|
23 | 23 |
|
24 | 24 | # %%
|
25 | 25 | # Display timeline
|
26 |
| -fig = plt.figure(figsize=(15, 8)) |
| 26 | +fig = plt.figure(figsize=(15, 5)) |
27 | 27 | ax = fig.add_axes([0.04, 0.04, 0.92, 0.92])
|
28 | 28 | n.display_timeline(ax)
|
29 | 29 |
|
30 | 30 | # %%
|
31 |
| -# Display timeline with event |
32 |
| -fig = plt.figure(figsize=(15, 8)) |
| 31 | +# Display timeline without event |
| 32 | +fig = plt.figure(figsize=(15, 5)) |
33 | 33 | ax = fig.add_axes([0.04, 0.04, 0.92, 0.92])
|
34 |
| -n.display_timeline(ax, event=True) |
35 |
| -plt.show() |
| 34 | +n.display_timeline(ax, event=False) |
| 35 | + |
| 36 | +# %% |
| 37 | +# Parameters timeline |
| 38 | +# ------------------- |
| 39 | +kw = dict(s=25, cmap="Spectral_r", zorder=10) |
| 40 | +fig = plt.figure(figsize=(15, 8)) |
| 41 | +ax = fig.add_axes([0.04, 0.54, 0.90, 0.44]) |
| 42 | +m = n.scatter_timeline(ax, "radius_e", factor=1e-3, vmin=50, vmax=150, **kw) |
| 43 | +cb = plt.colorbar( |
| 44 | + m["scatter"], cax=fig.add_axes([0.95, 0.54, 0.01, 0.44]), orientation="vertical" |
| 45 | +) |
| 46 | +cb.set_label("Effective radius (km)") |
| 47 | + |
| 48 | +ax = fig.add_axes([0.04, 0.04, 0.90, 0.44]) |
| 49 | +m = n.scatter_timeline(ax, "amplitude", factor=100, vmin=0, vmax=15, **kw) |
| 50 | +cb = plt.colorbar( |
| 51 | + m["scatter"], cax=fig.add_axes([0.95, 0.04, 0.01, 0.44]), orientation="vertical" |
| 52 | +) |
| 53 | +cb.set_label("Amplitude (cm)") |
| 54 | + |
| 55 | +# %% |
| 56 | +fig = plt.figure(figsize=(15, 5)) |
| 57 | +ax = fig.add_axes([0.04, 0.06, 0.90, 0.88]) |
| 58 | +m = n.scatter_timeline(ax, "speed_average", factor=100, vmin=0, vmax=40, **kw) |
| 59 | +cb = plt.colorbar( |
| 60 | + m["scatter"], cax=fig.add_axes([0.95, 0.04, 0.01, 0.92]), orientation="vertical" |
| 61 | +) |
| 62 | +cb.set_label("Maximum speed (cm/s)") |
| 63 | + |
| 64 | +# %% |
| 65 | +fig = plt.figure(figsize=(15, 5)) |
| 66 | +ax = fig.add_axes([0.04, 0.06, 0.90, 0.88]) |
| 67 | +m = n.scatter_timeline(ax, "radius_s", factor=1e-3, vmin=20, vmax=100, **kw) |
| 68 | +cb = plt.colorbar( |
| 69 | + m["scatter"], cax=fig.add_axes([0.95, 0.04, 0.01, 0.92]), orientation="vertical" |
| 70 | +) |
| 71 | +cb.set_label("Speed radius (km)") |
| 72 | + |
| 73 | +# %% |
| 74 | +# Remove dead branch |
| 75 | +# ------------------ |
| 76 | +# Remove all tiny segment with less than N obs which didn't join two segments |
| 77 | +# |
| 78 | +# .. warning:: |
| 79 | +# Must be explore, no solution to solve all the case |
| 80 | + |
| 81 | +n_clean = n.remove_dead_branch(nobs=51) |
| 82 | +fig = plt.figure(figsize=(15, 8)) |
| 83 | +ax = fig.add_axes([0.04, 0.54, 0.90, 0.40]) |
| 84 | +ax.set_title(f"Original network ({n.infos()})") |
| 85 | +n.display_timeline(ax) |
| 86 | +ax = fig.add_axes([0.04, 0.04, 0.90, 0.40]) |
| 87 | +ax.set_title(f"Clean network ({n_clean.infos()})") |
| 88 | +_ = n_clean.display_timeline(ax) |
36 | 89 |
|
37 | 90 | # %%
|
38 | 91 | # Keep close relative
|
39 | 92 | # -------------------
|
40 | 93 | # First choose an observation in the network
|
| 94 | +fig = plt.figure(figsize=(15, 5)) |
| 95 | +ax = fig.add_axes([0.04, 0.06, 0.90, 0.88]) |
| 96 | +n.display_timeline(ax) |
| 97 | +i = 1100 |
| 98 | +obs_args = n.time[i], n.segment[i] |
| 99 | +obs_kw = dict(color="black", markersize=30, marker=".") |
| 100 | +_ = ax.plot(*obs_args, **obs_kw) |
| 101 | + |
| 102 | +# %% |
| 103 | +fig = plt.figure(figsize=(15, 5)) |
| 104 | +ax = fig.add_axes([0.04, 0.06, 0.90, 0.88]) |
| 105 | +m = n.scatter_timeline( |
| 106 | + ax, n.obs_relative_order(i), vmin=-1.5, vmax=6.5, cmap=plt.get_cmap("jet", 8), s=10 |
| 107 | +) |
| 108 | +ax.plot(*obs_args, **obs_kw) |
| 109 | +cb = plt.colorbar( |
| 110 | + m["scatter"], cax=fig.add_axes([0.95, 0.04, 0.01, 0.92]), orientation="vertical" |
| 111 | +) |
| 112 | +cb.set_label("Relative order") |
| 113 | +# %% |
| 114 | +fig = plt.figure(figsize=(15, 5)) |
| 115 | +ax = fig.add_axes([0.04, 0.06, 0.90, 0.88]) |
| 116 | +close_to_i = n.relative(i, order=1) |
| 117 | +ax.set_title(f"Close segments ({close_to_i.infos()})") |
| 118 | +_ = close_to_i.display_timeline(ax) |
| 119 | +# %% |
| 120 | +fig = plt.figure(figsize=(15, 5)) |
| 121 | +ax = fig.add_axes([0.04, 0.06, 0.90, 0.88]) |
| 122 | +close_to_i = n.relative(i, order=2) |
| 123 | +ax.set_title(f"Close segments ({close_to_i.infos()})") |
| 124 | +_ = close_to_i.display_timeline(ax) |
| 125 | +# %% |
| 126 | +fig = plt.figure(figsize=(15, 5)) |
| 127 | +ax = fig.add_axes([0.04, 0.06, 0.90, 0.88]) |
| 128 | +close_to_i = n.relative(i, order=3) |
| 129 | +ax.set_title(f"Close segments ({close_to_i.infos()})") |
| 130 | +_ = close_to_i.display_timeline(ax) |
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