def create_dataframeBI(bi_path, section='[CONDUITS]'):
"""
given a path to a biuld instructions file, create a dataframe of data in a
given section
"""
headerdefs = funcs.complete_inp_headers(bi_path)
headerlist = headerdefs['headers'][section].split() + [';', 'Comment', 'Origin']
tempfilepath = txt.extract_section_from_inp(bi_path, section,
headerdefs=headerdefs,
skipheaders=True)
df = pd.read_table(tempfilepath, header=None, delim_whitespace=True,
skiprows=[0], index_col=0, names=headerlist, comment=None)
os.remove(tempfilepath) # clean up
return df
def create_dataframeINP(inp_path, section='[CONDUITS]', ignore_comments=True,
comment_str=';', comment_cols=True):
"""
given a path to an INP file, create a dataframe of data in the given
section.
"""
# find all the headers and their defs (section title with cleaned one-liner column headers)
headerdefs = funcs.complete_inp_headers(inp_path)
# create temp file with section isolated from inp file
tempfilepath = txt.extract_section_from_inp(inp_path, section,
headerdefs=headerdefs,
ignore_comments=ignore_comments)
if ignore_comments:
comment_str = None
if not tempfilepath:
# if this head (section) was not found in the textfile, return a
# blank dataframe with the appropriate schema
print('header "{}" not found in "{}"'.format(section, inp_path))
print('returning empty dataframe')
headerlist = headerdefs['headers'].get(section, 'blob').split() + [';', 'Comment', 'Origin']
blank_df = pd.DataFrame(data=None, columns=headerlist).set_index(headerlist[0])
return blank_df
if headerdefs['headers'][section] == 'blob':
# return the whole row, without specifc col headers
df = pd.read_table(tempfilepath, delim_whitespace=False, comment=comment_str)
elif section == '[CURVES]' or section == '[TIMESERIES]':
# return the whole row, without specifc col headers
df = pd.read_table(tempfilepath, delim_whitespace=False) # , index_col=0)#, skiprows=[0])
else:
# this section header is recognized and will be organized into known columns
headerlist = headerdefs['headers'][section].split()
if comment_cols:
headerlist = headerlist + [';', 'Comment', 'Origin']
df = pd.read_table(tempfilepath, header=None, delim_whitespace=True, skiprows=[0],
index_col=0, names=headerlist, comment=comment_str)
if comment_cols:
# add new blank comment column after a semicolon column
df[';'] = ';'
os.remove(tempfilepath)
return df.rename(index=str)
def generate_inp_from_diffs(basemodel, inpdiffs, target_dir):
"""
create a new inp with respect to a baseline inp and changes instructed
with a list of inp diff files (build instructions). This saves having to
recalculate the differences of each model from the baseline whenever we want
to combine versions.
"""
#step 1 --> combine the diff/build instructions
allheaders = funcs.complete_inp_headers(basemodel.inp.filePath)
combi_build_instr_file = os.path.join(target_dir, 'build_instructions.txt')
newinp = os.path.join(target_dir, 'new.inp')
with open(combi_build_instr_file, 'w') as f:
for header in allheaders['order']:
s = ''
section_header_written = False
for inp in inpdiffs:
sect_s = None
if not section_header_written:
sect_s = text.extract_section_from_inp(inp,
header,
cleanheaders=False,
return_string=True,
skipheaders=False)
section_header_written = True
else:
sect_s = text.extract_section_from_inp(inp,
header,
cleanheaders=False,
return_string=True,
skipheaders=True)
if sect_s:
#remove the extra space between data in the same table
#coming from diffrent models.
if sect_s[-2:] == '\n\n': #NOTE Check this section...
s += sect_s[:-1]
else:
s += sect_s
f.write(s + '\n')
#step 2 --> clean up the new combined diff instructions
df_dict = clean_inp_diff_formatting(
combi_build_instr_file) #makes more human readable
#step 3 --> create a new inp based on the baseline, with the inp_diff
#instructions applied
with open(newinp, 'w') as f:
for section in allheaders['order']:
print section
if section not in problem_sections and allheaders['headers'][
section] != 'blob':
#check if a changes from baseline spreadheet exists, and use this
#information if available to create the changes array
df = create_dataframeINP(basemodel.inp, section)
df['Origin'] = '' #add the origin column if not there
if section in df_dict:
df_change = df_dict[section]
ids_to_drop = df_change.loc[df_change['Comment'].isin(
['Removed', 'Altered'])].index
df = df.drop(ids_to_drop)
df = df.append(df_change.loc[df_change['Comment'].isin(
['Added', 'Altered'])])
new_section = df
else:
#blindly copy this section from the base model
new_section = create_dataframeINP(basemodel.inp,
section=section)
#write the section into the inp file and the excel file
vc_utils.write_inp_section(f, allheaders, section, new_section)
def generate_inp_from_diffs(basemodel, inpdiffs, target_dir):
"""
create a new inp with respect to a baseline inp and changes instructed
with a list of inp diff files (build instructions). This saves having to
recalculate the differences of each model from the baseline whenever we want
to combine versions.
NOTE THIS ISN'T USED ANYWHERE. DELETE ????
"""
#step 1 --> combine the diff/build instructions
allheaders = funcs.complete_inp_headers(basemodel.inp.path)
combi_build_instr_file = os.path.join(target_dir, 'build_instructions.txt')
newinp = os.path.join(target_dir, 'new.inp')
with open (combi_build_instr_file, 'w') as f:
for header in allheaders['order']:
s = ''
section_header_written = False
for inp in inpdiffs:
sect_s = None
if not section_header_written:
sect_s = text.extract_section_from_inp(inp, header,
cleanheaders=False,
return_string=True,
skipheaders=False)
section_header_written = True
else:
sect_s = text.extract_section_from_inp(inp, header,
cleanheaders=False,
return_string=True,
skipheaders=True)
if sect_s:
#remove the extra space between data in the same table
#coming from diffrent models.
if sect_s[-2:] == '\n\n': #NOTE Check this section...
s += sect_s[:-1]
else:
s += sect_s
f.write(s + '\n')
#step 2 --> clean up the new combined diff instructions
# df_dict = clean_inp_diff_formatting(combi_build_instr_file) #makes more human readable
#step 3 --> create a new inp based on the baseline, with the inp_diff
#instructions applied
with open (newinp, 'w') as f:
for section in allheaders['order']:
print(section)
if section not in problem_sections and allheaders['headers'][section] != 'blob':
#check if a changes from baseline spreadheet exists, and use this
#information if available to create the changes array
df = create_dataframeINP(basemodel.inp.path, section)
df['Origin'] = '' #add the origin column if not there
if section in df_dict:
df_change = df_dict[section]
ids_to_drop = df_change.loc[df_change['Comment'].isin(['Removed', 'Altered'])].index
df = df.drop(ids_to_drop)
df = df.append(df_change.loc[df_change['Comment'].isin(['Added', 'Altered'])])
new_section = df
else:
#blindly copy this section from the base model
new_section = create_dataframeINP(basemodel.inp.path, section=section)
#write the section into the inp file and the excel file
vc_utils.write_inp_section(f, allheaders, section, new_section)
