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imputation_policies.py
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imputation_policies.py
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import numpy as np
import pandas as pd
"Functions that map elevation to smb for each rgi"
def smb_elev_functs(rgi, elev, lat, lon):
if rgi in [1,2]:
a = -2.06716100e-03
c = 1.00007291e+02
p = 5.35472753e-01
return a / (c + elev) ** p
elif rgi in [3,4]:
m = 3.55847897e-08
q = -4.09376228e-05
return m * elev + q
elif rgi in [5]:
m = np.nan
q = np.nan
return m * elev + q
elif rgi in [6,]:
m, q = 9.79209658e-08, -1.18727280e-04
return m * elev + q
elif rgi in [7,9]:
m, q = 6.24861787e-08, -3.13537544e-05
return m * elev + q
elif rgi in [8,]:
m, q = 1.28063292e-08, -2.91968790e-05
return m * elev + q
elif rgi in [10]:
# mask 1
if (((lon>150.) & (lat<67.)) | ((lon<-170.) & (lat<68.))):
m, q = 2.73664257e-08, -5.57532056e-05
# mask 2
elif (lat > 70.5):
m, q = 3.20075411e-09, -1.69586219e-05
# mask 3
elif (((lon > 123.) & (lon < 150.)) & ((lat > 59.) & (lat < 71.))):
m, q = 8.98905066e-09, -4.55599936e-05
# mask 4
elif (((lon > 55.) & (lon < 98.)) & ((lat > 62.) & (lat < 71.))):
m, q = 2.93816867e-08, -4.90286962e-05
# mask 5
elif (((lon > 83.) & (lon < 121.)) & ((lat > 44.) & (lat < 60.))):
m, q = 1.20851920e-08, -7.95458401e-05
else:
raise ValueError(f"Region 10 point at which you want smb outside the 5 masks defined in smb study.")
return m * elev + q
elif rgi in [11]:
m, q = 3.35388079e-08, -1.09618747e-04
return m * elev + q
elif rgi in [12]:
m, q = 3.37561294e-08, -1.34817237e-04
return m * elev + q
elif rgi in [13]:
m, q = 7.52278648e-10, -7.91066609e-06
return m * elev + q
elif rgi in [14]:
m, q = 2.66700218e-09, -2.03540077e-05
return m * elev + q
elif rgi in [15]:
m, q = 3.13560412e-09, -4.80703842e-05
return m * elev + q
elif rgi in [16]:
# mask 1: Africa and Indonesia
if (lon > -20.0):
m, q = 1.25561251e-06, -2.45823803e-03
# mask 2: north
elif ((lon < -60.0) & (lat > -4.0)):
m, q = 8.61184690e-08, -2.96609257e-04
# mask 3: south west
elif ((lon < -74.0) & (lat < -4.0)):
m, q = 4.86895515e-08, -2.73268834e-04
# mask 4: south east
elif ((lon > -74.0) & (lon < -60.0) & (lat < -4.0)):
m, q = 0.0, -1.48720385e-04
return m * elev + q
elif rgi in [17]:
# mask 1: south
if (lat <= -52.0):
m, q = 1.94213019e-07, -1.50282861e-04
# mask 2: north
elif (lat > -52.0):
m, q = 1.08078913e-08, -8.79352844e-05
return m * elev + q
elif rgi in [18]:
m, q = 0.0, -2.47735564e-05
return m * elev + q
elif rgi in [19]:
m = np.nan
q = np.nan
return m * elev + q
else:
raise ValueError(f"rgi value {rgi} does not exist.")
