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 search_for_duplicates(inp_path, verbose=False):
"""
scan an inp file and determine if any element IDs are duplicated in
any section. Method: count the uniques and compare to total length
"""
headers = funcs.complete_inp_headers(inp_path)['headers']
dups_found = False
for header, cols, in headers.items():
if cols != 'blob':
df = dataframes.create_dataframeINP(inp_path, section=header)
elements = df.index
n_unique = len(elements.unique()) #number of unique elements
n_total = len(elements) #total number of elements
if verbose:
print('{} -> (uniques, total) -> ({}, {})'.format(
header, n_unique, n_total))
if n_unique != n_total:
dups = ', '.join(
df[df.index.duplicated()].index.unique().tolist())
print('duplicate found in {}\nsection: {}\n{}'.format(
inp_path, header, dups))
dups_found = True
return dups_found
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 test_complete_headers(test_model):
headers = functions.complete_inp_headers(test_model.inp.path)
sections_in_inp = [
'[TITLE]', '[OPTIONS]', '[EVAPORATION]', '[RAINGAGES]', '[SUBCATCHMENTS]', '[SUBAREAS]', '[INFILTRATION]',
'[JUNCTIONS]', '[OUTFALLS]', '[STORAGE]', '[CONDUITS]', '[PUMPS]', '[WEIRS]', '[XSECTIONS]', '[INFLOWS]',
'[CURVES]', '[TIMESERIES]', '[REPORT]', '[TAGS]', '[MAP]', '[COORDINATES]', '[VERTICES]', '[Polygons]',
'[SYMBOLS]'
]
assert (all(section in headers['headers'] for section in sections_in_inp))
def inline_comments_in_inp(filepath, overwrite=False):
"""
with an existing INP file, shift any comments that have been placed above the
element (behavoir from saving in GUI) and place them to the right, inline with
the element. To improve readability
"""
newfilename = os.path.splitext(
os.path.basename(filepath))[0] + '_unGUI.inp'
newfilepath = os.path.join(os.path.dirname(filepath), newfilename)
allheaders = complete_inp_headers(filepath)
with open(filepath) as oldf:
with open(newfilepath, 'w') as new:
comment_concat = [
] #to hold list of comments (handles multiline comments)
current_section = allheaders['order'][0]
for line in oldf:
#determine what section we are in by noting when we pass double brackets
if '[' and ']' in line:
current_section = line.strip()
#if line.count(';') == 1 and line.strip()[0]==';':
if len(line.strip()) > 1:
if line.strip()[0] == ';' and ''.join(
line.strip()[:2]) != ';;':
#print line.strip()[:2]
#this is a comment bc first char is ; and there
#seconf char is not (which would resember the header section)
words = line.split()
hdrs = allheaders['headers'][current_section].split(
) #headerrow.split()
perc_match_to_header = float(
len([x for x in words if x in hdrs])) / float(
len(hdrs))
if perc_match_to_header <= 0.75:
comment_concat.append(line.strip())
else:
#print comment_concat
#this row has data, tack any comment to the line end
comment_string = ''
if len(comment_concat) > 0:
comment_string = r' '.join(comment_concat)
newlinestring = line.strip() + comment_string + '\n'
new.write(newlinestring)
comment_concat = []
else:
#write the short line
new.write(line)
#rename files and remove old if we should overwrite
if overwrite:
os.remove(filepath)
os.rename(newfilepath, filepath)
def test_complete_headers(test_model):
headers = functions.complete_inp_headers(test_model.inp.path)
sections_in_inp = [
'[TITLE]', '[OPTIONS]', '[EVAPORATION]', '[RAINGAGES]',
'[SUBCATCHMENTS]', '[SUBAREAS]', '[INFILTRATION]', '[JUNCTIONS]',
'[OUTFALLS]', '[STORAGE]', '[CONDUITS]', '[PUMPS]', '[WEIRS]',
'[XSECTIONS]', '[INFLOWS]', '[CURVES]', '[TIMESERIES]', '[REPORT]',
'[TAGS]', '[MAP]', '[COORDINATES]', '[VERTICES]', '[Polygons]',
'[SYMBOLS]'
]
assert (all(section in headers['headers'] for section in sections_in_inp))
def test_complete_inp_headers():
headers = [
'[TITLE]', '[OPTIONS]', '[EVAPORATION]', '[JUNCTIONS]', '[OUTFALLS]',
'[CONDUITS]', '[XSECTIONS]', '[DWF]', '[REPORT]', '[TAGS]', '[MAP]',
'[COORDINATES]', '[VERTICES]',
]
h1 = funcs.complete_inp_headers(MODEL_XSECTION_BASELINE)
assert(all(h in h1['headers'] for h in headers))
assert(h1['order'] == headers)
def inline_comments_in_inp(filepath, overwrite=False):
"""
with an existing INP file, shift any comments that have been placed above the
element (behavoir from saving in GUI) and place them to the right, inline with
the element. To improve readability
"""
newfilename = os.path.splitext(os.path.basename(filepath))[0] + '_unGUI.inp'
newfilepath = os.path.join(os.path.dirname(filepath), newfilename)
