def expand_minmax(self):
sfile = open(self.destfor, 'r')
dfile = open(self.destcmp, 'w')
compare_format = Keyword(
KEYWORDS['compare']) + '(' + Regex(r'[^\s\(\)]*')('op') + ')'
case_var_format = Word(alphas + "_", alphanums + "_" + ".")('var')
case_format = Keyword(KEYWORDS['case']) + case_var_format + ":"
for line in sfile:
if KEYWORDS['compare'] in line:
res = compare_format.parseString(line)
op = res.op
varlist = OrderedDict()
while True:
l = sfile.readline()
if KEYWORDS['endcompare'] in l:
break
elif KEYWORDS['case'] in l:
res = case_format.parseString(l)
var = res.var
varlist[var] = ''
lcase = sfile.readline()
content = ''
while KEYWORDS['endcase'] not in lcase:
content += lcase
lcase = sfile.readline()
varlist[var] = content
self.roll_out_compare(varlist, op, dfile)
else:
dfile.write(line)
sfile.close()
dfile.close()
def expand_sync(self):
sfile = open(self.destbool, 'r')
dfile = open(self.dest, 'w')
sync_format = Keyword(KEYWORDS['sync']) + '(' + Regex(r'[_a-zA-Z]*')(
"header_name") + "." + Regex(r'[^\s\(\),]*')("field") + "," + Word(
nums)("val") + ')'
mirror_format = Keyword(KEYWORDS['mirror']) + '(' + Regex(
r'[_a-zA-Z]*')("header_name") + "." + Regex(r'[^\s\(\),]*')(
"field") + "," + Word(nums)("val") + ')'
active_sync, active_mirror = False, False
fields, field_name, val = [], None, None
for line in sfile:
if KEYWORDS['sync'] in line:
res = sync_format.parseString(line)
field_name, val = res.header_name + '.' + res.field, res.val
active_sync = True
self.sync_id += 1
elif KEYWORDS['mirror'] in line:
res = mirror_format.parseString(line)
field_name, val = res.header_name + '.' + res.field, res.val
active_mirror = True
self.mirror_id += 1
elif KEYWORDS['endsync'] in line:
active_sync = False
self.write_sync_action(fields, self.sync_id, field_name, val)
fields = []
indent = line[:-len(line.lstrip())]
dfile.write(APPLY_SYNC_STRING % (indent, self.sync_id))
elif KEYWORDS['endmirror'] in line:
active_mirror = False
self.write_mirror_action(fields, self.mirror_id, field_name,
val)
fields = []
indent = line[:-len(line.lstrip())]
dfile.write(APPLY_MIRROR_STRING % (indent, self.mirror_id))
elif active_sync or active_mirror:
fields.append(line.strip())
else:
dfile.write(line)
sfile.close()
dfile.close()
class DSLParser:
def __init__(self, n, colors, shapes, max_constant=5):
"""
:param n: length of side of the grids
:param colors: list of color names
:param shapes: list of shape names
"""
self.colors = Or([Keyword(w) for w in colors])
self.colors ^= Keyword("getMarkerColor()")
self.shapes = Or([Keyword(w) for w in shapes])
self.shapes ^= Keyword("getMarkerShape()")
self.positions = Or([Keyword(str(i)) for i in range(n)])
self.constants = Or([Keyword(str(i)) for i in range(1, max_constant)])
self.actions = (("move(" + self.positions + "," + self.positions + ")")
| "moveUp()" | "moveDown()" | "moveLeft()"
| "moveRight()" | "moveTop()" | "moveBottom()"
| "moveLeftmost()" | "moveRightmost()"
| "moveToMovableMarker()" | "pickMarker()"
| "putMarker()" | "fixMarker()")
self.conditions = (Group(self.shapes + "==" + self.shapes)
| Group(self.colors + "==" + self.colors)
| "markersPresent()" | "movableMarkersPresent()"
| "existMovableMarkers()" | "upperBoundary()"
| "lowerBoundary()" | "leftBoundary()"
| "rightBoundary()" | "true")
self.conditions = (self.conditions
| Group(Keyword("not") + self.conditions))
block = Forward()
stmt = (Group(
Keyword("while") + "(" + self.conditions + ")" + "{" +
Group(block) + "}") | Group(
Keyword("repeat") + "(" + self.constants + ")" + "{" +
Group(block) + "}") | Group(
Keyword("if") + "(" + self.conditions + ")" + "{" +
Group(block) + "}") | Group(
Keyword("ifelse") + "(" + self.conditions + ")" + "{" +
Group(block) + "}" + Keyword("else") + "{" +
Group(block) + "}") | Group(self.actions + ";"))
block << OneOrMore(stmt)
# stmt ^= block
self.statements = block
self.program = Keyword("def") + Keyword("run()") + "{" \
