image_pairs = read_evaluation_data(filename=fname,
struct_types=struct_types,
dyn_types=dyn_types)
cache_forces, data_forces = get_force_list(image_pairs)
force_error_list = compute_force_error_list(cache_forces, data_forces)
if by_atom:
force_norms_by_atom = compute_force_norms_by_atom(data_forces)
rms_force_error_by_atom = compute_rms_force_error_by_atom(
cache_forces, data_forces)
#net_rms_force_error_by_atom = np.sqrt(np.mean( collapse_sub_lists(rms_force_error_by_atom)**2))
force_cosines_by_atom = compute_force_cosines_by_atom(
cache_forces, data_forces)
force_angles_by_atom = deg * np.arccos(
collapse_sub_lists(force_cosines_by_atom))
mean_force_angles = np.mean(force_angles_by_atom)
rms_force_angles = np.sqrt(np.mean((force_angles_by_atom)**2))
X = scalex * force_angles_by_atom
Y = scaley * collapse_sub_lists(rms_force_error_by_atom)
error_rmse = scaley * rms_force_angles
error_mae = scaley * mean_force_angles
else:
image_force_norms = compute_force_norms_by_image(
data_forces) / np.sqrt(3)
rms_force_error_by_image = compute_rms_force_error_by_image(
cache_forces, data_forces) / np.sqrt(3)
net_rms_force_error_by_image = np.sqrt(
np.mean((rms_force_error_by_image)**2))
X = scalex * image_force_norms
Y = scaley * rms_force_error_by_image
error_rmse = scaley * net_rms_force_error_by_image
#xbins = pick_bins(xbin_size, X)
ybins = pick_bins(ybin_size, Y)
ax.hist2d(X / deg, Y, bins=(xbins / deg, ybins), vmin=1, cmap=cmap_tweaked)
ax.set_title(data_name, fontsize=8)
#ax_force_polar.legend(fontsize = 8)
ax.set_thetamin(0)
ax.set_thetamax(180)
ax.set_theta_zero_location(theta_zero_location)
ax.xaxis.set_major_locator(MultipleLocator(base=45 / deg))
#ax.xaxis.set_minor_locator(MultipleLocator(base=15/deg))
dtheta = 15
theta_grid = np.arange(0, 180 + dtheta / 2, dtheta)
for theta in theta_grid:
ax.axvline(theta / deg, color='grey', lw=0.8, zorder=-1)
#ax.set_thetagrids()
#ax.minorticks_on()
return error_rmse, error_mae
