[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

ipsuite/analysis/model/dynamics_checks.py

@@ -4,8 +4,7 @@
 import numpy as np
 import zntrack
 from ase.geometry import conditional_find_mic
-from ase.neighborlist import build_neighbor_list
-from ase.neighborlist import natural_cutoffs
+from ase.neighborlist import build_neighbor_list, natural_cutoffs
 
 from ipsuite import base
 from ipsuite.utils.ase_sim import get_energy
@@ -61,7 +60,6 @@ def initialize(self, atoms):
  self.is_initialized = True
 
  def check(self, atoms: ase.Atoms) -> bool:
-
  p1 = atoms.positions[self.idx_i]
  p2 = atoms.positions[self.idx_j]
  _, dists = conditional_find_mic(p1 - p2, atoms.cell, atoms.pbc)
@@ -80,7 +78,6 @@ def check(self, atoms: ase.Atoms) -> bool:
  atoms.numbers[first_atom] = 3
  atoms.numbers[second_atom] = 3
 
-
  if self.bonded_max_dist:
  max_dist = np.max(dists)
  too_far = max_dist > self.bonded_max_dist

ipsuite/analysis/model/plots.py

@@ -4,8 +4,8 @@
 import numpy as np
 import seaborn as sns
 from scipy.interpolate import interpn
-from scipy.stats import gaussian_kde, foldnorm
 from scipy.optimize import curve_fit
+from scipy.stats import foldnorm, gaussian_kde
 
 
 def density_scatter(ax, x, y, bins, **kwargs) -> None:
@@ -48,7 +48,13 @@ def density_scatter(ax, x, y, bins, **kwargs) -> None:
 
 
 def get_figure(
- true, prediction, datalabel: str, xlabel: str, ylabel: str, figsize: tuple = (10, 7), density=True,
+ true,
+ prediction,
+ datalabel: str,
+ xlabel: str,
+ ylabel: str,
+ figsize: tuple = (10, 7),
+ density=True,
 ) -> plt.Figure:
  """Create a correlation plot for true, prediction values.
 
@@ -104,31 +110,42 @@ def get_cdf_figure(x, y, figsize: tuple = (10, 7)):
  return fig
 
 
-def get_calibration_figure(error, std, markersize:float = 3.0, datalabel="", figsize: tuple = (10, 7)):
- fig, ax = plt.subplots(1,1,figsize=figsize, dpi=300)
+def get_calibration_figure(
+ error, std, markersize: float = 3.0, datalabel="", figsize: tuple = (10, 7)
+):
+ fig, ax = plt.subplots(1, 1, figsize=figsize, dpi=300)
 