+ Group(self.statements) + "}"
def parse_string(self, program):
return self.program.parseString(program, parseAll=True)
molecule_name = os.path.basename(elem)
molecule_name = os.path.splitext(molecule_name)[-2]
# parse lines for data extraction
for line in lines:
# find out units of distance in file
try:
res1 = split_bohr.parseString(line)
if res1[1] == 'Bohr':
unit_array.append(res1[1])
except Exception:
print('No units given in this line')
try:
res2 = split_angstrom.parseString(line)
if res2[1] == 'Angstrom':
unit_array.append(res2[1])
except Exception:
print('No units given in this line')
try:
parsed_lines = clt_parser.parseString(line)
list_conversion = list(parsed_lines)
df = df.append(
{
'label': list_conversion[0][0],
'atomic_number': float(list_conversion[1]),
'x': float(list_conversion[2]),
'y': float(list_conversion[3]),
def extract_moments(file_directory):
# list to host directory for each unique .mom file
mom_files = []
# search through entire file hierarchy to find all .mom files to parse
for root, dirs, files in os.walk(file_directory):
for i in files:
# select only .mom files and add them to list
if os.path.splitext(os.path.basename(root + '/' + i))[1] == '.mom':
mom_files.append(root + '/' + i)
# define parsing grammar
# find the df type from .mom file
df_parser = Keyword("! Based on DF-type :") + Word(alphas)
# parse floating point numbers
float_parser = Combine(Optional('-') + Word(nums) + '.' + Word(nums))
mom_parser = OneOrMore(float_parser)
# parse a line common to all .mom files with the following structure: ATOM X Y Z Type <ATOM-TYPE> Rank K
atom_line = Word(alphas + nums) + mom_parser + OneOrMore(Word(alphas)) + Word(nums)
# lists for storing information
error_array, df_array, coords, atom = ([],)*4
# json array to compile each atom multipole moment info
json_result = {'moments': []}
# empty dataframe to host atom information
df = pd.DataFrame(data={'atom': [], 'type': [], 'rank': []})
# dictionary to store sorted moment values
atom_mom = {}
# using pyparsing's 'search string' method
for elem in mom_files:
# open file
file_object = open(elem, 'r')
lines = file_object.readlines()
mom_name = os.path.basename(elem)
mom_name = os.path.splitext(mom_name)[-2]
# parse lines for data extraction
for line in lines:
# get df type first
try:
res1 = df_parser.parseString(line)
df_array.append(res1[1])
except Exception:
print('No DF-Type is specified in file')
error_array.append('No DF-Type is specified in file')
# get information about each atom
try:
res2 = atom_line.parseString(line)
atom.append(res2[0])
type = res2[5]
rank = (res2[7])
df = df.append({'atom': atom[len(atom) - 1], 'type': type, 'rank': rank}, ignore_index=True)
except Exception:
print('This is not a atom type line')
# get moment values
try:
res3 = mom_parser.parseString(line)
for i in res3:
coords.append(i)
except Exception:
print('This is not a moment value')
coords_float = []
for val in coords:
coords_float.append(np.float(val))
# assign the correct values from coords to the right moment value
for i in range(len(atom)):
# get atom information (+1 is so that Q0 is also counted as well as Q4: Q0, Q1, Q2, Q3, Q4)
name = df.iloc[i]['atom']
r = int(df.iloc[i]['rank']) + 1
array, tot, cum_sum = ([],)*3
# find correct number of coordinates to fill each moment configuration with
for ii in range(r):
s = 2 * ii + 1
tot.append(s)
cum_sum.append(np.cumsum(tot).tolist())
cum_sum = cum_sum[len(cum_sum) - 1]
total = np.sum(tot)
# value in tot is the number of values stored in each Q layer
for value in tot:
temporary_array = []
# k in range() ensures the correct number of moments is filled in each layer
for k in range(value):
temporary_array.append(coords_float[k])
array.append(temporary_array)
for kkk in temporary_array:
coords_float.pop(coords_float.index(kkk))
# fill a dictionary with atoms as keys and moments as values
atom_mom[atom[i]] = array
