initial bush fire

Author: gabrielaznar

climada/hazard/bush_fire.py

@@ -0,0 +1,66 @@
+"""
+Define BushFire class.
+"""
+
+__all__ = ['BushFire']
+
+import logging
+import numpy as np
+from scipy import sparse
+
+from climada.hazard.base import Hazard
+from climada.hazard.tag import Tag as TagHazard
+
+LOGGER = logging.getLogger(__name__)
+
+HAZ_TYPE = 'BF'
+""" Hazard type acronym for Bush Fire """
+
+class BushFire(Hazard):
+    """Contains bush fire events.
+
+    Attributes:
+    """
+
+    def __init__(self):
+        """Empty constructor. """
+        Hazard.__init__(self, HAZ_TYPE)
+
+    def read_firms_csv(self, firms, centroids, description=''):
+        """Read historical bush fires."""
+
+        # distinguish one event in file
+
+        # cut by centroids range
+        sel_lines = np.zeros(())
+        for event_lines in sel_lines:
+            bf_haz = self._one_firms_csv(centroids)
+            self.append(bf_haz)
+
+#        self.tag.description = description
+        raise NotImplementedError
+
+    @staticmethod
+    def _one_firms_csv(centroids):
+
+        bf_haz = BushFire()
+
+        # FILL these values
+#        bf_haz.tag =
+#        bf_haz.units =
+#        # following values are defined for each event
+#        bf_haz.centroids = centroids
+#        bf_haz.event_id = np.array([], int)
+#        bf_haz.frequency = np.array([])
+#        bf_haz.event_name = list()
+#        bf_haz.date = np.array([], int)
+#        bf_haz.orig = np.array([], bool)
+#        # following values are defined for each event and centroid
+#        bf_haz.intensity = sparse.lil_matrix((1, centroids.id.size))
+#        bf_haz.fraction = sparse.lil_matrix((1, centroids.id.size))
+
+        return bf_haz
+
+    def calc_probabilistic(self, n_ens):
+        """ For every event, compute n_ens probabilistic events."""
+        raise NotImplementedError