allheaders = complete_inp_headers(filepath)
with open(filepath) as oldf:
with open(newfilepath, 'w') as new:
comment_concat = [] #to hold list of comments (handles multiline comments)
current_section = allheaders['order'][0]
for line in oldf:
#determine what section we are in by noting when we pass double brackets
if '[' and ']' in line:
current_section = line.strip()
#if line.count(';') == 1 and line.strip()[0]==';':
if len(line.strip()) > 1:
if line.strip()[0]==';' and ''.join(line.strip()[:2]) != ';;':
#print line.strip()[:2]
#this is a comment bc first char is ; and there
#seconf char is not (which would resember the header section)
words = line.split()
hdrs = allheaders['headers'][current_section].split()#headerrow.split()
perc_match_to_header = float(len([x for x in words if x in hdrs])) / float(len(hdrs))
if perc_match_to_header <= 0.75:
comment_concat.append(line.strip())
else:
#print comment_concat
#this row has data, tack any comment to the line end
comment_string = ''
if len(comment_concat) > 0:
comment_string = r' '.join(comment_concat)
newlinestring = line.strip() + comment_string + '\n'
new.write(newlinestring)
comment_concat = []
else:
#write the short line
new.write(line)
#rename files and remove old if we should overwrite
if overwrite:
os.remove(filepath)
os.rename(newfilepath, filepath)
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 __init__(self, build_instr_file=None):
#create a change object for each section that is different from baseline
self.instructions = {}
self.metadata = {}
if build_instr_file:
#read the instructions and create a dictionary of Change objects
allheaders = funcs.complete_inp_headers(build_instr_file)
instructions = {}
for section in allheaders['order']:
change = INPDiff(build_instr_file=build_instr_file, section=section)
instructions.update({section:change})
self.instructions = instructions
#read the meta data
self.metadata = vc_utils.read_meta_data(build_instr_file)
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 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 __init__(self, build_instr_file=None):
#create a change object for each section that is different from baseline
self.instructions = {}
self.metadata = {}
if build_instr_file:
#read the instructions and create a dictionary of Change objects
allheaders = funcs.complete_inp_headers(build_instr_file)
instructions = {}
for section in allheaders['order']:
change = Change(build_instr_file=build_instr_file,
section=section)
instructions.update({section: change})
self.instructions = instructions
#read the meta data
self.metadata = vc_utils.read_meta_data(build_instr_file)
def test_complete_inp_headers():
headers = [
'[TITLE]',
'[OPTIONS]',
'[EVAPORATION]',
'[JUNCTIONS]',
'[OUTFALLS]',
'[CONDUITS]',
'[XSECTIONS]',
'[DWF]',
'[REPORT]',
'[TAGS]',
'[MAP]',
'[COORDINATES]',
'[VERTICES]',
]
h1 = funcs.complete_inp_headers(MODEL_XSECTION_BASELINE)
assert (all(h in h1['headers'] for h in headers))
assert (h1['order'] == headers)
def search_for_duplicates(inp_path, verbose = False):
"""
scan an inp file and determine if any element IDs are duplicated in
any section. Method: count the uniques and compare to total length
"""
headers = funcs.complete_inp_headers(inp_path)['headers']
dups_found = False
for header, cols, in headers.items():
if cols != 'blob':
df = dataframes.create_dataframeINP(inp_path, section=header)
elements = df.index
n_unique = len(elements.unique()) #number of unique elements
n_total = len(elements) #total number of elements
if verbose:
print('{} -> (uniques, total) -> ({}, {})'.format(header, n_unique , n_total))
if n_unique != n_total:
dups = ', '.join(df[df.index.duplicated()].index.unique().tolist())
print('duplicate found in {}\nsection: {}\n{}'.format(inp_path, header, dups))
dups_found = True
return dups_found
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.filePath)
#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, 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,
section=section)
#write the section
vc_utils.write_inp_section(f, allheaders, section, new_section)
def extract_section_from_inp(filepath, sectionheader, cleanheaders=True,
startfile=False, headerdefs=None, ignore_comments=False,
return_string=False, skiprows=0, skipheaders=False):
"""
INPUT path to text file (inp, rpt, etc) and a text string
matchig the section header of the to be extracted
creates a new text file in the same directory as the filepath
and returns the path to the new file.
