def __init__(self):
""" Virtually private constructor. """
self.base_dir = dirfuncs.guess_data_dir()
self.island_data_table = {}
if Imagery_Cache.__instance != None:
raise Exception("This class is a singleton!")
else:
Imagery_Cache.__instance = self
# 'app_jambi', # d
# 'app_kalbar',
# 'app_kaltim',
#'app_oki', # c 'mukti_prakarsa',
'app_riau',
'multipersada_gatramegah', #'musim_mas', # 'unggul_lestari',
# e
'gar_pgm',
'PTAgroAndalan',
'Bumitama_PTGemilangMakmurSubur',
]
my_sampler = sampler.Sampler()
base_dir = dirfuncs.guess_data_dir()
band_set = {
0: {
'blue_max', 'red_max', 'nir_max', 'swir1_max', 'VH_0', 'VH', 'VH_2',
'VV_0', 'VV', 'VV_2', 'EVI', 'swir2_max', 'brightness', 'wetness',
'greenness', 'slope'
},
1: {
'blue_max', 'red_max', 'nir_max', 'swir1_max', 'VH_0', 'VH', 'VV_0',
'VV', 'VV_2', 'EVI', 'swir2_max', 'wetness', 'greenness', 'slope'
},
2: {
'blue_max', 'red_max', 'nir_max', 'swir1_max', 'VH_0', 'VH', 'VV_0',
'VV', 'VV_2', 'brightness', 'swir2_max', 'wetness', 'greenness',
'slope'
},
# plt.figure()
# destination.plot(robust=True)
# plt.show()
image2 = False
#image3=False
return image3
if __name__ == "__main__":
wbt = WhiteboxTools()
wbt.set_verbose_mode(True)
name = 'Kalimantan'
file1 = "KalimantanFINAL_classified_by_ensemble_rf.tif"
year = 2017
base_dir = dir.guess_data_dir()
wbt.work_dir = os.path.join(base_dir, name, 'sklearn_test', str(year),
'test_temp', 'wkdir')
# wbt.majority_filter(file, "Kalimantan2018_Final_smooth3x3.tif", filterx=3, filtery=3)
#
base = os.path.join(base_dir, name, 'sklearn_test', str(year), 'test_temp',
'Kalimantan2017_Final_100m.tif')
out1 = "resample2017.tif"
out2 = "round2017.tif"
wbt.resample(file1, out1, "cc", base=base)
wbt.round(out1, out2)
wbt.majority_filter(out2,
"RES_RND_SMTH_Kalimantan2017_Final3x3.tif",
filterx=3,
filtery=3)
import sqlite3
import pandas as pd
import dirfuncs
conn = sqlite3.connect('data/hcs_database.db')
#conn = sqlite3.connect('hcs_database.db')
##TODO this should be moved to hcs_database.py
base_dir = dirfuncs.guess_data_dir()
concessions_csv = base_dir + 'concession_inventory.csv'
concession_df = pd.read_csv(concessions_csv)
assessment_year_dict = dict(zip(concession_df.app_key, concession_df.use_year))
leaf_nodes_dict = {
'Bumitama_PTDamaiAgroSejahtera': 10,
'Bumitama_PTHungarindoPersada': 6,
'PTMitraNusaSarana': 10,
'makmur_abadi': 10,
'sawit_perdana': 6,
'aneka_sawit': 10,
'PTMentariPratama': 6,
'PTSukajadiSawitMekar': 10,
'PTLabontaraEkaKarsa': 10,
'adi_perkasa': 8
}
features_dict = {
'Bumitama_PTDamaiAgroSejahtera': 0.33,
'Bumitama_PTHungarindoPersada': 0.33,
'PTMitraNusaSarana': 0.65,
'makmur_abadi': 0.33,
'sawit_perdana': 0.8,
'aneka_sawit': 0.65,
def ingest_kml_fixed_classes():
#input = 'C:\\Users\\ME\\Dropbox\\HCSproject\\data\\PoC\\supplementary_class\\impervious\\doc.kml'
imageSumatra = image_cache.get_band_by_context_year('nir_max', 'Sumatra', 2019)
imageKalimantan = image_cache.get_band_by_context_year('nir_max', 'Kalimantan', 2019)
imagePapua = image_cache.get_band_by_context_year('nir_max', 'Papua', 2019)
for landcover in supplemental_class_codes.keys():
print(landcover)
input = os.path.join(dirfuncs.guess_data_dir(), 'supplementary_class', landcover,'doc.kml')
srcDS = gdal.OpenEx(input)
output = os.path.join(dirfuncs.guess_data_dir(), 'supplementary_class', landcover, landcover + '.json')
#ds = gdal.VectorTranslate(output, srcDS, format='GeoJSON')
#file = glob.glob(output)
print(output)
with open(output) as f:
data = f.read()
#TODO on windows you may get a parsing error that does not make sense but it has to do with EOL characters
jsonload = js.loads(data)
features = jsonload["features"]
print(features)
# NOTE: buffer(0) is a trick for fixing scenarios where polygons have overlapping coordinates
#temp = GeometryCollection([shape(feature["geometry"]).buffer(0) for feature in features])
#TODO get the year from the json or doc.kml
#my_dict = sat_ops.s1_band_dict.copy()
my_dict = sat_ops.l8_band_dict.copy()
#my_dict.update(sat_ops.s2_band_dict)
#my_dict.update(sat_ops.dem_band_dict)
bands = my_dict.values()
print(bands)
