def two_bank_winding(table):
#global cell_style
excel.cell_style = 'Normal'
#rows = excel.cursor.columns(table=table)
#r = r""
sqlcommand = r"""SELECT
StationObjectID
,TransformerObjectID
,TransformerPhase
,TransformerEquipLoc
,TransformerBankID
,TransformerWindingConnection
FROM #QueryTable
WHERE TransformerWindingConnection <> 5 OR TransformerWindingConnection IS NULL"""
excel.cursor.execute(sqlcommand)
row = excel.cursor.fetchall()
df = pd.read_sql_query(sqlcommand, excel.conn)
countid = len(row)
if (countid > 0):
excel.cell_style = 'Bad'
c = str(countid) + " errors."
sheetname = excel.category + "-2bank_winding"
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
analysis.write_hyperlink(sheetname)
return c
def two_bank_phasing(table):
#global cell_style
excel.cell_style = 'Normal'
#rows = excel.cursor.columns(table=table)
#r = r""
sqlcommand = r"""SELECT
StationObjectID
,TransformerObjectID
,TransformerPhase
,TransformerEquipLoc
,TransformerBankID
,TransformerWindingConnection
FROM #QueryTable"""
excel.cursor.execute(sqlcommand)
row = excel.cursor.fetchall()
df = pd.read_sql_query(sqlcommand, excel.conn)
grouped_df = df.groupby("TransformerEquipLoc")
grouped_list = grouped_df["TransformerPhase"].agg(
lambda column: "".join(column))
grouped_list = grouped_list.reset_index(name="TransformerPhase")
grouped_list = grouped_list[grouped_list.TransformerPhase != 'ABC']
countid = len(row)
if (countid > 0):
excel.cell_style = 'Bad'
c = str(countid) + " errors."
sheetname = excel.category + "-2bank_phasing"
analysis.createsheet(sheetname)
analysis.writedf(grouped_list, sheetname)
analysis.write_hyperlink(sheetname)
return c
def two_bank_diff_id(table):
#global cell_style
excel.cell_style = 'Normal'
#rows = excel.cursor.columns(table=table)
#r = r""
sqlcommand = r"""SELECT
StationObjectID
,TransformerEquipLoc
,TransformerBankID
,COUNT(*) AS 'Count'
FROM #QueryTable
GROUP BY StationObjectID, TransformerEquipLoc, TransformerBankID
HAVING COUNT(TransformerBankID) <= 1"""
#excel.cursor.execute(sqlcommand)
#row = excel.cursor.fetchall()
df = pd.read_sql_query(sqlcommand, excel.conn)
countid = df['Count'].sum()
if (countid > 0):
excel.cell_style = 'Bad'
c = str(countid) + " errors."
sheetname = excel.category + "-2bank_diff_id"
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
analysis.write_hyperlink(sheetname)
return c
def xfmr_voltage(idcolumn, idcolumn1, table):
import pandas as pd
#c = "Error."
#facility_id = False
#global cell_style
excel.cell_style = 'Normal'
#global cell_alignment
excel.cell_alignment = 'False'
rows = excel.cursor.columns(table=table)
for row in rows:
if idcolumn in row:
for row in excel.cursor.columns(table=table):
if idcolumn1 in row:
#r = r""
sqlcommand = r"""SELECT {table}.{idcolumn}, {table}.{idcolumn1}, Count({table}.OBJECTID) AS [Count]
FROM {table}
GROUP BY {table}.{idcolumn}, {table}.{idcolumn1}
ORDER BY Count({table}.OBJECTID) DESC
""".format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)
excel.cursor.execute(sqlcommand)
row = excel.cursor.fetchall()
df = pd.read_sql_query(sqlcommand, excel.conn)
sheetname = excel.category + "-VoltageTable"
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
break
return
def two_bank_xfmr(table):
excel.c = "Error."
