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模組:沙盒/myselfwu/TRARanking

维基百科,自由的百科全书
local p = {}

ridershipDataYear = 2015
ridershipData = {}
ridershipData[  4] = { 33,  10595.83}
ridershipData[  6] = {220,     15.58}
ridershipData[  8] = {149,    459.16}
ridershipData[  9] = {191,     88.83}
ridershipData[ 10] = {216,     21.39}
ridershipData[ 12] = {121,   1204.41}
ridershipData[ 14] = {218,     18.59}
ridershipData[ 15] = {128,    988.50}
ridershipData[ 18] = {157,    387.15}
ridershipData[ 20] = {206,     37.38}
ridershipData[ 22] = {204,     43.93}
ridershipData[ 25] = { 83,   2585.13}
ridershipData[ 27] = {221,     14.50}
ridershipData[ 29] = {124,   1116.23}
ridershipData[ 31] = {189,     98.46}
ridershipData[ 32] = {219,     18.00}
ridershipData[ 34] = {131,    882.78}
ridershipData[ 35] = {186,    108.46}
ridershipData[ 36] = {137,    679.45}
ridershipData[ 37] = {210,     35.16}
ridershipData[ 40] = {177,    199.57}
ridershipData[ 41] = {147,    517.00}
ridershipData[ 42] = {207,     36.93}
ridershipData[ 43] = {144,    581.74}
ridershipData[ 45] = {136,    711.85}
ridershipData[ 51] = { 12,  29773.50}
ridershipData[ 52] = {146,    517.45}
ridershipData[ 53] = {211,     35.01}
ridershipData[ 54] = { 46,   6369.34}
ridershipData[ 55] = {192,     87.30}
ridershipData[ 56] = {188,     98.99}
ridershipData[ 57] = {118,   1263.14}
ridershipData[ 58] = {203,     45.42}
ridershipData[ 60] = {209,     35.30}
ridershipData[ 62] = { 90,   2281.00}
ridershipData[ 63] = {139,    666.36}
ridershipData[ 64] = {202,     47.31}
ridershipData[ 66] = {111,   1459.96}
ridershipData[ 67] = { 41,   7464.04}
ridershipData[ 68] = {201,     47.70}
ridershipData[ 69] = {119,   1212.00}
ridershipData[ 70] = { 28,  12598.60}
ridershipData[ 71] = {196,     63.85}
ridershipData[ 72] = {171,    268.62}
ridershipData[ 73] = { 35,  10302.15}
ridershipData[ 74] = {169,    275.15}
ridershipData[ 75] = { 66,   3744.06}
ridershipData[ 76] = {167,    284.54}
ridershipData[ 77] = { 74,   3270.15}
ridershipData[ 78] = {175,    227.56}
ridershipData[ 79] = {176,    206.37}
ridershipData[ 80] = {151,    434.00}
ridershipData[ 81] = {153,    418.31}
ridershipData[ 82] = {194,     67.90}
ridershipData[ 83] = { 86,   2470.99}
ridershipData[ 84] = {152,    433.19}
ridershipData[ 85] = { 97,   1824.52}
ridershipData[ 86] = {172,    266.98}
ridershipData[ 87] = {178,    193.74}
ridershipData[ 88] = {102,   1586.74}
ridershipData[ 89] = { 27,  12909.93}
ridershipData[ 90] = {127,   1007.88}
ridershipData[ 91] = {143,    582.42}
ridershipData[ 92] = { 19,  16458.60}
ridershipData[ 93] = { 51,   5265.09}
ridershipData[ 94] = { 30,  11903.49}
ridershipData[ 95] = { 75,   3175.05}
ridershipData[ 96] = { 15,  18782.70}
ridershipData[ 97] = { 23,  14813.89}
ridershipData[ 98] = { 10,  31786.13}
ridershipData[100] = {  1, 129531.90}
ridershipData[101] = { 39,   8271.57}
ridershipData[102] = {  7,  42642.00}
ridershipData[103] = {  9,  32282.51}
ridershipData[104] = { 68,   3629.62}
ridershipData[105] = { 16,  17731.15}
ridershipData[106] = {  2,  60655.54}
ridershipData[107] = { 17,  16515.90}
ridershipData[108] = {  3,  57239.99}
ridershipData[109] = { 32,  10629.09}
ridershipData[110] = { 34,  10317.25}
ridershipData[111] = { 73,   3317.87}
ridershipData[112] = { 40,   8006.98}
ridershipData[113] = { 36,   9829.50}
ridershipData[114] = { 37,   9512.10}
ridershipData[115] = {  6,  42805.93}
ridershipData[116] = {125,   1075.76}
ridershipData[117] = {159,    367.45}
ridershipData[118] = { 18,  16470.22}
ridershipData[119] = {197,     63.14}
ridershipData[120] = {170,    273.34}
ridershipData[121] = { 82,   2604.23}
ridershipData[122] = {198,     62.81}
ridershipData[123] = {150,    455.98}
ridershipData[124] = {183,    135.16}
ridershipData[125] = { 98,   1822.45}
ridershipData[126] = { 78,   2911.93}
ridershipData[127] = {145,    577.21}
ridershipData[128] = { 55,   5032.63}
ridershipData[129] = {181,    155.56}
ridershipData[130] = { 89,   2333.01}
ridershipData[131] = { 58,   4711.14}
ridershipData[132] = {110,   1462.35}
ridershipData[133] = {133,    860.56}
ridershipData[134] = {129,    928.27}
ridershipData[135] = {141,    593.36}
ridershipData[136] = {138,    671.33}
ridershipData[137] = { 26,  13145.89}
ridershipData[138] = {180,    173.96}
ridershipData[139] = {107,   1499.50}
ridershipData[140] = {101,   1647.82}
ridershipData[142] = {135,    764.33}
ridershipData[143] = { 62,   4028.48}
ridershipData[144] = { 21,  15554.18}
ridershipData[145] = { 52,   5101.13}
ridershipData[146] = {  4,  53734.24}
ridershipData[147] = {100,   1675.76}
ridershipData[148] = {106,   1530.50}
ridershipData[149] = { 11,  30009.09}
ridershipData[150] = { 93,   2011.05}
ridershipData[151] = { 20,  16436.81}
ridershipData[152] = {134,    830.84}
ridershipData[153] = { 85,   2480.92}
ridershipData[154] = { 45,   6525.21}
ridershipData[155] = { 79,   2834.31}
ridershipData[156] = {109,   1484.95}
ridershipData[157] = {165,    298.59}
ridershipData[158] = { 25,  14061.59}
ridershipData[159] = { 54,   5064.50}
ridershipData[160] = {173,    258.86}
ridershipData[161] = { 71,   3386.77}
ridershipData[162] = { 63,   4024.02}
ridershipData[163] = { 13,  21383.59}
ridershipData[164] = {115,   1318.92}
ridershipData[165] = {154,    411.59}
ridershipData[166] = {117,   1281.27}
ridershipData[167] = { 31,  11233.45}
ridershipData[168] = { 95,   2004.47}
ridershipData[169] = {122,   1199.62}
ridershipData[170] = { 70,   3415.14}
ridershipData[171] = {163,    309.18}
ridershipData[172] = { 42,   7272.10}
ridershipData[173] = { 49,   5618.13}
ridershipData[174] = { 44,   6670.35}
ridershipData[175] = {  5,  53051.45}
ridershipData[176] = { 67,   3652.96}
ridershipData[177] = {126,   1027.64}
ridershipData[178] = { 61,   4052.47}
ridershipData[179] = { 65,   3795.80}
ridershipData[180] = { 47,   6025.76}
ridershipData[181] = { 77,   2956.02}
ridershipData[183] = { 50,   5292.55}
ridershipData[184] = { 92,   2106.71}
ridershipData[185] = {  8,  41437.07}
ridershipData[186] = { 38,   8824.32}
ridershipData[187] = {116,   1284.53}
ridershipData[188] = { 88,   2412.45}
ridershipData[189] = {179,    180.73}
ridershipData[190] = { 14,  20853.86}
ridershipData[191] = {184,    128.10}
ridershipData[192] = {190,     92.57}
ridershipData[193] = {148,    470.81}
ridershipData[194] = {161,    336.66}
ridershipData[195] = { 57,   4880.48}
ridershipData[196] = {158,    382.86}
ridershipData[197] = {114,   1326.29}
ridershipData[198] = {205,     42.50}
ridershipData[199] = {132,    861.13}
ridershipData[200] = {168,    280.58}
ridershipData[201] = {185,    112.34}
ridershipData[203] = { 96,   1939.78}
ridershipData[204] = {217,     21.10}
ridershipData[205] = {224,      1.00}
ridershipData[206] = {222,      3.28}
ridershipData[209] = {223,      2.06}
ridershipData[211] = {156,    395.74}
ridershipData[213] = {199,     60.34}
ridershipData[215] = {142,    583.76}
ridershipData[217] = {162,    316.44}
ridershipData[219] = {123,   1189.00}
ridershipData[220] = {208,     35.96}
ridershipData[280] = { 29,  12193.60}
ridershipData[288] = { 22,  14967.51}
ridershipData["竹東車站"] = {120, 1206.64}
ridershipData["汐科車站"] = {24, 14123.95}
ridershipData["大橋車站"] = {43, 6885.47}
ridershipData["太原車站"] = {48, 5634.06}
ridershipData["浮洲車站"] = {53, 5073.10}
ridershipData["百福車站"] = {56, 4883.40}
ridershipData["六家車站"] = {59, 4404.37}
ridershipData["大慶車站"] = {60, 4404.08}
ridershipData["沙崙車站"] = {64, 3861.39}
ridershipData["北新竹車站"] = {69, 3525.51}
ridershipData["菁桐車站"] = {72, 3335.17}
ridershipData["新莊車站 (新竹市)"] = {76, 3048.27}
ridershipData["海科館車站"] = {80, 2762.70}
ridershipData["十分車站"] = {81, 2619.56}
ridershipData["三坑車站"] = {84, 2488.77}
ridershipData["南科車站"] = {87, 2452.31}
ridershipData["北湖車站"] = {91, 2133.21}
ridershipData["長榮大學車站"] = {94, 2010.16}
ridershipData["大村車站"] = {99, 1684.71}
ridershipData["內灣車站"] = {103, 1579.47}
ridershipData["嘉北車站"] = {104, 1579.00}
ridershipData["竹中車站"] = {105, 1559.03}
ridershipData["仁德車站"] = {108, 1485.15}
ridershipData["車埕車站"] = {112, 1450.56}
ridershipData["集集車站"] = {113, 1353.67}
ridershipData["平溪車站"] = {130, 897.52}
ridershipData["千甲車站"] = {140, 618.39}
ridershipData["水里車站"] = {155, 405.12}
ridershipData["濁水車站"] = {160, 337.84}
ridershipData["榮華車站"] = {164, 304.51}
ridershipData["合興車站"] = {166, 289.11}
ridershipData["橫山車站 (新竹縣)"] = {174, 252.37}
ridershipData["上員車站"] = {182, 144.75}
ridershipData["九讚頭車站"] = {187, 105.86}
ridershipData["嶺腳車站"] = {193, 69.31}
ridershipData["龍泉車站"] = {195, 64.07}
ridershipData["源泉車站"] = {200, 50.93}
ridershipData["望古車站"] = {212, 34.02}
ridershipData["大華車站"] = {213, 31.73}
ridershipData["富貴車站"] = {214, 30.85}
ridershipData["南樹林車站"] = {215, 23.28}

function p._formatnum(value, digit)
	if value == 0 then
		if digit == 1 then
			return 0
		else
			return ""
		end
	end
	result = p._formatnum(math.floor(value/10), digit+1)
	digitData = value%10
	if (digit > 3) and ((digit%3) == 1) then
		return result .. digitData .. ","
	else
		return result .. digitData
	end
end

function p.rank(frame)
    -- Allow for invocation via #invoke or directly from another module
    local args
	if frame == mw.getCurrentFrame() then
		args = frame.args
	else
		args = frame
	end
 	local stationID = args.stationID
	local requestField = args.requestField
	local nStationID = tonumber(stationID)
	local nRequestField
	if requestField == nil then
		nRequestField = 4
	else
		nRequestField = tonumber(requestField)
		if type(nRequestField) ~= "number" then
			return type(nRequestField) .. requestField
		end
		if nRequestField > 4 then
			nRequestField = 4
		elseif nRequestField < 1 then
			-- show one at least
			nRequestField = 4
		end
	end
	for indexID, targetRidership in pairs(ridershipData) do
		if (indexID == nStationID) or (indexID == stationID) then 
			if nRequestField == 1 then
				return p._formatnum(math.floor(targetRidership[2]+0.5),1)
			elseif nRequestField == 2 then
				return targetRidership[1]
			elseif nRequestField == 3 then
				return ridershipDataYear
			elseif nRequestField == 4 then
				return p._formatnum(math.floor(targetRidership[2]+0.5),1) .. ",第" .. targetRidership[1] .. "名(" .. ridershipDataYear .. "年)"
			end
		end
	end
	if nRequestField == 4 then
		return "無資料"
	else
		return "-"
	end
end
return p