def test_collapse2(self):
# parent with same ancestor
sids = numpy.array([
0x300a30000000000a, 0x300a30800000000a, 0x300a31000000000a,
0x300a31800000000a
])
ivs = pystare.to_compressed_range(sids.flatten())
#indices = numpy.zeros([srange.get_size_as_values()], dtype=numpy.int64)
#srange.copy_values(indices)
with self.subTest():
self.assertEqual(ivs.size, 1)
with self.subTest():
self.assertEqual(hex(ivs[0]), '0x300a300000000009')
def test_tocompressedrange(self):
# indices = numpy.array([4151504989081014892, 4161865161846704588, 3643626718498217164])
# expected = numpy.array([3643626718498217164, 4151504989081014892, 4161865161846704588])
indices = numpy.array([0x399d1bcabd6f926c,0x39c1ea506ef249cc,0x3290c3321a355ccc])
expected = numpy.array([0x3290c3300000000c,0x399d1bc80000000c,0x39c1ea500000000c])
compressed = pystare.to_compressed_range(indices)
# print('')
# print('indices','compressed','expected')
# for i in [0,1,2]:
# print(i,hex(indices[i]),hex(compressed[i]),hex(expected[i]))
# print('\nindices')
# for i in [0,1,2]:
# print(hex(indices[i]))
# print('\ncompressed')
# for i in [0,1,2]:
# print(hex(compressed[i]))
numpy.testing.assert_array_equal(compressed, expected)
def compress_sids(sids):
""" Dissolve STARE index values.
Combine/dissolve sibiling sids into the parent sids. That is:
1. Any 4 siblings with the same parent in the collection get replaced by the parent. And
2. Any child whose parents is in the collection will be removed
Parameters
------------
sids: array-like
A collection of SIDs to dissolve
Returns
---------
dissolved: numpy.array
Dissolved SIDs
See Also
----------
merge_stare
Examples
---------
>>> import starepandas
>>> # The two latter SIDs are contained in the first SID
>>> sids = [4035225266123964416, 4254212798004854789, 4255901647865118724]
>>> starepandas.compress_sids(sids)
array([4035225266123964416])
Notes
--------
.. image:: ../../../_static/dissolve.png
"""
sids = numpy.unique(sids)
s_range = pystare.to_compressed_range(sids)
expanded = pystare.expand_intervals(s_range, -1, multi_resolution=True)
return expanded
def dissolve(sids):
s_range = pystare.to_compressed_range(sids)
expanded = pystare.expand_intervals(s_range, -1, multi_resolution=True)
return expanded
def main():
print('MODIS Sketching')
data_sourcedir = '/home/mrilee/data/MODIS/'
# Geolocation files
mod03_catalog = gd.data_catalog({
'directory': data_sourcedir,
'patterns': ['MOD03*']
})
# Data files
mod05_catalog = gd.data_catalog({
'directory': data_sourcedir,
'patterns': ['MOD05*']
})
print('mod03 catalog\n', mod03_catalog.get_files())
print('mod03 catalog\n', mod03_catalog.get_tid_centered_index())
print('mod05 catalog\n', mod05_catalog.get_tid_centered_index())
modis_sets = SortedDict()
tKeys = list(mod05_catalog.tid_centered_index.keys())
for tid in tKeys:
# for tid in tKeys[0:1]:
# for tid in mod05_catalog.tid_centered_index: # replace with temporal comparison
if (len(mod05_catalog.tid_centered_index[tid]) > 1
or len(mod03_catalog.tid_centered_index[tid]) > 1):
raise NotImplementedError(
'sketchH only written for preselected pairs of MODIS files')
modis_sets[tid] = modis05_set(mod05_catalog.tid_centered_index[tid][0],
mod03_catalog.tid_centered_index[tid][0],
data_sourcedir=data_sourcedir)
modis_sets[tid].load_wv_nir().load_geo().make_sare()
# print(hex(tid),modis_sets[tid].data,modis_sets[tid].location)
# print(modis_sets[tid].info())
###########################################################################
proj = ccrs.PlateCarree()
