something new

data/passenger.csv

@@ -1,11 +1,11 @@
-Year,Ferry Passenger,Hotel & Motel Gross Business Sales,population
-2014,72187,32071,33256
-2015,65101,33439,33445
-2016,59194,34677,33081
-2017,57144,35603,32729
-2018,53920,35906,32664
-2019,41559,37496,32544
-2020,10987,19077,32255
-2021,25299,37829,32239
-2022,35683,53740,31834
-2023,41469,59158,31549
+Year,Cruise Ship Visitation,Ferry Passenger,Air Passenger,Hotel & Motel Gross Business Sales,total earnings,local residents
+2014,953100,72187,307742,32071,,33256
+2015,982500,65101,331079,33439,,33445
+2016,1015100,59194,339279,34677,,33081
+2017,1072300,57144,345454,35603,,32729
+2018,1151100,53920,358388,35906,,32664
+2019,1305700,41559,365349,37496,1032253389,32544
+2020,37,10987,154292,19077,62723855,32255
+2021,115800,25299,284039,37829,78383883,32239
+2022,1167194,35683,359312,53740,119520965,31834
+2023,1638902,41469,354905,59158,134631332,31549

data/passenger.csv.bak

@@ -0,0 +1,17 @@
+Year,Cruise Ship Visitation,Ferry Passenger,Air Passenger,Hotel & Motel Gross Business Sales,total earnings,local residents
+2008,,,291620,,,
+2009,,,264004,,,
+2010,,,285720,,,
+2011,,,291069,,,
+2012,931000,,288311,,,
+2013,985700,,289993,,,33138
+2014,953100,72187,307742,32071,,33256
+2015,982500,65101,331079,33439,,33445
+2016,1015100,59194,339279,34677,,33081
+2017,1072300,57144,345454,35603,,32729
+2018,1151100,53920,358388,35906,,32664
+2019,1305700,41559,365349,37496,1032253389,32544
+2020,37,10987,154292,19077,62723855,32255
+2021,115800,25299,284039,37829,78383883,32239
+2022,1167194,35683,359312,53740,119520965,31834
+2023,1638902,41469,354905,59158,134631332,31549

optimizer1.py

@@ -0,0 +1,53 @@
+import math
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+
+colorList = json.load(open('color/config.json','r'))["color"]
+
+p=3
+p1=100
+d=0.5
+p2=25
+p3=25
+cp=500
+c=15
+cmax=8000/0.2
+cb=200
+m=0.01
+r=0.85
+
+fac1=0.0625
+fac2=0.1875*0.2
+fac3=0.4375
+
+def t(x):
+    return max(0,500-2.2*x)
+def f1(x):
+    return x*t(x)+x/d*p1+x*(1-r)*p2+x*r*p3
+def f2(x):
+    return x*c+cp*x/d
+def f3(x):
+    return x/cb+x/(x+p)+m*x
+def f(x):
+    return fac1*f1(x)-fac2*f2(x)-fac3*f3(x)
+
+for x in np.arange(0.2,cb,0.2):
+    if (f2(x)>cmax):
+        cb = x
+        break
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+ranging=np.arange(0.01,cb,0.01)
+plt.plot(ranging,[f(x) for x in ranging],color=colorList[0],label='f(x)')
+# plt.plot(ranging,[f1(x) for x in ranging],color=colorList[1],label='f1(x)')
+# plt.plot(ranging,[f2(x) for x in ranging],color=colorList[2],label='f2(x)')
+# plt.plot(ranging,[f3(x) for x in ranging],color=colorList[3],label='f3(x)')
+
+plt.xlabel('x')
+plt.ylabel('f(x)')
+plt.legend()
+plt.savefig('result/optimized1.png')
+plt.show()

relation-plot.py

@@ -0,0 +1,48 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+
+colorList = json.load(open('color/config.json','r'))["color"]
+
+import csv
+
+data_pasg = {}
+data_temp = {}
+
+with open('data/passenger.csv', 'r') as f:
+    reader = csv.reader(f)
+    header = next(reader)
+    for row in reader:
+        data_pasg[row[0]] = np.array(row[1:],dtype=float)
+
+with open('data/temperature.csv', 'r') as f:
+    reader = csv.reader(f)
+    header = next(reader)
+    for row in reader:
+        data_temp[row[0]] = np.array(row[1:],dtype=float)
+
+xList = np.array([])
+yList = np.array([])
+for year in range(2014,2024):
+    plt.scatter(data_pasg[str(year)][0], data_temp[str(year)][2], color=colorList[0]) 
+    xList = np.append(xList,data_pasg[str(year)][0])
+    yList = np.append(yList,data_temp[str(year)][2])
+
+from scipy.optimize import curve_fit
+def linear(x,k,b):
+    return k*x+b
+valk,valb = curve_fit(linear,xList,yList)[0]
+residuals = yList - linear(xList,valk,valb)
+ss_res = np.sum(residuals**2)
+ss_tot = np.sum((yList-np.mean(yList))**2)
+r_squared = 1 - (ss_res / ss_tot)
+print("R-squared:", r_squared)
+
+plt.plot(np.arange(0,80000,1000),linear(np.arange(0,80000,1000),valk,valb),color=colorList[1])
+
+plt.xlabel('Passenger')
+plt.ylabel('SnowFall')
+plt.title('Relation between Passenger and SnowFall')
+plt.text( 60000, 110, "k = %f\nb = %f\nR^2 = %f"%(valk,valb,r_squared),color=colorList[1])
+# plt.show()
+plt.savefig('result/relation-plot0.png')

