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array([296.])
array('1987-04-04T18:00:00.000000000', dtype='datetime64[ns]')
array([-3.086806e+03, -3.054343e+03, -3.021880e+03, -2.989417e+03, + Geopotential_height (isentropic_level, y, x) float64 <Quantity([[[1063.6...
array([296.])
array('1987-04-04T18:00:00.000000000', dtype='datetime64[ns]')
array([-3.086806e+03, -3.054343e+03, -3.021880e+03, -2.989417e+03, -2.956954e+03, -2.924491e+03, -2.892028e+03, -2.859565e+03, -2.827102e+03, -2.794639e+03, -2.762176e+03, -2.729713e+03, -2.697250e+03, -2.664787e+03, -2.632324e+03, -2.599861e+03, @@ -592,8 +592,8 @@ The output is an xarray Dataset: 2.893464e+02, 3.218094e+02, 3.542724e+02, 3.867354e+02, 4.191984e+02, 4.516614e+02, 4.841244e+02, 5.165874e+02, 5.490504e+02, 5.815134e+02, 6.139764e+02, 6.464394e+02, - 6.789024e+02, 7.113654e+02])
array([-3977.054864, -3944.591864, -3912.128864, ..., 5404.752136, - 5437.215136, 5469.678136])
[34456 values with dtype=float64]
[34456 values with dtype=float64]
Magnitude | [[[900.3412466141666 900.6282002263883 902.6954128823945 ... nan nan nan] |
---|---|
Units | hectopascal |
Magnitude | [[[287.25340245256615 287.2795573040488 287.4678010688309 ... nan nan |
---|---|
Units | kelvin |
Magnitude | [[[-13.192940863873579 -13.65521105416572 -13.558986233473753 ... nan |
---|---|
Units | meter/second |
Magnitude | [[[0.06423312212716725 0.07479514517840302 -0.16838919556261756 ... nan |
---|---|
Units | meter/second |
Magnitude | [[[0.008842348434263451 0.008867832268411289 0.008968565810944646 ... |
---|---|
Units | dimensionless |
Magnitude | [[[1063.6606513260513 1061.4421473443042 1041.4938116153148 ... nan nan |
---|---|
Units | meter |
PandasIndex(Index([296.0], dtype='float64', name='isentropic_level'))
PandasIndex(Index([-3086.8056464496035, -3054.3426464496033, -3021.8796464496036, + 6.789024e+02, 7.113654e+02])
array([-3977.054864, -3944.591864, -3912.128864, ..., 5404.752136, + 5437.215136, 5469.678136])
[34456 values with dtype=float64]
[34456 values with dtype=float64]
Magnitude | [[[900.3412466141666 900.6282002263883 902.6954128823937 ... nan nan nan] |
---|---|
Units | hectopascal |
Magnitude | [[[287.25340245256615 287.2795573040488 287.46780106883085 ... nan nan |
---|---|
Units | kelvin |
Magnitude | [[[-13.192940863873584 -13.655211054165733 -13.558986233473757 ... nan |
---|---|
Units | meter/second |
Magnitude | [[[0.0642331221271153 0.07479514517833352 -0.1683891955627046 ... nan |
---|---|
Units | meter/second |
Magnitude | [[[0.008842348434263434 0.00886783226841127 0.008968565810944629 ... nan |
---|---|
Units | dimensionless |
Magnitude | [[[1063.6606513260613 1061.4421473443138 1041.4938116153235 ... nan nan |
---|---|
Units | meter |
PandasIndex(Index([296.0], dtype='float64', name='isentropic_level'))
PandasIndex(Index([-3086.8056464496035, -3054.3426464496033, -3021.8796464496036, -2989.4166464496034, -2956.953646449603, -2924.4906464496034, -2892.0276464496037, -2859.5646464496035, -2827.1016464496033, -2794.6386464496036, @@ -602,7 +602,7 @@ The output is an xarray Dataset: 516.587353550397, 549.0503535503967, 581.5133535503965, 613.9763535503971, 646.4393535503968, 678.9023535503966, 711.3653535503963], - dtype='float64', name='y', length=118))
PandasIndex(Index([ -3977.054863757302, -3944.5918637573022, -3912.1288637573025, + dtype='float64', name='y', length=118))
PandasIndex(Index([ -3977.054863757302, -3944.5918637573022, -3912.1288637573025, -3879.6658637573023, -3847.202863757302, -3814.7398637573024, -3782.276863757302, -3749.813863757302, -3717.3508637573022, -3684.8878637573025, @@ -611,7 +611,7 @@ The output is an xarray Dataset: 5274.900136242699, 5307.363136242699, 5339.826136242698, 5372.289136242698, 5404.752136242698, 5437.215136242698, 5469.678136242697], - dtype='float64', name='x', length=292))
array(['1987-04-04T18:00:00.000000000'], dtype='datetime64[ns]')
array([1000., 975., 950., 925., 900., 875., 850., 825., 800., 775., + History: Translated to CF-1.0 Conventions by Netcdf-Java...
