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| # n [1/min] | P [kW] | |
|---|---|---|
| 250.0 | 3.0 | |
| 260.0 | 3.2 | |
| 270.0 | 3.4 | |
| 280.0 | 3.6 | |
| 290.0 | 3.8 | |
| 300.0 | 4.1 | |
| 310.0 | 4.3 | |
| 320.0 | 4.5 | |
| 330.0 | 4.8 | |
| 340.0 | 5.0 | |
| 350.0 | 5.3 | |
| 360.0 | 5.5 | |
| 370.0 | 5.8 | |
| 380.0 | 6.0 | |
| 390.0 | 6.3 | |
| 400.0 | 6.6 | |
| 410.0 | 6.9 | |
| 420.0 | 7.1 | |
| 430.0 | 7.4 | |
| 440.0 | 7.7 | |
| 450.0 | 8.0 | |
| 460.0 | 8.3 | |
| 470.0 | 8.6 | |
| 480.0 | 8.9 | |
| 490.0 | 9.2 | |
| 500.0 | 9.5 | |
| 510.0 | 9.9 | |
| 520.0 | 10.2 | |
| 530.0 | 10.5 | |
| 540.0 | 10.8 | |
| 550.0 | 11.2 | |
| 560.0 | 11.5 | |
| 570.0 | 11.9 | |
| 580.0 | 12.2 | |
| 590.0 | 12.6 | |
| 600.0 | 12.9 | |
| 610.0 | 13.3 | |
| 620.0 | 13.7 | |
| 630.0 | 14.0 | |
| 640.0 | 14.4 | |
| 650.0 | 14.8 | |
| 660.0 | 15.2 | |
| 670.0 | 15.6 | |
| 680.0 | 15.9 | |
| 690.0 | 16.3 | |
| 700.0 | 16.7 | |
| 710.0 | 17.1 | |
| 720.0 | 17.5 | |
| 730.0 | 17.9 | |
| 740.0 | 18.4 | |
| 750.0 | 18.8 | |
| 760.0 | 19.2 | |
| 770.0 | 19.6 | |
| 780.0 | 20.0 | |
| 790.0 | 20.5 | |
| 800.0 | 20.9 | |
| 810.0 | 21.3 | |
| 820.0 | 21.8 | |
| 830.0 | 22.2 | |
| 840.0 | 22.7 | |
| 850.0 | 23.1 | |
| 860.0 | 23.6 | |
| 870.0 | 24.1 | |
| 880.0 | 24.5 | |
| 890.0 | 25.0 | |
| 900.0 | 25.5 | |
| 910.0 | 25.9 | |
| 920.0 | 26.4 | |
| 930.0 | 26.9 | |
| 940.0 | 27.4 | |
| 950.0 | 27.9 | |
| 960.0 | 28.3 | |
| 970.0 | 28.8 | |
| 980.0 | 29.3 | |
| 990.0 | 29.8 | |
| 1000.0 | 30.3 | |
| 1010.0 | 30.9 | |
| 1020.0 | 31.4 | |
| 1030.0 | 31.9 | |
| 1040.0 | 32.4 | |
| 1050.0 | 32.9 | |
| 1060.0 | 33.4 | |
| 1070.0 | 34.0 | |
| 1080.0 | 34.5 | |
| 1090.0 | 35.0 | |
| 1100.0 | 35.6 | |
| 1110.0 | 36.1 | |
| 1120.0 | 36.7 | |
| 1130.0 | 37.2 | |
| 1140.0 | 37.8 | |
| 1150.0 | 38.3 | |
| 1160.0 | 38.9 | |
| 1170.0 | 39.4 | |
| 1180.0 | 40.0 | |
| 1190.0 | 40.6 | |
| 1200.0 | 41.1 | |
| 1210.0 | 41.7 | |
| 1220.0 | 42.3 | |
| 1230.0 | 42.9 | |
| 1240.0 | 43.5 | |
| 1250.0 | 44.0 | |
| 1260.0 | 44.6 | |
| 1270.0 | 45.2 | |
| 1280.0 | 45.8 | |
| 1290.0 | 46.4 | |
| 1300.0 | 47.0 | |
| 1310.0 | 47.6 | |
| 1320.0 | 48.2 | |
| 1330.0 | 48.8 | |
| 1340.0 | 49.5 | |
| 1350.0 | 50.1 | |
| 1360.0 | 50.7 | |
| 1370.0 | 51.3 | |
| 1380.0 | 51.9 | |
| 1390.0 | 52.6 | |
| 1400.0 | 53.2 | |
| 1410.0 | 53.8 | |
| 1420.0 | 54.5 | |
| 1430.0 | 55.1 | |
| 1440.0 | 55.8 | |
| 1450.0 | 56.4 | |
| 1460.0 | 57.1 | |
| 1470.0 | 57.7 | |
| 1480.0 | 58.4 | |
| 1490.0 | 59.0 | |
| 1500.0 | 59.7 | |
| 1510.0 | 60.4 | |
| 1520.0 | 61.0 | |
| 1530.0 | 61.7 | |
| 1540.0 | 62.4 | |
| 1550.0 | 63.1 | |
| 1560.0 | 63.7 | |
| 1570.0 | 64.4 | |
