From fa976bb264d38bbbb906033522ade2027b391f72 Mon Sep 17 00:00:00 2001
From: James Harle <jdha@noc.ac.uk>
Date: Wed, 8 Jan 2020 12:30:26 +0000
Subject: [PATCH] 2to3 in tide
---
pynemo/tide/nemo_bdy_tide.py | 8 +-
pynemo/tide/nemo_bdy_tide2.py | 231 +--------------------------------
pynemo/tide/nemo_bdy_tide3.py | 4 +-
pynemo/tide/tpxo_extract_HC.py | 8 +-
4 files changed, 11 insertions(+), 240 deletions(-)
diff --git a/pynemo/tide/nemo_bdy_tide.py b/pynemo/tide/nemo_bdy_tide.py
index 983a77c..0fdf5ab 100644
--- a/pynemo/tide/nemo_bdy_tide.py
+++ b/pynemo/tide/nemo_bdy_tide.py
@@ -77,8 +77,8 @@ class Extract:
nb.close() # Close Bathymetry file
# Find nearest neighbours on the source grid to each dst bdy point
- source_tree = sp.cKDTree(zip(lon, lat))
- dst_pts = zip(dst_lon, dst_lat)
+ source_tree = sp.cKDTree(list(zip(lon, lat)))
+ dst_pts = list(zip(dst_lon, dst_lat))
nn_dist, self.nn_id = source_tree.query(dst_pts, k=4, eps=0, p=2,
distance_upper_bound=0.5)
@@ -106,7 +106,7 @@ class Extract:
# Need to identify missing points and throw a warning and set values to zero
mv = np.sum(self.wei,axis=1) == 0
- print '##WARNING## There are', np.sum(mv), 'missing values, these will be set to ZERO'
+ print('##WARNING## There are', np.sum(mv), 'missing values, these will be set to ZERO')
def extract_con(self, con):
@@ -150,7 +150,7 @@ class Extract:
else:
# throw some warning
- print '##WARNING## Missing constituent values will be set to ZERO'
+ print('##WARNING## Missing constituent values will be set to ZERO')
self.harm_Im[con] = np.zeros(self.nn_id[:,0].size)
self.harm_Re[con] = np.zeros(self.nn_id[:,0].size)
diff --git a/pynemo/tide/nemo_bdy_tide2.py b/pynemo/tide/nemo_bdy_tide2.py
index 710eff4..581a012 100644
--- a/pynemo/tide/nemo_bdy_tide2.py
+++ b/pynemo/tide/nemo_bdy_tide2.py
@@ -1,230 +1 @@
-'''
-
-
-
-'''
-# pylint: disable=E1103
-# pylint: disable=no-name-in-module
-import numpy as np
-import scipy.spatial as sp
-from netCDF4 import Dataset
-import copy # DEBUG ONLY- allows multiple runs without corruption
-from pynemo import nemo_bdy_grid_angle
-from pynemo.nemo_bdy_lib import rot_rep
-
-
-"
-import foo
-method_to_call = getattr(foo, 'bar')
-result = method_to_call()
-You could shorten lines 2 and 3 to:
-
-result = getattr(foo, 'bar')()
-"
-
-
-class Extract:
-
- def __init__(self, setup, DstCoord, Grid):
-
- self.g_type = Grid.grid_type
- DC = copy.deepcopy(DstCoord)
- dst_lon = DC.bdy_lonlat[self.g_type]['lon'][Grid.bdy_r == 0]
- dst_lat = DC.bdy_lonlat[self.g_type]['lat'][Grid.bdy_r == 0]
- self.dst_dep = DC.depths[self.g_type]['bdy_hbat'][Grid.bdy_r == 0]
- self.harm_Im = {} # tidal boundary data: Imaginary
- self.harm_Re = {} # tidal boundary data: Real
-
- # Modify lon for 0-360 TODO this needs to be auto-dectected
-
- dst_lon = np.array([x if x > 0 else x+360 for x in dst_lon])
-
- fileIDb = '../data/tide/grid_tpxo7.2.nc' # TPX bathymetry file
- nb = Dataset(fileIDb) # Open the TPX bathybetry file using the NetCDF4-Python library
-
- # Open the TPX Datafiles using the NetCDF4-Python library
-
- if self.g_type == 't':
- self.fileID = '../data/tide/h_tpxo7.2.nc' # TPX sea surface height file
