In [1]:
import h5py
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
%matplotlib inline
In [2]:
ds = xr.open_dataset('/data/sallen/results/MIDOSS/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS.nc')
mesh = xr.open_dataset('~/MEOPAR/grid/mesh_mask201702.nc')
hdf = h5py.File('/data/sallen/results/MIDOSS/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS.hdf5', 'r')
In [3]:
ds
Out[3]:
xarray.Dataset
- grid_x: 396
- grid_x_latlon: 397
- grid_y: 896
- grid_y_latlon: 897
- grid_z: 40
- time: 191
- time(time)datetime64[ns]2018-01-15T00:30:00 ... 2018-01-22T22:30:00
- standard_name :
- time
- long_name :
- time
- tz_name :
- UTC
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'2018-01-22T12:30:00.000000000', '2018-01-22T13:30:00.000000000', '2018-01-22T14:30:00.000000000', '2018-01-22T15:30:00.000000000', '2018-01-22T16:30:00.000000000', '2018-01-22T17:30:00.000000000', '2018-01-22T18:30:00.000000000', '2018-01-22T19:30:00.000000000', '2018-01-22T20:30:00.000000000', '2018-01-22T21:30:00.000000000', '2018-01-22T22:30:00.000000000'], dtype='datetime64[ns]') - grid_y(grid_y)int160 1 2 3 4 5 ... 891 892 893 894 895
- standard_name :
- model_y_index
- long_name :
- y index
array([ 0, 1, 2, ..., 893, 894, 895], dtype=int16)
- grid_x(grid_x)int160 1 2 3 4 5 ... 391 392 393 394 395
- standard_name :
- model_x_index
- long_name :
- x index
array([ 0, 1, 2, ..., 393, 394, 395], dtype=int16)
- grid_z(grid_z)int160 1 2 3 4 5 6 ... 34 35 36 37 38 39
- standard_name :
- model_level_index
- long_name :
- depth level
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], dtype=int16) - grid_y_latlon(grid_y_latlon)float320.0 1.0 2.0 ... 894.0 895.0 896.0
- standard_name :
- model_latlon_y_index
- long_name :
- latlon y index
- units :
- degrees_north
array([ 0., 1., 2., ..., 894., 895., 896.], dtype=float32)
- grid_x_latlon(grid_x_latlon)float320.0 1.0 2.0 ... 394.0 395.0 396.0
- standard_name :
- model_latlon_x_index
- long_name :
- latlon x index
- units :
- degrees_east
array([ 0., 1., 2., ..., 394., 395., 396.], dtype=float32)
- OilConcentration_2D(time, grid_y, grid_x)float64...
- standard_name :
- OilConcentration_2D
- long_name :
- OilConcentration_2D
- units :
- ppm
[67769856 values with dtype=float64]
- Thickness_2D(time, grid_y, grid_x)float64...
- standard_name :
- Thickness_2D
- long_name :
- Thickness_2D
- units :
- microns
[67769856 values with dtype=float64]
- Dissolution_3D(time, grid_z, grid_y, grid_x)float64...
- standard_name :
- Dissolution_3D
- long_name :
- Dissolution_3D
- units :
- Kg/m3
[2710794240 values with dtype=float64]
- OilConcentration_3D(time, grid_z, grid_y, grid_x)float64...
- standard_name :
- OilConcentration_3D
- long_name :
- OilConcentration_3D
- units :
- Kg/m3
[2710794240 values with dtype=float64]
- Latitude(grid_y_latlon, grid_x_latlon)float64...
- standard_name :
- Latitude
- long_name :
- Latitude
- units :
- degrees_north
[356109 values with dtype=float64]
- Longitude(grid_y_latlon, grid_x_latlon)float64...
- standard_name :
- Longitude
- long_name :
- Longitude
- units :
- degrees_east
[356109 values with dtype=float64]
- Beaching_Time(grid_y, grid_x)datetime64[ns]...
- standard_name :
- Beaching_Time
- long_name :
- Beaching Time
[354816 values with dtype=datetime64[ns]]
- Beaching_Volume(grid_y, grid_x)datetime64[ns]...