array(['1987-04-04T18:00:00.000000000'], dtype='datetime64[ns]')
array([1000., 975., 950., 925., 900., 875., 850., 825., 800., 775., 750., 725., 700., 650., 600., 550., 500., 450., 400., 350., - 300., 275., 250., 225., 200., 175., 150., 125., 100.])
array([-3.086806e+03, -3.054343e+03, -3.021880e+03, -2.989417e+03, + 300., 275., 250., 225., 200., 175., 150., 125., 100.])
array([-3.086806e+03, -3.054343e+03, -3.021880e+03, -2.989417e+03, -2.956954e+03, -2.924491e+03, -2.892028e+03, -2.859565e+03, -2.827102e+03, -2.794639e+03, -2.762176e+03, -2.729713e+03, -2.697250e+03, -2.664787e+03, -2.632324e+03, -2.599861e+03, @@ -501,12 +501,12 @@ Preview the data: 2.893464e+02, 3.218094e+02, 3.542724e+02, 3.867354e+02, 4.191984e+02, 4.516614e+02, 4.841244e+02, 5.165874e+02, 5.490504e+02, 5.815134e+02, 6.139764e+02, 6.464394e+02, - 6.789024e+02, 7.113654e+02])
array([-3977.054864, -3944.591864, -3912.128864, ..., 5404.752136, - 5437.215136, 5469.678136])
[999224 values with dtype=float32]
[1 values with dtype=|S1]
[34456 values with dtype=float64]
[34456 values with dtype=float64]
[999224 values with dtype=float32]
[999224 values with dtype=float32]
[999224 values with dtype=float32]
[999224 values with dtype=float32]
PandasIndex(DatetimeIndex(['1987-04-04 18:00:00'], dtype='datetime64[ns]', name='time', freq=None))
PandasIndex(Index([1000.0, 975.0, 950.0, 925.0, 900.0, 875.0, 850.0, 825.0, 800.0, + 6.789024e+02, 7.113654e+02])
array([-3977.054864, -3944.591864, -3912.128864, ..., 5404.752136, + 5437.215136, 5469.678136])
[999224 values with dtype=float32]
[1 values with dtype=|S1]
[34456 values with dtype=float64]
[34456 values with dtype=float64]
[999224 values with dtype=float32]
[999224 values with dtype=float32]
[999224 values with dtype=float32]
[999224 values with dtype=float32]
PandasIndex(DatetimeIndex(['1987-04-04 18:00:00'], dtype='datetime64[ns]', name='time', freq=None))
PandasIndex(Index([1000.0, 975.0, 950.0, 925.0, 900.0, 875.0, 850.0, 825.0, 800.0, 775.0, 750.0, 725.0, 700.0, 650.0, 600.0, 550.0, 500.0, 450.0, 400.0, 350.0, 300.0, 275.0, 250.0, 225.0, 200.0, 175.0, 150.0, 125.0, 100.0], - dtype='float64', name='isobaric'))
PandasIndex(Index([-3086.8056464496035, -3054.3426464496033, -3021.8796464496036, + dtype='float64', name='isobaric'))
PandasIndex(Index([-3086.8056464496035, -3054.3426464496033, -3021.8796464496036, -2989.4166464496034, -2956.953646449603, -2924.4906464496034, -2892.0276464496037, -2859.5646464496035, -2827.1016464496033, -2794.6386464496036, @@ -515,7 +515,7 @@ Preview the data: 516.587353550397, 549.0503535503967, 581.5133535503965, 613.9763535503971, 646.4393535503968, 678.9023535503966, 711.3653535503963], - dtype='float64', name='y', length=118))
PandasIndex(Index([ -3977.054863757302, -3944.5918637573022, -3912.1288637573025, + dtype='float64', name='y', length=118))
PandasIndex(Index([ -3977.054863757302, -3944.5918637573022, -3912.1288637573025, -3879.6658637573023, -3847.202863757302, -3814.7398637573024, -3782.276863757302, -3749.813863757302, -3717.3508637573022, -3684.8878637573025, @@ -524,7 +524,7 @@ Preview the data: 5274.900136242699, 5307.363136242699, 5339.826136242698, 5372.289136242698, 5404.752136242698, 5437.215136242698, 5469.678136242697], - dtype='float64', name='x', length=292))
array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000', + geospatial_lon_max: ...