transf = ccrs.Geodetic()
def init_figure(proj):
plt.figure()
ax = plt.axes(projection=proj)
# ax.set_global()
# ax.coastlines()
return ax
vmin = np.amin(np.array([a.vmin() for a in modis_sets.values()]))
vmax = np.amax(np.array([a.vmax() for a in modis_sets.values()]))
tKeys = list(modis_sets.keys())
tKeys = [tKeys[1]]
# tid = tKeys[0]
# tKeys = tKeys[1:]
# tKeys = tKeys[-2:-1]
# for tid in mod05_catalog.tid_centered_index: # replace with temporal comparison
# if True:
for tid in tKeys:
if False:
plt.scatter(modis_sets[tid].geo_lon,
modis_sets[tid].geo_lat,
s=1,
c=modis_sets[tid].data_wv_nir,
transform=transf,
vmin=vmin,
vmax=vmax)
tmp_data_src_name = mod05_catalog.tid_centered_index[tid][0][0:-4]
# print('tmp_data_src_name: ',tmp_data_src_name)
# clons,clats,cintmat = ps.triangulate_indices(modis_sets[tid].cover)
tmp_cover = ps.to_compressed_range(modis_sets[tid].cover)
# tmp_cover = ps.expand_intervals(tmp_cover,2)
tmp_cover = ps.expand_intervals(tmp_cover, 4)
# tmp_cover = ps.expand_intervals(tmp_cover,6,result_size_limit=1600)
if False:
clons, clats, cintmat = ps.triangulate_indices(tmp_cover)
ctriang = tri.Triangulation(clons, clats, cintmat)
spart_nmax = -1
# tmp_cover = tmp_cover[0:10]
idx_all = {}
for sid in tmp_cover:
# print(sid,' sid, indexing cover sid: 0x%016x'%sid)
idx_all[sid] = np.where(
ps.cmp_spatial(np.array([sid], dtype=np.int64),
modis_sets[tid].sare) != 0)
spart_nmax = max(spart_nmax,
len(modis_sets[tid].sare[idx_all[sid]]))
# print('max spart items to write per file: ',spart_nmax)
spart_nmax = None # Variable lengths
spart_names = []
for sid in tmp_cover:
idx = idx_all[sid][0]
# print(sid,' sid,cover sid: 0x%016x'%sid,' len(idx)=%i'%(len(idx)))
# print('idx: ',idx)
# print('idx type: ',type(idx))
spart_h5_namebase = 't%016x.%s' % (tid,
tmp_data_src_name + '.sketchJ0')
# MAKE
spart_h5 = sare_partition(sid,
spart_h5_namebase,
src_name=tmp_data_src_name,
var_nmax=spart_nmax)
# CHECK
# print(' data_wv_nir mn,mx: ',np.amin(modis_sets[tid].data_wv_nir),np.amax(modis_sets[tid].data_wv_nir))
# WRITE
if True:
spart_h5.write1(
shape=[modis_sets[tid].nAcross, modis_sets[tid].nAlong],
dataset_name='wv_nir',
vars={
'sare':
modis_sets[tid].sare[idx],
'src_coord':
np.arange(len(modis_sets[tid].sare))[idx],
'Water_Vapor_Near_Infrared':
modis_sets[tid].data_wv_nir.flatten()[idx]
})
# print('')
spart_names.append(spart_h5.fname)
if False:
ax = init_figure(proj)
ax.triplot(ctriang,
'b-',
transform=transf,
lw=1.0,
markersize=3,
alpha=0.5)
plt.scatter(modis_sets[tid].geo_lon.flatten()[idx],
modis_sets[tid].geo_lat.flatten()[idx],
s=1,
c=modis_sets[tid].data_wv_nir.flatten()[idx],
transform=transf,
vmin=vmin,
vmax=vmax)
plt.show()
# READ
spart_h5_1 = sare_partition(sid, spart_h5_namebase)
(s5_shape, s5_name, s5_vars, s5_vars_dtype,
s5_metadata_dtype) = spart_h5_1.read1()
# print('found and loaded %s of type %s.'%(s5_name,s5_vars_dtype))
idx = s5_vars['src_coord']
if False:
ax = init_figure(proj)
ax.triplot(ctriang,
'g-',
transform=transf,
lw=1.0,
markersize=3,
alpha=0.5)
plt.scatter(modis_sets[tid].geo_lon.flatten()[idx],
modis_sets[tid].geo_lat.flatten()[idx],
s=1,
c=modis_sets[tid].data_wv_nir.flatten()[idx],
transform=transf,
vmin=vmin,
vmax=vmax)
plt.show()
if False:
ax = init_figure(proj)
ax.triplot(ctriang,
'r-',
transform=transf,
lw=1.0,
markersize=3,
alpha=0.5)
lat, lon = ps.to_latlon(s5_vars['sare'])
plt.scatter(lon,
lat,
s=1,
c=s5_vars['Water_Vapor_Near_Infrared'],
transform=transf,
vmin=vmin,
vmax=vmax)
plt.show()