- standard_name :
- Beaching_Volume
- long_name :
- Beaching Volume
[354816 values with dtype=datetime64[ns]]
- Oil_Arrival_Time(grid_y, grid_x)datetime64[ns]...
- standard_name :
- Oil_Arrival_Time
- long_name :
- Oil Arrival Time
[354816 values with dtype=datetime64[ns]]
- nco_openmp_thread_number :
- 1
- history :
- Thu Jul 9 21:44:38 2020: ncks -4 -L4 -A /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_oil_times.nc /localscratch/allen.34754583.0/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS.nc Thu Jul 9 21:40:29 2020: ncrcat -4 -L4 -O -o /localscratch/allen.34754583.0/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T0030.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T01:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T02:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T03:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T04:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T05:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T06:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T07:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T08:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T09:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T10:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T11:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T12:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T13:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-15T14:30.nc 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/localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T00:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T01:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T02:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T03:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T04:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T05:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T06:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T07:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T08:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T09:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T10:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T11:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T12:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T13:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T14:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T15:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T16:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-16T17:30.nc 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/localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-22T18:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-22T19:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-22T20:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-22T21:30.nc /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_2018-01-22T22:30.nc
- NCO :
- 4.6.6
- history_of_appended_files :
- Thu Jul 9 21:44:38 2020: Appended file /localscratch/allen.34754583.0/tmpr5j7osuz/Lagrangian_AKNS_crude_15jan18-22jan18_AKNS_oil_times.nc had no "history" attribute
In [132]:
imin, imax = 250, 340
jmin, jmax = 180, 280
In [60]:
fig, axs = plt.subplots(1, 2, figsize=(15, 5))
it = 2
ds.Thickness_2D[it, imin:imax, jmin:jmax].plot(ax=axs[0], cmap='copper')