array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000', '2017-09-05T18:00:00.000000000', '2017-09-05T21:00:00.000000000', '2017-09-06T00:00:00.000000000', '2017-09-06T03:00:00.000000000', '2017-09-06T06:00:00.000000000', '2017-09-06T09:00:00.000000000', - '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')
[1 values with dtype=datetime64[ns]]
array([50. , 49.5, 49. , 48.5, 48. , 47.5, 47. , 46.5, 46. , 45.5, 45. , 44.5, + '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')
[1 values with dtype=datetime64[ns]]
array([50. , 49.5, 49. , 48.5, 48. , 47.5, 47. , 46.5, 46. , 45.5, 45. , 44.5, 44. , 43.5, 43. , 42.5, 42. , 41.5, 41. , 40.5, 40. , 39.5, 39. , 38.5, 38. , 37.5, 37. , 36.5, 36. , 35.5, 35. , 34.5, 34. , 33.5, 33. , 32.5, 32. , 31.5, 31. , 30.5, 30. , 29.5, 29. , 28.5, 28. , 27.5, 27. , 26.5, 26. , 25.5, 25. , 24.5, 24. , 23.5, 23. , 22.5, 22. , 21.5, 21. , 20.5, 20. , 19.5, 19. , 18.5, 18. , 17.5, 17. , 16.5, 16. , 15.5, 15. , 14.5, - 14. , 13.5, 13. , 12.5, 12. , 11.5, 11. , 10.5, 10. ], dtype=float32)
array([ 100., 200., 300., 500., 700., 1000., 2000., 3000., + 14. , 13.5, 13. , 12.5, 12. , 11.5, 11. , 10.5, 10. ], dtype=float32)
array([ 100., 200., 300., 500., 700., 1000., 2000., 3000., 5000., 7000., 10000., 15000., 20000., 25000., 30000., 35000., 40000., 45000., 50000., 55000., 60000., 65000., 70000., 75000., - 80000., 85000., 90000., 92500., 95000., 97500., 100000.])
array([ 10000., 15000., 20000., 25000., 30000., 35000., 40000., 45000., + 80000., 85000., 90000., 92500., 95000., 97500., 100000.])
array([ 10000., 15000., 20000., 25000., 30000., 35000., 40000., 45000., 50000., 55000., 60000., 65000., 70000., 75000., 80000., 85000., - 90000., 92500., 95000., 97500., 100000.])
array([250. , 250.5, 251. , 251.5, 252. , 252.5, 253. , 253.5, 254. , 254.5, + 90000., 92500., 95000., 97500., 100000.])
array([250. , 250.5, 251. , 251.5, 252. , 252.5, 253. , 253.5, 254. , 254.5, 255. , 255.5, 256. , 256.5, 257. , 257.5, 258. , 258.5, 259. , 259.5, 260. , 260.5, 261. , 261.5, 262. , 262.5, 263. , 263.5, 264. , 264.5, 265. , 265.5, 266. , 266.5, 267. , 267.5, 268. , 268.5, 269. , 269.5, @@ -503,30 +503,30 @@ First, some general imports... 300. , 300.5, 301. , 301.5, 302. , 302.5, 303. , 303.5, 304. , 304.5, 305. , 305.5, 306. , 306.5, 307. , 307.5, 308. , 308.5, 309. , 309.5, 310. , 310.5, 311. , 311.5, 312. , 312.5, 313. , 313.5, 314. , 314.5, - 315. ], dtype=float32)
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[2960469 values with dtype=float32]
[2960469 values with dtype=float32]
[2960469 values with dtype=float32]
[2960469 values with dtype=float32]
[1 values with dtype=int32]
PandasIndex(DatetimeIndex(['2017-09-05 12:00:00', '2017-09-05 15:00:00', + 315. ], dtype=float32)
[2005479 values with dtype=float32]
[2960469 values with dtype=float32]
[2960469 values with dtype=float32]
[2960469 values with dtype=float32]
[2960469 values with dtype=float32]
[2960469 values with dtype=float32]
[1 values with dtype=int32]
PandasIndex(DatetimeIndex(['2017-09-05 12:00:00', '2017-09-05 15:00:00', '2017-09-05 18:00:00', '2017-09-05 21:00:00', '2017-09-06 00:00:00', '2017-09-06 03:00:00', '2017-09-06 06:00:00', '2017-09-06 09:00:00', '2017-09-06 12:00:00'], - dtype='datetime64[ns]', name='time1', freq=None))
PandasIndex(Index([50.0, 49.5, 49.0, 48.5, 48.0, 47.5, 47.0, 46.5, 46.0, 45.5, 45.0, 44.5, + dtype='datetime64[ns]', name='time1', freq=None))