| 1580.0 | 65.1 | |
| 1590.0 | 65.8 | |
| 1600.0 | 66.5 | |
| 1610.0 | 67.2 | |
| 1620.0 | 67.9 | |
| 1630.0 | 68.6 | |
| 1640.0 | 69.3 | |
| 1650.0 | 70.0 | |
| 1650.0 | 70.0 | |
| 1660.0 | 70.4 | |
| 1670.0 | 70.8 | |
| 1680.0 | 71.2 | |
| 1690.0 | 71.7 | |
| 1700.0 | 72.1 | |
| 1710.0 | 72.5 | |
| 1720.0 | 72.9 | |
| 1730.0 | 73.3 | |
| 1740.0 | 73.7 | |
| 1750.0 | 74.2 | |
| 1760.0 | 74.6 | |
| 1770.0 | 75.0 | |
| 1780.0 | 75.4 | |
| 1790.0 | 75.8 | |
| 1800.0 | 76.2 | |
| 1810.0 | 76.7 | |
| 1820.0 | 77.1 | |
| 1830.0 | 77.5 | |
| 1840.0 | 77.9 | |
| 1850.0 | 78.3 | |
| 1860.0 | 78.7 | |
| 1870.0 | 79.1 | |
| 1880.0 | 79.6 | |
| 1890.0 | 80.0 | |
| 1900.0 | 80.4 | |
| 1910.0 | 80.8 | |
| 1920.0 | 81.2 | |
| 1930.0 | 81.6 | |
| 1940.0 | 82.1 | |
| 1950.0 | 82.5 | |
| 1960.0 | 82.9 | |
| 1970.0 | 83.3 | |
| 1980.0 | 83.7 | |
| 1990.0 | 84.1 | |
| 2000.0 | 84.5 | |
| 2010.0 | 85.0 | |
| 2020.0 | 85.4 | |
| 2030.0 | 85.8 | |
| 2040.0 | 86.2 | |
| 2050.0 | 86.6 | |
| 2060.0 | 87.0 | |
| 2070.0 | 87.4 | |
| 2080.0 | 87.9 | |
| 2090.0 | 88.3 | |
| 2100.0 | 88.7 | |
| 2110.0 | 89.1 | |
| 2120.0 | 89.5 | |
| 2130.0 | 89.9 | |
| 2140.0 | 90.3 | |
| 2150.0 | 90.8 | |
| 2160.0 | 91.2 | |
| 2170.0 | 91.6 | |
| 2180.0 | 92.0 | |
| 2190.0 | 92.4 | |
| 2200.0 | 92.8 | |
| 2210.0 | 93.2 | |
| 2220.0 | 93.7 | |
| 2230.0 | 94.1 | |
| 2240.0 | 94.5 | |
| 2250.0 | 94.9 | |
| 2260.0 | 95.3 | |
| 2270.0 | 95.7 | |
| 2280.0 | 96.1 | |
| 2290.0 | 96.6 | |
| 2300.0 | 97.0 | |
| 2310.0 | 97.4 | |
| 2320.0 | 97.8 | |
| 2330.0 | 98.2 | |
| 2340.0 | 98.6 | |
| 2350.0 | 99.0 | |
| 2360.0 | 99.5 | |
| 2370.0 | 99.9 | |
| 2380.0 | 100.3 | |
| 2390.0 | 100.7 | |
| 2400.0 | 101.1 | |
| 2410.0 | 101.5 | |
| 2420.0 | 101.9 | |
| 2430.0 | 102.3 | |
| 2440.0 | 102.8 | |
| 2450.0 | 103.2 | |
| 2460.0 | 103.6 | |
| 2470.0 | 104.0 | |
| 2480.0 | 104.4 | |
| 2490.0 | 104.8 | |
| 2500.0 | 105.2 | |
| 2510.0 | 105.7 | |
| 2520.0 | 106.1 | |
| 2530.0 | 106.5 | |
| 2540.0 | 106.9 | |
| 2550.0 | 107.3 | |
| 2560.0 | 107.7 | |
| 2570.0 | 108.1 | |
| 2580.0 | 108.5 | |
| 2590.0 | 109.0 | |
| 2600.0 | 109.4 | |
| 2610.0 | 109.8 | |
| 2620.0 | 110.2 | |
| 2630.0 | 110.6 | |
| 2640.0 | 111.0 | |
| 2650.0 | 111.4 | |
| 2660.0 | 111.8 | |
| 2670.0 | 112.3 | |
| 2680.0 | 112.7 | |
| 2690.0 | 113.1 | |
| 2700.0 | 113.5 | |
| 2710.0 | 113.9 | |
| 2720.0 | 114.3 | |
| 2730.0 | 114.7 | |
| 2740.0 | 115.1 | |
| 2750.0 | 115.6 | |
| 2760.0 | 116.0 | |
| 2770.0 | 116.4 | |
| 2780.0 | 116.8 | |
| 2790.0 | 117.2 | |
| 2800.0 | 117.6 | |
| 2810.0 | 118.0 | |
| 2820.0 | 118.4 | |
| 2830.0 | 118.9 | |
| 2840.0 | 119.3 | |
| 2850.0 | 119.7 | |
| 2860.0 | 120.1 | |
| 2870.0 | 120.5 | |
