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to_excel update for ids #118 from dt #67

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Jan 9, 2025
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to_excel update for ids #118 from dt #67

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37d08e9
for excel update for ids
ichrys03 Dec 11, 2024
daebeff
toexcel() update with example with 4 cases
ichrys03 Jan 7, 2025
32382c5
updated example toexcel.py
ichrys03 Jan 8, 2025
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192 changes: 132 additions & 60 deletions epyt/epanet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -384,24 +384,46 @@ def to_dict(self):
dict_values = vars(self)
return dict_values

def to_excel(self, filename=None, attributes=None, allValues=False):
""" Save to an Excel file the values of EpytValues class
def to_excel(self, filename=None, attributes=None, allValues=False,
node_id_list=None, link_id_list=None, both=False, header=True):
"""
Save to an Excel file the values of EpytValues class.

:param self: Values to add to the Excel file
:type self: EpytValues class
:param filename: excel filename, defaults to None
:param filename: Excel filename, defaults to None
:type filename: str, optional
:param attributes: attributes to add to the file, defaults to None
:param attributes: Attributes to add to the file, defaults to None
:type attributes: str or list of str, optional
:param allValues: 'True' if all the values will be included in a
separate sheet, defaults to False
:param allValues: If True, writes all the values into a separate "All values" sheet, defaults to False
:type allValues: bool, optional
:param node_id_list: Array of IDs for node-related attributes
:type node_id_list: list or np.ndarray, optional
:param link_id_list: Array of IDs for link-related attributes
:type link_id_list: list or np.ndarray, optional
:param both: If True, and ID array available, print both 'Index' and 'Id'. If no ID array, just Index.
If False and ID array available, print only 'Id'; if no ID array, print only 'Index'.
:type both: bool, optional
:param header: If False, remove the first row from all sheets and do not write column headers
:type header: bool, optional
:return: None

"""
node_keywords = ['nodequality', 'head', 'demand', 'pressure']
link_keywords = [
'linkquality', 'flow', 'velocity', 'headloss',
'Status', 'Setting', 'ReactionRate', 'StatusStr', 'FrictionFactor'
]

def is_node_attribute(key):
key_lower = key.lower()
return (any(re.search(r'\b' + kw + r'\b', key_lower) for kw in node_keywords)
or 'node' in key_lower)

def is_link_attribute(key):
key_lower = key.lower()
return (any(re.search(r'\b' + kw + r'\b', key_lower) for kw in link_keywords)
or 'link' in key_lower)

if filename is None:
rand_id = ''.join(random.choices(string.ascii_letters
+ string.digits, k=5))
rand_id = ''.join(random.choices(string.ascii_letters + string.digits, k=5))
filename = 'ToExcelfile_' + rand_id + '.xlsx'
if '.xlsx' not in filename:
filename = filename + '.xlsx'
Expand All @@ -414,58 +436,108 @@ def to_excel(self, filename=None, attributes=None, allValues=False):
else:
dictValss[i] = dictVals[i]
dictVals = dictValss

def process_dataframe(key, df):
header_labels = []
num_rows = len(df)
chosen_id_array = None

if is_node_attribute(key) and node_id_list is not None:
chosen_id_array = node_id_list
elif is_link_attribute(key) and link_id_list is not None:
chosen_id_array = link_id_list

print_id_col = False
print_index_col = False

if both:
print_index_col = True
if chosen_id_array is not None:
print_id_col = True
else:
if chosen_id_array is not None:
print_id_col = True
else:
print_index_col = True

if print_id_col and chosen_id_array is not None:
if len(chosen_id_array) != num_rows:
warnings.warn(
f"ID array length does not match rows for '{key}'. Truncating."
)
adjusted_id_array = chosen_id_array[:num_rows]
df.insert(0, 'Id', adjusted_id_array)
header_labels.append('Id')

if print_index_col:
index_values = list(range(1, num_rows + 1))
if print_id_col:
id_col = df.pop('Id')
df.insert(0, 'Index', index_values)
df.insert(1, 'Id', id_col)
header_labels.insert(0, 'Index')
else:
df.insert(0, "Index", index_values)
header_labels.insert(0, 'Index')

num_data_columns = df.shape[1] - len(header_labels)
if 'Time' in dictVals and len(dictVals['Time']) == num_data_columns:
header_labels.extend(dictVals['Time'])
else:
data_column_names = [f"Column{i + 1}" for i in range(num_data_columns)]
header_labels.extend(data_column_names)