####
## # Define the layout of the virtual data set.
## var_ns = [len(idx_all[i][0]) for i in idx_all]
## var_n_cumul = [0]
## for i in range(len(idx_all)):
## var_n_cumul.append(var_n_cumul[-1]+var_ns[i])
## # var_n_total = sum([len(i) for i in idx_all])
## var_n_total = var_n_cumul[-1]
##
## print('')
## print('var_ns: ',var_ns)
## print('var_n_cumul: ',var_n_cumul)
## print('var_n_total: ',var_n_total)
##
## layout = h5.VirtualLayout(shape=(var_n_total,),dtype=s5_vars_dtype)
## print('layout: ',layout)
##
## layout_md = h5.VirtualLayout(shape=(len(spart_names),),dtype=s5_metadata_dtype)
## print('layout_md: ',layout_md)
## print('')
##
## layout_idx = np.arange(var_n_total)
##
## for i in range(len(spart_names)):
## print(i,' part: ',var_n_cumul[i],var_n_cumul[i+1],', ',var_ns[i])
## print('')
##
## # print('s5_metadata_dtype: ',s5_metadata_dtype)
## ds_name = 'wv_nir'
## for i in range(len(spart_names)):
## # Just concatenate
## #++ layout[var_n_cumul[i]:var_n_cumul[i+1]] = h5.VirtualSource(spart_names[i],ds_name,shape=(var_ns[i],))
## # What I would like to do...
## if var_ns[i] != 0:
## with h5.File(spart_names[i]) as h:
## vs = h5.VirtualSource(spart_names[i],ds_name,shape=(var_ns[i],))
## for j in range(h[ds_name]['src_coord'].shape[0]): # Should be var_ns[i]
## layout[h[ds_name]['src_coord'][j]] = vs[j] # next step, try [j,k] for a virtual image....
## # else: # zero-case
## # layout[var_n_cumul[i]:var_n_cumul[i+1]] = h5.VirtualSource(spart_names[i],ds_name,shape=(var_ns[i],))
##
## for i in range(len(spart_names)):
## layout_md[i] = h5.VirtualSource(spart_names[i],'metadata',shape=(1,))
##
## vds_fname = '.'.join([tmp_data_src_name,ds_name,'sketchJ0.vds.h5'])
## print('writing ',vds_fname)
## with h5.File(vds_fname,'w',libver='latest') as f:
## f.create_virtual_dataset('wv_nir',layout) # fillvalue?
## f.create_virtual_dataset('metadata',layout_md) # fillvalue?
## # f.create_dataset(['image_lookup']...)
print('MODIS Sketching Done')
return
def main():
print('MODIS Sketching')
data_sourcedir = '/home/mrilee/data/MODIS/'
# Geolocation files
mod03_catalog = gd.data_catalog({
'directory': data_sourcedir,
'patterns': ['MOD03*']
})
# Data files
mod05_catalog = gd.data_catalog({
'directory': data_sourcedir,
'patterns': ['MOD05*']
})
print('mod03 catalog\n', mod03_catalog.get_files())
print('mod03 catalog\n', mod03_catalog.get_tid_centered_index())