axs[1].plot(ds.time[0:24], ds.Thickness_2D[0:24].max(axis=1).max(axis=1));
In [67]:
fig, axs = plt.subplots(1, 2, figsize=(15, 5))
it = 8
ds.OilConcentration_2D[it, imin:imax, jmin:jmax].plot(ax=axs[0], cmap='copper')
axs[1].plot(ds.time[0:24], ds.OilConcentration_2D[0:24].max(axis=1).max(axis=1));
axs[1].plot(ds.time[0:24], ds.OilConcentration_2D[0:24].sum(axis=1).sum(axis=1), 'o-');
In [50]:
fig, axs = plt.subplots(1, 2, figsize=(15, 5))
it = 40
ds.Dissolution_3D[it, :, imin:imax, jmin:jmax].sum(axis=0).plot(ax=axs[0], cmap='copper')
axs[1].plot(ds.time[0:it], ds.Dissolution_3D[0:it].max(axis=1).max(axis=1).max(axis=1));
axs[1].plot(ds.time[0:it], ds.Dissolution_3D[0:it].sum(axis=1).sum(axis=1).sum(axis=1), 'o-');
In [43]:
fig, ax = plt.subplots(1, 1)
for j in range(jmin, jmax-40):
plt.plot(ds.Oil_Arrival_Time[:, j], label=str(j));
ax.set_xlim(imin+20, imax-20)
ax.legend();
In [78]:
fig, ax = plt.subplots(1, 1)
for j in range(240, 248):
plt.plot(ds.Beaching_Time[:, j], label=str(j));
ax.set_xlim(470, 490)
ax.legend();
In [82]:
fig, ax = plt.subplots(1, 1)
ax.pcolormesh(mesh.tmask[0, 0])
ax.set_ylim(imin, imax)
ax.set_xlim(jmin, jmax)
ax.plot([240, 241], [480, 478], 'rx');
In [18]:
it = 2
fig, ax = plt.subplots(1, 1)
ax.pcolormesh(mesh.tmask[0, 0])
ax.set_ylim(imin, imax)
ax.set_xlim(jmin, jmax)
for i in range(imin, imax):
for j in range(jmin, jmax):
if ds.OilConcentration_2D[it, i, j] != 0:
# print(i, j, ds.time[it].values, ds.OilConcentration_2D[it, i, j].values)
plt.plot(j, i, 'bo')
if ds.Beaching_Time[i, j] > ds.time[0].values and ds.Beaching_Time[i, j] <= ds.time[it].values:
# print(i, j, ds.time[it].values, ds.Beaching_Time[i, j].values)
plt.plot(j, i, 'rx')
In [106]:
ds.Dissolution_3D[17, :, imin:imax, jmin:jmax].sum(axis=0).plot(cmap='copper')
Out[106]:
<matplotlib.collections.QuadMesh at 0x1319e7190>
In [91]:
it = 18
imin, imax = 250, 280
jmin, jmax = 240, 270
fig, ax = plt.subplots(1, 1, figsize=(7, 7))
ax.pcolormesh(mesh.tmask[0, 0])
ax.set_ylim(imin, imax)
ax.set_xlim(jmin, jmax)
for i in range(imin, imax):
for j in range(jmin, jmax):
if ds.OilConcentration_2D[it, i, j] > 0.2:
# print(i, j, ds.time[it].values, ds.OilConcentration_2D[it, i, j].values)
plt.plot(j, i, 'bo')
if ds.Beaching_Time[i, j] > ds.time[0].values and ds.Beaching_Time[i, j] <= ds.time[it].values:
# print(i, j, ds.time[it].values, ds.Beaching_Time[i, j].values)
plt.plot(j, i, 'rx')
if ds.OilConcentration_3D[it, :, i, j].sum() > 0.1:
plt.plot(j, i, 'm+')
In [89]:
plt.plot(ds.OilConcentration_3D[it, :, 266, 250:255], -mesh.gdepw_1d[0, ::-1] , 'o-');
plt.ylim(-10, 0);
Out[89]:
(-10.0, 0.0)
In [123]:
plt.pcolormesh(mesh.mbathy[0, imin:imax, jmin:jmax])
plt.colorbar();
In [146]:
it = 20
fig, ax = plt.subplots(1, 1)
ax.pcolormesh(mesh.mbathy[0])
imin, imax = 475, 490
jmin, jmax = 230, 255
ax.set_ylim(imin, imax)
ax.set_xlim(jmin, jmax)
for i in range(imin, imax):
for j in range(jmin, jmax):
if ds.OilConcentration_2D[it, i, j] != 0:
# print(i, j, ds.time[it].values, ds.OilConcentration_2D[it, i, j].values)
plt.plot(j, i, 'bo')
if ds.Beaching_Time[i, j] > ds.time[0].values and ds.Beaching_Time[i, j] <= ds.time[it].values:
# print(i, j, ds.time[it].values, ds.Beaching_Time[i, j].values)
plt.plot(j, i, 'rx')
if ds.Dissolution_3D[it, :, i, j].sum() != 0:
plt.plot(j, i, 'm+')
In [152]:
plt.plot(ds.Dissolution_3D[20, :, :, :].sum(axis=1).sum(axis=1), -mesh.gdepw_1d[0, ::-1], 'o-');
In [155]:
it = 20
fig, ax = plt.subplots(1, 1)
ax.pcolormesh(mesh.mbathy[0])
imin, imax = 475, 490
jmin, jmax = 230, 255
ax.set_ylim(imin, imax)
ax.set_xlim(jmin, jmax)
for i in range(imin, imax):
for j in range(jmin, jmax):
if ds.OilConcentration_2D[it, i, j] != 0:
# print(i, j, ds.time[it].values, ds.OilConcentration_2D[it, i, j].values)
plt.plot(j, i, 'bo')
if ds.Beaching_Time[i, j] > ds.time[0].values and ds.Beaching_Time[i, j] <= ds.time[it].values:
# print(i, j, ds.time[it].values, ds.Beaching_Time[i, j].values)
plt.plot(j, i, 'rx')
if ds.OilConcentration_3D[it, :, i, j].sum() != 0:
plt.plot(j, i, 'm+')
In [3]:
list(hdf.keys())
Out[3]:
['Grid', 'Results', 'Time']
In [11]:
list(hdf['Results'].keys())
Out[11]:
['Group_1', 'Number', 'OilSpill', 'Percentage Contaminated']
In [26]:
list(hdf['Results']['OilSpill']['Data_2D'].keys())
Out[26]:
['Beaching Time', 'Beaching Volume', 'Oil Arrival Time', 'OilConcentration_2D', 'Thickness_2D']
In [24]:
BV = hdf['Results']['OilSpill']['Data_2D']['Beaching Volume']['Beaching Volume_00191'][:]
print (BV.max(), BV.sum())
2988.221332146034 23186.377452579443
In [27]:
dset = hdf['Results']
In [28]:
OilSpill = dset['OilSpill']
In [29]:
OilSpill.keys()
Out[29]:
<KeysViewHDF5 ['Beached', 'Data_2D', 'Data_3D', 'Freazed', 'Latitude', 'Latitude average', 'Longitude', 'Longitude average', 'Model ID', 'Origin ID', 'Particle State', 'Surface State', 'Thickness', 'Volume', 'X Pos', 'Y Pos', 'Z Pos', 'oil']>
In [98]:
OilSpill['oil']['oil_00100'][5]
Out[98]:
1.0
In [93]:
print(np.array(OilSpill['Volume']['Volume_00001']).sum())
print(np.array(OilSpill['Volume']['Volume_00002']).sum())
print(np.array(OilSpill['Volume']['Volume_00005']).sum())
print(np.array(OilSpill['Volume']['Volume_00010']).sum())
print(np.array(OilSpill['Volume']['Volume_00020']).sum())
print(np.array(OilSpill['Volume']['Volume_00150']).sum())
1000.0 2721.92683305835 2969.9065364908465 3700.706849569166 3583.5208952447265 23186.377452579436
In [26]:
Xpos, Ypos, Zpos = OilSpill['X Pos'], OilSpill['Y Pos'], OilSpill['Z Pos']
In [74]:
fig, axs = plt.subplots(1, 2, figsize=(10, 4))
axs[0].plot(Xpos['X Position_00001'][:], Ypos['Y Position_00001']);
axs[0].plot(Xpos['X Position_00002'][:], Ypos['Y Position_00002']);
axs[1].plot(Xpos['X Position_00001'][:], Zpos['Z Position_00001'], '+')
axs[1].plot(Xpos['X Position_00002'][:], Zpos['Z Position_00002'], '+');
axs[1].plot(Xpos['X Position_00005'][:], Zpos['Z Position_00005'], '+');
axs[1].plot(Xpos['X Position_00010'][:], Zpos['Z Position_00010'], '+');
axs[1].plot(Xpos['X Position_00020'][:], Zpos['Z Position_00020'], '+');
In [102]:
beach = OilSpill['Beached']
plt.plot(OilSpill['Volume']['Volume_00100'][20:50])
print (beach['Beached_00034'][22], beach['Beached_00035'][22], beach['Beached_00036'][22],beach['Beached_00037'][22])
print (OilSpill['Volume']['Volume_00034'][22], OilSpill['Volume']['Volume_00035'][22],
OilSpill['Volume']['Volume_00036'][22], OilSpill['Volume']['Volume_00037'][22],
OilSpill['Volume']['Volume_00050'][22], OilSpill['Volume']['Volume_00100'][22])
print (((np.array(beach['Beached_00020'])-1) * np.array(OilSpill['Volume']['Volume_00020'])).sum())
print (((np.array(beach['Beached_00025'])-1) * np.array(OilSpill['Volume']['Volume_00025'])).sum())
print (((np.array(beach['Beached_00030'])-1) * np.array(OilSpill['Volume']['Volume_00030'])).sum())
print (((np.array(beach['Beached_00035'])-1) * np.array(OilSpill['Volume']['Volume_00035'])).sum())
print (((np.array(beach['Beached_00040'])-1).sum()))
print (((np.array(beach['Beached_00040'])-1) * np.array(OilSpill['Volume']['Volume_00040'])).sum())
print (((np.array(beach['Beached_00045'])-1).sum()))
print (((np.array(beach['Beached_00045'])-1) * np.array(OilSpill['Volume']['Volume_00045'])).sum())
print (((np.array(beach['Beached_00050'])-1).sum()))
print (((np.array(beach['Beached_00050'])-1) * np.array(OilSpill['Volume']['Volume_00050'])).sum())
print (((np.array(beach['Beached_00100'])-1).sum()))
print (((np.array(beach['Beached_00100'])-1) * np.array(OilSpill['Volume']['Volume_00100'])).sum())
print (((np.array(beach['Beached_00150'])-1).sum()))
print (((np.array(beach['Beached_00150'])-1) * np.array(OilSpill['Volume']['Volume_00150'])).sum())
1.0 1.0 2.0 2.0 1.7359286863328873 1.756633565258116 1.7875118665240224 1.7875118665240224 1.7875118665240224 1.7875118665240224 0.0 0.0 0.0 143.15198917198393 613.0 1212.1263948074986 778.0 1633.8117332375552 937.0 2092.8311814562553 1999.0 20198.1561204334 2000.0 23186.377452579436
In [111]:
print (beach['Beached_00020'][8], OilSpill['Volume']['Volume_00020'][8])
print (beach['Beached_00050'][8], OilSpill['Volume']['Volume_00050'][8])
print (beach['Beached_00060'][8], OilSpill['Volume']['Volume_00060'][8])
print (beach['Beached_00065'][8], OilSpill['Volume']['Volume_00065'][8])
print (beach['Beached_00070'][8], OilSpill['Volume']['Volume_00070'][8])
print (beach['Beached_00100'][8], OilSpill['Volume']['Volume_00100'][8])
plt.plot(OilSpill['Volume']['Volume_00100'][0:10])
1.0 1.7917604476223639 1.0 3.0459969727190566 1.0 4.565037337742477 1.0 8.374284442517059 2.0 11.953906790881833 2.0 11.953906790881833
Out[111]:
[<matplotlib.lines.Line2D at 0x7f134c210210>]
In [46]:
data2d = OilSpill['Data_2D']
data2d.keys()
print(data2d['Beaching Time']['Beaching Time_00001'][:].min())
0.0
In [56]:
plt.plot(OilSpill['Longitude']['Longitude_00001'][:], OilSpill['Latitude']['Latitude_00001'][:], 'o')
plt.plot(OilSpill['Longitude']['Longitude_00002'][:], OilSpill['Latitude']['Latitude_00002'][:], 'o')
plt.plot(OilSpill['Longitude']['Longitude_00003'][:], OilSpill['Latitude']['Latitude_00003'][:], 'o')
plt.plot(OilSpill['Longitude']['Longitude_00004'][:], OilSpill['Latitude']['Latitude_00004'][:], 'o')
Out[56]:
[<matplotlib.lines.Line2D at 0x7f0d5c0d2590>]
In [69]:
for mypoint in range(15):
mylats = np.zeros((10, 1))
mylons = np.zeros_like(mylats)
for counter in range(1, 10):
key1 = f'Longitude_0000{counter}'
key2 = f'Latitude_0000{counter}'
mylons[counter] = OilSpill['Longitude'][key1][mypoint]
mylats[counter] = OilSpill['Latitude'][key2][mypoint]
plt.plot(mylons[1:], mylats[1:], 'o-')
In [100]:
plt.plot(OilSpill['Particle State']['Particle State_00100'][:], '+')
Out[100]:
[<matplotlib.lines.Line2D at 0x7f0d5c3ae850>]
In [99]:
plt.plot(OilSpill['Surface State']['Surface State_00010'][:], '+')
Out[99]:
[<matplotlib.lines.Line2D at 0x7f0d5a95e890>]
In [107]:
plt.plot(OilSpill['Volume']['Volume_00050'][:], '+')
Out[107]:
[<matplotlib.lines.Line2D at 0x7f0d5a6a17d0>]