excel.facility_id = False
#global cell_style
excel.cell_style = 'Normal'
#global cell_alignment
excel.cell_alignment = 'False'
#r = r""
sqlcommand = r"""DROP TABLE IF EXISTS #QueryTable"""
excel.cursor.execute(sqlcommand)
#r = r""
sqlcommand = r"""SELECT
s.OBJECTID AS StationObjectID
,s.gs_guid AS StationGuid
,t.OBJECTID AS TransformerObjectID
,t.gs_guid AS TransformerGuid
,t.gs_phase AS TransformerPhase
,t.gs_equipment_location AS TransformerEquipLoc
,t.gs_bank_id AS TransformerBankID
,t.gs_winding_connection AS TransformerWindingConnection
INTO #QueryTable
FROM GS_TRANSFORMER t
JOIN GS_ATTACHED_ASSEMBLIES aa ON aa.gs_display_feature_guid = t.gs_guid
JOIN gs_station s ON s.gs_guid = aa.gs_network_feature_guid
WHERE s.gs_guid IN
(
SELECT aa.gs_network_feature_guid FROM gs_attached_assemblies aa
JOIN GS_TRANSFORMER t
ON t.gs_guid = aa.gs_display_feature_guid
GROUP BY aa.gs_network_feature_guid
HAVING COUNT(aa.gs_network_feature_guid) = 2
)"""
excel.cursor.execute(sqlcommand)
#r = r""
sqlcommand = r"""SELECT StationObjectID, TransformerObjectID, TransformerPhase,
TransformerEquipLoc, TransformerBankID, TransformerWindingConnection FROM #QueryTable ORDER BY StationObjectID"""
#excel.cursor.execute(sqlcommand)
#row = excel.cursor.fetchall()
df = pd.read_sql_query(sqlcommand, excel.conn)
sheetname = excel.category + "-2bank"
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
analysis.write_hyperlink(sheetname)
return
def span_assembly(table):
import pandas as pd
excel.c = "Error."
excel.facility_id = False
#global cell_style
excel.cell_style = 'Normal'
#global cell_alignment
excel.cell_alignment = 'False'
rows = excel.cursor.columns(table=table)
for row in rows:
if excel.idcolumn in row:
for row in excel.cursor.columns(table=table):
if excel.idcolumn1 in row:
#r = r""
sqlcommand = r"""SELECT gs_assembly_ref.gs_assembly_name, gs_assembly_ref.gs_engineering_analysis_name, Count({table}.gs_guid) AS [Count], {table}.gs_subtype_cd, gs_assembly_ref.gs_assembly_description
FROM ({table} INNER JOIN gs_attached_assemblies ON {table}.gs_guid = gs_attached_assemblies.gs_network_feature_guid) INNER JOIN gs_assembly_ref ON gs_attached_assemblies.gs_assembly_guid = gs_assembly_ref.gs_guid
GROUP BY gs_assembly_ref.gs_assembly_name, gs_assembly_ref.gs_engineering_analysis_name, {table}.gs_subtype_cd, gs_assembly_ref.gs_assembly_description, gs_assembly_ref.gs_is_flow_assembly
HAVING (((gs_assembly_ref.gs_is_flow_assembly)='true' Or (gs_assembly_ref.gs_is_flow_assembly)='true'))
ORDER BY gs_assembly_ref.gs_assembly_name, gs_assembly_ref.gs_engineering_analysis_name, Count({table}.gs_guid) DESC
""".format(idcolumn=excel.idcolumn,
idcolumn1=excel.idcolumn1,
table=table)
excel.cursor.execute(sqlcommand)
row = excel.cursor.fetchall()
df = pd.read_sql_query(sqlcommand, excel.conn)
sheetname = excel.category + "-AssemblyTable"
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
df = df[df.duplicated(subset=['gs_assembly_name'],
keep=False)]
sheetname = excel.category + "-DupAssemblyTable"
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
analysis.write_hyperlink(sheetname)
break
return
def is_feeder_bay(idcolumn, table):
import pandas as pd
c = "Error."
facility_id = False
for row in excel.cursor.columns(table=table):
if "gs_facility_id" in row:
facility_id = True
global cell_style
cell_style = 'Normal'
global cell_alignment
cell_alignment = 'False'
rows = excel.cursor.columns(table=table)
if int(analysis.sumrows(table)) == 0:
cell_style = 'Bad'
c = "This table is empty."
