def build(self, baseline_dir, target_path):
"""
build a complete INP file with the build instructions committed to a
baseline model.
"""
basemodel = swmmio.Model(baseline_dir)
allheaders = funcs.complete_inp_headers(basemodel.inp.path)
#new_inp = os.path.join(target_dir, 'model.inp')
with open (target_path, 'w') as f:
for section in allheaders['order']:
#check if the section is not in problem_sections and there are changes
#in self.instructions and commit changes to it from baseline accordingly
if (section not in problem_sections
and allheaders['headers'][section] != 'blob'
and section in self.instructions):
#df of baseline model section
basedf = create_dataframeINP(basemodel.inp.path, section)
#grab the changes to
changes = self.instructions[section]
#remove elements that have alterations and or tagged for removal
remove_ids = changes.removed.index | changes.altered.index
new_section = basedf.drop(remove_ids)
#add elements
new_section = pd.concat([new_section, changes.altered, changes.added])
else:
#section is not well understood or is problematic, just blindly copy
new_section = create_dataframeINP(basemodel.inp.path, section=section)
#write the section
vc_utils.write_inp_section(f, allheaders, section, new_section)
def create_inp_build_instructions(inpA, inpB, path, filename, comments=''):
"""
pass in two inp file paths and produce a spreadsheet showing the differences
found in each of the INP sections. These differences should then be used
whenever we need to rebuild this model from the baseline reference model.
Note: this should be split into a func that creates a overall model "diff"
that can then be written as a BI file or used programmatically
"""
allsections_a = funcs.complete_inp_headers(inpA)
modela = swmmio.Model(inpA)
modelb = swmmio.Model(inpB)
#create build insructions folder
if not os.path.exists(path):
os.makedirs(path)
filepath = os.path.join(path, filename) + '.txt'
# xlpath = os.path.join(path, filename) + '.xlsx'
# excelwriter = pd.ExcelWriter(xlpath)
# vc_utils.create_change_info_sheet(excelwriter, modela, modelb)
problem_sections = [
'[TITLE]', '[CURVES]', '[TIMESERIES]', '[RDII]', '[HYDROGRAPHS]'
]
with open(filepath, 'w') as newf:
#write meta data
metadata = {
#'Baseline Model':modela.inp.path,
#'ID':filename,
'Parent Models': {
'Baseline': {
inpA: vc_utils.modification_date(inpA)
},
'Alternatives': {
inpB: vc_utils.modification_date(inpB)
}
},
'Log': {
filename: comments
}
}
#print metadata
vc_utils.write_meta_data(newf, metadata)
for section in allsections_a['order']:
if section not in problem_sections:
#calculate the changes in the current section
changes = INPDiff(modela, modelb, section)
data = pd.concat(
[changes.removed, changes.added, changes.altered])
#vc_utils.write_excel_inp_section(excelwriter, allsections_a, section, data)
vc_utils.write_inp_section(
newf,
allsections_a,
section,
data,
pad_top=False,
na_fill='NaN') #na fill fixes SNOWPACK blanks spaces issue
def replace_inp_section(inp_path, modified_section_header, new_data):
"""
modify an existing inp file by passing in new data (Pandas Dataframe)
and the section header that should be modified. This function will overwrite
all data in the old section with the passed data
:param inp_path: path to inp file to be changed
:param modified_section_header: section for which data should be change
:param new_data: pd.DataFrame of data to overwrite data in the modified section
:return: swmmio.Model instantiated with modified inp file
"""
sections = get_inp_sections_details(inp_path)
m = swmmio.Model(inp_path)
with tempfile.TemporaryDirectory() as tempdir:
with open(inp_path) as oldf:
tmp_inp_path = os.path.join(tempdir, f'{m.inp.name}.inp')
with open(tmp_inp_path, 'w') as new:
# write each line as is from the original model until we find the
# header of the section we wish to overwrite
found_section = False
found_next_section = False
for line in oldf:
if modified_section_header in line:
# write the replacement data in the new file now
write_inp_section(new,
sections,
modified_section_header,
new_data,
pad_top=False)
found_section = True
if (found_section
and any(es in line for es in sections.keys())
and modified_section_header not in line):
found_next_section = True
if found_next_section or not found_section:
