|
17 | 17 | sst = f[0] # Select the SST variable
|
18 | 18 |
|
19 | 19 | # Collapse data by area mean (average over spatial dimensions)
|
20 |
| -am = sst.collapse("area: mean") # equivalent to "X Y: mean" |
21 |
| -am.squeeze(inplace=True) |
| 20 | +am_max = sst.collapse("area: maximum") # equivalent to "X Y: mean" |
| 21 | +am_min = sst.collapse("area: minimum") # equivalent to "X Y: mean" |
| 22 | +#am.squeeze(inplace=True)cf.seasons() |
| 23 | +print("AM SEASONAL IS", am_min, am_max) |
| 24 | +# REDUCE TO TEST |
| 25 | +am_min = am_min[-100:] # final 100 points |
| 26 | +am_max = am_max[-100:] # final 100 points |
22 | 27 |
|
23 | 28 | # Check available coordinates (already found 'dimensioncoordinate0' as the
|
24 | 29 | # time coordinate)
|
25 |
| -print("Available coordinates:", am.coordinates()) |
| 30 | +###print("Available coordinates:", am.coordinates()) |
| 31 | + |
| 32 | +###am_dim_key, am_data = am.coordinate("dimensioncoordinate0", item=True) |
| 33 | +am_sub_1 = am_min.collapse("T: mean", group=cf.mam()) |
| 34 | +am_sub_2 = am_min.collapse("T: mean", group=cf.jja()) |
| 35 | +am_sub_3 = am_min.collapse("T: mean", group=cf.son()) |
| 36 | +am_sub_4 = am_min.collapse("T: mean", group=cf.djf()) |
| 37 | +am_sub_5 = am_max.collapse("T: mean", group=cf.mam()) |
| 38 | +am_sub_6 = am_max.collapse("T: mean", group=cf.jja()) |
| 39 | +am_sub_7 = am_max.collapse("T: mean", group=cf.son()) |
| 40 | +am_sub_8 = am_max.collapse("T: mean", group=cf.djf()) |
| 41 | + |
| 42 | + |
| 43 | + |
| 44 | +""" |
| 45 | +am_sub_1 = am.subspace(**{am_dim_key: cf.mam()}) |
| 46 | +am_sub_2 = am.subspace(**{am_dim_key: cf.month(3)}) |
| 47 | +am_sub_3 = am.subspace(**{am_dim_key: cf.month(4)}) |
| 48 | +am_sub_4 = am.subspace(**{am_dim_key: cf.month(5)}) |
| 49 | +am_sub_2 = am_sub_2 - am_sub_1 |
| 50 | +am_sub_3 = am_sub_3 - am_sub_1 |
| 51 | +am_sub_4 = am_sub_4 - am_sub_1 |
| 52 | +""" |
26 | 53 |
|
27 |
| -am_dim_key, am_data = am.coordinate("dimensioncoordinate0", item=True) |
28 |
| -am_sub = am.subspace(**{am_dim_key: cf.mam()}) |
29 | 54 |
|
30 | 55 | cfp.gopen(file="global_avg_sst_plot.png")
|
| 56 | +#cfp.lineplot( |
| 57 | +# am, |
| 58 | +# color="blue", |
| 59 | +# title="Global Average Sea Surface Temperature", |
| 60 | +# ylabel="Temperature (K)", |
| 61 | +# xlabel="Time" |
| 62 | +#) |
| 63 | +cfp.lineplot( |
| 64 | + am_sub_1, |
| 65 | + color="red", |
| 66 | +) |
| 67 | +cfp.lineplot( |
| 68 | + am_sub_2, |
| 69 | + color="green", |
| 70 | +) |
31 | 71 | cfp.lineplot(
|
32 |
| - am, |
| 72 | + am_sub_3, |
33 | 73 | color="blue",
|
34 |
| - title="Global Average Sea Surface Temperature", |
35 |
| - ylabel="Temperature (K)", |
36 |
| - xlabel="Time" |
37 | 74 | )
|
38 | 75 | cfp.lineplot(
|
39 |
| - am_sub, |
| 76 | + am_sub_4, |
| 77 | + color="purple", |
| 78 | +) |
| 79 | +cfp.lineplot( |
| 80 | + am_sub_5, |
40 | 81 | color="red",
|
41 | 82 | )
|
| 83 | +cfp.lineplot( |
| 84 | + am_sub_6, |
| 85 | + color="green", |
| 86 | +) |
| 87 | +cfp.lineplot( |
| 88 | + am_sub_7, |
| 89 | + color="blue", |
| 90 | +) |
| 91 | +cfp.lineplot( |
| 92 | + am_sub_8, |
| 93 | + color="purple", |
| 94 | +) |
42 | 95 | cfp.gclose()
|
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