else:
for row in excel.cursor.columns(table=table):
if idcolumn in row:
r = r""
sqlcommand = r.join(
("SELECT {table}.OBJECTID, {table}.{idcolumn}".format(
idcolumn=idcolumn, table=table)))
if (facility_id == True
and (not (idcolumn == "gs_facility_id"))):
sqlcommand = r.join((
sqlcommand,
""",{table}.gs_facility_id, {table}.gs_overcurrent_device_subtype, {table}.gs_is_feeder_bay
FROM {table}
WHERE ({table}.{idcolumn} = '4' AND ({table}.gs_is_feeder_bay <> '1' OR {table}.gs_is_feeder_bay IS NULL)) OR ({table}.gs_overcurrent_device_subtype <> '4' AND {table}.gs_is_feeder_bay <> '0')
GROUP BY {table}.{idcolumn}, {table}.OBJECTID, {table}.gs_facility_id, {table}.gs_overcurrent_device_subtype, {table}.gs_is_feeder_bay"""
.format(idcolumn=idcolumn, table=table)))
else:
sqlcommand = r.join((
sqlcommand,
""", {table}.gs_overcurrent_device_subtype, {table}.gs_is_feeder_bay
FROM {table}
WHERE ({table}.{idcolumn} = '4' AND ({table}.gs_is_feeder_bay <> '1' OR {table}.gs_is_feeder_bay IS NULL)) OR ({table}.gs_overcurrent_device_subtype <> '4' AND {table}.gs_is_feeder_bay <> '0')
GROUP BY {table}.{idcolumn}, {table}.OBJECTID, {table}.gs_overcurrent_device_subtype, {table}.gs_is_feeder_bay"""
.format(idcolumn=idcolumn, table=table)))
excel.cursor.execute(sqlcommand)
rows = excel.cursor.fetchall()
totalrows = analysis.sumrows(table)
if not rows:
cell_style = 'Normal'
cell_alignment = 'False'
c = "All are populated correctly."
else:
percent = int(round(int(len(rows)) / int(totalrows) * 100))
cell_style = 'Bad'
c = str(len(rows)) + " (" + str(
percent) + "%)" + " incorrect values."
r = r""
sqlcommand = r.join(
("SELECT {table}.OBJECTID, {table}.{idcolumn}".format(
idcolumn=idcolumn, table=table)))
if (facility_id == True
and (not (idcolumn == "gs_facility_id"))):
sqlcommand = r.join((
sqlcommand,
""",{table}.gs_facility_id, {table}.gs_overcurrent_device_subtype, {table}.gs_is_feeder_bay
FROM {table}
WHERE ({table}.{idcolumn} = '4' AND ({table}.gs_is_feeder_bay <> '1' OR {table}.gs_is_feeder_bay IS NULL)) OR ({table}.gs_overcurrent_device_subtype <> '4' AND {table}.gs_is_feeder_bay <> '0')
GROUP BY {table}.{idcolumn}, {table}.OBJECTID, {table}.gs_facility_id, {table}.gs_overcurrent_device_subtype, {table}.gs_is_feeder_bay"""
.format(idcolumn=idcolumn, table=table)))
else:
sqlcommand = r.join((
sqlcommand,
""", {table}.gs_overcurrent_device_subtype, {table}.gs_is_feeder_bay
FROM {table}
WHERE ({table}.{idcolumn} = '4' AND ({table}.gs_is_feeder_bay <> '1' OR {table}.gs_is_feeder_bay IS NULL)) OR ({table}.gs_overcurrent_device_subtype <> '4' AND {table}.gs_is_feeder_bay <> '0')
GROUP BY {table}.{idcolumn}, {table}.OBJECTID, {table}.gs_overcurrent_device_subtype, {table}.gs_is_feeder_bay"""
.format(idcolumn=idcolumn, table=table)))
df = pd.read_sql_query(sqlcommand, excel.conn)
sheetname = excel.category + "-FeederBay"
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
analysis.write_hyperlink(sheetname)
break
else:
cell_style = 'Neutral'
c = "This field needs to be added to the database."
return c
def neutral(idcolumn, table):
import pandas as pd
c = "Error."
global cell_style
cell_style = 'Normal'
global cell_alignment
cell_alignment = 'False'
for row in excel.cursor.columns(table=table):
if idcolumn in row:
file_list = []
sqlcommand = r"""SELECT {table}.{idcolumn}, {table}.gs_subtype_cd, Count({table}.OBJECTID) AS [Count]
FROM {table}
WHERE {table}.gs_subtype_cd = 1
AND ({table}.{idcolumn} IS null OR {table}.{idcolumn} = '' OR {table}.{idcolumn} LIKE '%UNK%')
GROUP BY {table}.{idcolumn}, {table}.gs_subtype_cd
ORDER BY {table}.{idcolumn}, {table}.gs_subtype_cd, Count({table}.OBJECTID) DESC
""".format(idcolumn=idcolumn, table=table)
excel.cursor.execute(sqlcommand)
row = excel.cursor.fetchall()
totalrows = analysis.sumrows(table)
try:
if int(analysis.sumrows(table)) == int(row[0][2]):
cell_style = 'Normal'
cell_alignment = 'False'
if row[0][0] == None:
c = "All fields are NULL."