df.columns = header_labels
return df

with pd.ExcelWriter(filename, mode="w") as writer:
for key in dictVals:
if 'Time' not in key:
if not attributes:
df = pd.DataFrame(dictVals[key])
df.insert(0, "Index", list(range(1, len(dictVals[key]) + 1)), True)
df.set_index("Index", inplace=True)
df.to_excel(writer, sheet_name=key,
header=dictVals['Time'])
else:
if not isList(attributes):
attributes = [attributes]
if key in attributes:
df = pd.DataFrame(dictVals[key])
df.insert(0, "Index", list(range(1, len(dictVals[key]) + 1)), True)
df.set_index("Index", inplace=True)
df.to_excel(writer,
sheet_name=key,
header=dictVals['Time'])

for key, data in dictVals.items():
if key == 'Time':
continue
if attributes and key not in attributes:
continue
if not isinstance(data, (list, np.ndarray, pd.Series, pd.DataFrame)):
continue

df = pd.DataFrame(data)
df = process_dataframe(key, df)

if not header:
df.columns = df.iloc[0]
df = df.iloc[1:]
df.to_excel(writer, sheet_name=key, index=False, header=True)
else:
df.to_excel(writer, sheet_name=key, index=False, header=True)

if allValues:
worksheet_name = 'All values'
first_iter = True
titleFormat = writer.book.add_format(
{'bold': True, 'align': 'center',
'valign': 'vcenter', 'font_size': 16})
for key in dictVals:
if key != 'Time' and not attributes:
df = pd.DataFrame(dictVals[key])
df.insert(0, "Index", list(range(1, len(dictVals[key]) + 1)), True)
df.set_index("Index", inplace=True)
if first_iter:
df.to_excel(
writer,
sheet_name='All values',
header=dictVals['Time'],
startrow=1
)
writer.book.worksheets()[-1].write(0, 1, key,
titleFormat)
first_iter = False
else:
startrow = writer.book.worksheets()[-1].dim_rowmax \
+ 3
writer.book.worksheets()[-1].write(startrow - 1,
1,
key,
titleFormat)
df.to_excel(
writer,
sheet_name='All values',
header=dictVals['Time'],
startrow=startrow
)
for key, data in dictVals.items():
if key == 'Time':
continue
if attributes and key not in attributes:
continue
if not isinstance(data, (list, np.ndarray, pd.Series, pd.DataFrame)):
print(f"Skipping key '{key}' due to unsupported data type: {type(data)}")
continue

df = pd.DataFrame(data)
df = process_dataframe(key, df)


worksheet = writer.sheets.get(worksheet_name)
if first_iter:
df.to_excel(writer, sheet_name=worksheet_name, index=False, header=header, startrow=1)
worksheet = writer.sheets[worksheet_name]
worksheet.write(0, 0, key)
first_iter = False
else:
startrow = worksheet.dim_rowmax + 3
worksheet.write(startrow - 1, 0, key)
df.to_excel(writer, sheet_name=worksheet_name, index=False, header=header, startrow=startrow)

print(f"Data successfully exported to {filename}")

def to_json(self, filename=None):
""" Transforms val class values to json object and saves them
Expand Down
19 changes: 19 additions & 0 deletions epyt/examples/python/EX_to_excel_json.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,5 +57,24 @@
comp_values.to_excel(f"to_excel_{selected_attribute}_example",
attributes=selected_attribute)

# Retrieve node and link IDs from the network
nodeid = d.getNodeNameID()
linkid = d.getLinkNameID()

# Below are four example scenarios demonstrating how to use comp_values.to_excel()
# with different parameter configurations:

# 1) Scenario 1: Include both index and node/link IDs in the output.
comp_values.to_excel("case1", node_id_list=nodeid, link_id_list=linkid, both=True)

# 2) Scenario 2: Include only node/link IDs (no index).
comp_values.to_excel("case2", node_id_list=nodeid, link_id_list=linkid, both=False)

# 3) Scenario 3: Use the default settings (only index is included).
comp_values.to_excel("case3")

# 4) Scenario 4: Use the default settings but suppress the column headers.
comp_values.to_excel("case4", header=False)

# Unload library
d.unload()