In [125]:
plt.pcolormesh(OilSpill['Data_3D']['OilConcentration_3D']['OilConcentration_3D_00010'][36].transpose()[250:275, 250:275])
plt.colorbar();
In [130]:
for i in range(259, 262):
for j in range(260, 265):
plt.plot(OilSpill['Data_3D']['OilConcentration_3D']['OilConcentration_3D_00010'][:, j, i])
In [113]:
print(OilSpill['Data_3D'].keys())
print(OilSpill['Data_2D'].keys())
<KeysViewHDF5 ['Dissolution_3D', 'OilConcentration_3D']> <KeysViewHDF5 ['Beaching Time', 'Beaching Volume', 'Oil Arrival Time', 'OilConcentration_2D', 'Thickness_2D']>
In [190]:
imin, imax = 257, 263
jmin, jmax = 259, 266
fig, axs = plt.subplots(3, 2, figsize=(15, 22))
it = 9
colour = axs[0, 0].pcolormesh(ds.Longitude[imin:imax, jmin+1:jmax+1], ds.Latitude[imin:imax, jmin+1:jmax+1],
ds.OilConcentration_2D[it, imin:imax, jmin:jmax], cmap='copper')
colour = axs[0, 1].pcolormesh(hdf['Grid']['Longitude'][imin:imax, jmin:jmax], hdf['Grid']['Latitude'][imin:imax, jmin:jmax],
OilSpill['Data_2D']['Thickness_2D']['Thickness_2D_00010'][jmin:jmax, imin:imax].transpose(),
cmap='copper')
colour = axs[1, 0].pcolormesh(ds.Longitude[imin:imax, jmin:jmax], ds.Latitude[imin:imax, jmin:jmax],
ds.OilConcentration_3D[it, :, imin:imax, jmin:jmax].sum(axis=0), cmap='copper')
colour = axs[1, 1].pcolormesh(ds.Longitude[imin:imax, jmin:jmax], ds.Latitude[imin:imax, jmin:jmax],
OilSpill['Data_3D']['OilConcentration_3D']['OilConcentration_3D_00010'][:, jmin:jmax, imin:imax].sum(axis=0).transpose(),
cmap='copper')
axs[2, 1].hist2d(OilSpill['Longitude']['Longitude_00010'][:], OilSpill['Latitude']['Latitude_00010'][:],
cmap='copper', bins=5);
axs[0, 0].set_title('Oil Concentration 2D from NetCDF')
axs[0, 1].set_title('Oil Concentration 2D from HDF5 - with HDF5 lat/lon')
axs[1, 0].set_title('Oil Concentration 3D from NetCDF')
axs[1, 1].set_title('Oil Concentration 3D from HDF5 - with NetCDF lat/lon')
axs[2, 1].set_title('Lagrangian particle position with 2D histogram')
for i in range(3):
for ax in axs[i]:
ax.set_ylim(48.375, 48.405)
ax.set_xlim(-122.91, -122.890)
ax.plot(OilSpill['Longitude']['Longitude_00010'][:], OilSpill['Latitude']['Latitude_00010'][:], 'o');
In [186]:
imin, imax = 257, 263
jmin, jmax = 259, 266
fig, axs = plt.subplots(3, 2, figsize=(15, 22))
it = 9
colour = axs[1, 0].pcolormesh(ds.Longitude[imin:imax, jmin:jmax], ds.[imin:imax, jmin:jmax],
ds.OilConcentration_3D[it, :, imin:imax, jmin:jmax].sum(axis=0), cmap='copper')
colour = axs[1, 1].pcolormesh(ds.Longitude[imin:imax, jmin:jmax], ds.Latitude[imin:imax, jmin:jmax],
OilSpill['Data_3D']['OilConcentration_3D']['OilConcentration_3D_00010'][:, jmin:jmax, imin:imax].sum(axis=0).transpose(),
cmap='copper')
axs[2, 1].hist2d(OilSpill['Longitude']['Longitude_00010'][:], OilSpill['Latitude']['Latitude_00010'][:],
cmap='copper', bins=5);
axs[0, 0].set_title('Thickness from NetCDF')
axs[0, 1].set_title('Thickness from HDF5 - with NetCDF lat/lon')
axs[1, 0].set_title('Oil Concentration 3D from NetCDF')
axs[1, 1].set_title('Oil Concentration 3D from HDF5 - with NetCDF lat/lon')
axs[2, 1].set_title('Lagrangian particle position with 2D histogram')
for i in range(3):
for ax in axs[i]:
ax.set_ylim(48.375, 48.405)
ax.set_xlim(-122.91, -122.890)
ax.plot(OilSpill['Longitude']['Longitude_00010'][:], OilSpill['Latitude']['Latitude_00010'][:], 'o');
