General improvements following reviews.

- `conversion_factor()` function used to convert from eV to Hartree.
- cclib is just imported when invoking `.parse_output()`.
- `.get_gradient()` renamed to `._get_gradient()` so that it becomes
  a private function.

qcengine/programs/orca.py

@@ -3,13 +3,13 @@
 """
 
 import string
-import cclib
 import io
 import numpy as np
 from typing import Any, Dict, List, Optional, Set, Tuple
 
-from qcelemental.models import AtomicResult
+from qcelemental.constants import conversion_factor
 from qcelemental.util import parse_version, which
+from qcelemental.models import AtomicResult
 
 from ..exceptions import InputError, UnknownError
 from ..util import execute
@@ -230,12 +230,16 @@ def build_input(
  }
 
  def parse_output(self, outfiles: Dict[str, str], input_model: "AtomicInput") -> "AtomicResult":
+ try:
+ import cclib
+ except ImportError:
+ raise("You need to install the cclib python module...")
 
  data = cclib.io.ccread(io.StringIO(outfiles["stdout"]))
 
  properties = {}
  extras = {}
- extras["ev_to_hartrees"] = 0.0367493
+ extras["ev_to_hartrees"] = conversion_factor("ev", "hartree")
 
  # Process basis set data
  properties["calcinfo_nbasis"] = data.nbasis
@@ -262,7 +266,7 @@ def parse_output(self, outfiles: Dict[str, str], input_model: "AtomicInput") ->
  extras["CURRENT ENERGY"] = final_energy
 
  elif input_model.driver == "gradient":
- gradient = self.get_gradient(outfiles["outfiles"]["dispatch.engrad"])
+ gradient = self._get_gradient(outfiles["outfiles"]["dispatch.engrad"])
  output_data["return_result"] = gradient
  extras["CURRENT ENERGY"] = final_energy
  extras["CURRENT GRADIENT"] = gradient
@@ -277,7 +281,7 @@ def parse_output(self, outfiles: Dict[str, str], input_model: "AtomicInput") ->
 
  return AtomicResult(**output_data)
 
- def get_gradient(self, gradient_file):
+ def _get_gradient(self, gradient_file):
  """Get gradient from engrad Orca file"""
  copy = False
  found = False