PandasIndex(Index([50.0, 49.5, 49.0, 48.5, 48.0, 47.5, 47.0, 46.5, 46.0, 45.5, 45.0, 44.5, 44.0, 43.5, 43.0, 42.5, 42.0, 41.5, 41.0, 40.5, 40.0, 39.5, 39.0, 38.5, 38.0, 37.5, 37.0, 36.5, 36.0, 35.5, 35.0, 34.5, 34.0, 33.5, 33.0, 32.5, 32.0, 31.5, 31.0, 30.5, 30.0, 29.5, 29.0, 28.5, 28.0, 27.5, 27.0, 26.5, 26.0, 25.5, 25.0, 24.5, 24.0, 23.5, 23.0, 22.5, 22.0, 21.5, 21.0, 20.5, 20.0, 19.5, 19.0, 18.5, 18.0, 17.5, 17.0, 16.5, 16.0, 15.5, 15.0, 14.5, 14.0, 13.5, 13.0, 12.5, 12.0, 11.5, 11.0, 10.5, 10.0], - dtype='float32', name='latitude'))
PandasIndex(Index([ 100.0, 200.0, 300.0, 500.0, 700.0, 1000.0, 2000.0, + dtype='float32', name='latitude'))
PandasIndex(Index([ 100.0, 200.0, 300.0, 500.0, 700.0, 1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 35000.0, 40000.0, 45000.0, 50000.0, 55000.0, 60000.0, 65000.0, 70000.0, 75000.0, 80000.0, 85000.0, 90000.0, 92500.0, 95000.0, 97500.0, 100000.0], - dtype='float64', name='isobaric3'))
PandasIndex(Index([ 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 35000.0, 40000.0, + dtype='float64', name='isobaric3'))
PandasIndex(Index([ 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 35000.0, 40000.0, 45000.0, 50000.0, 55000.0, 60000.0, 65000.0, 70000.0, 75000.0, 80000.0, 85000.0, 90000.0, 92500.0, 95000.0, 97500.0, 100000.0], - dtype='float64', name='isobaric1'))
PandasIndex(Index([250.0, 250.5, 251.0, 251.5, 252.0, 252.5, 253.0, 253.5, 254.0, 254.5, + dtype='float64', name='isobaric1'))
PandasIndex(Index([250.0, 250.5, 251.0, 251.5, 252.0, 252.5, 253.0, 253.5, 254.0, 254.5, ... 310.5, 311.0, 311.5, 312.0, 312.5, 313.0, 313.5, 314.0, 314.5, 315.0], - dtype='float32', name='longitude', length=131))
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array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000', + grid_mapping: LatLon_361X720-0p25S-180p00E
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array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000', '2017-09-05T18:00:00.000000000', '2017-09-05T21:00:00.000000000', '2017-09-06T00:00:00.000000000', '2017-09-06T03:00:00.000000000', '2017-09-06T06:00:00.000000000', '2017-09-06T09:00:00.000000000', - '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')
[1 values with dtype=datetime64[ns]]
array([50. , 49.5, 49. , 48.5, 48. , 47.5, 47. , 46.5, 46. , 45.5, 45. , 44.5, + '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')
[1 values with dtype=datetime64[ns]]
array([50. , 49.5, 49. , 48.5, 48. , 47.5, 47. , 46.5, 46. , 45.5, 45. , 44.5, 44. , 43.5, 43. , 42.5, 42. , 41.5, 41. , 40.5, 40. , 39.5, 39. , 38.5, 38. , 37.5, 37. , 36.5, 36. , 35.5, 35. , 34.5, 34. , 33.5, 33. , 32.5, 32. , 31.5, 31. , 30.5, 30. , 29.5, 29. , 28.5, 28. , 27.5, 27. , 26.5, 26. , 25.5, 25. , 24.5, 24. , 23.5, 23. , 22.5, 22. , 21.5, 21. , 20.5, 20. , 19.5, 19. , 18.5, 18. , 17.5, 17. , 16.5, 16. , 15.5, 15. , 14.5, - 14. , 13.5, 13. , 12.5, 12. , 11.5, 11. , 10.5, 10. ], dtype=float32)
array([ 100., 200., 300., 500., 700., 1000., 2000., 3000., + 14. , 13.5, 13. , 12.5, 12. , 11.5, 11. , 10.5, 10. ], dtype=float32)
array([ 100., 200., 300., 500., 700., 1000., 2000., 3000., 5000., 7000., 10000., 15000., 20000., 25000., 30000., 35000., 40000., 45000., 50000., 55000., 60000., 65000., 70000., 75000., - 80000., 85000., 90000., 92500., 95000., 97500., 100000.])