# write the lines from the original file
# if we haven't found the section to modify.
# if we have found the section and we've found the NEXT section
# continue writing original file's lines
new.write(line)
if not found_section:
# the header doesn't exist in the old model
# so we should append it to the bottom of file
write_inp_section(new, sections, modified_section_header,
new_data)
# rename files and remove old if we should overwrite
os.remove(inp_path)
shutil.copy2(tmp_inp_path, inp_path)
return swmmio.Model(inp_path)
def save(self, dir, filename):
"""
save the current BuildInstructions instance to file (human readable)
"""
if not os.path.exists(dir):
os.makedirs(dir)
filepath = os.path.join(dir, filename)
with open (filepath, 'w') as f:
vc_utils.write_meta_data(f, self.metadata)
for section, change_obj in self.instructions.items():
section_df = pd.concat([change_obj.removed, change_obj.altered, change_obj.added])
vc_utils.write_inp_section(f, allheaders=None, sectionheader=section,
section_data=section_df, pad_top=False, na_fill='NaN')
def create_inp_build_instructions(inpA, inpB, path, filename, comments=''):
"""
pass in two inp file paths and produce a spreadsheet showing the differences
found in each of the INP sections. These differences should then be used
whenever we need to rebuild this model from the baseline reference model.
Note: this should be split into a func that creates a overall model "diff"
that can then be written as a BI file or used programmatically
"""
allsections_a = funcs.complete_inp_headers(inpA)
modela = swmmio.Model(inpA)
modelb = swmmio.Model(inpB)
#create build insructions folder
if not os.path.exists(path):
os.makedirs(path)
filepath = os.path.join(path, filename) + '.txt'
# xlpath = os.path.join(path, filename) + '.xlsx'
# excelwriter = pd.ExcelWriter(xlpath)
# vc_utils.create_change_info_sheet(excelwriter, modela, modelb)
problem_sections = ['[TITLE]', '[CURVES]', '[TIMESERIES]', '[RDII]', '[HYDROGRAPHS]']
with open (filepath, 'w') as newf:
#write meta data
metadata = {
#'Baseline Model':modela.inp.path,
#'ID':filename,
'Parent Models':{
'Baseline':{inpA:vc_utils.modification_date(inpA)},
'Alternatives':{inpB:vc_utils.modification_date(inpB)}
},
'Log':{filename:comments}
}
#print metadata
vc_utils.write_meta_data(newf, metadata)
for section in allsections_a['order']:
if section not in problem_sections:
#calculate the changes in the current section
changes = INPDiff(modela, modelb, section)
data = pd.concat([changes.removed, changes.added, changes.altered])
#vc_utils.write_excel_inp_section(excelwriter, allsections_a, section, data)
vc_utils.write_inp_section(newf, allsections_a, section, data, pad_top=False, na_fill='NaN') #na fill fixes SNOWPACK blanks spaces issue
def save(self, dir, filename):
"""
save the current BuildInstructions instance to file (human readable)
"""
if not os.path.exists(dir):
os.makedirs(dir)
filepath = os.path.join(dir, filename)
with open(filepath, 'w') as f:
vc_utils.write_meta_data(f, self.metadata)
for section, change_obj in self.instructions.iteritems():
section_df = pd.concat(
[change_obj.removed, change_obj.altered, change_obj.added])
vc_utils.write_inp_section(f,
allheaders=None,
sectionheader=section,
section_data=section_df,
pad_top=False,
na_fill='NaN')
def build(self, baseline_dir, target_path):
"""
build a complete INP file with the build instructions committed to a
baseline model.
"""
basemodel = swmmio.Model(baseline_dir)
allheaders = get_inp_sections_details(basemodel.inp.path)
# new_inp = os.path.join(target_dir, 'model.inp')
with open(target_path, 'w') as f:
for section, _ in allheaders.items():
# check if the section is not in problem_sections and there are changes
# in self.instructions and commit changes to it from baseline accordingly
if (section not in problem_sections
and allheaders[section]['columns'] != ['blob']
and section in self.instructions):
# df of baseline model section
basedf = dataframe_from_bi(basemodel.inp.path, section)
basedf[';'] = ';'
# grab the changes to
changes = self.instructions[section]
# remove elements that have alterations and or tagged for removal
remove_ids = changes.removed.index | changes.altered.index
new_section = basedf.drop(remove_ids)
# add elements
new_section = pd.concat(
[new_section, changes.altered, changes.added])
else:
# section is not well understood or is problematic, just blindly copy
new_section = dataframe_from_bi(basemodel.inp.path,
section=section)
new_section[';'] = ';'
# write the section
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.