if row[0][0] == 0:
c = "All fields are populated with '0'."
else:
c = "All fields are populated with '" + str(
row[0][0]) + "'."
if (row[0][0] == None) or (row[0][0] == "") or (row[0][0]
== "UNK"):
cell_style = 'Bad'
sqlcommand = r"""SELECT {table}.OBJECTID, {table}.{idcolumn}
FROM {table}
WHERE {table}.gs_subtype_cd = 1
AND ({table}.{idcolumn} IS null OR {table}.{idcolumn} = '' OR {table}.{idcolumn} LIKE '%UNK%')
GROUP BY {table}.OBJECTID, {table}.{idcolumn}
ORDER BY {table}.OBJECTID, {table}.{idcolumn}
""".format(idcolumn=idcolumn, table=table)
df = pd.read_sql_query(sqlcommand, excel.conn)
sheetname = excel.category + "-" + idcolumn[len("gs_"
):]
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
else:
for row in row:
if (row[0] == None) or (row[0] == "") or (row[0]
== "UNK"):
cell_style = 'Bad'
fcol = row[0]
if fcol == 0:
fcol = "0"
if fcol == None:
fcol = "NULL"
fnum = row[2]
fper = int(round(int(fnum) / int(totalrows) * 100))
file_list.append({
"fcol": fcol,
"fnum": fnum,
"fper": fper
})
cell_alignment = 'True'
c = r""
c = c.join(
("{fnum} ({fper}%) populated with '{fcol}'.\n".format(
fcol=fl['fcol'], fnum=fl['fnum'], fper=fl['fper'])
for fl in file_list))
if cell_style == 'Bad':
sqlcommand = r"""SELECT {table}.OBJECTID, {table}.{idcolumn}
FROM {table}
WHERE {table}.gs_subtype_cd = 1
AND ({table}.{idcolumn} IS null OR {table}.{idcolumn} ='' OR {table}.{idcolumn} LIKE '%UNK%')
GROUP BY {table}.OBJECTID, {table}.{idcolumn}
ORDER BY {table}.OBJECTID, {table}.{idcolumn}
""".format(idcolumn=idcolumn, table=table)
df = pd.read_sql_query(sqlcommand, excel.conn)
sheetname = excel.category + "-" + idcolumn[len("gs_"
):]
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
analysis.write_hyperlink(sheetname)
break
except IndexError:
break
else:
cell_style = 'Neutral'
c = "This field needs to be added to the database."
return c
def duplicate_xfmr(idcolumn, idcolumn1, table):
import pandas as pd
c = "Error."
facility_id = False
global cell_style
cell_style = 'Normal'
global cell_alignment
cell_alignment = 'False'
#rows = excel.cursor.columns(table=table)
for row in excel.cursor.columns(table=table):
if "gs_facility_id" in row:
facility_id = True
else:
facility_id = False
if int(analysis.sumrows(table)) == 0:
cell_style = 'Bad'
c = "This table is empty."
else:
for row in excel.cursor.columns(table=table):
if idcolumn in row:
for row in excel.cursor.columns(table=table):
if idcolumn1 in row:
r = r""
sqlcommand = r.join(
("SELECT {table}.OBJECTID, ".format(table=table)))
if (facility_id == True
and (not (idcolumn == "gs_facility_id"
or idcolumn1 == "gs_facility_id"))):
sqlcommand = r.join((
sqlcommand,
"{table}.gs_facility_id, {table}.{idcolumn}, {table}.{idcolumn1}"
.format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
else:
sqlcommand = r.join(
(sqlcommand,
"{table}.{idcolumn}, {table}.{idcolumn1}".