array([250. , 250.5, 251. , 251.5, 252. , 252.5, 253. , 253.5, 254. , 254.5, + 80000., 85000., 90000., 92500., 95000., 97500., 100000.])
array([250. , 250.5, 251. , 251.5, 252. , 252.5, 253. , 253.5, 254. , 254.5, 255. , 255.5, 256. , 256.5, 257. , 257.5, 258. , 258.5, 259. , 259.5, 260. , 260.5, 261. , 261.5, 262. , 262.5, 263. , 263.5, 264. , 264.5, 265. , 265.5, 266. , 266.5, 267. , 267.5, 268. , 268.5, 269. , 269.5, @@ -971,27 +971,27 @@ if we look at just a single data variable? 300. , 300.5, 301. , 301.5, 302. , 302.5, 303. , 303.5, 304. , 304.5, 305. , 305.5, 306. , 306.5, 307. , 307.5, 308. , 308.5, 309. , 309.5, 310. , 310.5, 311. , 311.5, 312. , 312.5, 313. , 313.5, 314. , 314.5, - 315. ], dtype=float32)
PandasIndex(DatetimeIndex(['2017-09-05 12:00:00', '2017-09-05 15:00:00', + 315. ], dtype=float32)
PandasIndex(DatetimeIndex(['2017-09-05 12:00:00', '2017-09-05 15:00:00', '2017-09-05 18:00:00', '2017-09-05 21:00:00', '2017-09-06 00:00:00', '2017-09-06 03:00:00', '2017-09-06 06:00:00', '2017-09-06 09:00:00', '2017-09-06 12:00:00'], - dtype='datetime64[ns]', name='time1', freq=None))
PandasIndex(Index([50.0, 49.5, 49.0, 48.5, 48.0, 47.5, 47.0, 46.5, 46.0, 45.5, 45.0, 44.5, + dtype='datetime64[ns]', name='time1', freq=None))
PandasIndex(Index([50.0, 49.5, 49.0, 48.5, 48.0, 47.5, 47.0, 46.5, 46.0, 45.5, 45.0, 44.5, 44.0, 43.5, 43.0, 42.5, 42.0, 41.5, 41.0, 40.5, 40.0, 39.5, 39.0, 38.5, 38.0, 37.5, 37.0, 36.5, 36.0, 35.5, 35.0, 34.5, 34.0, 33.5, 33.0, 32.5, 32.0, 31.5, 31.0, 30.5, 30.0, 29.5, 29.0, 28.5, 28.0, 27.5, 27.0, 26.5, 26.0, 25.5, 25.0, 24.5, 24.0, 23.5, 23.0, 22.5, 22.0, 21.5, 21.0, 20.5, 20.0, 19.5, 19.0, 18.5, 18.0, 17.5, 17.0, 16.5, 16.0, 15.5, 15.0, 14.5, 14.0, 13.5, 13.0, 12.5, 12.0, 11.5, 11.0, 10.5, 10.0], - dtype='float32', name='latitude'))
PandasIndex(Index([ 100.0, 200.0, 300.0, 500.0, 700.0, 1000.0, 2000.0, + dtype='float32', name='latitude'))
PandasIndex(Index([ 100.0, 200.0, 300.0, 500.0, 700.0, 1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 35000.0, 40000.0, 45000.0, 50000.0, 55000.0, 60000.0, 65000.0, 70000.0, 75000.0, 80000.0, 85000.0, 90000.0, 92500.0, 95000.0, 97500.0, 100000.0], - dtype='float64', name='isobaric3'))
PandasIndex(Index([250.0, 250.5, 251.0, 251.5, 252.0, 252.5, 253.0, 253.5, 254.0, 254.5, + dtype='float64', name='isobaric3'))
PandasIndex(Index([250.0, 250.5, 251.0, 251.5, 252.0, 252.5, 253.0, 253.5, 254.0, 254.5, ... 310.5, 311.0, 311.5, 312.0, 312.5, 313.0, 313.5, 314.0, 314.5, 315.0], - dtype='float32', name='longitude', length=131))
array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000', + _metpy_axis: time
array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000', '2017-09-05T18:00:00.000000000', '2017-09-05T21:00:00.000000000', '2017-09-06T00:00:00.000000000', '2017-09-06T03:00:00.000000000', '2017-09-06T06:00:00.000000000', '2017-09-06T09:00:00.000000000', - '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')
array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000', + '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')
array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000', '2017-09-05T18:00:00.000000000', '2017-09-05T21:00:00.000000000', '2017-09-06T00:00:00.000000000', '2017-09-06T03:00:00.000000000', '2017-09-06T06:00:00.000000000', '2017-09-06T09:00:00.000000000', - '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')
[1 values with dtype=datetime64[ns]]
PandasIndex(DatetimeIndex(['2017-09-05 12:00:00', '2017-09-05 15:00:00', + '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')
[1 values with dtype=datetime64[ns]]
PandasIndex(DatetimeIndex(['2017-09-05 12:00:00', '2017-09-05 15:00:00', '2017-09-05 18:00:00', '2017-09-05 21:00:00', '2017-09-06 00:00:00', '2017-09-06 03:00:00', '2017-09-06 06:00:00', '2017-09-06 09:00:00', '2017-09-06 12:00:00'], - dtype='datetime64[ns]', name='time1', freq=None))
