How to create a Dynamic Regression Model (ARIMAX) in R? - dynamic
I created an ARIMAX model. Unfortunately, the forecasting that is created is not as it should be.
I want to predict the clickthrough rate "ctr" and use 6 explanatory variables.
enter image description here
#Step 4: Fit the forecasting model (ARIMAX) to the data
Fit <- auto.arima(Oct_Mar[, "ctr"], xreg = as.matrix(Oct_Mar[, 2:7]))
#Step 5: Predict values for Apr
Forecast_ctr <- Fit %>% forecast(xreg = as.matrix(Oct_Mar[, 2:7], h=30)
#Step 6: Forecast values
Forecast_ctr
#Step 7: Plot the forecast
autoplot(Forecast_ctr)+ xlab("Anzahl an Tagen")+ ylab("CTR")
Dataset:
structure(list(date = c("2019.10.01", "2019.10.02", "2019.10.03",
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0.0212945438945016, 0.021660499704709, 0.0203399246100257, 0.021042755155702,
0.0207706911083365, 0.0191080416224264, 0.0188521002714409, 0.0176177277744931,
0.0170654092407268, 0.0167173964870618, 0.0169957130997929, 0.0173290930652611,
0.018723068602435, 0.0190819762151085, 0.0178334964691514, 0.0160768402598991,
0.0172883539665594, 0.0176745227466401)), row.names = c(NA, -183L
), class = "data.frame")
Related
Is there a way to create a dynamic regression model (ARIMAX) with multiple explanatory variables?
Is there a way to create a dynamic regression model (ARIMAX) with multiple explanatory variables?
The explanatory variables are introduced into the model with the regression part (xreg()).
(Fit <- auto.arima(Oct_Mar[, "ctr"],
xreg = Oct_Mar[, "price_purchase"]))`
As long as I use a variable it works to create the model.
As soon as I want to use several variables:
(Fit <- auto.arima(Oct_Mar[, "ctr"],
xreg = Oct_Mar[, "price_purchase"],
xreg = Oct_Mar[, "price_view"],
xreg = Oct_Mar[, "price_cart"]))`
Error:
Error in auto.arima(Oct_Mar[, "ctr"], xreg = Oct_Mar[, "price_purchase"], :
formal argument "xreg" matched by multiple actual arguments
When I try it like this:
(Fit <- auto.arima(Oct_Mar[, "ctr"],
xreg = Oct_Mar[, 2:6])) `
Error:
Error in auto.arima(Oct_Mar[, "ctr"], xreg = Oct_Mar[, 2:6]) :
xreg should be a numeric matrix or a numeric vector
All chosen variables should be numeric.
Data Subset:
structure(list(date = c("2019.10.01", "2019.10.02", "2019.10.03",
"2019.10.04", "2019.10.05", "2019.10.06", "2019.10.07", "2019.10.08",
"2019.10.09", "2019.10.10", "2019.10.11", "2019.10.12", "2019.10.13",
"2019.10.14", "2019.10.15", "2019.10.16", "2019.10.17", "2019.10.18",
"2019.10.19", "2019.10.20", "2019.10.21", "2019.10.22", "2019.10.23",
"2019.10.24", "2019.10.25", "2019.10.26", "2019.10.27", "2019.10.28",
"2019.10.29", "2019.10.30", "2019.10.31", "2019.11.01", "2019.11.02",
"2019.11.03", "2019.11.04", "2019.11.05", "2019.11.06", "2019.11.07",
"2019.11.08", "2019.11.09", "2019.11.10", "2019.11.11", "2019.11.12",
"2019.11.13", "2019.11.14", "2019.11.15", "2019.11.16", "2019.11.17",
"2019.11.18", "2019.11.19", "2019.11.20", "2019.11.21", "2019.11.22",
"2019.11.23", "2019.11.24", "2019.11.25", "2019.11.26", "2019.11.27",
"2019.11.28", "2019.11.29", "2019.11.30", "2019.12.01", "2019.12.02",