  x = np.linspace(1e-6, 5e3, 5)
  noise_level_2 = x
 
- quantiles_lower_01 = [foldnorm.ppf(0.15, 0.,0.,i) for i in noise_level_2]
- quantiles_upper_01 = [foldnorm.ppf(0.85, 0.,0.,i) for i in noise_level_2]
- quantiles_lower_05 = [foldnorm.ppf(0.05, 0.,0.,i) for i in noise_level_2]
- quantiles_upper_05 = [foldnorm.ppf(0.95, 0.,0.,i) for i in noise_level_2]
- quantiles_lower_005 = [foldnorm.ppf(0.005, 0.,0.,i) for i in noise_level_2]
- quantiles_upper_005 = [foldnorm.ppf(0.995, 0.,0.,i) for i in noise_level_2]
-
- ax.scatter(std, error, s=markersize, alpha=0.3, color="tab:blue", rasterized=True, linewidth=0., label=datalabel)
+ quantiles_lower_01 = [foldnorm.ppf(0.15, 0.0, 0.0, i) for i in noise_level_2]
+ quantiles_upper_01 = [foldnorm.ppf(0.85, 0.0, 0.0, i) for i in noise_level_2]
+ quantiles_lower_05 = [foldnorm.ppf(0.05, 0.0, 0.0, i) for i in noise_level_2]
+ quantiles_upper_05 = [foldnorm.ppf(0.95, 0.0, 0.0, i) for i in noise_level_2]
+ quantiles_lower_005 = [foldnorm.ppf(0.005, 0.0, 0.0, i) for i in noise_level_2]
+ quantiles_upper_005 = [foldnorm.ppf(0.995, 0.0, 0.0, i) for i in noise_level_2]
+
+ ax.scatter(
+ std,
+ error,
+ s=markersize,
+ alpha=0.3,
+ color="tab:blue",
+ rasterized=True,
+ linewidth=0.0,
+ label=datalabel,
+ )
  ax.loglog()
- ax.plot(x, quantiles_upper_05, color='gray', alpha=0.5)
- ax.plot(x, quantiles_lower_05, color='gray', alpha=0.5)
- ax.plot(x, quantiles_upper_01, color='gray', alpha=0.5)
- ax.plot(x, quantiles_lower_01, color='gray', alpha=0.5)
- ax.plot(x, quantiles_upper_005, color='gray', alpha=0.5)
- ax.plot(x, quantiles_lower_005, color='gray', alpha=0.5)
+ ax.plot(x, quantiles_upper_05, color="gray", alpha=0.5)
+ ax.plot(x, quantiles_lower_05, color="gray", alpha=0.5)
+ ax.plot(x, quantiles_upper_01, color="gray", alpha=0.5)
+ ax.plot(x, quantiles_lower_01, color="gray", alpha=0.5)
+ ax.plot(x, quantiles_upper_005, color="gray", alpha=0.5)
+ ax.plot(x, quantiles_lower_005, color="gray", alpha=0.5)
 
- ax.plot(np.logspace(-3,100.0),np.logspace(-3,100.0),linestyle="--", color="grey")
- ax.set_xlim(np.min(std)/1.5, np.max(std)*1.5)
- ax.set_ylim(np.min(error)/1.5, np.max(error)*1.5)
+ ax.plot(np.logspace(-3, 100.0), np.logspace(-3, 100.0), linestyle="--", color="grey")
+ ax.set_xlim(np.min(std) / 1.5, np.max(std) * 1.5)
+ ax.set_ylim(np.min(error) / 1.5, np.max(error) * 1.5)
 
  ax.set_xlabel(r"$\sigma_{f_{i\alpha}}(A)$ [eV/$\AA$] ")
  ax.set_ylabel(r"$|\Delta f_{i\alpha}(A)|$ [eV/$\AA$] ")
@@ -138,34 +155,35 @@ def get_calibration_figure(error, std, markersize:float = 3.0, datalabel="", fig
 
 def gauss(x, *p):
  m, s = p
- return np.exp(-((x-m)/s)**2*0.5)/np.sqrt(2*np.pi*s**2)
+ return np.exp(-(((x - m) / s) ** 2) * 0.5) / np.sqrt(2 * np.pi * s**2)
 
 
 def slice_ensemble_uncertainty(true, pred_ens, slice_start, slice_end):
  pred_mean = np.mean(pred_ens, axis=1)
  pred_std = np.std(pred_ens, axis=1)
- 
- isel = np.where((slice_start<pred_std) & (pred_std<slice_end))[0]
- 
- error_true = np.reshape(true[isel,0]-pred_mean[isel,0], -1)
- error_pred = np.reshape(pred_ens[isel,:,0]-pred_mean[isel,np.newaxis,0], -1)
+
+ isel = np.where((slice_start < pred_std) & (pred_std < slice_end))[0]
+
+ error_true = np.reshape(true[isel, 0] - pred_mean[isel, 0], -1)
+ error_pred = np.reshape(pred_ens[isel, :, 0] - pred_mean[isel, np.newaxis, 0], -1)
  return error_true, error_pred
 
-def slice_uncertainty(true, pred_mean, pred_std, slice_start, slice_end): 
- isel = np.where((slice_start<pred_std) & (pred_std<slice_end))[0]
-
- error_true = np.reshape(true[isel]-pred_mean[isel], -1)
+
+def slice_uncertainty(true, pred_mean, pred_std, slice_start, slice_end):
+ isel = np.where((slice_start < pred_std) & (pred_std < slice_end))[0]
+
+ error_true = np.reshape(true[isel] - pred_mean[isel], -1)
  error_pred = pred_std[isel]
  return error_true, error_pred
 
 
 def get_gaussianicity_figure(error_true, error_pred):
  true_kde_sel = gaussian_kde(error_true)
- ens_kde_sel =  gaussian_kde(error_pred)
+ ens_kde_sel = gaussian_kde(error_pred)
 
  bounds = 1.5 * max(np.max(np.abs(error_true)), np.max(np.abs(error_pred)))
 
- xgrid = np.linspace(-bounds,bounds,400)
+ xgrid = np.linspace(-bounds, bounds, 400)
 
  ens_sel = ens_kde_sel(xgrid)
  true_sel = true_kde_sel(xgrid)
@@ -175,11 +193,11 @@ def get_gaussianicity_figure(error_true, error_pred):
 
  fig, ax = plt.subplots()
 
- ax.semilogy(xgrid,gauss(xgrid,0,std), 'k--', label="Gaussian")
- ax.semilogy(xgrid, true_sel, 'r-', label="empirical")
- ax.semilogy(xgrid, ens_sel, 'b-', label="predicted")
+ ax.semilogy(xgrid, gauss(xgrid, 0, std), "k--", label="Gaussian")
+ ax.semilogy(xgrid, true_sel, "r-", label="empirical")
+ ax.semilogy(xgrid, ens_sel, "b-", label="predicted")
  ymax = 5 * max(np.max(true_sel), np.max(ens_sel))
- ax.set_ylim(1e-6,ymax)
+ ax.set_ylim(1e-6, ymax)
  ax.set_yscale("log")
 
  ax.set_xlabel(r"$\Delta (S)$ / meV/Ang")

ipsuite/analysis/model/predict.py

@@ -10,7 +10,14 @@
 
 from ipsuite import base, models, utils
 from ipsuite.analysis.model.math import decompose_stress_tensor, force_decomposition
-from ipsuite.analysis.model.plots import get_cdf_figure, get_figure, get_hist, get_calibration_figure, get_gaussianicity_figure, slice_ensemble_uncertainty
+from ipsuite.analysis.model.plots import (
+ get_calibration_figure,
+ get_cdf_figure,
+ get_figure,
+ get_gaussianicity_figure,
+ get_hist,
+ slice_ensemble_uncertainty,
+)
 from ipsuite.geometry import BarycenterMapping
 from ipsuite.utils.ase_sim import freeze_copy_atoms
 
@@ -316,20 +323,22 @@ def get_plots(self, save=False):
  )
 
  gaussianicy_figures = []
- for (start, end) in self.force_dist_slices:
- error_true, error_pred = slice_ensemble_uncertainty(self.content["forces_true"], self.content["forces_ensemble"], start, end)
- fig = get_gaussianicity_figure(
- error_true, error_pred
+ for start, end in self.force_dist_slices:
+ error_true, error_pred = slice_ensemble_uncertainty(
+ self.content["forces_true"],
+ self.content["forces_ensemble"],
+ start,
+ end,
  )
+ fig = get_gaussianicity_figure(error_true, error_pred)
  gaussianicy_figures.append(fig)
  if save:
  forces_plot.savefig(self.plots_dir / "forces.png")
  forces_cdf_plot.savefig(self.plots_dir / "forces_cdf.png")
- 
+
  for ii, fig in enumerate(gaussianicy_figures):
  fig.savefig(self.plots_dir / f"forces_gaussianicity_{ii}.png")
 
-
  def run(self):
  self.nwd.mkdir(exist_ok=True, parents=True)
  self.get_data()