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array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000', + grid_mapping: LatLon_361X720-0p25S-180p00E
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array(['2017-09-05T12:00:00.000000000', '2017-09-05T15:00:00.000000000', '2017-09-05T18:00:00.000000000', '2017-09-05T21:00:00.000000000', '2017-09-06T00:00:00.000000000', '2017-09-06T03:00:00.000000000', '2017-09-06T06:00:00.000000000', '2017-09-06T09:00:00.000000000', - '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')
[1 values with dtype=datetime64[ns]]
array([50. , 49.5, 49. , 48.5, 48. , 47.5, 47. , 46.5, 46. , 45.5, 45. , 44.5, + '2017-09-06T12:00:00.000000000'], dtype='datetime64[ns]')
[1 values with dtype=datetime64[ns]]
array([50. , 49.5, 49. , 48.5, 48. , 47.5, 47. , 46.5, 46. , 45.5, 45. , 44.5, 44. , 43.5, 43. , 42.5, 42. , 41.5, 41. , 40.5, 40. , 39.5, 39. , 38.5, 38. , 37.5, 37. , 36.5, 36. , 35.5, 35. , 34.5, 34. , 33.5, 33. , 32.5, 32. , 31.5, 31. , 30.5, 30. , 29.5, 29. , 28.5, 28. , 27.5, 27. , 26.5, 26. , 25.5, 25. , 24.5, 24. , 23.5, 23. , 22.5, 22. , 21.5, 21. , 20.5, 20. , 19.5, 19. , 18.5, 18. , 17.5, 17. , 16.5, 16. , 15.5, 15. , 14.5, - 14. , 13.5, 13. , 12.5, 12. , 11.5, 11. , 10.5, 10. ], dtype=float32)
array([ 100., 200., 300., 500., 700., 1000., 2000., 3000., + 14. , 13.5, 13. , 12.5, 12. , 11.5, 11. , 10.5, 10. ], dtype=float32)
array([ 100., 200., 300., 500., 700., 1000., 2000., 3000., 5000., 7000., 10000., 15000., 20000., 25000., 30000., 35000., 40000., 45000., 50000., 55000., 60000., 65000., 70000., 75000., - 80000., 85000., 90000., 92500., 95000., 97500., 100000.])
array([250. , 250.5, 251. , 251.5, 252. , 252.5, 253. , 253.5, 254. , 254.5, + 80000., 85000., 90000., 92500., 95000., 97500., 100000.])
array([250. , 250.5, 251. , 251.5, 252. , 252.5, 253. , 253.5, 254. , 254.5, 255. , 255.5, 256. , 256.5, 257. , 257.5, 258. , 258.5, 259. , 259.5, 260. , 260.5, 261. , 261.5, 262. , 262.5, 263. , 263.5, 264. , 264.5, 265. , 265.5, 266. , 266.5, 267. , 267.5, 268. , 268.5, 269. , 269.5, @@ -1960,28 +1960,28 @@ the CRS using the ``.assign_crs()`` method: 300. , 300.5, 301. , 301.5, 302. , 302.5, 303. , 303.5, 304. , 304.5, 305. , 305.5, 306. , 306.5, 307. , 307.5, 308. , 308.5, 309. , 309.5, 310. , 310.5, 311. , 311.5, 312. , 312.5, 313. , 313.5, 314. , 314.5, - 315. ], dtype=float32)
array(<metpy.plots.mapping.CFProjection object at 0x7fcc283726d0>, - dtype=object)
PandasIndex(DatetimeIndex(['2017-09-05 12:00:00', '2017-09-05 15:00:00', + 315. ], dtype=float32)
array(<metpy.plots.mapping.CFProjection object at 0x7fd64d0c3810>, + dtype=object)
PandasIndex(DatetimeIndex(['2017-09-05 12:00:00', '2017-09-05 15:00:00', '2017-09-05 18:00:00', '2017-09-05 21:00:00', '2017-09-06 00:00:00', '2017-09-06 03:00:00', '2017-09-06 06:00:00', '2017-09-06 09:00:00', '2017-09-06 12:00:00'], - dtype='datetime64[ns]', name='time1', freq=None))
PandasIndex(Index([50.0, 49.5, 49.0, 48.5, 48.0, 47.5, 47.0, 46.5, 46.0, 45.5, 45.0, 44.5, + dtype='datetime64[ns]', name='time1', freq=None))
PandasIndex(Index([50.0, 49.5, 49.0, 48.5, 48.0, 47.5, 47.0, 46.5, 46.0, 45.5, 45.0, 44.5, 44.0, 43.5, 43.0, 42.5, 42.0, 41.5, 41.0, 40.5, 40.0, 39.5, 39.0, 38.5, 38.0, 37.5, 37.0, 36.5, 36.0, 35.5, 35.0, 34.5, 34.0, 33.5, 33.0, 32.5, 32.0, 31.5, 31.0, 30.5, 30.0, 29.5, 29.0, 28.5, 28.0, 27.5, 27.0, 26.5, 26.0, 25.5, 25.0, 24.5, 24.0, 23.5, 23.0, 22.5, 22.0, 21.5, 21.0, 20.5, 20.0, 19.5, 19.0, 18.5, 18.0, 17.5, 17.0, 16.5, 16.0, 15.5, 15.0, 14.5, 14.0, 13.5, 13.0, 12.5, 12.0, 11.5, 11.0, 10.5, 10.0], - dtype='float32', name='latitude'))
PandasIndex(Index([ 100.0, 200.0, 300.0, 500.0, 700.0, 1000.0, 2000.0, + dtype='float32', name='latitude'))