relation-plot2.py

@@ -0,0 +1,36 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+
+colorList = json.load(open('color/config.json','r'))["color"]
+
+import csv
+
+with open('data/passenger.csv', 'r') as f:
+    reader = csv.reader(f)
+    header = next(reader)
+    data = [row for row in reader]
+
+data = np.array(data).astype(int).T
+
+xList = data[1]
+yList = data[2]/data[1]
+
+plt.scatter(xList,yList,color=colorList[0])
+
+from scipy.optimize import curve_fit
+def linear(x,k,b):
+    return k*x+b
+valk,valb = curve_fit(linear,xList,yList)[0]
+residuals = yList - linear(xList,valk,valb)
+ss_res = np.sum(residuals**2)
+ss_tot = np.sum((yList-np.mean(yList))**2)
+r_squared = 1 - (ss_res / ss_tot)
+print("R-squared:", r_squared)
+
+plt.plot(np.arange(0,80000,1000),linear(np.arange(0,80000,1000),valk,valb),color=colorList[1])
+
+bar_width = 0.25
+
+plt.legend()
+plt.show()

relation-plot3.py

@@ -0,0 +1,37 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+
+colorList = json.load(open('color/config.json','r'))["color"]
+
+import csv
+
+with open('data/passenger.csv', 'r') as f:
+    reader = csv.reader(f)
+    header = next(reader)
+    data = [row for row in reader]
+
+data = np.array(data).astype(int).T
+
+xList = data[1]
+yList = data[2]
+
+plt.scatter(xList,yList,color=colorList[0])
+
+from scipy.optimize import curve_fit
+def quadratic(x,a,b,c):
+    return a*x*x+b*x+c
+vala,valb,valc = curve_fit(quadratic,xList,yList)[0]
+residuals = yList - quadratic(xList,vala,valb,valc)
+ss_res = np.sum(residuals**2)
+ss_tot = np.sum((yList-np.mean(yList))**2)
+r_squared = 1 - (ss_res / ss_tot)
+print("R-squared:", r_squared)
+
+plt.plot(np.arange(0,80000,1000),quadratic(np.arange(0,80000,1000),vala,valb,valc),color=colorList[1])
+plt.xlabel('Ferry Passenger Count (Number of Passengers)')
+plt.ylabel('Hotel & Motel Gross Business Sales (USD)')
+
+
+plt.legend()
+plt.show()

simple-plot.py

@@ -17,7 +17,30 @@ bar_width = 0.25
 for i in range(1,len(data)):
     plt.bar(data[0]+(i-2)*bar_width, data[i], label=header[i], color=colorList[i-1], width=bar_width)
 
+ax2 = plt.twinx()
+ax2.plot(data[0],data[2]/data[1],label="income/pop",color=colorList[len(data)-1])
+
+# with open('data/temperature.csv', 'r') as f:
+#     reader = csv.reader(f)
+#     header = next(reader)
+#     data = [row for row in reader]
+
 plt.legend()
+
+# data = np.array(data).astype(float).T
+# ax2 = plt.twinx()
+# for i in range(1,len(data)):
+#     if i <=2 :
+#     #     data[i] = ( data[i] - 32 ) / 1.8
+#     #     ax2.plot(data[0], data[i], label=header[i], color=colorList[i-1+len(data)-1])
+#         pass
+#     else:
+#         ax2.plot(data[0], data[i], label=header[i], color=colorList[i-1+len(data)-1])
+
+# ax2.set_ylabel('Temperature (Celcius)')
+# # ax2.set_ylim(-10,20)
+# ax2.legend(loc='upper left')
+
 plt.xlabel('Year')
 
 plt.show()

simplePlot.py

@@ -0,0 +1,27 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+
+colorList = json.load(open('color/config.json','r'))["color"]
+
+import csv
+
+with open('data/passenger.csv', 'r') as f:
+    reader = csv.reader(f)
+    header = next(reader)
+    data = [row for row in reader]
+
+data = np.array(data).astype(float).T
+
+bar_width = 0.35
+
+plt.bar(data[0], data[1], label=header[1], color=colorList[0], width=bar_width)
+plt.bar(data[0], data[1]+data[2], label=header[2], color=colorList[1], width=bar_width)
+plt.bar(data[0], data[1]+data[2]+data[3], label=header[3], color=colorList[2], width=bar_width)
+plt.plot(data[0],data[4], label=header[4], color=colorList[3])
+plt.plot(data[0],data[5], label=header[5], color=colorList[4])
+
+plt.legend()
+plt.xlabel('Year')
+
+plt.show()