print('mod05 catalog\n', mod05_catalog.get_tid_centered_index())
modis_sets = SortedDict()
tKeys = list(mod05_catalog.tid_centered_index.keys())
for tid in tKeys:
# for tid in tKeys[0:1]:
# for tid in mod05_catalog.tid_centered_index: # replace with temporal comparison
if (len(mod05_catalog.tid_centered_index[tid]) > 1
or len(mod03_catalog.tid_centered_index[tid]) > 1):
raise NotImplementedError(
'sketchH only written for preselected pairs of MODIS files')
modis_sets[tid] = modis05_set(mod05_catalog.tid_centered_index[tid][0],
mod03_catalog.tid_centered_index[tid][0],
data_sourcedir=data_sourcedir)
modis_sets[tid].load_wv_nir().load_geo().make_sare()
# print(hex(tid),modis_sets[tid].data,modis_sets[tid].location)
# print(modis_sets[tid].info())
###########################################################################
proj = ccrs.PlateCarree()
transf = ccrs.Geodetic()
def init_figure(proj):
plt.figure()
ax = plt.axes(projection=proj)
# ax.set_global()
# ax.coastlines()
return ax
vmin = np.amin(np.array([a.vmin() for a in modis_sets.values()]))
vmax = np.amax(np.array([a.vmax() for a in modis_sets.values()]))
tKeys = list(modis_sets.keys())
tid = tKeys[0]
# tKeys = tKeys[1:]
# tKeys = tKeys[-2:-1]
# for tid in mod05_catalog.tid_centered_index: # replace with temporal comparison
# if True:
for tid in tKeys:
if False:
plt.scatter(modis_sets[tid].geo_lon,
modis_sets[tid].geo_lat,
s=1,
c=modis_sets[tid].data_wv_nir,
transform=transf,
vmin=vmin,
vmax=vmax)
tmp_data_src_name = mod05_catalog.tid_centered_index[tid][0][0:-4]
# print('tmp_data_src_name: ',tmp_data_src_name)
# clons,clats,cintmat = ps.triangulate_indices(modis_sets[tid].cover)
tmp_cover = ps.to_compressed_range(modis_sets[tid].cover)
# tmp_cover = ps.expand_intervals(tmp_cover,2)
tmp_cover = ps.expand_intervals(tmp_cover, 5)
clons, clats, cintmat = ps.triangulate_indices(tmp_cover)
ctriang = tri.Triangulation(clons, clats, cintmat)
spart_nmax = -1
# tmp_cover = tmp_cover[0:10]
idx_all = {}
for sid in tmp_cover:
# print(sid,' sid, indexing cover sid: 0x%016x'%sid)
idx_all[sid] = np.where(
ps.cmp_spatial(np.array([sid], dtype=np.int64),
modis_sets[tid].sare) != 0)
spart_nmax = max(spart_nmax,
len(modis_sets[tid].sare[idx_all[sid]]))
# print('max spart items to write per file: ',spart_nmax)
spart_names = []
for sid in tmp_cover:
idx = idx_all[sid][0]
# print(sid,' sid,cover sid: 0x%016x'%sid,' len(idx)=%i'%(len(idx)))
# print('idx: ',idx)
# print('idx type: ',type(idx))
spart_h5_namebase = 't%016x.%s' % (tid,