PandasIndex(Index([ 100.0, 200.0, 300.0, 500.0, 700.0, 1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 35000.0, 40000.0, 45000.0, 50000.0, 55000.0, 60000.0, 65000.0, 70000.0, 75000.0, 80000.0, 85000.0, 90000.0, 92500.0, 95000.0, 97500.0, 100000.0], - dtype='float64', name='isobaric3'))
PandasIndex(Index([250.0, 250.5, 251.0, 251.5, 252.0, 252.5, 253.0, 253.5, 254.0, 254.5, + dtype='float64', name='isobaric3'))
PandasIndex(Index([250.0, 250.5, 251.0, 251.5, 252.0, 252.5, 253.0, 253.5, 254.0, 254.5, ... 310.5, 311.0, 311.5, 312.0, 312.5, 313.0, 313.5, 314.0, 314.5, 315.0], - dtype='float32', name='longitude', length=131))
array([ 0., 100000., 200000.])
array([ 0., 100000., 200000.])
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array([ 0., 100000., 200000.])
array([ 0., 100000., 200000.])
array(<metpy.plots.mapping.CFProjection object at 0x7fd63c65d290>, + dtype=object)
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array([[-97.5 , -96.35096705, -95.20229214], + [40.29828138, 40.29252697, 40.27526648]])
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Magnitude | [[0 1 2] |
---|---|
Units | degree_Celsius |
PandasIndex(Index([0.0, 100000.0, 200000.0], dtype='float64', name='y'))
PandasIndex(Index([0.0, 100000.0, 200000.0], dtype='float64', name='x'))
Magnitude | [[0 1 2] |
---|---|
Units | degree_Celsius |
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PandasIndex(Index([0.0, 100000.0, 200000.0], dtype='float64', name='x'))
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index 09275fffc52..4b45e7c3129 100644
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index 42b3d06ed21..9e71d6705e0 100644
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+ GitHub
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index 68749dbab51..5b9e35e16a8 100644
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index a0a92526209..b86a84baebf 100644
--- a/dev/api/generated/metpy.calc.bunkers_storm_motion.html
+++ b/dev/api/generated/metpy.calc.bunkers_storm_motion.html
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index fbcb2d3abde..a030c65b30e 100644
--- a/dev/api/generated/metpy.calc.cape_cin.html
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index 6387a67c564..9c06db18b9b 100644
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index 7314b7e702d..dfb75c1588c 100644
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index 71fde648bd2..a24ba2e71d3 100644
--- a/dev/api/generated/metpy.calc.density.html
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index e6212821087..0aa27b51af3 100644
--- a/dev/api/generated/metpy.calc.dewpoint_from_specific_humidity.html
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Download Python source code: Gradient.py
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[<Quantity(272.583109, 'kelvin')>]
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So as we expect the parcel has warmed and dried out through the combined dry and moist ascent
due to the release of latent heat and subsequent precipitating out of moisture from the
parcel
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diff --git a/dev/examples/calculations/QVector.html b/dev/examples/calculations/QVector.html
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diff --git a/dev/examples/calculations/Shearing_Deformation.html b/dev/examples/calculations/Shearing_Deformation.html
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diff --git a/dev/examples/calculations/Sounding_Calculations.html b/dev/examples/calculations/Sounding_Calculations.html
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diff --git a/dev/examples/calculations/Static_Stability.html b/dev/examples/calculations/Static_Stability.html
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print((static_stability(p, T) * (p / Rd)).to('K/Pa'))