format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
if not ((idcolumn == "gs_phase") or
(idcolumn1 == "gs_phase")):
sqlcommand = r.join((
sqlcommand,
",{table}.gs_phase FROM {table} WHERE ((({table}.{idcolumn}) In (SELECT {idcolumn} FROM {table} As Tmp GROUP BY {idcolumn}, {idcolumn1}, gs_phase HAVING Count(*)>1 ))) GROUP BY {table}.OBJECTID, {table}.{idcolumn}, {table}.{idcolumn1}"
.format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
if (facility_id == True and
(not (idcolumn == "gs_facility_id"
or idcolumn1 == "gs_facility_id"))):
sqlcommand = r.join(
(sqlcommand,
", {table}.gs_facility_id".format(
table=table)))
else:
sqlcommand = r.join((
sqlcommand,
" FROM {table} WHERE ((({table}.{idcolumn}) In (SELECT {idcolumn} FROM {table} As Tmp GROUP BY {idcolumn}, {idcolumn1} HAVING Count(*)>1 ))) GROUP BY {table}.OBJECTID, {table}.{idcolumn}, {table}.{idcolumn1}"
.format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
if (facility_id == True and
(not (idcolumn == "gs_facility_id"
or idcolumn1 == "gs_facility_id"))):
sqlcommand = r.join(
(sqlcommand,
", {table}.gs_facility_id".format(
table=table)))
if not ((idcolumn == "gs_phase") or
(idcolumn1 == "gs_phase")):
sqlcommand = r.join((
sqlcommand,
", {table}.gs_phase ORDER BY {table}.{idcolumn}, {table}.OBJECTID, {table}.{idcolumn1}, {table}.gs_phase"
.format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
if (facility_id == True and
(not (idcolumn == "gs_facility_id"
or idcolumn1 == "gs_facility_id"))):
sqlcommand = r.join(
(sqlcommand,
", {table}.gs_facility_id".format(
table=table)))
else:
sqlcommand = r.join((
sqlcommand,
" ORDER BY {table}.{idcolumn}, {table}.OBJECTID, {table}.{idcolumn1}"
.format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
if (facility_id == True and
(not (idcolumn == "gs_facility_id"
or idcolumn1 == "gs_facility_id"))):
sqlcommand = r.join(
(sqlcommand,
", {table}.gs_facility_id".format(
table=table)))
excel.cursor.execute(sqlcommand)
row = excel.cursor.fetchall()
if not row:
cell_style = 'Normal'
cell_alignment = 'False'
c = "No duplicates found."
else:
cell_style = 'Bad'
c = str(len(row)) + " duplicates exist."
r = r""
sqlcommand = r.join(
("SELECT {table}.OBJECTID, ".format(
table=table)))
if (facility_id == True and
(not (idcolumn == "gs_facility_id"
or idcolumn1 == "gs_facility_id"))):
sqlcommand = r.join((
sqlcommand,
"{table}.gs_facility_id, {table}.{idcolumn}, {table}.{idcolumn1}"
.format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
else:
sqlcommand = r.join(
(sqlcommand,
"{table}.{idcolumn}, {table}.{idcolumn1}".
format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
if not ((idcolumn == "gs_phase") or
(idcolumn1 == "gs_phase")):
sqlcommand = r.join((
sqlcommand,
",{table}.gs_phase FROM {table} WHERE ((({table}.{idcolumn}) In (SELECT {idcolumn} FROM {table} As Tmp GROUP BY {idcolumn}, {idcolumn1}, gs_phase HAVING Count(*)>1 ))) GROUP BY {table}.OBJECTID, {table}.{idcolumn}, {table}.{idcolumn1}"
.format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
if (facility_id == True and
(not (idcolumn == "gs_facility_id"
or idcolumn1 == "gs_facility_id"))):
sqlcommand = r.join(
(sqlcommand,
", {table}.gs_facility_id".format(
table=table)))
else:
sqlcommand = r.join((
sqlcommand,
" FROM {table} WHERE ((({table}.{idcolumn}) In (SELECT {idcolumn} FROM {table} As Tmp GROUP BY {idcolumn}, {idcolumn1} HAVING Count(*)>1 ))) GROUP BY {table}.OBJECTID, {table}.{idcolumn}, {table}.{idcolumn1}"
.format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
if (facility_id == True and
(not (idcolumn == "gs_facility_id"
or idcolumn1 == "gs_facility_id"))):
sqlcommand = r.join(
(sqlcommand,
", {table}.gs_facility_id".format(
table=table)))
if not ((idcolumn == "gs_phase") or
(idcolumn1 == "gs_phase")):
sqlcommand = r.join((
sqlcommand,
", {table}.gs_phase ORDER BY {table}.{idcolumn}, {table}.OBJECTID, {table}.{idcolumn1}, {table}.gs_phase"
.format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
if (facility_id == True and
(not (idcolumn == "gs_facility_id"
or idcolumn1 == "gs_facility_id"))):
sqlcommand = r.join(
(sqlcommand,
", {table}.gs_facility_id".format(
table=table)))
else:
sqlcommand = r.join((
sqlcommand,
" ORDER BY {table}.{idcolumn}, {table}.OBJECTID, {table}.{idcolumn1}"
.format(idcolumn=idcolumn,
idcolumn1=idcolumn1,
table=table)))
if (facility_id == True and
(not (idcolumn == "gs_facility_id"
or idcolumn1 == "gs_facility_id"))):
sqlcommand = r.join(
(sqlcommand,
", {table}.gs_facility_id".format(
table=table)))
df = pd.read_sql_query(sqlcommand, excel.conn)
sheetname = excel.category + "-" + idcolumn[
len("gs_"):]
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
analysis.write_hyperlink(sheetname)
break
else:
cell_style = 'Neutral'
c = "The field ''" + str(
idcolumn1) + "' needs to be added to the database."