"""
#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 merge_models(inp1, inp2, target='merged_model.inp'):
"""
Merge two separate swmm models into one model. This creates a diff, ignores
removed sections, and uses inp1 settings where conflicts exist (altered sections in diff)
:param inp1: swmmio.Model.inp object to be combined with inp2
:param inp2: swmmio.Model.inp object to be combined with inp1
:param target: path of new model
:return: path to target
"""
# model object to store resulting merged model
m3 = swmmio.Model(inp1)
inp_diff = INPDiff(inp1, inp2)
with open(target, 'w') as newf:
for section, _ in inp_diff.all_inp_objects.items():
# don't consider the "removed" parts of the diff
# print('{}: {}'.format(section,inp_diff.all_inp_objects[section]['columns']))
# check if the section is not in problem_sections and there are changes
# in self.instructions and commit changes to it from baseline accordingly
col_order = []
if (section not in problem_sections and
inp_diff.all_inp_objects[section]['columns'] != ['blob']
and section in inp_diff.diffs):
# df of baseline model section
basedf = dataframe_from_inp(
m3.inp.path,
section,
additional_cols=[';', 'Comment', 'Origin'])
basedf[';'] = ';'
col_order = basedf.columns
# grab the changes to
changes = inp_diff.diffs[section]
# remove elements that have alterations keep ones tagged for removal
# (unchanged, but not present in m2)
remove_ids = changes.altered.index
new_section = basedf.drop(remove_ids)
# add elements
new_section = pd.concat(
[new_section, changes.altered, changes.added],
axis=0,
sort=False)
else:
# section is not well understood or is problematic, just blindly copy
new_section = dataframe_from_inp(
m3.inp.path,
section,
additional_cols=[';', 'Comment', 'Origin'])
new_section[';'] = ';'
# print ('dealing with confusing section: {}\n{}'.format(section, new_section))
# print(new_section.head())
# write the section
new_section = new_section[col_order]
new_section[';'] = ';'
vc_utils.write_inp_section(newf,
inp_diff.all_inp_objects,
section,
new_section,
pad_top=True)
return target
def replace_inp_section(inp_path,
modified_section_header,
new_data,
overwrite=True):
"""
modify an existing model by passing in new data (Pandas Dataframe)
and the section header that should be modified. This funciton will overwrite
all data in the old section with the passed data
"""
tmpfilename = os.path.splitext(os.path.basename(inp_path))[0] + '_mod.inp'
wd = os.path.dirname(inp_path)
tmpfilepath = os.path.join(os.path.dirname(inp_path), tmpfilename)
allheaders = complete_inp_headers(inp_path)
basemodel = swmmio.Model(inp_path)
with open(inp_path) as oldf:
with open(tmpfilepath, 'w') as new:
#create the companion excel file
#create the MS Excel writer object
# xlpath = os.path.join(wd, basemodel.inp.name + '_modified.xlsx')
# excelwriter = pd.ExcelWriter(xlpath)
# vc_utils.create_info_sheet(excelwriter, basemodel)
#write each line as is from the original model until we find the
#header of the section we wish to overwrite
found_section = False
found_next_section = False
for line in oldf:
if modified_section_header in line:
#write the replacement data in the new file now
vc_utils.write_inp_section(new,
allheaders,
modified_section_header,
new_data,
pad_top=False)
found_section = True
if (found_section and not found_next_section
and line.strip() in allheaders['headers']
and modified_section_header != line.strip()):
found_next_section = True
new.write('\n\n') #add some space before the next section
if found_next_section or not found_section:
#write the lines from the original file
#if we haven't found the section to modify.
#if we have found the section and we've found the NEXT section
#continue writing original file's lines
new.write(line)
if not found_section:
#the header doesn't exist in the old model
#so we should append it to the bottom of file
vc_utils.write_inp_section(new, allheaders,
modified_section_header, new_data)
# excelwriter.save()
#rename files and remove old if we should overwrite
if overwrite:
os.remove(inp_path)
os.rename(tmpfilepath, inp_path)
return swmmio.Model(inp_path)
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)