| 2880.0 | 120.9 | |
| 2890.0 | 121.3 | |
| 2900.0 | 121.7 | |
| 2910.0 | 122.2 | |
| 2920.0 | 122.6 | |
| 2930.0 | 123.0 | |
| 2940.0 | 123.4 | |
| 2950.0 | 123.8 | |
| 2960.0 | 124.2 | |
| 2970.0 | 124.6 | |
| 2980.0 | 125.0 | |
| 2990.0 | 125.4 | |
| 3000.0 | 125.9 | |
| 3010.0 | 126.3 | |
| 3020.0 | 126.7 | |
| 3030.0 | 127.1 | |
| 3040.0 | 127.5 | |
| 3050.0 | 127.9 | |
| 3060.0 | 128.3 | |
| 3070.0 | 128.7 | |
| 3080.0 | 129.1 | |
| 3090.0 | 129.6 | |
| 3100.0 | 130.0 | |
| 3110.0 | 130.4 | |
| 3120.0 | 130.8 | |
| 3130.0 | 131.2 | |
| 3140.0 | 131.6 | |
| 3150.0 | 132.0 | |
| 3160.0 | 132.4 | |
| 3170.0 | 132.9 | |
| 3180.0 | 133.3 | |
| 3190.0 | 133.7 | |
| 3200.0 | 134.1 | |
| 3210.0 | 134.5 | |
| 3220.0 | 134.9 | |
| 3230.0 | 135.3 | |
| 3240.0 | 135.7 | |
| 3250.0 | 136.1 | |
| 3260.0 | 136.6 | |
| 3270.0 | 137.0 | |
| 3280.0 | 137.4 | |
| 3290.0 | 137.8 | |
| 3300.0 | 138.2 | |
| 3310.0 | 138.6 | |
| 3320.0 | 139.0 | |
| 3330.0 | 139.4 | |
| 3340.0 | 139.8 | |
| 3350.0 | 140.2 | |
| 3360.0 | 140.7 | |
| 3370.0 | 141.1 | |
| 3380.0 | 141.5 | |
| 3390.0 | 141.9 | |
| 3400.0 | 142.3 | |
| 3410.0 | 142.7 | |
| 3420.0 | 143.1 | |
| 3430.0 | 143.5 | |
| 3440.0 | 143.9 | |
| 3450.0 | 144.4 | |
| 3460.0 | 144.8 | |
| 3470.0 | 145.2 | |
| 3480.0 | 145.6 | |
| 3490.0 | 146.0 | |
| 3500.0 | 146.4 | |
| 3510.0 | 146.8 | |
| 3520.0 | 147.2 | |
| 3530.0 | 147.6 | |
| 3540.0 | 148.1 | |
| 3550.0 | 148.5 | |
| 3560.0 | 148.9 | |
| 3570.0 | 149.3 | |
| 3580.0 | 149.7 | |
| 3590.0 | 150.1 | |
| 3600.0 | 150.5 | |
| 3610.0 | 150.9 | |
| 3620.0 | 151.3 | |
| 3630.0 | 151.7 | |
| 3640.0 | 152.2 | |
| 3650.0 | 152.6 | |
| 3660.0 | 153.0 | |
| 3670.0 | 153.4 | |
| 3680.0 | 153.8 | |
| 3690.0 | 154.2 | |
| 3700.0 | 154.6 | |
| 3710.0 | 155.0 | |
| 3720.0 | 155.4 | |
| 3730.0 | 155.8 | |
| 3740.0 | 156.3 | |
| 3750.0 | 156.7 | |
| 3760.0 | 157.1 | |
| 3770.0 | 157.5 | |
| 3780.0 | 157.9 | |
| 3790.0 | 158.3 | |
| 3800.0 | 158.7 | |
| 3810.0 | 159.1 | |
| 3820.0 | 159.5 | |
| 3830.0 | 159.9 | |
| 3840.0 | 160.4 | |
| 3850.0 | 160.8 | |
| 3860.0 | 161.2 | |
| 3870.0 | 161.6 | |
| 3880.0 | 162.0 | |
| 3890.0 | 162.4 | |
| 3900.0 | 162.8 | |
| 3910.0 | 163.2 | |
| 3920.0 | 163.6 | |
| 3930.0 | 164.0 | |
| 3940.0 | 164.4 | |
| 3950.0 | 164.9 | |
| 3960.0 | 165.3 | |
| 3970.0 | 165.7 | |
| 3980.0 | 166.1 | |
| 3990.0 | 166.5 | |
| 4000.0 | 166.9 | |
| 4010.0 | 167.3 | |
| 4020.0 | 167.7 | |
| 4030.0 | 168.1 | |
| 4040.0 | 168.5 | |
| 4050.0 | 169.0 | |
| 4060.0 | 169.4 | |
| 4070.0 | 169.8 | |
| 4080.0 | 170.2 | |
| 4090.0 | 170.6 | |
| 4100.0 | 171.0 | |
| 4110.0 | 171.4 | |
| 4120.0 | 171.8 | |
| 4130.0 | 172.2 | |
