def read_appropriate_pairs(filename):
"""
This function reads appropriate_pairs excel file and returns appropriate_pairs as a list of lists
It takes 1 argument:
i) filename : A string , which is filename of excel file
It returns 2 variables:
i) from_district : A list , which consists of ids of from_district
ii) to_district : A list , which consists of ids of to_district
"""
#Get the full_path using current_directory
current_directory = util.get_current_directory()
full_path = current_directory + "/Appropriate_Pairs/" + filename
all_data = pd.ExcelFile(full_path)
worksheet = all_data.parse('Sheet1')
#Get the columns into columns
columns = worksheet.columns
#It is easier to work with list objects,so cast them to list objects.
columns = list(columns)
from_district = worksheet['From_District']
to_district = worksheet['To_District']
return from_district , to_district
def read_new_district_names(filename):
"""
This function gives new_district_names
"""
current_directory = util.get_current_directory()
full_path = current_directory + "/Coords/" + filename
all_data = pd.ExcelFile(full_path)
worksheet_1 = all_data.parse('Sheet1')
#Get the columns into columns
columns_1 = worksheet_1.columns
#It is easier to work with list objects,so cast them to list objects.
columns_1 = list(columns_1)
if IS_DEBUG == True:
print("The columns in the first worksheet are: " , columns_1)
print("The columns in the second workshett are: " , columns_2)
#Read all the x_coordinates and y_coordinates
old_district_names = worksheet_1['AD'].values
#Get rid of empty districts
new_district_names = []
for i in range(len(old_district_names)):
if type(old_district_names[i]) == type(" "):
new_district_names.append(old_district_names[i])
return new_district_names
def read_cloud_solution(filename):
"""
This function reads solution of the optimization problem which is solved by cplex_cloud
"""
current_directory = util.get_current_directory()
full_path = current_directory + "/Solutions/" + filename
solution_file = open(full_path , 'r')
content = solution_file.read()
is_solution_start = False
is_solution_end = False
solution_array = []
for i in range(len(content)) :
if content[i : i + 5] == "y = [" :
is_solution_start = True
if is_solution_start == True and is_solution_end == False :
if 48 <= ord(content[i]) <= 49 :
solution_array.append(int(content[i]))
if is_solution_start == True and content[i] == "]" :
is_solution_end = True
return solution_array
def combine_queries(length_of_appropriate_pairs):
"""
This function reads all the cleaned query files, then it combines them in an array which consists of len(appropriate_pairs) lists each of which
consists of 60 data points
It takes no argument.
It returns 1 variable:
combined_data_points : A list of lists , whose length is len(appropriate_pairs) , each element list has length 60
"""
#Get the full path using current_directory
current_directory = util.get_current_directory()
full_path = current_directory + "/New_Data_Points"
#Initialize the variables
combined_data_points = []
for i in range(length_of_appropriate_pairs):
combined_data_points.append([])
#Iterate over all excel queries and add the data points accordingly
for filename in os.listdir(full_path):
if filename.endswith(".xlsx") and filename[0] == 'w':
d_excel_file = pd.ExcelFile(full_path + "/" + filename)
worksheet_1 = d_excel_file.parse('Sheet1' , header = None)
for i in range(len(worksheet_1)):
if worksheet_1.iloc[i,2] != 0 :
combined_data_points[i].append(worksheet_1.iloc[i,2])
else:
print("You are not a excel file dute.")
return combined_data_points
def read_query_file(filename):
"""
This function read a query file.
It takes 1 argument:
i) filename : A string , name of the excel file to read
It returns 3 variables:
i) from_district : A list which consists of ids of from_district
ii) to_district : A list which consists of ids of to_district
iii) duration : A list which consists of travel time between from_district and to_district such as "8 mins"
"""
#Get the full path using current directory
current_directory = util.get_current_directory()
full_path = current_directory + "/Data_Points/" + filename
#Open excel file
d_excel_file = pd.ExcelFile(full_path)
worksheet = d_excel_file.parse('Sheet1' , header = None)
#Initialize the variables
from_district = []
to_district = []
duration = []
#Fill the variables usign excel data
for i in range(len(worksheet)):
from_district.append(worksheet.iloc[i , 0])
to_district.append(worksheet.iloc[i , 1])
duration.append(worksheet.iloc[i , 2])
return from_district , to_district , duration
def read_binary_txt(filename):
"""
This function reads solution txt which consist of NUMBER_OF_DISTRICT binary values in line 1
It takes 1 argument:
filename : A string , which is the name of the txt file
It returns 1 variable:
solution_array : A list , which consist of NUMBER_OF_DISTRICT binary values
"""
current_directory = util.get_current_directory()
full_path = current_directory + "/Solutions/" + filename
#Open the file and read the content of it into the variable "content"
file = open(full_path , 'r')
content = file.read()
#Get rid of \n at the at of the content
content = content.strip()
#Collect all the elements in array
solution_array = []
for element in content:
if element != ' ':
solution_array.append(element)
#Make the elements integer and win the war.
for i in range(len(solution_array)):
solution_array[i] = int(solution_array[i])
return solution_array
def polygon_coords(filename):
"""
This function reads x-y data of polygon coordinates and return it
"""
#Get full_path using current_directory
current_directory = util.get_current_directory()
full_path = current_directory + "\Coords/" + filename
all_data = pd.ExcelFile(full_path)
worksheet_1 = all_data.parse('Sheet1')
#Get the columns into columns
columns_1 = worksheet_1.columns
#It is easier to work with list objects,so cast them to list objects.
columns_1 = list(columns_1)
id_district = worksheet_1['shapeid'].values
x_district = worksheet_1['x'].values
y_district = worksheet_1['y'].values
#Initialize variables
lat = []
longs = []
for i in range(util.VIS_NUMBER_OF_DISTRICT):
lat.append([])
longs.append([])
for i in range(len(x_district)):
if pd.isna(x_district[i]) != True and pd.isna(y_district[i]) != True and pd.isna(id_district[i]) != True :
while x_district[i] > 100 :
x_district[i] /= 10
while y_district[i] > 100 :
y_district[i] /= 10
lat[int(id_district[i]/10)].append(y_district[i])
longs[int(id_district[i]/10)].append(x_district[i])
return lat,longs
def read_availability_matrix(filename):
"""
This function reads filename.xlsx and returns availability matrix.
"""
#Get full path using current_directory
current_directory = util.get_current_directory()
full_path = current_directory + "/Availability_Matrix/" + filename
#Open excel file to write
excel_file = pd.ExcelFile(full_path)
worksheet = excel_file.parse('Sheet1' , header = None)
#Initialize availability_matrix
availability_matrix = []
for i in range(len(worksheet)):
availability_matrix.append([])
for k in range(util.NUMBER_OF_DISTRICT) :
availability_matrix[i].append(worksheet.iloc[i][k])
return availability_matrix