tmp_data_src_name + '.sketchH')
spart_h5 = sare_partition(sid,
spart_h5_namebase,
src_name=tmp_data_src_name,
var_nmax=spart_nmax)
spart_h5.write1(
shape=[modis_sets[tid].nAcross, modis_sets[tid].nAlong],
dataset_name='wv_nir',
vars={
'sare':
modis_sets[tid].sare[idx],
'src_coord':
np.arange(len(modis_sets[tid].sare))[idx],
'Water_Vapor_Near_Infrared':
modis_sets[tid].data_wv_nir.flatten()[idx]
})
spart_names.append(spart_h5.fname)
if False:
ax = init_figure(proj)
ax.triplot(ctriang,
'b-',
transform=transf,
lw=1.0,
markersize=3,
alpha=0.5)
plt.scatter(modis_sets[tid].geo_lon.flatten()[idx],
modis_sets[tid].geo_lat.flatten()[idx],
s=1,
c=modis_sets[tid].data_wv_nir.flatten()[idx],
transform=transf,
vmin=vmin,
vmax=vmax)
plt.show()
spart_h5_1 = sare_partition(sid, spart_h5_namebase)
(s5_shape, s5_name, s5_vars, s5_vars_dtype,
s5_metadata_dtype) = spart_h5_1.read1()
# print('found and loaded %s of type %s.'%(s5_name,s5_vars_dtype))
idx = s5_vars['src_coord']
if False:
ax = init_figure(proj)
ax.triplot(ctriang,
'g-',
transform=transf,
lw=1.0,
markersize=3,
alpha=0.5)
plt.scatter(modis_sets[tid].geo_lon.flatten()[idx],
modis_sets[tid].geo_lat.flatten()[idx],
s=1,
c=modis_sets[tid].data_wv_nir.flatten()[idx],
transform=transf,
vmin=vmin,
vmax=vmax)
plt.show()
if False:
ax = init_figure(proj)
ax.triplot(ctriang,
'r-',
transform=transf,
lw=1.0,
markersize=3,
alpha=0.5)
lat, lon = ps.to_latlon(s5_vars['sare'])
plt.scatter(lon,
lat,
s=1,
c=s5_vars['Water_Vapor_Near_Infrared'],
transform=transf,
vmin=vmin,
vmax=vmax)
plt.show()
####
layout = h5.VirtualLayout(shape=(len(spart_names), spart_nmax),
dtype=s5_vars_dtype)
layout_md = h5.VirtualLayout(shape=(len(spart_names), 1),
dtype=s5_metadata_dtype)
# print('s5_metadata_dtype: ',s5_metadata_dtype)
ds_name = 'wv_nir'
for i in range(len(spart_names)):
layout[i] = h5.VirtualSource(spart_names[i],
ds_name,
shape=(spart_nmax, ))
layout_md[i] = h5.VirtualSource(spart_names[i],
'metadata',
shape=(1, ))
vds_fname = '.'.join([tmp_data_src_name, ds_name, 'vds.h5'])
print('writing ', vds_fname)
with h5.File(vds_fname, 'w', libver='latest') as f:
f.create_virtual_dataset('wv_nir', layout) # fillvalue?
f.create_virtual_dataset('metadata', layout_md) # fillvalue?
print('MODIS Sketching Done')
return
def dominica1(proj, transf):
"Example and analysis for pystare bug#27."
sids = numpy.array([
2521898143583305738, 2521997099629805578, 2521989952804225034,
2521991052315852810, 2521893745536794634, 2521897044071677962,
2521897593827491850, 2521898693339119626, 2521899792850747402,
2521991602071666698, 2521889347490283530, 2521899243094933514,
2521900892362375178, 2521901442118189066, 2521901991874002954,
2521903091385630730, 2521887698222841866, 2521886048955400202,
2521992701583294474, 2521902541629816842, 2521906389920514058,
2521890447001911306, 2521993251339108362, 2521886598711214090,
2521908588943769610, 2521892646025166858, 2521894295292608522,
2521989403048411146
])
sids = numpy.array([
2521902129312956427, 2521902404190863371, 2521899930289700875,
2521900067728654347, 2521900205167607819, 2521991876949573643,
2521992701583294475, 2521992839022247947, 2521992976461201419,
2521997099629805579, 2521889484929237003, 2521991739510620171,
2521902679068770315, 2521908726382723083, 2521894432731561995,
2521901579557142539, 2521989952804225035, 2521990365121085451,
2521991327193759755, 2521997237068759051, 2521997511946665995,
2521898418461212683, 2521898555900166155, 2521898693339119627,
2521889759807143947, 2521903091385630731, 2521901716996096011,
2521897456388538379, 2521902266751909899, 2521897868705398795,
2521898968217026571, 2521990227682131979, 2521889622368190475,
2521901167240282123, 2521887973100748811, 2521902816507723787,
2521892920903073803, 2521894020414701579, 2521890721879818251,
2521993526217015307, 2521901304679235595, 2521901854435049483,
2521902953946677259, 2521898006144352267, 2521890859318771723,
2521886461272260619, 2521888110539702283, 2521898830778073099,
2521903228824584203, 2521897044071677963, 2521903366263537675,
2521899105655980043, 2521903503702491147, 2521886048955400203,
2521991602071666699, 2521899380533886987, 2521890584440864779,
2521992014388527115, 2521886873589121035, 2521887011028074507,
2521889347490283531, 2521890447001911307, 2521886736150167563,
2521893745536794635, 2521893882975748107, 2521894157853655051,
2521897181510631435, 2521897731266445323, 2521899792850747403,
2521887698222841867, 2521900892362375179, 2521901442118189067,
2521906389920514059, 2521908588943769611, 2521901991874002955,
2521902541629816843, 2521909001260630027, 2521989540487364619,
2521897318949584907, 2521898143583305739, 2521898281022259211,
2521897593827491851, 2521993113900154891, 2521993251339108363,
2521993388778061835, 2521993663655968779, 2521886598711214091,
2521886186394353675
])
expanded_stare_test = numpy.array([
0x22ff8a000000000b, 0x22ff8a200000000b, 0x22ff8a600000000b,
0x22ff8a800000000b, 0x22ff8aa00000000b, 0x22ff8ac00000000b,
0x22ff8ae00000000b, 0x22ff8b800000000b, 0x22ff8bc00000000b,
0x22ff8be00000000b, 0x22ff8d000000000a, 0x22ff8e000000000a,
0x22ff90400000000b, 0x22ff91000000000a, 0x22ff91a00000000b,
0x22ff940000000009, 0x22ff96200000000b, 0x22ff96800000000a,
0x22ff97800000000b, 0x22ff97c00000000b, 0x22ff97e00000000b,
0x22ff980000000009, 0x22ff9c800000000b, 0x22ff9e800000000b,
0x22ff9ea00000000b, 0x22ff9ee00000000b, 0x22ffe8200000000b,
0x22ffe8800000000b, 0x22ffe8c00000000b, 0x22ffe8e00000000b,
0x22ffe9c00000000b, 0x22ffea000000000a, 0x22ffeb000000000a,
0x22ffeb800000000a, 0x22ffef000000000b, 0x22ffef200000000b,
0x22ffef600000000b
])
expanded_stare_test = numpy.array([
0x22ff8a000000000b, 0x22ff8a200000000b, 0x22ff8a600000000b,
0x22ff8a800000000a, 0x22ff8b000000000a, 0x22ff8b800000000a,
0x22ff8b800000000b, 0x22ff8bc00000000b, 0x22ff8be00000000b,
