diff --git a/sample/mrt_classes.py b/sample/mrt_classes.py
index f181d8076cf4e2d02a0e84993e5b17404834164f..eeae75b7e40e08a4a49a67ac7bf104afe7393944 100644
--- a/sample/mrt_classes.py
+++ b/sample/mrt_classes.py
@@ -160,9 +160,9 @@ class ReClass:
self.models = [x for x in list(self.downstream_df) if x.lower() not in ['description', 'newval']]
pd.testing.assert_series_equal(left=self.rules_df.newval, right=self.downstream_df.newval), "Difference detected between upstream and downstream sheets"
- if self.verbose:
- print('Models are: ')
- print('\n'.join(self.models))
+ if self.verbose:
+ print('Models are: ')
+ print('\n'.join(self.models))
if self.models:
# Create model objects and attach as attributes
@@ -209,7 +209,7 @@ class ReClass:
print('\nWarning, not all valid values for are employed in a Reclass Rule')
for s in self.not_classified_lst:
print("{}".format(s.name))
- print('\n'.join([str(x) for x in s]))
+ print('\n'.join([' {}'.format(str(x)) for x in s]))
def parse_rules(self):
"""
@@ -222,11 +222,15 @@ class ReClass:
# Create dummy rule (Rule999) to catch all values in the combined raster RAT.
# For dict merge: https://stackoverflow.com/questions/38987/
# how-do-i-merge-two-dictionaries-in-a-single-expression-in-python-taking-union-o
- data_p1 = dict(zip(self.source_rasters, ['n'] * len(self.source_rasters)))
- data_p2 = {'newval': 0, 'reclass': True, 'description': 'No other rule applies'}
+ data_p1 = dict.fromkeys(self.source_rasters, 'n')
+ data_p2 = {'newval': 0, 'reclass': True, 'description': 'No other rule applies', 'join_type': 'and'}
dummy_rule = pd.Series(data={**data_p1, **data_p2}, name=999)
self.rules_df = self.rules_df.append(dummy_rule)
+ # Dict to map user provided information to AND or OR join type for rule
+ join_types = {'and': '&', 'or': '|', 'en': '&', 'of': '|', 'And': '&', 'Or': '|', 'AND': '&', 'OR': '|',
+ 'En': '&', 'Of': '|', 'EN': '&', 'OF': '|'}
+
for row in self.rules_df.iterrows():
series = row[1]
rule = Rule()
@@ -236,7 +240,11 @@ class ReClass:
rule.apply = True if series.loc['reclass'] == 1 else False
rule.comment = series.loc['remark'] # idem
rule.name = 'rule{:03}'.format(row[0])
- rule.join_type = series.loc['join_type']
+ try:
+ rule.join_type = join_types[series.loc['join_type']]
+ except KeyError:
+ print('Invalid join type indicator "{0}" for rule {1}'.format(series.loc['join_type'], row[0]))
+ raise
# Build query based on the dataframe contents for each source raster
for reclass_rast in self.source_rasters:
@@ -245,8 +253,7 @@ class ReClass:
rule.queries.append('{0}.isin({1})'.format(reclass_rast, reclass_rast_query))
if rule.queries:
# Build query using the rule join type as connector between the individual queries.
- join_type = rule.join_type
- rule.query = ' {} '.format(join_type).join(rule.queries)
+ rule.query = ' {} '.format(rule.join_type).join(rule.queries)
else:
rule.apply = False # never apply a rule that does not have a valid query
@@ -400,7 +407,7 @@ class ReClass:
assert os.path.isdir(self.base_out), 'Invalid output directory'
excel_out = '{0}_report.xlsx'.format(self.project_name)
- with pd.ExcelWriter(os.path.join(self.base_out, excel_out), mode='a') as writer:
+ with pd.ExcelWriter(os.path.join(self.base_out, excel_out), mode='w') as writer:
# Report on input
metadata = {'input_XLS': self.__xls,
@@ -425,10 +432,11 @@ class ReClass:
self.rules_df.to_excel(writer, sheet_name='ReclassRules', index=False, freeze_panes=(1, 2))
# Write downstream table for applied rules only
- applied_newvals = [getattr(getattr(self, rule), 'newval') for rule in self.rules if
- getattr(getattr(self, rule), 'apply')]
- self.downstream_df.loc[self.downstream_df.newval.isin(applied_newvals), :] \
- .to_excel(writer, sheet_name='downstream', index=False, freeze_panes=(1,2))
+ if self.models:
+ applied_newvals = [getattr(getattr(self, rule), 'newval') for rule in self.rules if
+ getattr(getattr(self, rule), 'apply')]
+ self.downstream_df.loc[self.downstream_df.newval.isin(applied_newvals), :] \
+ .to_excel(writer, sheet_name='downstream', index=False, freeze_panes=(1,2))
# Report on combi raster RAT
self.combi_raster.rat.drop(labels=self.combi_raster.rule_cols + ['geometry'], axis=1) \
@@ -601,9 +609,12 @@ class CombinedRaster:
self.raster = rio.open(os.path.join(rast_dir, rast_in))
try:
self.rat = gp.read_file(os.path.join(rast_dir, '{}.tif.vat.dbf'.format(self.basename)))
+ # AGPro delivers columnnames inconsistently: sometimes Count, sometimes COUNT.
+ self.rat.columns = [x.capitalize() if x.lower() in ['count', 'value'] else x for x in list(self.rat)]
except ValueError:
print('Raster Attribute Table not found')
raise
+
self.source_rasters = [x for x in list(self.rat) if x not in ['Value', 'Count', 'geometry']]
self.pxl_area_m2 = np.square(self.raster.res[0])
self.pixel_area_ha = np.divide(self.pxl_area_m2, 10000)
diff --git a/sample/mrt_helpers.py b/sample/mrt_helpers.py
index 6e45048b8a10eddc5d5a5df560d4b9a56020a810..4edfa243301ab89c91fbd8b2f204f57b30604d83 100644
--- a/sample/mrt_helpers.py
+++ b/sample/mrt_helpers.py
@@ -64,6 +64,7 @@ def parse_rule(rule, category=None, negate=True):
raise
else:
raise Exception('unexpected reclass rule found: {0}'.format(rule))
+ # out = False
return out