else:
cell_style = 'Neutral'
c = "The field ''" + str(
idcolumn) + "' needs to be added to the database."
return c
def duplicate_system():
import pandas as pd
c = "Error."
global cell_style
cell_style = 'Normal'
global cell_alignment
cell_alignment = 'False'
table = "gs_service_point"
#rows = excel.cursor.columns(table=table)
if int(analysis.sumrows(table)) == 0:
cell_style = 'Bad'
c = "This table is empty."
else:
r = r""
sqlcommand = r.join((r"""SELECT t.id AS 'ID',COUNT(T.ID) AS 'Count'
FROM (SELECT gs_service_map_location AS 'ID' FROM GS_SERVICE_POINT"""))
table = "gs_transformer"
for row in excel.cursor.columns(table=table):
if "gs_equipment_location" in row:
sqlcommand = r.join((sqlcommand, """
UNION ALL
SELECT gs_equipment_location AS 'ID' FROM gs_transformer
WHERE (((gs_transformer.gs_equipment_location)
In (SELECT gs_equipment_location
FROM gs_transformer As Tmp
GROUP BY gs_equipment_location, gs_bank_id, gs_phase HAVING Count(*)>1 ))) """
))
table = "gs_overcurrent_device"
for row in excel.cursor.columns(table=table):
if "gs_equipment_location" in row:
sqlcommand = r.join((sqlcommand, """
UNION ALL
SELECT GS_EQUIPMENT_LOCATION AS 'ID' FROM GS_OVERCURRENT_DEVICE"""
))
table = "gs_switch"
for row in excel.cursor.columns(table=table):
if "gs_equipment_location" in row:
sqlcommand = r.join((sqlcommand, """
UNION ALL
SELECT GS_EQUIPMENT_LOCATION AS 'ID' FROM GS_SWITCH"""))
table = "gs_capacitor_bank"
for row in excel.cursor.columns(table=table):
if "gs_equipment_location" in row:
sqlcommand = r.join((sqlcommand, """
UNION ALL
SELECT GS_EQUIPMENT_LOCATION AS 'ID' FROM GS_CAPACITOR_BANK"""
))
table = "gs_generator"
for row in excel.cursor.columns(table=table):
if "gs_equipment_location" in row:
sqlcommand = r.join((sqlcommand, """
UNION ALL
SELECT GS_EQUIPMENT_LOCATION AS 'ID' FROM GS_GENERATOR"""))
table = "gs_motor"
for row in excel.cursor.columns(table=table):
if "gs_equipment_location" in row:
sqlcommand = r.join((sqlcommand, """
UNION ALL
SELECT GS_EQUIPMENT_LOCATION AS 'ID' FROM GS_MOTOR"""))
# table = "gs_street_light"
# for row in excel.cursor.columns(table=table):
# if "gs_equipment_location" in row:
# sqlcommand = r.join((sqlcommand, """
# UNION ALL
# SELECT GS_EQUIPMENT_LOCATION AS 'ID' FROM GS_STREET_LIGHT"""))
table = "gs_voltage_regulator"
for row in excel.cursor.columns(table=table):
if "gs_equipment_location" in row:
sqlcommand = r.join((sqlcommand, """
UNION ALL
SELECT GS_EQUIPMENT_LOCATION AS 'ID' FROM GS_VOLTAGE_REGULATOR"""
))
sqlcommand = r.join(
(sqlcommand, """) AS T group by t.id having count(*)>1"""))
excel.cursor.execute(sqlcommand)
row = excel.cursor.fetchall()
if not row:
cell_style = 'Normal'
cell_alignment = 'False'
c = "No duplicates found."
else:
cell_style = 'Bad'
c = str(len(row)) + " duplicates exist."
df = pd.read_sql_query(sqlcommand, excel.conn)
sheetname = excel.category + "-SystemDuplicates"
analysis.createsheet(sheetname)
analysis.writedf(df, sheetname)
analysis.write_hyperlink(sheetname)
return c