| 4140.0 | 172.6 | |
| 4150.0 | 173.0 | |
| 4160.0 | 173.5 | |
| 4170.0 | 173.9 | |
| 4180.0 | 174.3 | |
| 4190.0 | 174.7 | |
| 4200.0 | 175.1 | |
| 4210.0 | 175.5 | |
| 4220.0 | 175.9 | |
| 4230.0 | 176.3 | |
| 4240.0 | 176.7 | |
| 4250.0 | 177.1 | |
| 4260.0 | 177.5 | |
| 4270.0 | 178.0 | |
| 4280.0 | 178.4 | |
| 4290.0 | 178.8 | |
| 4300.0 | 179.2 | |
| 4310.0 | 179.6 | |
| 4320.0 | 180.0 | |
| 4320.0 | 180.0 | |
| 4330.0 | 180.0 | |
| 4340.0 | 180.0 | |
| 4350.0 | 180.0 | |
| 4360.0 | 180.0 | |
| 4370.0 | 180.0 | |
| 4380.0 | 180.0 | |
| 4390.0 | 180.0 | |
| 4400.0 | 180.0 | |
| 4410.0 | 180.0 | |
| 4420.0 | 180.0 | |
| 4430.0 | 180.0 | |
| 4440.0 | 180.0 | |
| 4450.0 | 180.0 | |
| 4460.0 | 180.0 | |
| 4470.0 | 180.0 | |
| 4480.0 | 180.0 | |
| 4490.0 | 180.0 | |
| 4500.0 | 180.0 | |
| 4510.0 | 180.0 | |
| 4520.0 | 180.0 | |
| 4530.0 | 180.0 | |
| 4540.0 | 180.0 | |
| 4550.0 | 180.0 | |
| 4560.0 | 180.0 | |
| 4570.0 | 180.0 | |
| 4580.0 | 180.0 | |
| 4590.0 | 180.0 | |
| 4600.0 | 180.0 | |
| 4610.0 | 180.0 | |
| 4620.0 | 180.0 | |
| 4630.0 | 180.0 | |
| 4640.0 | 180.0 | |
| 4650.0 | 180.0 | |
| 4660.0 | 180.0 | |
| 4670.0 | 180.0 | |
| 4680.0 | 180.0 | |
| 4690.0 | 180.0 | |
| 4700.0 | 180.0 | |
| 4710.0 | 180.0 | |
| 4720.0 | 180.0 | |
| 4730.0 | 180.0 | |
| 4740.0 | 180.0 | |
| 4750.0 | 180.0 | |
| 4760.0 | 180.0 | |
| 4770.0 | 180.0 | |
| 4780.0 | 180.0 | |
| 4790.0 | 180.0 | |
| 4800.0 | 180.0 | |
| 4810.0 | 180.0 | |
| 4820.0 | 180.0 | |
| 4830.0 | 180.0 | |
| 4840.0 | 180.0 | |
| 4850.0 | 180.0 | |
| 4860.0 | 180.0 | |
| 4870.0 | 180.0 | |
| 4880.0 | 180.0 | |
| 4890.0 | 180.0 | |
| 4900.0 | 180.0 | |
| 4910.0 | 180.0 | |
| 4920.0 | 180.0 | |
| 4930.0 | 180.0 | |
| 4940.0 | 180.0 | |
| 4950.0 | 180.0 | |
| 4960.0 | 180.0 | |
| 4970.0 | 180.0 | |
| 4980.0 | 180.0 | |
| 4990.0 | 180.0 | |
| 5000.0 | 180.0 | |
| 5010.0 | 180.0 | |
| 5020.0 | 180.0 | |
| 5030.0 | 180.0 | |
| 5040.0 | 180.0 | |
| 5050.0 | 180.0 | |
| 5060.0 | 180.0 | |
| 5070.0 | 180.0 | |
| 5080.0 | 180.0 | |
| 5090.0 | 180.0 | |
| 5100.0 | 180.0 | |
| 5110.0 | 180.0 | |
| 5120.0 | 180.0 | |
| 5130.0 | 180.0 | |
| 5140.0 | 180.0 | |
| 5150.0 | 180.0 | |
| 5160.0 | 180.0 | |
| 5170.0 | 180.0 | |
| 5180.0 | 180.0 | |
| 5190.0 | 180.0 | |
| 5200.0 | 180.0 | |
| 5210.0 | 180.0 | |
| 5220.0 | 180.0 | |
| 5230.0 | 180.0 | |
| 5240.0 | 180.0 | |
| 5250.0 | 180.0 | |
| 5260.0 | 180.0 | |
| 5270.0 | 180.0 | |
| 5280.0 | 180.0 | |
| 5290.0 | 180.0 | |
| 5300.0 | 180.0 | |
| 5310.0 | 180.0 | |
| 5320.0 | 180.0 | |
| 5330.0 | 180.0 | |
| 5340.0 | 180.0 | |
| 5350.0 | 180.0 | |
| 5360.0 | 180.0 | |
| 5370.0 | 180.0 | |
| 5380.0 | 180.0 | |
| 5390.0 | 180.0 | |
