Dissolve#
[1]:
import geopandas
import starepandas
import numpy
import pystare
import matplotlib.pyplot as plt
import datetime
import multiprocessing
[2]:
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
Manual merging SIDs#
We are extracting all SIDS from the dataframe and merge them in a compressed range
[3]:
continent = world[world.continent=='South America']
continent = starepandas.STAREDataFrame(continent, add_sids=True, level=7, add_trixels=True, n_partitions=1)
sids = numpy.concatenate(list(continent['sids']))
[4]:
sids = numpy.unique(sids)
s_range = pystare.to_compressed_range(sids)
sids1 = pystare.expand_intervals(s_range , -1, multi_resolution=True)
[5]:
# Equivalent to
sids2 = starepandas.compress_sids(sids)
[6]:
continent
[6]:
| pop_est | continent | name | iso_a3 | gdp_md_est | geometry | sids | trixels | |
|---|---|---|---|---|---|---|---|---|
| 9 | 44293293 | South America | Argentina | ARG | 879400.0 | MULTIPOLYGON (((-68.63401 -52.63637, -68.25000... | [1831874332805038087, 1831909517177126919, 183... | MULTIPOLYGON (((-68.10744 -55.31801, -67.97225... |
| 10 | 17789267 | South America | Chile | CHL | 436100.0 | MULTIPOLYGON (((-68.63401 -52.63637, -68.63335... | [1822268999224786951, 1822339367968964615, 182... | MULTIPOLYGON (((-70.49911 -54.87622, -70.66819... |
| 20 | 2931 | South America | Falkland Is. | FLK | 281.8 | POLYGON ((-61.20000 -51.85000, -60.00000 -51.2... | [1807104534854500359, 1807245272342855687, 180... | MULTIPOLYGON (((-60.67409 -51.68503, -59.57634... |
| 28 | 3360148 | South America | Uruguay | URY | 73250.0 | POLYGON ((-57.62513 -30.21629, -56.97603 -30.1... | [2632353982198054918, 2661627379775963142, 266... | MULTIPOLYGON (((-55.17662 -32.24790, -55.77960... |
| 29 | 207353391 | South America | Brazil | BRA | 3081000.0 | POLYGON ((-53.37366 -33.76838, -53.65054 -33.2... | [2749447572509687812, 2796735368597078020, 286... | MULTIPOLYGON (((-53.69934 -4.63080, -61.44765 ... |
| 30 | 11138234 | South America | Bolivia | BOL | 78350.0 | POLYGON ((-69.52968 -10.95173, -68.78616 -11.0... | [2684145377912815620, 2692026677260713989, 270... | MULTIPOLYGON (((-62.76428 -18.88667, -66.06621... |
| 31 | 31036656 | South America | Peru | PER | 410400.0 | POLYGON ((-69.89364 -4.29819, -70.79477 -4.251... | [2693152577167556612, 2699907976608612356, 268... | MULTIPOLYGON (((-74.96857 -15.27649, -72.04021... |
| 32 | 47698524 | South America | Colombia | COL | 688000.0 | POLYGON ((-66.87633 1.25336, -67.06505 1.13011... | [2666130979403333637, 2667256879310176261, 266... | MULTIPOLYGON (((-75.36119 8.42106, -76.79015 5... |
| 40 | 31304016 | South America | Venezuela | VEN | 468600.0 | POLYGON ((-60.73357 5.20028, -60.60118 4.91810... | [2486549944261935109, 2743255123022053381, 274... | MULTIPOLYGON (((-67.79536 8.98813, -70.10240 1... |
| 41 | 737718 | South America | Guyana | GUY | 6093.0 | POLYGON ((-56.53939 1.89952, -56.78270 1.86371... | [2767743445995880454, 2758489956136517639, 275... | MULTIPOLYGON (((-58.56295 5.89722, -59.38264 7... |
| 42 | 591919 | South America | Suriname | SUR | 8547.0 | POLYGON ((-54.52475 2.31185, -55.09759 2.52375... | [2759017721717850119, 2759052906089938951, 275... | MULTIPOLYGON (((-56.93751 2.38886, -56.40173 3... |
| 44 | 16290913 | South America | Ecuador | ECU | 182400.0 | POLYGON ((-75.37322 -0.15203, -75.23372 -0.911... | [2668523516705374214, 2669086466658795526, 267... | MULTIPOLYGON (((-78.07509 -2.31151, -78.84541 ... |
| 156 | 6943739 | South America | Paraguay | PRY | 64670.0 | POLYGON ((-58.16639 -20.17670, -57.87067 -20.7... | [2639390856615821318, 2792372506458062854, 279... | MULTIPOLYGON (((-61.01690 -22.26119, -61.90025... |
[7]:
fig, ax = plt.subplots(dpi=100, figsize=(7, 7))
ax.grid(True)
continent.plot(ax=ax, trixels=True, boundary=False, column='name', zorder=0, linewidth=2, alpha=0.2)
continent.plot(ax=ax, trixels=True, boundary=True, column='name', zorder=0, linewidth=0.5)
df = starepandas.STAREDataFrame(sids=[sids2], add_trixels=True)
df.plot(ax=ax, trixels=True, boundary=True, color='black', zorder=2, linewidth=2)
#plt.savefig('dissolve.png')
[7]:
<AxesSubplot:>
High level functions#
[8]:
west = world[world['continent'].isin(['Europe', 'North America'])]
west = starepandas.STAREDataFrame(west, add_sids=True, level=4, add_trixels=False)
[9]:
west.stare_dissolve(by='continent', aggfunc='first', geom=False)
[9]:
| sids | pop_est | name | iso_a3 | gdp_md_est | |
|---|---|---|---|---|---|
| continent | |||||
| Europe | [648518346341351428, 900719925474099204, 10448... | 142257519 | Russia | RUS | 3745000.0 |
| North America | [1170935903116328964, 1173187702930014212, 117... | 35623680 | Canada | CAN | 1674000.0 |
[10]:
west.dissolve(by='continent', aggfunc='sum')
[10]:
| geometry | pop_est | gdp_md_est | |
|---|---|---|---|
| continent | |||
| Europe | MULTIPOLYGON (((-53.555 2.335, -53.779 2.377, ... | 746398461 | 25284877.0 |
| North America | MULTIPOLYGON (((-155.222 19.240, -155.542 19.0... | 573042112 | 23505137.0 |
Multiprocessing#
By continent#
[11]:
start = datetime.datetime.now()
west.stare_dissolve(by='continent', geom=False, num_workers=1)
print(datetime.datetime.now() - start)
0:00:07.565706
[12]:
start = datetime.datetime.now()
west.stare_dissolve(by='continent', geom=False, num_workers=4)
print(datetime.datetime.now() - start)
0:00:03.978145
[13]:
trixels = west.make_trixels(num_workers=4, wrap_lon=False)
trixels = starepandas.split_antimeridian_series(trixels, drop=True)
west.set_trixels(trixels, inplace=True)
[14]:
west.reset_index().plot(column='continent')
[14]:
<AxesSubplot:>
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