optionally inserts row at begining of file with one-line headers that are
defined in swmmio.defs.sectionheaders (useful for creating dataframes)
"""
#headers = she.complete_inp_headers(inp)['headers']
if not headerdefs:
allheaders = complete_inp_headers(filepath)['headers']
else:
allheaders = headerdefs['headers']
with open(filepath) as f:
startfound = False
endfound = False
#outFilePath = inp.dir + "\\" + inp.name + "_" + section + ".txt"
wd = os.path.dirname(filepath)
newfname = sectionheader + '_' + random_alphanumeric(6)
outfilepath = os.path.join(wd, newfname + txt)
out_string = '' #populate if we only want a string returned
line_count = 0
with open(outfilepath, 'w') as newf:
for line in f:
if startfound and line.strip() in allheaders:
endfound = True
#print 'end found: {}'.format(line.strip())
break
elif not startfound and sectionheader in line:
startfound = True
#replace line with usable headers
if cleanheaders:
if allheaders[sectionheader] != 'blob':
line = allheaders[sectionheader] + '\n'
else:
line = sectionheader + '\n'
if startfound:
if ignore_comments:
#ignore anything after a comment
#THIS IS HACK GARBAGE
if ';' in line:
s = line.split(';')[0] + '\n'
out_string += s #build the overall out string (optional use)
newf.write(s)
else:
newf.write(line)
out_string += line
elif skipheaders:
#check if we're at a inp header row with ';;' or the section
#header e.g. [XSECTIONS]. If so, skip the row bc skipheader = True
if line.strip()[:2] != ";;" and line.strip() != sectionheader:
newf.write(line)
out_string += line
elif line_count >= skiprows:
newf.write(line)
out_string += line
line_count += 1
if not startfound:
#input section header was not found in the file
os.remove(outfilepath)
return None
if return_string:
#return a string rep of the extracted section
os.remove(outfilepath) #not needed so delete (maybe wasteful to make in 1st place)
return out_string
if startfile:
os.startfile(outfilepath)
return outfilepath
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)
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 extract_section_from_inp(filepath,
sectionheader,
cleanheaders=True,
startfile=False,
headerdefs=None,
ignore_comments=False,
return_string=False,
skiprows=0,
skipheaders=False):
"""
INPUT path to text file (inp, rpt, etc) and a text string
matchig the section header of the to be extracted
creates a new text file in the same directory as the filepath
and returns the path to the new file.
optionally inserts row at begining of file with one-line headers that are
defined in swmmio.defs.sectionheaders (useful for creating dataframes)
"""
#headers = she.complete_inp_headers(inp)['headers']
if not headerdefs:
allheaders = complete_inp_headers(filepath)['headers']
else:
allheaders = headerdefs['headers']
with open(filepath) as f:
startfound = False
endfound = False
#outFilePath = inp.dir + "\\" + inp.name + "_" + section + ".txt"
wd = os.path.dirname(filepath)
newfname = sectionheader + '_' + random_alphanumeric(6)
outfilepath = os.path.join(wd, newfname + txt)
out_string = '' #populate if we only want a string returned
line_count = 0
with open(outfilepath, 'w') as newf:
for line in f:
if startfound and line.strip() in allheaders:
endfound = True
#print 'end found: {}'.format(line.strip())
break
elif not startfound and sectionheader in line:
startfound = True
#replace line with usable headers
if cleanheaders:
if allheaders[sectionheader] != 'blob':
line = allheaders[sectionheader] + '\n'
else:
line = sectionheader + '\n'
if startfound:
if ignore_comments:
#ignore anything after a comment
#THIS IS HACK GARBAGE
if ';' in line:
s = line.split(';')[0] + '\n'
out_string += s #build the overall out string (optional use)
newf.write(s)
else:
newf.write(line)
out_string += line
elif skipheaders:
#check if we're at a inp header row with ';;' or the section
#header e.g. [XSECTIONS]. If so, skip the row bc skipheader = True
if line.strip()[:2] != ";;" and line.strip(
) != sectionheader:
newf.write(line)
out_string += line
elif line_count >= skiprows:
newf.write(line)
out_string += line
line_count += 1
if not startfound:
#input section header was not found in the file
os.remove(outfilepath)
return None
if return_string:
#return a string rep of the extracted section
os.remove(
outfilepath
) #not needed so delete (maybe wasteful to make in 1st place)
return out_string
if startfile:
os.startfile(outfilepath)
return outfilepath