| 5400.0 | 180.0 | |
| 5410.0 | 180.0 | |
| 5420.0 | 180.0 | |
| 5430.0 | 180.0 | |
| 5440.0 | 180.0 | |
| 5450.0 | 180.0 | |
| 5460.0 | 180.0 | |
| 5470.0 | 180.0 | |
| 5480.0 | 180.0 | |
| 5490.0 | 180.0 | |
| 5500.0 | 180.0 | |
| 5510.0 | 180.0 | |
| 5520.0 | 180.0 | |
| 5530.0 | 180.0 | |
| 5540.0 | 180.0 | |
| 5550.0 | 180.0 | |
| 5560.0 | 180.0 | |
| 5570.0 | 180.0 | |
| 5580.0 | 180.0 | |
| 5590.0 | 180.0 | |
| 5600.0 | 180.0 | |
| 5610.0 | 180.0 | |
| 5620.0 | 180.0 | |
| 5630.0 | 180.0 | |
| 5640.0 | 180.0 | |
| 5650.0 | 180.0 | |
| 5660.0 | 180.0 | |
| 5670.0 | 180.0 | |
| 5680.0 | 180.0 | |
| 5690.0 | 180.0 | |
| 5700.0 | 180.0 | |
| 5710.0 | 180.0 | |
| 5720.0 | 180.0 | |
| 5730.0 | 180.0 | |
| 5740.0 | 180.0 | |
| 5750.0 | 180.0 | |
| 5760.0 | 180.0 | |
| 5770.0 | 180.0 | |
| 5780.0 | 180.0 | |
| 5790.0 | 180.0 | |
| 5800.0 | 180.0 | |
| 5810.0 | 180.0 | |
| 5820.0 | 180.0 | |
| 5830.0 | 180.0 | |
| 5840.0 | 180.0 | |
| 5850.0 | 180.0 | |
| 5860.0 | 180.0 | |
| 5870.0 | 180.0 | |
| 5880.0 | 180.0 | |
| 5890.0 | 180.0 | |
| 5900.0 | 180.0 | |
| 5910.0 | 180.0 | |
| 5920.0 | 180.0 | |
| 5930.0 | 180.0 | |
| 5940.0 | 180.0 | |
| 5950.0 | 180.0 | |
| 5960.0 | 180.0 | |
| 5970.0 | 180.0 | |
| 5980.0 | 180.0 | |
| 5990.0 | 180.0 | |
| 6000.0 | 180.0 | |
| 6010.0 | 180.0 | |
| 6020.0 | 180.0 | |
| 6030.0 | 180.0 | |
| 6040.0 | 180.0 | |
| 6050.0 | 180.0 | |
| 6060.0 | 180.0 | |
| 6070.0 | 180.0 | |
| 6080.0 | 180.0 | |
| 6090.0 | 180.0 | |
| 6100.0 | 180.0 | |
| 6110.0 | 180.0 | |
| 6120.0 | 180.0 | |
| 6130.0 | 180.0 | |
| 6140.0 | 180.0 | |
| 6150.0 | 180.0 | |
| 6160.0 | 180.0 | |
| 6170.0 | 180.0 | |
| 6180.0 | 180.0 | |
| 6190.0 | 180.0 | |
| 6200.0 | 180.0 | |
| 6210.0 | 180.0 | |
| 6220.0 | 180.0 | |
| 6230.0 | 180.0 | |
| 6240.0 | 180.0 | |
| 6250.0 | 180.0 | |
| 6260.0 | 180.0 | |
| 6270.0 | 180.0 | |
| 6280.0 | 180.0 | |
| 6290.0 | 180.0 | |
| 6300.0 | 180.0 | |
| 6310.0 | 180.0 | |
| 6320.0 | 180.0 | |
| 6330.0 | 180.0 | |
| 6340.0 | 180.0 | |
| 6350.0 | 180.0 | |
| 6360.0 | 180.0 | |
| 6370.0 | 180.0 | |
| 6380.0 | 180.0 | |
| 6390.0 | 180.0 | |
| 6400.0 | 180.0 | |
| 6410.0 | 180.0 | |
| 6420.0 | 180.0 | |
| 6430.0 | 180.0 | |
| 6440.0 | 180.0 | |
| 6450.0 | 180.0 | |
| 6460.0 | 180.0 | |
| 6470.0 | 180.0 | |
| 6480.0 | 180.0 | |
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| { | |
| "metadata": { | |
| "name": "", | |
| "signature": "sha256:db29df71954fe1168badcedd77462d0f3f8a0550ae4449d1bbab66ba5a87ae79" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "heading", | |
| "level": 1, | |
| "metadata": {}, | |
| "source": [ | |