-[-4.41283219227208e-05 -2.4492777247121155e-05 9.116458268710324e-05 8.845234936113418e-05 6.224860528096752e-05 0.00015314927360383487 0.00019881695313443988 0.00030101997736649576 0.00026111189622417404 0.001085062948164048 0.003740321802149091 0.004248281970347817 0.0036657831964964018 0.0016752453464420483 0.0007380480366923099 0.00019231433878668947 0.0002557493550907393 0.00024017381030490135 0.0010992754328914723 0.000915641843948739 0.00025827670773766095 0.0008820697439592232 0.0003589161178615257 0.0004817507957698896 0.0006973708206233451 0.0006126559385666097 0.00047078968526674726 0.0004870196826060473 0.0004875042540964733 0.0004723683427336631 0.00018331961002751509 0.00014636894085019007 0.00013915628085254793 -0.0015920843524442835 -0.0015653462261827026 9.863194193151853e-05 0.0001579458273752061 0.00041106154962424813 0.00039799924225680105 0.0006038463463312496 0.0032368837310525183 0.004009490876985825 0.0037125198804787668 0.0023375347706963796 0.001366027873753104 0.0012474317985632058 0.0014544559226790695 0.0024234211184320383 0.0033852295736065785 0.00393333469570616 0.0024998389621420955 0.0026161133002877935 0.0025584158650765487 0.0061453098584766585 0.006346575700474026 0.003115891778520376 0.0028392731739860125 0.0018886781873633822 0.0023835929582128146 0.0013562543705426307 0.0011897088702191683 0.004713351681793958 0.0032926213870648154 0.002904873402537745 0.005189222385428262 0.005480289289528526 0.0014028667954945192 0.005193086215585504 0.005625967270339566 0.005397711905806318 0.01000895177150228 0.011789360222267875 0.0027175297861060685] kelvin / pascal
+[-4.412832192334464e-05 -2.449277724743241e-05 9.116458268710324e-05 8.845234936113418e-05 6.224860528096752e-05 0.00015314927360352893 0.00019881695313411532 0.0003010199773665337 0.00026111189622417404 0.001085062948164048 0.0037403218021489384 0.004248281970348124 0.0036657831964967114 0.0016752453464420483 0.0007380480366923099 0.00019231433878668947 0.0002557493550907393 0.00024017381030490135 0.0010992754328866168 0.0009156418439438833 0.00025827670773887395 0.0008820697439593748 0.0003589161178615257 0.0004817507957698896 0.0006973708206233451 0.0006126559385666097 0.00047078968526674726 0.00048701968260597434 0.0004875042540964733 0.0004723683427325029 0.00018331961002751509 0.00014636894085019007 0.00013915628085254793 -0.0015920843524442835 -0.0015653462261827026 9.863194193125203e-05 0.0001579458273746663 0.00041106154962431264 0.00039799924225680105 0.0006038463463312496 0.0032368837310525183 0.004009490876983789 0.003712519880476728 0.0023375347706966346 0.001366027873753104 0.0012474317985632058 0.0014544559226790695 0.0024234211184300538 0.003385229573605462 0.00393333469570616 0.0024998389621420955 0.0026161133002917765 0.0025584158650765487 0.0061453098584766585 0.006346575700474026 0.003115891778520376 0.0028392731739860125 0.0018886781873633822 0.0023835929582128146 0.0013562543705426307 0.0011897088702191683 0.004713351681793958 0.0032926213870685993 0.002904873402541409 0.005189222385428262 0.005480289289528999 0.0014028667954945192 0.005193086215585504 0.005625967270339566 0.005397711905806259 0.010008951771502746 0.01178936022226881 0.0027175297861041976] kelvin / pascal
-Total running time of the script: (0 minutes 0.013 seconds)
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Download Python source code: Static_Stability.py
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Download Python source code: Thickness_Hydrostatic.py
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Download Python source code: Total_Deformation.py
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Download Python source code: Vorticity.py
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Computation times#
-00:04.356 total execution time for examples_calculations files:
+00:02.984 total execution time for examples_calculations files: