Lint fix.

jarvis/analysis/solarefficiency/solar.py

@@ -4,15 +4,18 @@
 Please find more detailsin:
 https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.9b02166
 """
+
 import numpy as np
 import os
 from scipy.interpolate import interp1d
 from numpy import interp
 import scipy.constants as constants
+
 try:
     from scipy.integrate import simps
-except:
+except Exception:
     from scipy.integrate import simpson as simps
+
     pass
 import matplotlib.pyplot as plt
 
@@ -73,7 +76,7 @@ def calculate_SQ(
         # units of W/(m**3), different than solar_spectra_irradiance!!! (This
         # is intentional, it is for convenience)
         blackbody_irradiance = (
-            2.0 * h * c ** 2 / (solar_spectra_wavelength_meters ** 5)
+            2.0 * h * c**2 / (solar_spectra_wavelength_meters**5)
         ) * (
             1.0
             / (
@@ -113,7 +116,7 @@ def calculate_SQ(
         )
 
         bandgap_blackbody = (
-            (2.0 * h * c ** 2 / (bandgap_wavelength ** 5))
+            (2.0 * h * c**2 / (bandgap_wavelength**5))
             * (
                 1.0
                 / (
@@ -277,7 +280,7 @@ def slme(
         # units of W/(m**3), different than solar_spectra_irradiance!!! (This
         # is intentional, it is for convenience)
         blackbody_irradiance = (
-            2.0 * h * c ** 2 / (solar_spectra_wavelength_meters ** 5)
+            2.0 * h * c**2 / (solar_spectra_wavelength_meters**5)
         ) * (
             1.0
             / (
@@ -302,7 +305,7 @@ def slme(
         # units of nm
         material_wavelength_for_absorbance_data = (
             (c * h_e) / (material_energy_for_absorbance_data + 0.00000001)
-        ) * 10 ** 9
+        ) * 10**9
 
         # absorbance interpolation onto each solar spectrum wavelength
 

jarvis/analysis/structure/spacegroup.py

@@ -12,7 +12,7 @@
 
 try:
     import spglib
-except Exception as exp:
+except Exception:
     # print(exp)
     pass
 # from numpy import gcd

jarvis/core/utils.py

@@ -1,6 +1,7 @@
 """Set of useful utility functions."""
 
 from collections import OrderedDict
+from collections import defaultdict
 from scipy import sparse
 from scipy.sparse.linalg import spsolve
 import random
@@ -338,7 +339,7 @@ def cos_formula(a, b, c):
 def baseline_als(y, lam, p, niter=10):
     """
     Adaptive Least Squares fitting for baseline correction
-    
+
     Parameters:
     y: array_like
         Input signal
@@ -348,32 +349,39 @@ def baseline_als(y, lam, p, niter=10):
         Asymmetry
     niter: int, optional
         Number of iterations
-    
+
     Returns:
     array_like
         The estimated baseline
     """
     L = len(y)
-    D = sparse.diags([1,-2,1],[0,-1,-2], shape=(L,L-2))
+    D = sparse.diags([1, -2, 1], [0, -1, -2], shape=(L, L - 2))
     w = np.ones(L)
     for i in range(niter):
         W = sparse.spdiags(w, 0, L, L)
         Z = W + lam * D.dot(D.transpose())
-        z = spsolve(Z, w*y)
-        w = p * (y > z) + (1-p) * (y < z)
+        z = spsolve(Z, w * y)
+        w = p * (y > z) + (1 - p) * (y < z)
     return z
-
-def recast_array(x_original = [],y_original = [],x_new = np.arange(0, 90, 1),tol=0.1):
-    x_original=np.array(x_original)
+
+
+def recast_array(
+    x_original=[], y_original=[], x_new=np.arange(0, 90, 1), tol=0.1
+):
+    x_original = np.array(x_original)
     # Initialize the new y array with NaNs or a default value
     y_new = np.full_like(x_new, 0, dtype=np.float64)
 
     # Fill the corresponding bins
     for x_val, y_val in zip(x_original, y_original):
-        closest_index = np.abs(x_new - x_val).argmin()  # Find the closest x_new index
+        closest_index = np.abs(
+            x_new - x_val
+        ).argmin()  # Find the closest x_new index
         y_new[closest_index] = y_val
-    #y_new[y_new<tol]=0
+    # y_new[y_new<tol]=0
     return x_new, y_new
+
+
 # def is_xml_valid(xsd="jarvisdft.xsd", xml="JVASP-1002.xml"):
 #   """Check if XML is valid."""
 #   xml_file = etree.parse(xml)

jarvis/db/figshare.py

@@ -671,6 +671,7 @@ def get_wann_phonon(jid="JVASP-1002", factor=15.633302):
     # Requires phonopy
     from jarvis.io.phonopy.outputs import get_phonon_tb
     from jarvis.io.vasp.outputs import Vasprun
+    from jarvis.io.wannier.outputs import WannierHam
 
     fls = data("raw_files")