| "We have a LogLog diagram and need the values" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 3, | |
| "metadata": {}, | |
| "source": [ | |
| "So, let's Un-LogLog it" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import numpy as np\n", | |
| "%matplotlib inline\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "from scipy.optimize import curve_fit" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 14 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Get some Datapoints, e.g. with [DigitizeIT](http://www.digitizeit.de/)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# for the E2-180 curve\n", | |
| "logx = np.array([250.0, 1650.0, 4320.0, 10000.0]) # rpm\n", | |
| "logy = np.array([3.0, 70.0, 180.0, 180.0]) # power" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 15 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Because the diagram is [LogLog](http://en.wikipedia.org/wiki/Log-log_plot) and has only straight lines, that means we can fit a function of the form $P(n)=c \\cdot n^m$ to find the parameters `c` (constant) and `m` (slope)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "def loglog(n, c, m):\n", | |
| " return c * n**m" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 16 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now lets fit the three parts of the LogLog graph to find the parameters." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "var={} # Dictionary to save the optimal parameters\n", | |
| "for p in range(3):\n", | |
| " # do the curve fitting\n", | |
| " popt, pcov = curve_fit(loglog, logx[p:p+2], logy[p:p+2])\n", | |
| " \n", | |
| " # Save the optimal parameters\n", | |
| " var[p] = [popt[0], popt[1]]\n", | |
| " \n", | |
| " # Print them\n", | |
| " print('Constant: %.6f, Slope: %.3f for %i. part of the graph' % (popt[0], popt[1], p+1))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Constant: 0.000298, Slope: 1.669 for 1. part of the graph\n", | |
| "Constant: 0.048736, Slope: 0.981 for 2. part of the graph\n", | |
| "Constant: 180.000001, Slope: -0.000 for 3. part of the graph\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 17 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "So now we have the variables for the function $P(n)=c \\cdot n^m$, we can calc a continuous function" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "n=[]\n", | |
| "P=[]\n", | |
| "for p in range(3):\n", | |
| " x=np.arange(logx[p], logx[p+1]+1, 10)\n", | |
| " n.extend(x)\n", | |
| " P.extend(loglog(x, var[p][0], var[p][1]))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 22 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Let's plot it and see, if the original datapoints and the found curve are fitting" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "plt.loglog(n, P, label='Curve Fit', ls='--')\n", | |