def smb_elev_functs_hugo(rgi=None):
"""
returns rate of change of hugonnet dmdtda wrt elevation
- m [d(dmdtda) / dh] = [m w.e./year / m a.s.l]
- q [dmdtda] = [m w.e./year]
"""
df = pd.DataFrame(index=range(1, 20), columns=['m', 'q'])
df.loc[1, ['m', 'q']] = [0.0004589, -1.03817] # updated
df.loc[2, ['m', 'q']] = [0.000335, -1.0190] # updated
df.loc[3, ['m', 'q']] = [0.000216, -0.4851] # updated
df.loc[4, ['m', 'q']] = [0.00065118, -0.87902] # updated
df.loc[5, ['m', 'q']] = [0.0005457, -0.70308] # updated
df.loc[6, ['m', 'q']] = [0.000837, -1.08213] # updated
df.loc[7, ['m', 'q']] = [0.000863, -0.524328] # updated
df.loc[8, ['m', 'q']] = [0.000562, -1.08825] # updated
df.loc[9, ['m', 'q']] = [0.0006414, -0.40542] # updated
df.loc[10, ['m', 'q']] = [0.0003031, -1.03351] # updated
df.loc[11, ['m', 'q']] = [0.00041039, -1.73905] # updated
df.loc[12, ['m', 'q']] = [0.0001430, -0.91881] # updated
df.loc[13, ['m', 'q']] = [0.0004552, -1.9562] # updated
df.loc[14, ['m', 'q']] = [0.0001617, -0.91912] # updated
df.loc[15, ['m', 'q']] = [0.0003172, -2.05440] # updated
df.loc[16, ['m', 'q']] = [0.0005212, -2.88924] # updated
df.loc[17, ['m', 'q']] = [0.0004511, -1.0511] # updated
df.loc[18, ['m', 'q']] = [0.0001404, -0.44008] # updated
df.loc[19, ['m', 'q']] = [0.0004052, -0.19093] # updated
return df
def velocity_median_rgi(rgi=None):
"""
median velocities for each rgi
Note: rgi 6, 9, 14, 15, 18 are taken from other rgis
returns: vector of median velocities
"""
df = pd.DataFrame(index=range(1, 20), columns=['v50', 'v100', 'v150', 'v300', 'v450', 'vgfa'])
df.loc[1, df.columns] = np.array([50.29, 50.56, 50.71, 51.40, 51.91, 55.85])
df.loc[2, df.columns] = np.array([13.81, 17.59, 19.25, 19.61, 18.52, 26.73])
df.loc[3, df.columns] = np.array([17.64, 17.64, 17.63, 17.67, 17.71, 17.95])
df.loc[4, df.columns] = np.array([7.69, 7.79, 7.91, 8.20, 8.53, 10.34])
df.loc[5, df.columns] = np.array([7.37, 7.37, 7.37, 7.37, 7.63, 9.15])
df.loc[7, df.columns] = np.array([9.96, 9.89, 9.85, 9.85, 10.18, 12.16])
df.loc[8, df.columns] = np.array([19.42, 19.65, 19.80, 20.18, 20.31, 19.95])
df.loc[10, df.columns] = np.array([10.46, 9.87, 9.13, 11.54, 12.77, 10.42])
df.loc[11, df.columns] = np.array([7.63, 7.98, 8.31, 9.15, 9.63, 10.51])
df.loc[12, df.columns] = np.array([8.99, 9.50, 10.03, 10.44, 10.57, 10.20])
df.loc[13, df.columns] = np.array([7.14, 7.15, 6.91, 5.93, 5.18, 4.83])
df.loc[16, df.columns] = np.array([6.67, 8.85, 10.37, 10.86, 9.93, 10.86])
df.loc[17, df.columns] = np.array([14.50, 14.24, 14.21, 14.54, 15.25, 15.12])
df.loc[19, df.columns] = np.array([21.57, 21.57, 21.57, 21.57, 21.57, 24.00])
df.loc[6, df.columns] = df.loc[7]
df.loc[9, df.columns] = df.loc[7]
df.loc[14, df.columns] = df.loc[13]
df.loc[15, df.columns] = df.loc[13]
df.loc[18, df.columns] = df.loc[11]
rgi_median_velocities = df.loc[rgi].to_numpy()
return rgi_median_velocities