def impf_dem(x_paa=1, x_mdd=1):
impf = ImpactFunc()
impf.haz_type = 'TC'
impf.id = 1
impf.intensity_unit = 'm/s'
impf.intensity = np.linspace(0, 150, num=100)
impf.mdd = np.repeat(1, len(impf.intensity)) * x_mdd
impf.paa = np.arange(0, len(impf.intensity)) / len(impf.intensity) * x_paa
impf.check()
impf_set = ImpactFuncSet()
impf_set.append(impf)
return impf_set
def __init__(self):
ImpactFunc.__init__(self)
self.id = 1
self.name = 'flood_1m'
self.intensity_unit = 'm'
self.haz_type = HAZ_TYPE
self.intensity = np.arange(0, 1000, 0.5)
self.paa = np.ones(self.intensity.size)
self.mdd = np.zeros(self.intensity.size)
for idx in range(self.mdd.size):
if self.intensity[idx] >= 1:
self.mdd[idx] = 1.0
def create_impact_func_lin(haz_type, imp_id, m, q = 0):
"""
Create CLIMADA Impact function
Parameters
----------
haz_type : str
hazard type ("HL").
imp_id : int
id of Impact function.
m : float
Impact function parameter.
q : float
Impact function parameter (middle_x).
Returns
-------
imp_fun : entity.impact_funcs.base.ImpactFunc
CLIMADA Impact function.
"""
name = {1: "Hail (Meshs) on infrastructure",
2: "Hail (Meshs) on grape production",
3: "Hail (Meshs) on fruit production",
4: "Hail (Meshs) on agriculture (without fruits and grape production",
5: "Hail (duration) on grape production",
6: "Hail (duration) on fruit production",
7: "Hail (duration) on agriculture (without fruits and grape production",
8: "Hail (duration) on infrastructure",
9: "Hail (duration) on agriculture (fruit + grape + rest)",
10: "Hail (Meshs) on agriculture (fruit + grape + rest="
}
imp_fun= ImpactFunc()
imp_fun.haz_type = haz_type
imp_fun.id = imp_id
imp_fun.name = name[imp_id]
if imp_id <=4 or imp_id == 10:
imp_fun.intensity_unit = 'mm'
num = 150
else:
imp_fun.intensity_unit = "min"
num = 120
x = np.arange(num)
imp_fun.intensity = np.linspace(0, num, num=num)
imp_fun.mdd = np.linspace(q, m*num, num = num)
for i in range(len(imp_fun.mdd)):
if imp_fun.mdd[i]>1:
imp_fun.mdd[i] = 1
imp_fun.paa = np.linspace(1, 1, num=num)
imp_fun.check()
return imp_fun
def create_impact_func(haz_type, imp_id, L, x_0, k, y=None):
"""
Create CLIMADA Impact function
Parameters
----------
haz_type : str
hazard type ("HL").
imp_id : int
id of Impact function.
L : float
Impact function parameter (max_y).
x_0 : float
Impact function parameter (middle_x).
k : float
Impact function parameter (width).
y : float
Values for mdd. If == 0 then the fuction will call sigmoid()
Returns
-------
imp_fun : entity.impact_funcs.base.ImpactFunc
CLIMADA Impact function.
"""
name = {1: "Hail (Meshs) on infrastructure",
2: "Hail (Meshs) on grape production",
3: "Hail (Meshs) on fruit production",
4: "Hail (Meshs) on agriculture (without fruits and grape production",
5: "Hail (duration) on grape production",
6: "Hail (duration) on fruit production",
7: "Hail (duration) on agriculture (without fruits and grape production",
8: "Hail (duration) on infrastructure",
9: "Hail (duration) on agriculture (fruit + grape + field)",
10: "Hail (Meshs) on agriculture (fruit + grape + field)",
11: "Hail (Meshs) Category 20-40mm on infrastructure",
12: "Hail (Meshs) Category 41-60mm on infrastructure",
13: "Hail (Meshs) Category +60mm on infrastructure"}
imp_fun= ImpactFunc()
imp_fun.haz_type = haz_type
imp_fun.id = imp_id
imp_fun.name = name[imp_id]
if imp_id <=4 or imp_id >= 10:
imp_fun.intensity_unit = 'mm'
num = 150
else:
imp_fun.intensity_unit = "min"
num = 120
x = np.arange(num)
imp_fun.intensity = np.linspace(0, num, num=num)
if type(y) == type(x): #workaround to check wether y is an numpy.ndarray.
imp_fun.mdd = y
else:
imp_fun.mdd = sigmoid(x, L, x_0, k)
imp_fun.paa = np.linspace(1, 1, num=num)
imp_fun.check()
return imp_fun
def call_impact_functions(with_without_error):
"""get curve for the impact function:
Parameters:
with_without_error (bool): rather to give best estimate or to add a random variation. Default: True
Returns: climada impact functions set
"""
# get the data from the studies:
directory_if = '../../input_data/impact_functions/'
file_low = pd.read_csv(''.join([directory_if, 'impact_low.csv']))
function_low = impact_functions_random(file_low, 'low', with_without_error)
file_moderate = pd.read_csv(''.join([directory_if, 'impact_moderate.csv']))
function_moderate = impact_functions_random(file_moderate, 'moderate',
with_without_error)
file_high = pd.read_csv(''.join([directory_if, 'impact_high.csv']))
function_high = impact_functions_random(file_high, 'high',
with_without_error)
# make impact function set:
if_heat_set = ImpactFuncSet()
x = np.linspace(20, 40, num=30)
if_heat1 = ImpactFunc()
if_heat1.haz_type = 'heat'
if_heat1.id = 1
if_heat1.name = 'low physical activity'
if_heat1.intensity_unit = 'Degrees C'
if_heat1.intensity = x
if_heat1.mdd = (sigmoid(x, *function_low)) / 100
if_heat1.mdd[if_heat1.mdd < 0] = 0 # to avoid having negative values
if_heat1.mdd[if_heat1.mdd > 100] = 100 # to avoid having values over a 100
if_heat1.paa = np.linspace(1, 1, num=30)
if_heat_set.append(if_heat1)
if_heat2 = ImpactFunc()
if_heat2.haz_type = 'heat'
if_heat2.id = 2
if_heat2.name = 'medium physical activity'
if_heat2.intensity_unit = 'Degrees C'
if_heat2.intensity = x
if_heat2.mdd = (sigmoid(x, *function_moderate)) / 100
if_heat2.mdd[if_heat2.mdd < 0] = 0
if_heat2.mdd[if_heat2.mdd > 100] = 100
if_heat2.paa = np.linspace(1, 1, num=30)
if_heat_set.append(if_heat2)
if_heat3 = ImpactFunc()
if_heat3.haz_type = 'heat'
if_heat3.id = 3
if_heat3.name = 'high physical activity'
if_heat3.intensity_unit = 'Degrees C'
if_heat3.intensity = x
if_heat3.mdd = (sigmoid(x, *function_high)) / 100
if_heat3.mdd[if_heat3.mdd < 0] = 0
if_heat3.mdd[if_heat3.mdd > 100] = 100
if_heat3.paa = np.linspace(1, 1, num=30)
if_heat_set.append(if_heat3)
return if_heat_set