# need to loop over elements in dataframe to add each value for every atom in atom_mom, here we need to do
# the JSON method too and probably wipe the contents of the dataframe at the beginning of each new loop
for idx, kk in enumerate(atom_mom):
# need to reshape lists, first, find max value list can be
max_len = max(len(i) for i in atom_mom[kk])
# pad short lists with NaN
for col in atom_mom[kk]:
col.extend((max_len - len(col)) * [np.nan])
# convert to array
arr = np.asarray(atom_mom[kk]).T
# create indices depending on rank
indices = []
for ind in range(1, len(atom_mom[kk])):
one = f'{ind}s'
two = f'{ind}c'
indices.append(one)
indices.append(two)
indices.insert(0, '0')
# create df depending on rank also
df_mom = pd.DataFrame(
arr,
columns=[f'Q{i}' for i in range(0, len(atom_mom[kk]))],
index=indices
)
moments_string = df_mom.to_string().splitlines()
mom_data = {
'atom': atom[idx],
'scheme': mom_name,
'type': df['type'][idx],
'rank': df['rank'][idx],
'moments': moments_string,
'file': elem
}
json_result['moments'].append({kk: mom_data})
# save this information to a json file called mom_test.json
# with open("mom_test.json", "w") as mom_json:
# json.dump(json_result, mom_json, indent=4)
return json_result
def extract_coordinates(file_directory):
# list molecule files: below is an example for arg:
clt_files = glob.glob(file_directory + '*.clt')
xyz_files = glob.glob(file_directory + '*.xyz')
# empty dict to host df's
molecules_from_files = {}
# definitions of all the parsing tools used
clt_parser = Word(alphas + nums) + Word(nums + '.' + nums) + Word(printables + '.' + printables) + \
Word(printables + '.' + printables) + Word(printables + '.' + printables)
xyz_parser = Word(alphas) + Word(printables + '.' + printables) + Word(printables + '.' + printables) + \
Word(printables + '.' + printables)
bohr = Keyword('Bohr')
angstrom = Keyword('Angstrom')
word = ~bohr + Word(alphas)
sentence = OneOrMore(word)
split_bohr = sentence('unit') + bohr + sentence('degree')
split_angstrom = Keyword('Units') + angstrom
unit_array = []
error_array = []
# empty dataframe to host data
df = pd.DataFrame(data={'label': [], 'atomic_number': [], 'x': [], 'y': [], 'z': []})
if len(clt_files) or len(xyz_files) or (len(clt_files) + len(xyz_files)) > 1:
# extract information from .clt files
for elem in clt_files:
# open file
file_object = open(elem, 'r')
lines = file_object.readlines()
molecule_name = os.path.basename(elem)
molecule_name = os.path.splitext(molecule_name)[-2]
# parse lines for data extraction
for line in lines:
# find out units of distance in file
try:
res1 = split_bohr.parseString(line)
if res1[1] == 'Bohr':
unit_array.append(res1[1])
except Exception:
print('invalid unit line')
try:
res2 = split_angstrom.parseString(line)
if res2[1] == 'Angstrom':
unit_array.append(res2[1])
except Exception:
print('not valid data line')
try:
parsed_lines = clt_parser.parseString(line)
list_conversion = list(parsed_lines)
df = df.append(
{'label': list_conversion[0][0], 'atomic_number': float(list_conversion[1]),
'x': float(list_conversion[2]), 'y': float(list_conversion[3]),
'z': float(list_conversion[4])}, ignore_index=True)
print('valid data line')
except Exception:
print('not valid data line')
# perform necessary unit conversions
if len(unit_array) == 1:
if unit_array[0] == 'Angstrom':
print('Units are Angstrom')
elif unit_array[0] == 'Bohr':
df['x'] = df['x'] / 1.89
df['y'] = df['y'] / 1.89
df['z'] = df['z'] / 1.89
# ambiguous case, just have to assume it's bohr but add line in summary file explaining ambiguity
elif len(unit_array) == 2:
print('ambiguous units')
unit_array.clear()
unit_array.append('Unknown')
error_array.append('Ambiguous unit in file.')
molecules_from_files[str(molecule_name)] = df
if len(clt_files) > 1:
print('there are too many clt files, ambiguous')
error_array.append('There are too many .clt files, see file origin above for file used.')
# extract information from .xyz files:
for elem in xyz_files:
# empty dataframe to host data
df_2 = pd.DataFrame(data={'label': [], 'atomic_number': [], 'x': [], 'y': [], 'z': []})
# open file
file_object = open(elem, 'r')
lines = file_object.readlines()
molecule_name = os.path.basename(elem)
molecule_name = os.path.splitext(molecule_name)[-2]
# parse lines for data extraction
for line in lines:
try:
parsed_lines = xyz_parser.parseString(line)
list_conversion = list(parsed_lines)
atomic_number_df = atom_database.database[atom_database.database['symbol'] == list_conversion[0]]
atomic_number = atomic_number_df.iloc[0][0]
df_2 = df_2.append(
{'label': list_conversion[0], 'atomic_number': atomic_number,
'x': float(list_conversion[1]), 'y': float(list_conversion[2]),
'z': float(list_conversion[3])}, ignore_index=True)
print('valid data line')
except Exception:
print('not valid data line')
molecules_from_files[str(molecule_name)] = df_2
if len(error_array) == 0:
error_array.append('No errors')
# create a json object that will be saved to a summary file
for key, value in molecules_from_files.items():
coord_string = value.to_string().splitlines()
coord_data = {
'coordinates': {
'file': file_directory,
'data frame': df.to_json(),
'errors': error_array,
'units': unit_array[0],
'coordinates': coord_string
}
}
# save information to json file called coord_test.json
# with open("coord_test.json", "w") as coord_json:
# json.dump(coord_data, coord_json, indent=4)
else:
# extract information from .clt files
for elem in clt_files:
# open file
file_object = open(elem, 'r')
lines = file_object.readlines()
molecule_name = os.path.basename(elem)
molecule_name = os.path.splitext(molecule_name)[-2]
# parse lines for data extraction
for line in lines:
# find out units of distance in file
try:
res1 = split_bohr.parseString(line)
if res1[1] == 'Bohr':
unit_array.append(res1[1])
except Exception:
print('invalid unit line')
try:
res2 = split_angstrom.parseString(line)
if res2[1] == 'Angstrom':
unit_array.append(res2[1])
except Exception:
print('not valid data line')
try:
parsed_lines = clt_parser.parseString(line)
list_conversion = list(parsed_lines)
df = df.append(
{'label': list_conversion[0][0], 'atomic_number': float(list_conversion[1]),
'x': float(list_conversion[2]), 'y': float(list_conversion[3]),
'z': float(list_conversion[4])}, ignore_index=True)
print('valid data line')
except Exception:
print('not valid data line')
# perform necessary unit conversions
if len(unit_array) == 1:
if unit_array[0] == 'Angstrom':
print('Units are Angstrom')
elif unit_array[0] == 'Bohr':
df['x'] = df['x'] / 1.89
df['y'] = df['y'] / 1.89
df['z'] = df['z'] / 1.89
# ambiguous case, just have to assume it's bohr but add line in summary file explaining ambiguity
elif len(unit_array) == 2:
print('ambiguous units')
unit_array.clear()
unit_array.append('Unknown')
error_array.append('Ambiguous unit in file.')
# extract information from .xyz files:
for elem in xyz_files:
# empty dataframe to host data
df_2 = pd.DataFrame(data={'label': [], 'atomic_number': [], 'x': [], 'y': [], 'z': []})
# open file
file_object = open(elem, 'r')
lines = file_object.readlines()
molecule_name = os.path.basename(elem)
molecule_name = os.path.splitext(molecule_name)[-2]
# parse lines for data extraction
for line in lines:
try:
parsed_lines = xyz_parser.parseString(line)
list_conversion = list(parsed_lines)
atomic_number_df = atom_database.database[atom_database.database['symbol'] == list_conversion[0]]
atomic_number = atomic_number_df.iloc[0][0]
df_2 = df_2.append(
{'label': list_conversion[0], 'atomic_number': atomic_number,
'x': float(list_conversion[1]), 'y': float(list_conversion[2]),
'z': float(list_conversion[3])}, ignore_index=True)
print('valid data line')
except Exception:
print('not valid data line')
if len(error_array) == 0:
error_array.append('No errors')
# create a json object that will be saved to a summary file
coord_string = df.to_string().splitlines()
coord_data = {
'coordinates': {
'file': file_directory,
'data frame': df.to_json(),
'errors': error_array,
'units': unit_array[0],
'coordinates': coord_string
}
}
# with open("coord_test.json", "w") as coord_json:
# json.dump(coord_data, coord_json, indent=4)
return coord_data
# using pyparsing's 'search string' method
for elem in mom_files:
# open file
file_object = open(elem, 'r')
lines = file_object.readlines()
mom_name = os.path.basename(elem)
mom_name = os.path.splitext(mom_name)[-2]
# parse lines for data extraction
for line in lines:
# get df type first
try:
res1 = df_parser.parseString(line)
df_array.append(res1[1])
except Exception:
print('No DF-Type is specified in file')
error_array.append('No DF-Type is specified in file')
# get information about each atom
try:
res2 = atom_line.parseString(line)
atom.append(res2[0])
type = res2[5]
rank = (res2[7])
df = df.append({'atom': atom[len(atom) - 1], 'type': type, 'rank': rank}, ignore_index=True)
except Exception:
print('This is not a atom type line')
molecule_name = os.path.basename(elem)
molecule_name = os.path.splitext(molecule_name)[-2]
# parse lines for data extraction
for line in lines:
# find out units of distance in file
try:
res1 = split_bohr.parseString(line)
if res1[1] == 'Bohr':
unit_array.append(res1[1])
except Exception:
print('invalid unit line')
try:
res2 = split_angrstom.parseString(line)
if res2[1] == 'Angstrom':
unit_array.append(res2[1])
except Exception:
print('not valid data line')
try:
parsed_lines = clt_parser.parseString(line)
list_conversion = list(parsed_lines)
df = df.append(
{
'label': list_conversion[0][0],
'atomic_number': float(list_conversion[1]),
'x': float(list_conversion[2]),
'y': float(list_conversion[3]),