shapes = ((shape(feature["geometry"]).buffer(0), (feature['properties']['Description']),
feature['properties']['Name']) for feature in features)
for geom in shapes:
print(geom)
get_reference = True
xmin, ymin, xmax, ymax = geom[0].bounds
year_list = geom[1].split(sep=',')
year_list = map(int, year_list)
year_list = list(year_list)
year_list.sort(reverse=True)
my_year = year_list[0]
if my_year == 2018: my_year=2019
if my_year == 2016: my_year=2017 # TODO these only appliy because we are just using landsat 8 data aggregated in 2 year chuncks
name = geom[2]
for band in bands:
if get_reference:
bbox = box(xmin, ymin, xmax, ymax)
geo = gpd.GeoDataFrame({'geometry': bbox}, index=[0], crs=from_epsg(4326))
geo = geo.to_crs(crs=from_epsg(4326))
coords = getFeatures(geo)
if 'kal' in name:
out_img = imageKalimantan.rio.clip(coords, imageKalimantan.rio.crs)
island = 'Kalimantan'
elif 'sum' in name:
out_img = imageSumatra.rio.clip(coords, imageSumatra.rio.crs)
island = 'Sumatra'
elif 'pap' in name:
out_img = imagePapua.rio.clip(coords, imagePapua.rio.crs)
island = 'Papua'
else:
raise RuntimeError
# meta = out_img.meta.copy()
print(island)
trans = out_img.transform
crs = out_img.rio.crs
height = out_img.rio.height
width = out_img.rio.width
dtype = rio.int16
# burned = rioft.rasterize(shapes=geom, fill=0)
out_class = os.path.join(dirfuncs.guess_data_dir(), 'supplementary_class', landcover, name + '.tif')
with rio.open(out_class, 'w+', driver='GTiff',
height=height, width=width,
crs=crs, dtype=dtype, transform=trans, count=1) as out:
out_arr = out.read(1)
burned = rioft.rasterize(shapes=[(geom[0], supplemental_class_codes[landcover])], fill=-9999,
out=out_arr, transform=out.transform)
burned = np.where(burned != supplemental_class_codes[landcover], -9999,
burned) # NoData the other pixels
out.write_band(1, burned)
out.close()
get_reference=False
out_band = os.path.join(dirfuncs.guess_data_dir(), 'supplementary_class', landcover, 'out',
name + '_' + band + '.tif')
if os.path.isfile(out_band): continue # this is a hack because I have hit errors in the middle of long batch jobs and had find way to restart
image = image_cache.get_band_by_context_year(band, island, my_year)
out_img = image.rio.clip(coords, image.rio.crs)
if out_img.dtype == 'float64':
out_img.data = np.float32(out_img)
dtype = rio.float32
print('Writing: ', out_band)
with rio.open(out_band, 'w+', driver='GTiff',
height=height, width=width,
crs=crs, dtype=dtype, transform=trans, count=1) as out2:
out2.write_band(1, out_img[0])
out2.close()
"""
Running regression to explore how different parameterizations affect accuracy
across all analyses in a summary table.
"""
# =============================================================================
# Imports
# =============================================================================
import dirfuncs
import pandas as pd
import statsmodels.formula.api as smf
dropbox_dir = dirfuncs.guess_data_dir()
# =============================================================================
# Load data
# =============================================================================
data_dir = dropbox_dir
result_csv = data_dir + 'result.12252019.csv'
result_df = pd.read_csv(result_csv)
# =============================================================================
# Run regression
# =============================================================================
mod = smf.ols(formula="two_class_score_weighted ~ bands + class_scheme",
data=result_df)
res = mod.fit()
print(res.summary())
# Parameters
#================================================================================
#A shape file - study area
lon_edge=2
# lon_end=119
# lat_start = -5
# lat_end = 5
#lat_edge = 2.5 #PAPUA
lat_edge = 2
#site = 'Kalimantan'
start = 1
end = 50
years= [#2017,2015,
2019]
site = 'None'
out_path = dirfuncs.guess_data_dir()
#Take a set of years
#Take a set of bands
# take a step
# for each grid cell
# for each band
def get_grid_polygons(lon_start, lon_end, lat_start,lat_end):
print(lon_start, lon_end, lat_start,lat_end)
polys = [];
lon = lon_start
cell_id=0
while lon < lon_end :
x1 = lon;
x2 = lon + lon_edge;
