Merge branch 'update_evaluator_modules' into build_taproom

devtools/conda-envs/test_env.yaml

@@ -17,7 +17,10 @@ dependencies:
   - plumed
   - intermol
   - openff-interchange >=0.3.7
+<<<<<<< HEAD
   - openff-toolkit >=0.11
+=======
+>>>>>>> update_evaluator_modules
   - openff-units >=0.2.0
   - openff-utilities
 

paprika/analysis/utils.py

@@ -1,4 +1,4 @@
-import numpy as np
+import numpy
 
 
 def get_factors(n):
@@ -17,7 +17,7 @@ def get_factors(n):
 
     """
     factors = []
-    sqrt_n = int(round(np.sqrt(n) + 0.5))
+    sqrt_n = int(round(numpy.sqrt(n) + 0.5))
     i = 1
     while i <= sqrt_n:
         if n % i == 0:
@@ -26,6 +26,7 @@ def get_factors(n):
             if j != i:
                 factors.append(int(j))
         i += 1
+
     return sorted(factors, key=int)
 
 
@@ -45,19 +46,24 @@ def get_nearest_max(n):
 
     """
     max_factors = 0
+
     if n % 2 == 0:
         beg = n - 100
         end = n
     else:
         beg = n - 101
         end = n - 1
+
     if beg < 0:
         beg = 0
+
+    most_factors = 0
     for i in range(beg, end + 2, 2):
         num_factors = len(get_factors(i))
         if num_factors >= max_factors:
             max_factors = num_factors
             most_factors = i
+
     return most_factors
 
 
@@ -72,12 +78,12 @@ def get_block_sem(data_array):
 
     Parameters
     ----------
-    data_array: :class:`np.array`
+    data_array: :class:`numpy.array`
         Array containing data values.
 
     Returns
     -------
-    np.max(sems): float
+    numpy.max(sems): float
         The maximum SEM obtained from te blocking curve.
 
     """
@@ -86,11 +92,11 @@ def get_block_sem(data_array):
     block_sizes = get_factors(len(data_array))
 
     # An array to store means for each block ... make it bigger than we need.
-    block_means = np.zeros([block_sizes[-1]], np.float64)
+    block_means = numpy.zeros([block_sizes[-1]], numpy.float64)
 
     # Store the SEM for each block size, except the last two size for which
     # there will only be two or one blocks total and thus very noisy.
-    sems = np.zeros([len(block_sizes) - 2], np.float64)
+    sems = numpy.zeros([len(block_sizes) - 2], numpy.float64)
 
     # Check each block size except the last two.
     for size_idx in range(len(block_sizes) - 2):
@@ -102,15 +108,15 @@ def get_block_sem(data_array):
             data_beg_idx = blk_idx * block_sizes[size_idx]
             data_end_idx = (blk_idx + 1) * block_sizes[size_idx]
             # Compute the mean of this block and store in array
-            block_means[blk_idx] = np.mean(data_array[data_beg_idx:data_end_idx])
+            block_means[blk_idx] = numpy.mean(data_array[data_beg_idx:data_end_idx])
         # Compute the standard deviation across all blocks, devide by
         # num_blocks-1 for SEM
-        sems[size_idx] = np.std(block_means[0:num_blocks], ddof=0) / np.sqrt(
+        sems[size_idx] = numpy.std(block_means[0:num_blocks], ddof=0) / numpy.sqrt(
             num_blocks - 1
         )
         # Hmm or should ddof=1? I think 0, see Flyvbjerg -----^
 
-    return np.max(sems)
+    return numpy.max(sems)
 
 
 def get_subsampled_indices(N, g, conservative=False):
@@ -138,16 +144,16 @@ def get_subsampled_indices(N, g, conservative=False):
 
     # if conservative, assume integer g and round up
     if conservative:
-        g = np.ceil(g)
+        g = numpy.ceil(g)
 
     # initialize
     indices = [0]
     g_idx = 1.0
-    int_step = int(np.round(g_idx * g))
+    int_step = int(numpy.round(g_idx * g))
 
     while int_step < N:
         indices.append(int_step)
         g_idx += 1.0
-        int_step = int(np.round(g_idx * g))
+        int_step = int(numpy.round(g_idx * g))
 
     return indices

paprika/io.py

@@ -6,13 +6,13 @@
 import traceback
 from enum import Enum
 
-import numpy as np
-import pytraj as pt
+import numpy
+import pytraj
 from openff.units import unit as openff_unit
 from parmed import Structure
 from parmed.amber import AmberParm
 
-from paprika.restraints import BiasPotentialType, DAT_restraint, RestraintType
+from paprika.restraints import DAT_restraint
 
 # https://stackoverflow.com/questions/27909658/json-encoder-and-decoder-for-complex-numpy-arrays
 # https://stackoverflow.com/a/24375113/901925
@@ -80,11 +80,11 @@ def default(self, obj):
             logging.warning("Encountered Structure, which does not store filename.")
             return ""
 
-        if isinstance(obj, np.ndarray):
+        if isinstance(obj, numpy.ndarray):
             if obj.flags["C_CONTIGUOUS"]:
                 obj_data = obj.data
             else:
-                cont_obj = np.ascontiguousarray(obj)
+                cont_obj = numpy.ascontiguousarray(obj)
                 assert cont_obj.flags["C_CONTIGUOUS"]
                 obj_data = cont_obj.data
             data_b64 = base64.b64encode(obj_data)
@@ -97,23 +97,25 @@ def default(self, obj):
         elif isinstance(
             obj,
             (
-                np.int_,
-                np.intc,
-                np.intp,
-                np.int8,
-                np.int16,
-                np.int32,
-                np.int64,
-                np.uint8,
-                np.uint16,
-                np.uint32,
-                np.uint64,
+                numpy.int_,
+                numpy.intc,
+                numpy.intp,
+                numpy.int8,
+                numpy.int16,
+                numpy.int32,
+                numpy.int64,
+                numpy.uint8,
+                numpy.uint16,
+                numpy.uint32,
+                numpy.uint64,
             ),
         ):
             return int(obj)
-        elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):
+        elif isinstance(
+            obj, (numpy.float_, numpy.float16, numpy.float32, numpy.float64)
+        ):
             return float(obj)
-        elif isinstance(obj, (np.ndarray,)):
+        elif isinstance(obj, (numpy.ndarray,)):
             return obj.tolist()
         elif isinstance(obj, openff_unit.Quantity):
             return serialize_quantity(obj)
@@ -139,7 +141,7 @@ def __init__(self, *args, **kwargs):
     def custom_object_hook(self, obj):
         if "__ndarray__" in obj:
             data = base64.b64decode(obj["__ndarray__"])
-            return np.frombuffer(data, obj["dtype"]).reshape(obj["shape"])
+            return numpy.frombuffer(data, obj["dtype"]).reshape(obj["shape"])
 
         if "@type" in obj:
             if obj["@type"] == "openff.units.unit.Quantity":
@@ -343,18 +345,18 @@ def load_trajectory(window, trajectory, topology, single_topology=False):
             )
         logger.debug(f"Loading {os.path.join(window, topology)} and {trajectory_path}")
         try:
-            traj = pt.iterload(trajectory_path, os.path.join(window, topology))
+            traj = pytraj.iterload(trajectory_path, os.path.join(window, topology))
         except ValueError as e:
             formatted_exception = traceback.format_exception(None, e, e.__traceback__)
             logger.info(
                 f"Failed trying to load {os.path.join(window, topology)} and {trajectory_path}: "
                 f"{formatted_exception}"
             )
     elif isinstance(topology, str) and single_topology:
-        traj = pt.iterload(trajectory_path, os.path.join(topology))
+        traj = pytraj.iterload(trajectory_path, os.path.join(topology))
     else:
         try:
-            traj = pt.iterload(trajectory_path, topology)
+            traj = pytraj.iterload(trajectory_path, topology)
         except BaseException:
             raise Exception("Tried to load `topology` object directly and failed.")
 
@@ -384,7 +386,7 @@ def read_restraint_data(
 
     Returns
     -------
-    data: :class:`np.array`
+    data: :class:`numpy.array`
         The values for this restraint in this window
     """
 
@@ -397,7 +399,7 @@ def read_restraint_data(
         and not restraint.mask4
     ):
         data = openff_unit.Quantity(
-            pt.distance(
+            pytraj.distance(
                 trajectory, " ".join([restraint.mask1, restraint.mask2]), image=True
             ),
             units=openff_unit.angstrom,
@@ -407,7 +409,7 @@ def read_restraint_data(
         restraint.mask1 and restraint.mask2 and restraint.mask3 and not restraint.mask4
     ):
         data = openff_unit.Quantity(
-            pt.angle(
+            pytraj.angle(
                 trajectory,
                 " ".join([restraint.mask1, restraint.mask2, restraint.mask3]),
             ),
@@ -416,7 +418,7 @@ def read_restraint_data(
 
     elif restraint.mask1 and restraint.mask2 and restraint.mask3 and restraint.mask4:
         data = openff_unit.Quantity(
-            pt.dihedral(
+            pytraj.dihedral(
                 trajectory,
                 " ".join(
                     [restraint.mask1, restraint.mask2, restraint.mask3, restraint.mask4]

paprika/simulate/amber.py

@@ -1,7 +1,7 @@
 import abc
 import logging
 import os
-import subprocess as sp
+import subprocess
 from collections import OrderedDict
 from enum import Enum
 
@@ -633,11 +633,11 @@ def run(self, soft_minimize=False, overwrite=False, fail_ok=False):
             logger.debug("Exec line: " + " ".join(exec_list))
 
             # Execute
-            amber_output = sp.Popen(
+            amber_output = subprocess.Popen(
                 exec_list,
                 cwd=self.path,
-                stdout=sp.PIPE,
-                stderr=sp.PIPE,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
                 env=os.environ,
             )
 

paprika/simulate/gromacs.py

@@ -2,7 +2,7 @@
 import glob
 import logging
 import os
-import subprocess as sp
+import subprocess
 from collections import OrderedDict
 from enum import Enum
 
@@ -623,11 +623,11 @@ def run(self, run_grompp=True, overwrite=False, fail_ok=False):
                     grompp_list += ["-n", self.index_file]
 
                 # Run GROMPP
-                grompp_output = sp.Popen(
+                grompp_output = subprocess.Popen(
                     grompp_list,
                     cwd=self.path,
-                    stdout=sp.PIPE,
-                    stderr=sp.PIPE,
+                    stdout=subprocess.PIPE,
+                    stderr=subprocess.PIPE,
                     env=os.environ,
                 )
                 grompp_stdout = grompp_output.stdout.read().splitlines()
@@ -707,11 +707,11 @@ def run(self, run_grompp=True, overwrite=False, fail_ok=False):
                     mdrun_list += ["-plumed", self.plumed_file]
 
             # Run MDRUN
-            mdrun_output = sp.Popen(
+            mdrun_output = subprocess.Popen(
                 mdrun_list,
                 cwd=self.path,
-                stdout=sp.PIPE,
-                stderr=sp.PIPE,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
                 env=os.environ,
             )
             mdrun_out = mdrun_output.stdout.read().splitlines()

paprika/simulate/namd.py

@@ -1,12 +1,12 @@
 import abc
 import logging
 import os
-import subprocess as sp
+import subprocess
 from collections import OrderedDict
 from enum import Enum
 
-import numpy as np
-import parmed as pmd
+import numpy
+import parmed
 
 from paprika.utils import get_dict_without_keys
 
@@ -584,14 +584,14 @@ def _get_cell_basis_vectors(self):
         """
         Function to calculate the PBC cell basis vectors (needed when running a simulation for the first time).
         """
-        structure = pmd.load_file(
+        structure = parmed.load_file(
             os.path.join(self.path, self.topology),
             os.path.join(self.path, self.coordinates),
             structure=True,
         )
         coordinates = structure.coordinates
-        masses = np.ones(len(coordinates))
-        center = pmd.geometry.center_of_mass(coordinates, masses)
+        masses = numpy.ones(len(coordinates))
+        center = parmed.geometry.center_of_mass(coordinates, masses)
 
         self.cell_basis_vectors["cellOrigin"] = list(center)
 
@@ -878,11 +878,11 @@ def run(self, overwrite=True, fail_ok=False):
             logger.debug("Exec line: " + " ".join(exec_list))
 
             # Execute
-            namd_output = sp.Popen(
+            namd_output = subprocess.Popen(
                 exec_list,
                 cwd=self.path,
                 stdout=open(os.path.join(self.path, self.logfile), "w"),
-                stderr=sp.PIPE,
+                stderr=subprocess.PIPE,
                 env=os.environ,
             )
             namd_stderr = namd_output.stderr.read().splitlines()