0x22ff8c0000000009, 0x22ff8d000000000a, 0x22ff8e0000000009,
0x22ff8e000000000a, 0x22ff900000000008, 0x22ff90400000000b,
0x22ff91000000000a, 0x22ff91a00000000b, 0x22ff940000000009,
0x22ff96200000000b, 0x22ff96800000000a, 0x22ff97800000000b,
0x22ff97c00000000b, 0x22ff97e00000000b, 0x22ff980000000008,
0x22ff980000000009, 0x22ff9c800000000b, 0x22ff9e800000000b,
0x22ff9ea00000000b, 0x22ff9ee00000000b, 0x22ffa00000000007,
0x22ffc00000000007, 0x22ffe00000000007, 0x22ffe8200000000b,
0x22ffe8800000000b, 0x22ffe8c00000000b, 0x22ffe8e00000000b,
0x22ffe9c00000000b, 0x22ffea000000000a, 0x22ffeb000000000a,
0x22ffeb800000000a, 0x22ffef000000000b, 0x22ffef200000000b,
0x22ffef600000000b
])
print('compressed = pystare.to_compressed_range(sids)')
k = 0
for i in sids:
print(k, hex(i))
k += 1
print('')
compressed = pystare.to_compressed_range(sids)
# compressed = compressed[0:6] # Broken
# compressed = compressed[0:5] # Broken
# compressed = compressed[0:4] # Broken # fixed
# compressed = compressed[0:3] # Okay
print('compressed = pystare.to_compressed_range(sids)')
k = 0
for i in compressed:
print(k, hex(i))
k += 1
print('')
expanded = pystare.expand_intervals(compressed, -1, False)
print('expanded = pystare.expand_intervals(compressed,-1,False)')
k = 0
for i in expanded:
print(k, hex(i))
k += 1
print('')
expanded_mr = pystare.expand_intervals(compressed, -1, True)
print('expanded = pystare.expand_intervals(compressed,-1,True)')
k = 0
for i in expanded_mr:
print(k, hex(i))
k += 1
print('')
ncols = 4 # 5
fig, axs = plt.subplots(nrows=1,
ncols=ncols,
subplot_kw={
'projection': proj,
'transform': transf
})
# plt.figure()
# plt.subplot(projection=proj,transform=transf)
iax = 0
ax = axs[iax]
# ax.set_global()
ax.coastlines()
ax.title.set_text('1. Original\n(res=0xA)')
ax.annotate(
'1. sids\n2. compressed = pystare.to_compressed_range(sids) # Intervals. Terminators expressed as dots.\n3. expanded = pystare.expand_intervals(compressed,-1,False) # Intervals expanded according to embedded resolution.\n4. expanded_multires = pystare.expand_intervals(compressed,-1,True) # Intervals expanded into coarsest common ancestors.',
xy=(50, 50),
xycoords='figure pixels')
plot2(sids, c0='r', c1='b', transf=transf, ax=ax)
iax += 1
ax = axs[iax]
# ax.set_global()
ax.coastlines()
ax.title.set_text('2. Compressed\n(Incorrect usage)')
plot2(compressed, c0='r', c1='b', transf=transf, ax=ax)
iax += 1
ax = axs[iax]
# ax.set_global()
ax.coastlines()
ax.title.set_text('3. Expand Compressed\n(mono-res)')
plot2(expanded, c0='r', c1='b', transf=transf, ax=ax)
iax += 1
ax = axs[iax]
# ax.set_global()
ax.coastlines()
ax.title.set_text('4. Expand Compressed\n(multi-res)')
plot2(expanded_mr, c0='r', c1='b', transf=transf, ax=ax)
iax += 1
# ax=axs[iax]
# # ax.set_global()
# ax.coastlines()
# ax.title.set_text('5. Expand Compressed\n(multi-res)\nfrom STARE lib tests')
# plot2(expanded_stare_test,c0='r',c1='b',transf=transf,ax=ax)
# iax += 1
plt.show()
# compressed = pystare.to_compressed_range(flat_list)
# compressed.size
# 22
# we get
#
# array([2521886048955400202, 2521887148467027967, 2521887698222841866,
# 2521889347490283530, 2521890447001911306, 2521892646025166858,
# 2521893745536794634, 2521894845048422399, 2521897044071677961,
# 2521899243094933514, 2521900342606561279, 2521900892362375178,
# 2521903641141444607, 2521906389920514058, 2521908588943769610,
# 2521989403048411146, 2521990502560038911, 2521991052315852810,
# 2521992151827480575, 2521992701583294474, 2521993801094922239,
# 2521997099629805578])
return