| "plt.loglog(logx, logy, label='Original', alpha=0.6)\n", | |
| "\n", | |
| "plt.xlabel('n / 1/min')\n", | |
| "plt.ylabel('P / kW')\n", | |
| "plt.title('Leistungsdiagramm E2-180')\n", | |
| "plt.ylim(0, 200)\n", | |
| "plt.legend(loc='best')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 23, | |
| "text": [ | |
| "<matplotlib.legend.Legend at 0x10a340f50>" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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zmeEJp9kYkepPHH+ToN7noUJVb9Y86ut3sbn8eQxeAOIiRpCVeKk+j0P1Ge15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\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x107733350>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 23 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "You see, exactly!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 3, | |
| "metadata": {}, | |
| "source": [ | |
| "Plot it with normal scale axes" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "plt.figure(figsize=(10,6))\n", | |
| "plt.plot(n, P)\n", | |
| "\n", | |
| "plt.xlabel('n / 1/min')\n", | |
| "plt.ylabel('P / kW')\n", | |
| "plt.title('Leistungsdiagramm E2-180')\n", | |
| "plt.ylim(0, 200)\n", | |
| "plt.savefig('Leistung-E2-180.png', dpi=300, bbox_inches='tight')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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9H5VPXkTprCmpcd1PriXZf+voAipAhZw/6Zjy5y/lTjLxupgTybay1+6hYuo1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\nTkRERMRj/w8/5hGOx9j7TwAAAABJRU5ErkJggg==\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10a357a90>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 24 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now dump it to csv for use with `Excel` or something you guys are using..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data = np.array([n, P])\n", | |
| "np.savetxt('Leistung-E2-180.csv', data.T, fmt='%.1f', header='n [1/min], P [kW]', delimiter=',')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 25 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Remember:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "" | |
| ] | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ] | |
| } |
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| # -*- coding: utf-8 -*- | |
| # <nbformat>3.0</nbformat> | |
| # <headingcell level=1> | |
| # We have a LogLog diagram and need the values | |
| # <markdowncell> | |
| #  | |
| # <headingcell level=3> | |
| # So, let's Un-LogLog it | |
| # <codecell> | |
| import numpy as np | |
| %matplotlib inline | |
| import matplotlib.pyplot as plt | |
| from scipy.optimize import curve_fit | |
| # <markdowncell> | |
| # Get some Datapoints, e.g. with [DigitizeIT](http://www.digitizeit.de/) | |
| # <codecell> | |
| # for the E2-180 curve | |
| logx = np.array([250.0, 1650.0, 4320.0, 10000.0]) # rpm | |
| logy = np.array([3.0, 70.0, 180.0, 180.0]) # power | |
| # <markdowncell> | |
| # Because the diagram is [LogLog](http://en.wikipedia.org/wiki/Log-log_plot) and has only straight lines, that means we can fit a function of the form $P(n)=c \cdot n^m$ to find the parameters `c` (constant) and `m` (slope). | |
| # <codecell> | |
| def loglog(n, c, m): | |
| return c * n**m | |
| # <markdowncell> | |
| # Now lets fit the three parts of the LogLog graph to find the parameters. | |
| # <codecell> | |
| var={} # Dictionary to save the optimal parameters | |
| for p in range(3): | |
| # do the curve fitting | |
| popt, pcov = curve_fit(loglog, logx[p:p+2], logy[p:p+2]) | |
| # Save the optimal parameters | |
| var[p] = [popt[0], popt[1]] | |
| # Print them | |
| print('Constant: %.6f, Slope: %.3f for %i. part of the graph' % (popt[0], popt[1], p+1)) | |
| # <markdowncell> | |
| # So now we have the variables for the function $P(n)=c \cdot n^m$, we can calc a continuous function | |
| # <codecell> | |
| n=[] | |
| P=[] | |
| for p in range(3): | |
| x=np.arange(logx[p], logx[p+1]+1, 10) | |
| n.extend(x) | |
| P.extend(loglog(x, var[p][0], var[p][1])) | |
| # <markdowncell> | |
| # Let's plot it and see, if the original datapoints and the found curve are fitting | |
| # <codecell> | |
| plt.loglog(n, P, label='Curve Fit', ls='--') | |
| plt.loglog(logx, logy, label='Original', alpha=0.6) | |
| plt.xlabel('n / 1/min') | |
| plt.ylabel('P / kW') | |
| plt.title('Leistungsdiagramm E2-180') | |
| plt.ylim(0, 200) | |
| plt.legend(loc='best') | |
| # <markdowncell> | |
| # You see, exactly! | |
| # <headingcell level=3> | |
| # Plot it with normal scale axes | |
| # <codecell> | |
| plt.figure(figsize=(10,6)) | |
| plt.plot(n, P) | |
| plt.xlabel('n / 1/min') | |
| plt.ylabel('P / kW') | |
| plt.title('Leistungsdiagramm E2-180') | |
| plt.ylim(0, 200) | |
| plt.savefig('Leistung-E2-180.png', dpi=300, bbox_inches='tight') | |
| # <markdowncell> | |
| # Now dump it to csv for use with `Excel` or something you guys are using... | |
| # <codecell> | |
| data = np.array([n, P]) | |
| np.savetxt('Leistung-E2-180.csv', data.T, fmt='%.1f', header='n [1/min], P [kW]', delimiter=',') | |
| # <markdowncell> | |
| # Remember: | |
| # <markdowncell> | |
| #  | |
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