"2019.12.03", "2019.12.04", "2019.12.05", "2019.12.06", "2019.12.07",
"2019.12.08", "2019.12.09", "2019.12.10", "2019.12.11", "2019.12.12",
"2019.12.13", "2019.12.14", "2019.12.15", "2019.12.16", "2019.12.17",
"2019.12.18", "2019.12.19", "2019.12.20", "2019.12.21", "2019.12.22",
"2019.12.23", "2019.12.24", "2019.12.25", "2019.12.26", "2019.12.27",
"2019.12.28", "2019.12.29", "2019.12.30", "2019.12.31", "2020.01.01",
"2020.01.02", "2020.01.03", "2020.01.04", "2020.01.05", "2020.01.06",
"2020.01.07", "2020.01.08", "2020.01.09", "2020.01.10", "2020.01.11",
"2020.01.12", "2020.01.13", "2020.01.14", "2020.01.15", "2020.01.16",
"2020.01.17", "2020.01.18", "2020.01.19", "2020.01.20", "2020.01.21",
"2020.01.22", "2020.01.23", "2020.01.24", "2020.01.25", "2020.01.26",
"2020.01.27", "2020.01.28", "2020.01.29", "2020.01.30", "2020.01.31",
"2020.02.01", "2020.02.02", "2020.02.03", "2020.02.04", "2020.02.05",
"2020.02.06", "2020.02.07", "2020.02.08", "2020.02.09", "2020.02.10",
"2020.02.11", "2020.02.12", "2020.02.13", "2020.02.14", "2020.02.15",
"2020.02.16", "2020.02.17", "2020.02.18", "2020.02.19", "2020.02.20",
"2020.02.21", "2020.02.22", "2020.02.23", "2020.02.24", "2020.02.25",
"2020.02.26", "2020.02.27", "2020.02.28", "2020.02.29", "2020.03.01",
"2020.03.02", "2020.03.03", "2020.03.04", "2020.03.05", "2020.03.06",
"2020.03.07", "2020.03.08", "2020.03.09", "2020.03.10", "2020.03.11",
"2020.03.12", "2020.03.13", "2020.03.14", "2020.03.15", "2020.03.16",
"2020.03.17", "2020.03.18", "2020.03.19", "2020.03.20", "2020.03.21",
"2020.03.22", "2020.03.23", "2020.03.24", "2020.03.25", "2020.03.26",
"2020.03.27", "2020.03.28", "2020.03.29", "2020.03.30", "2020.03.31"
), price_view = c(35.79, 180.16, 437.57, 10.3, 74.26, 79.8, 89.84,
121.24, 461.95, 142.06, 241.71, 52, 43.24, 41.16, 167.05, 764.06,
91.64, 189.82, 38.59, 152.64, 86.23, 321.33, 411.83, 256.88,
352.39, 76.32, 360.11, 123.53, 43.41, 149.38, 14.16, 489.07,
1661.74, 1253.07, 25.71, 154.42, 990.89, 1645.93, 144.12, 84.43,
240.25, 148.18, 41.13, 262.56, 168.78, 860.85, 239.31, 372.98,
165.64, 134.32, 20.7, 43.73, 765.76, 51.48, 599.49, 893.79, 155.29,
334.37, 46.82, 1814.72, 196.27, 1302.48, 40.16, 1161.68, 381.48,
184.48, 48.91, 221.11, 434.73, 149.27, 77.22, 882.49, 106.05,
669.23, 282.86, 179.67, 12.97, 460.24, 38.59, 278.26, 243.76,
1904.79, 84.93, 32.18, 25.71, 496.54, 29.6, 1466.83, 164.33,
234.76, 19.95, 308.37, 1130.02, 7.47, 79.8, 65.9, 746.45, 1347.78,
1270.82, 69.42, 231.41, 195.6, 715.33, 208.47, 720.46, 414.68,
24.45, 217.82, 434.45, 483.92, 1500.42, 318.15, 339.29, 267.45,
133.85, 9.03, 11.81, 280.57, 916.74, 58.51, 339.78, 33.98, 263.58,
19.31, 239.88, 489.07, 84.92, 344.9, 95.24, 99.1, 142.58, 480.58,
104.74, 14.83, 252, 1039.41, 28.3, 328.97, 341.55, 278.26, 43.73,
91.35, 102.32, 131.25, 155.15, 77.74, 14.67, 132.63, 1185.36,
291.13, 1106.59, 849.42, 117.63, 171.32, 167.31, 252.23, 248.14,
111.15, 257.15, 27.62, 169.86, 101.89, 282.89, 298.57, 86.49,
196.32, 1415.45, 898.35, 334.6, 17.99, 13.62, 566.27, 60.41,
36.34, 62.04, 308.81, 32.95, 127.44, 836.57, 221.34, 360.34,
159.31, 20.57), view = c(1206151, 1152770, 1087372, 1344804,
1270060, 1262993, 1159265, 1323522, 1301376, 1240347, 1445162,
1432321, 1583572, 1376274, 1462409, 1443323, 1337174, 1413405,
1382403, 1443838, 1342668, 1353053, 1318395, 1252747, 1369922,
1288939, 1330209, 1220710, 1187883, 1169955, 1207854, 1402754,
1513400, 1524803, 1743304, 1670637, 1644359, 1748812, 1789808,
1783142, 1845552, 1907417, 1892753, 1920411, 2864410, 5691766,
5986292, 5759703, 1905351, 1627672, 1598554, 1573101, 1471242,
1474138, 1500022, 1496128, 1557252, 1547199, 1560191, 1727852,
1644405, 1706901, 1629904, 1547658, 1468085, 1540157, 1652208,
1725106, 1724452, 1627222, 1651328, 1605421, 1650612, 1634861,
1760750, 2167056, 2875847, 2780816, 2665285, 2528244, 2387520,
2340327, 2471739, 2372930, 2326654, 2322753, 2240514, 2058141,
2089081, 2474226, 2294820, 1603749, 1427733, 1700904, 1765457,
1754424, 1738774, 1696188, 1701769, 1585870, 1556542, 1557542,
1618230, 1645866, 1627433, 1612956, 1555416, 1773179, 1826768,
2021676, 2104199, 1801073, 1733142, 1593991, 1645225, 1557626,
1637470, 1721003, 1545472, 1594688, 1565742, 1651606, 1999670,
2217825, 1985751, 1680034, 1608904, 1620473, 1628906, 1726835,
1589058, 1714745, 1751044, 1896265, 2429526, 2268487, 1935249,
1916034, 2239698, 1916650, 1981570, 1948648, 1987134, 1749514,
1822349, 1830307, 1748590, 1734610, 1798308, 162557, 1000204,
1257475, 1770064, 2416707, 2477258, 2487470, 2457500, 2210539,
2377633, 2026050, 2301337, 2218894, 2012789, 1700619, 1481115,
1562027, 1560348, 1338829, 1244973, 1142989, 1260747, 1316975,
1387394, 1319559, 1440470, 1451015, 1439649, 1390411, 1336076,
1369834, 1255626, 1244163, 1283731), price_cart = c(29.51, 1415.48,
99.86, 358.57, 617.51, 1052.79, 1747.79, 190.56, 128.28, 252.38,
250.91, 720.48, 33.42, 643, 191.77, 460.11, 408.5, 789.9, 577.94,
49.36, 380.7, 19.56, 994.86, 756.71, 223.66, 437.33, 1684.28,
366.16, 968.34, 1683.07, 550.77, 503.09, 29.09, 179.67, 210.62,
22.66, 131.66, 68.96, 360.06, 494.22, 1023.62, 1569.92, 28.29,
694.97, 127.05, 37.85, 282.89, 178.9, 913.28, 1022.42, 424.7,
573.7, 1029.34, 30.12, 20.82, 17.99, 107.53, 41.19, 85.82, 1002.55,
140.98, 167.03, 231.67, 25.71, 205.64, 30.81, 51.22, 65.9, 7.08,
308.63, 227.79, 16.22, 7.89, 62.52, 48.88, 586.63, 602.07, 1312.26,
128.32, 179.9, 849.42, 100.9, 1284.2, 12.84, 128.42, 59.18, 176.99,
38.02, 48.88, 694.54, 262.3, 1402.84, 1453.18, 3.84, 453.01,
76.93, 7.04, 865.93, 865.4, 40.75, 1423.07, 1534.66, 679.27,
11.25, 102.63, 436.3, 853.93, 694.97, 850.47, 477.49, 1234.97,
10.27, 23.94, 643.23, 89.84, 290.34, 320.99, 6.44, 140.28, 188.89,
56.88, 1326.31, 194.34, 140.28, 771.96, 140.03, 20.21, 1464.39,
59.18, 57.92, 1156.81, 50.43, 300.12, 38.1, 832.71, 57.91, 174.5,
100.36, 248.14, 109.34, 100.7, 242.7, 266.67, 592.01, 242.18,
22.66, 566.04, 38.61, 812.06, 123.92, 168.6, 172.03, 49.91, 16.73,
108.04, 347.47, 97.79, 111.15, 514.79, 126.1, 178.87, 870.03,
529.31, 43.5, 2110.48, 771.94, 15.32, 105.25, 7.14, 312.67, 61.75,
165.51, 48.37, 643.49, 303.48, 35.78, 154.42, 209.71, 76.69,
25.46, 1415.45, 123.53, 602.31), cart = c(16658, 17268, 19323,
43826, 35493, 32145, 18052, 18442, 18432, 18997, 21450, 20691,
24833, 44821, 49513, 45272, 40368, 40127, 39455, 40533, 36675,
36945, 36407, 35721, 36800, 34776, 34256, 17838, 17455, 16996,
16798, 18911, 19350, 20211, 21960, 19231, 19670, 19446, 77319,
70093, 71585, 75135, 69669, 71613, 170183, 481862, 405584, 426261,
83117, 72450, 72311, 75530, 70171, 64801, 68099, 71405, 71622,
71324, 71504, 92345, 81760, 84473, 80869, 70192, 66718, 71048,
83618, 84231, 80773, 80675, 81420, 78947, 80162, 82360, 86689,
109721, 183764, 155406, 146906, 137487, 127900, 124577, 127381,
126700, 124797, 127554, 123966, 120940, 127769, 148663, 148608,
119062, 57614, 71342, 95608, 80629, 78782, 79099, 77396, 74671,
72772, 74827, 72221, 73406, 72999, 71182, 70235, 79414, 104791,
103481, 102597, 94354, 90666, 83642, 83223, 73075, 73582, 73849,
70067, 71600, 72179, 130757, 208231, 169156, 137970, 116560,
104701, 102836, 101145, 101605, 90864, 92635, 95114, 100283,
158447, 131720, 118661, 126405, 132399, 98277, 96270, 95284,
96886, 89046, 91384, 89585, 83771, 83241, 84151, 6104, 40574,
48944, 81869, 146953, 135144, 132819, 134255, 131648, 141696,
122204, 122752, 120927, 112159, 102239, 95998, 97600, 99032,
79662, 76622, 69585, 73822, 74488, 75621, 69098, 73761, 76429,
75664, 77671, 77090, 77835, 68888, 69091, 73986), price_purchase = c(130.76,
419.6, 251.74, 252.88, 64.02, 272.59, 172.72, 88.81, 28.73, 1003.86,
346.47, 130.48, 29.86, 280.11, 358.57, 385.83, 287.61, 22.95,
58.08, 854.08, 28.28, 62.91, 994.86, 51.22, 9.01, 77.21, 244.15,
366.16, 366.8, 213.25, 35.52, 566.3, 35.78, 1106.82, 64.35, 722.18,
131.66, 166.1, 823.9, 138.23, 334.6, 328.19, 243.51, 488.8, 159.57,
106.8, 54.03, 27, 308.63, 1022.42, 463.31, 144.66, 44.53, 25.48,
126.18, 365.52, 133.92, 97.27, 12.84, 1002.55, 107.41, 132.31,
131.2, 789.57, 230.2, 12.36, 229.86, 1386.91, 154.19, 18.19,
76.96, 882.49, 191.55, 46.08, 24.17, 102.65, 326.62, 924.06,
923.73, 88.29, 41.16, 128.42, 326.88, 137.96, 30.68, 108.88,
181.19, 241.34, 128.32, 137.46, 1279.81, 643.23, 1275.16, 717.245,
159.33, 745.37, 288.27, 177.26, 168.58, 66.85, 331.51, 437.31,
643.23, 9.3, 0.85, 436.3, 105.51, 7.7, 79.44, 1321.37, 160.89,
107.21, 172.25, 514.79, 141.06, 900.64, 153.22, 924.4, 176.34,
94.98, 162.6, 1326.25, 39.9, 38.15, 2162.22, 180.95, 153.41,
720.48, 720.48, 15.42, 140.28, 514.02, 720.47, 174.7, 197.69,
411.08, 741.07, 230.12, 501.89, 109.34, 643.26, 23.17, 242.48,
1317.36, 69.76, 178.11, 153.55, 32.18, 812.06, 302.2, 153.34,
172.03, 128.68, 939.54, 108.04, 165.89, 56.63, 43.76, 171.17,
98.59, 21.95, 280.28, 181.47, 730.01, 159.31, 60.75, 31.15, 1412.39,
7.14, 942.84, 321.06, 165.51, 284.95, 169.42, 303.48, 224.3,
416.43, 385.85, 492.08, 334.6, 1415.45, 123.53, 308.55), purchase = c(19307,
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0.0185813515317095, 0.0180607614170281, 0.0187505760492009, 0.0169511102933927,
0.0178137558385877, 0.0184785698285323, 0.0201169476464591, 0.0220364679005246,
0.0212945438945016, 0.021660499704709, 0.0203399246100257, 0.021042755155702,
0.0207706911083365, 0.0191080416224264, 0.0188521002714409, 0.0176177277744931,
0.0170654092407268, 0.0167173964870618, 0.0169957130997929, 0.0173290930652611,
0.018723068602435, 0.0190819762151085, 0.0178334964691514, 0.0160768402598991,
0.0172883539665594, 0.0176745227466401)), row.names = c(NA, -183L
), class = "data.frame")
As the error explains, xreg should be a numeric matrix, not a data frame. The following works. Fit <- auto.arima(Oct_Mar[, "ctr"], xreg = as.matrix(Oct_Mar[, 2:6]))
i need to find the coordinated of the peaks as well as delineate their boundaries (i.e. peak 1 begins at time 65 and ends at time 71)
import numpy, sys from matplotlib import pyplot from scipy.signal import find_peaks, peak_widths t=[0.0, 0.187, 0.373, 0.56, 0.747, 0.933, 1.12, 1.307, 1.493, 1.68, 1.867, 2.053, 2.24, 2.427, 2.613, 2.8, 2.987, 3.173, 3.36, 3.547, 3.733, 3.92, 4.107, 4.293, 4.48, 4.667, 4.853, 5.04, 5.227, 5.413, 5.6, 5.787, 5.973, 6.16, 6.347, 6.533, 6.72, 6.907, 7.093, 7.28, 7.467, 7.653, 7.84, 8.027, 8.213, 8.4, 8.587, 8.773, 8.96, 9.147, 9.333, 9.52, 9.707, 9.893, 10.08, 10.267, 10.453, 10.64, 10.827, 11.013, 11.2, 11.387, 11.573, 11.76, 11.947, 12.133, 12.32, 12.507, 12.693, 12.88, 13.067, 13.253, 13.44, 13.627, 13.813, 14.0, 14.187, 14.373, 14.56, 14.747, 14.933, 15.12, 15.307, 15.493, 15.68, 15.867, 16.053, 16.24, 16.427, 16.613, 16.8, 16.987, 17.173, 17.36, 17.547, 17.733, 17.92, 18.107, 18.293, 18.48, 18.667, 18.853, 19.04, 19.227, 19.413, 19.6, 19.787, 19.973, 20.16, 20.347, 20.533, 20.72, 20.907, 21.093, 21.28, 21.467, 21.653, 21.84, 22.027, 22.213, 22.4, 22.587, 22.773, 22.96, 23.147, 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162.027, 162.213, 162.4, 162.587, 162.773, 162.96, 163.147, 163.333, 163.52, 163.707, 163.893, 164.08, 164.267] a= [0.0, 0.187, 0.373, 0.56, 0.747, 0.933, 1.12, 1.307, 1.493, 1.68, 1.867, 2.053, 2.24, 2.427, 2.613, 2.8, 2.987, 3.173, 3.36, 3.547, 3.733, 3.92, 4.107, 4.293, 4.48, 4.667, 4.853, 5.04, 5.227, 5.413, 5.6, 5.787, 5.973, 6.16, 6.347, 6.533, 6.72, 6.907, 7.093, 7.28, 7.467, 7.653, 7.84, 8.027, 8.213, 8.4, 8.587, 8.773, 8.96, 9.147, 9.333, 9.52, 9.707, 9.893, 10.08, 10.267, 10.453, 10.64, 10.827, 11.013, 11.2, 11.387, 11.573, 11.76, 11.947, 12.133, 12.32, 12.507, 12.693, 12.88, 13.067, 13.253, 13.44, 13.627, 13.813, 14.0, 14.187, 14.373, 14.56, 14.747, 14.933, 15.12, 15.307, 15.493, 15.68, 15.867, 16.053, 16.24, 16.427, 16.613, 16.8, 16.987, 17.173, 17.36, 17.547, 17.733, 17.92, 18.107, 18.293, 18.48, 18.667, 18.853, 19.04, 19.227, 19.413, 19.6, 19.787, 19.973, 20.16, 20.347, 20.533, 20.72, 20.907, 21.093, 21.28, 21.467, 21.653, 21.84, 22.027, 22.213, 22.4, 22.587, 22.773, 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161.653, 161.84, 162.027, 162.213, 162.4, 162.587, 162.773, 162.96, 163.147, 163.333, 163.52, 163.707, 163.893, 164.08, 164.267] a=numpy.array(a) t=numpy.array(t) mean = numpy.mean(a) std = numpy.std(a) peaks, _ = find_peaks(a,prominence=1) pyplot.plot(a) pyplot.plot(peaks,a[peaks],"o") plot
How to check if a record already exists or not in database in DRF?
I am working on DRF. I am creating an API of POST request that overriding its features. I want to add one functionality if a particular field value already exists in DB then, user cannot create post request and instead of it getting a message that he couldn't do it.
views.py
class PizzaOrderViewSet(viewsets.ModelViewSet):
http_method_names = ['get', 'put', 'patch', 'post']
def create(self, request, *args, **kwargs):
data = request.data
order = PizzaOrder.objects.create(
name=data['name'], flavours=data['flavours'],
number=data['number'], size=data['size'],
customer_name=data['customer_name'],
customer_address=data['customer_address']
)
order.save()
serializer = PizzaOrderSerializer(order)
return Response(serializer.data)
serializers.py
class PizzaOrderSerializer(serializers.ModelSerializer):
class Meta:
model = PizzaOrder
validators = [
UniqueTogetherValidator(
queryset=PizzaOrder.objects.all(),
fields=['name', 'flavours', 'size', 'customer_name', 'customer_address'],
message='This field should be unique'
)
]
models.py
from django.db import models
from datetime import datetime
FLAVOURS_CHOICE = (
('Margarita', 'Margarita'),
('Marinara', 'Marinara'),
('Salami', 'Salami'),
)
SIZE_CHOICE = (
('Small', 'Small'),
('Medium', 'Medium'),
('Large', 'Large'),
)
STATUS_CHOICE = (
('Open', 'Open'),
('Accepted', 'Accepted'),
('Preparing', 'Preparing'),
('OnWay', 'OnWay'),
('Delivered', 'Delivered'),
)
class PizzaOrder(models.Model):
name = models.CharField(max_length=50, blank=False)
flavours = models.CharField(max_length=20, choices=FLAVOURS_CHOICE)
quantity = models.IntegerField()
size = models.CharField(max_length=10, choices=SIZE_CHOICE)
customer_name = models.CharField(max_length=30, blank=False)
customer_address = models.TextField(blank=False)
ordered_time = models.DateTimeField(default=datetime.now, editable=False)
status = models.CharField(max_length=20, choices=STATUS_CHOICE, default='Open')
def __str__(self):
return self.name
class Meta:
ordering = ['-ordered_time']
unique_together = ('name', 'flavours', 'quantity', 'size')
How could I achieve this?
How can I add From date to To date to print a set of records in report in odoo 10?
I am creating a Bank module, where the customers can buy loans from the bank. In that I want to print a report statement. Before printing the records of a particular customer, i want to filter the dates. I want to filter it by giving From date to To date. So the loan amounts where the customer bought from the bank in that given dates should print. Thank You, Hope I will gain some knowledge.
below code for wizard
class BankCustomer(models.TransientModel):
_name = 'bank.customer'
_description = 'Bank Customer Report'
date_from = fields.Date(string='From date', required=True,default=lambda *a: time.strftime('%Y-%m-01'))
date_to = fields.Date(string='To date', required=True,default=lambda *a: str(datetime.now() + relativedelta.relativedelta(months=+1, day=1, days=-1))[:10])
#api.multi
def pdf_bank_customer_report(self):
data = self.read()[0]
datas = {
'ids': [],
'model': 'bank.customer',
'form': data
}
return self.env['report'].with_context(landscape=True).get_action(self, 'module_name.template_name', data=datas)
class BankCustomerReport(models.AbstractModel):
_name = 'report.module_name.template_name
def get(self):
self.env.cr.execute(""" you query """+ datas['date_from'],datas['date_to'] ))
line_list = [i for i in self.env.cr.dictfetchall()]
finallist = []
import datetime
for fin in line_list:
#sale_date = datetime.datetime.strptime(fin['date'], '%Y-%m-%d').strftime('%d-%m-%y')
finallist.append({
'date': fin['date'],
'here gat you requirened field from query'
})
finally pass thislist to report template
return finallist
#api.model
def render_html(self, docids, data=None):
Report = self.env['report']
sale_report = Report._get_report_from_name('module_name.report_template_name')
context = dict(self._context or {})
active_ids = context.get('active_ids', [])
register = self.env['bank.customer'].browse(active_ids)
docargs = {
'doc_ids': self.ids,
'doc_model': sale_report.model,
'docs': register,
'details':self.get_det,
}
return Report.render('module_name.report_template_name', docargs)
Odoo: Two selection fields, second selection depends on the first selected value
I have a model with two selection fields. In the first selection field the user chooses a project, and in second the user chooses a tranche, depends on the selected first field. (for one project, we have many tranche)
I use this declaration to my function:
#api.multi
#api.onchange('project')
def _get_tranche(self):
print res
return res`
If I'll print in console the result of returned value is correct, and I have exactly what I search, but in the view, the second field value does not displays computed values to choose after setting the second field value.
Is there any method to make it work properly?
This is my code:
#api.model
def _get_project(self):
values = {'$top' : 100,
'$filter' : "'name==:1&&parent==null'",
'$params' : '\'["*"]\'' }
response = requests.get('http://localhost:8081/rest/Project/name',
auth=('admin', 'admin'),
params=values)
data = response.json()
res = []
for record in data['__ENTITIES']:
res.append((record['__KEY'], record['name']))
return res
#property
def ret_value(self):
return self.project
#api.multi
#api.onchange('project')
def _get_tranche(self):
if self.ret_value == False:
return []
values = {'$top' : 100,
'$filter' : "'parent.ID==:1'",
'$params' : '\'[' + self.ret_value + ']\'' }
response = requests.get('http://localhost:8081/rest/Project/name',
auth=('admin', 'admin'),
params=values)
data = response.json()
res = []
for record in data['__ENTITIES']:
res.append((record['__KEY'], record['name']))
print res
return res
_columns = {
'name': fields.char("Name", required=True),
'description' : fields.text(),
'project' :fields.selection(selection=_get_project, string="Project"),
'tranche' : fields.selection(selection=_get_tranche, string="Tranche"),
}
NB: a tranche is a project with parent_id (not null) = id of a project, I need do the following: when I select a project X from the first field selection "project", I can select the tranches of this project (project with parent_id=X) from the second field selection "tranche" what I can't understand is the result of print in the console is correct, but in the view the field tranche is empty??!!
Now I am trying this:
_columns = {
'name': fields.char("Name", required=True),
'description' : fields.text(),
'project' :fields.selection(selection=_get_project, string="Project"),
'tranche' : fields.selection(selection=_get_tranche,
string="Tranche", compute='_return_tranche'),
}
#api.depends('project')
def _return_tranche(self):
if self.ret_value == False:
self.tranche = []
return
values = {'$top' : 100,
'$filter' : "'parent.ID==:1'",
'$params' : '\'[' + self.ret_value + ']\'' }
response = requests.get('http://localhost:8081/rest/Project/name',
auth=('admin', 'admin'),
params=values)
data = response.json()
res = []
for record in data['__ENTITIES']:
res.append((str(record['__KEY']),str(record['name'])))
print res
self.tranche = res
return
#api.model
def _get_tranche(self):
values = {'$top' : 100,
'$filter' : "'name==:1&&parent!=null'",
'$params' : '\'["*"]\'' }
response = requests.get('http://localhost:8081/rest/Project/name',
auth=('admin', 'admin'),
params=values)
data = response.json()
res = []
for record in data['__ENTITIES']:
res.append((record['__KEY'], record['name']))
return res
But I get this error
File "/opt/odoo/odoo/openerp/fields.py", line 1529, in convert_to_cache
raise ValueError("Wrong value for %s: %r" % (self, value))
ValueError: Wrong value for crm.lead.tranche: [('6', 'Tranche I'), ('7', 'Tranche II'), ('8', 'Tranche III')]
Help me please...