- self.var_Im = 'hIm'
- self.var_Re = 'hRe'
- elif (self.g_type == 'u') or (self.g_type == 'v') :
- self.fileID = '../data/tide/u_tpxo7.2.nc' # TPX velocity file
- self.var_Im = 'UIm'
- self.var_Re = 'URe'
- self.var_Im2 = 'VIm'
- self.var_Re2 = 'VRe'
- self.key_tr = setup['trans']
-
- # Determine the grid angle for rotating vector qunatities
- maxJ = DC.lonlat['t']['lon'].shape[0]
- maxI = DC.lonlat['t']['lon'].shape[1]
- GridAngles = nemo_bdy_grid_angle.GridAngle(setup['dst_hgr'], 1, maxI, 1, maxJ, self.g_type)
- dst_gcos = np.ones([maxJ, maxI])
- dst_gsin = np.zeros([maxJ,maxI])
- dst_gcos[1:,1:] = GridAngles.cosval
- dst_gsin[1:,1:] = GridAngles.sinval
-
- # Retain only boundary points rotation information
- self.gcos = np.zeros(Grid.bdy_i.shape[0])
- self.gsin = np.zeros(Grid.bdy_i.shape[0])
- for p in range(Grid.bdy_i.shape[0]):
- self.gcos[p] = dst_gcos[Grid.bdy_i[p,1], Grid.bdy_i[p,0]]
- self.gsin[p] = dst_gsin[Grid.bdy_i[p,1], Grid.bdy_i[p,0]]
-
- if self.g_type == 'u':
- self.rot_dir = 'i'
- elif self.g_type == 'v':
- self.rot_dir = 'j'
- else:
- print 'You should not see this message!'
-
- # We will average velocities onto the T grid as there is a rotation to be done
- # Also need to account for east-west wrap-around
- nc = Dataset('../data/tide/h_tpxo7.2.nc')
- lon = np.ravel(np.concatenate([nc.variables['lon_z'][-2:,:],
- nc.variables['lon_z'][:,:],
- nc.variables['lon_z'][0:2,:]]))
- lat = np.ravel(np.concatenate([nc.variables['lat_z'][-2:,:],
- nc.variables['lat_z'][:,:],
- nc.variables['lat_z'][0:2,:]]))
- bat = np.ravel(np.concatenate([nb.variables['hz'][-2:,:],
- nb.variables['hz'][:,:],
- nb.variables['hz'][0:2,:]]))
- msk = np.ravel(np.concatenate([nb.variables['mz'][-2:,:],
- nb.variables['mz'][:,:],
- nb.variables['mz'][0:2,:]]))
-
- # Pull out the constituents that are avaibable
- self.cons = []
- for ncon in range(nc.variables['con'].shape[0]):
- self.cons.append(nc.variables['con'][ncon,:].tostring().strip())
-
- nc.close() # Close Datafile
- nb.close() # Close Bathymetry file
-
- # Find nearest neighbours on the source grid to each dst bdy point
- source_tree = sp.cKDTree(zip(lon, lat))
- dst_pts = zip(dst_lon, dst_lat)
- # Upper bound set at 0.5 deg as the TPXO7.2 data are at 0.25 deg resolution and
- # we don't want to grab points from further afield
- nn_dist, self.nn_id = source_tree.query(dst_pts, k=4, eps=0, p=2,
- distance_upper_bound=0.5)
-
- # Create a weighting index for interpolation onto dst bdy point
- # need to check for missing values
-
- ind = nn_dist == np.inf
-
- self.nn_id[ind] = 0 # better way of carrying None in the indices?
- dx = (lon[self.nn_id] - np.repeat(np.reshape(dst_lon,[dst_lon.size, 1]),4,axis=1) ) * np.cos(np.repeat(np.reshape(dst_lat,[dst_lat.size, 1]),4,axis=1) * np.pi / 180.)
- dy = lat[self.nn_id] - np.repeat(np.reshape(dst_lat,[dst_lat.size, 1]),4,axis=1)
-
- dist_tot = np.power((np.power(dx, 2) + np.power(dy, 2)), 0.5)
-
- self.msk = msk[self.nn_id]
- self.bat = bat[self.nn_id]
-
- dist_tot[ind | self.msk] = np.nan
-
- dist_wei = 1/( np.divide(dist_tot,(np.repeat(np.reshape(np.nansum(dist_tot,axis=1),[dst_lat.size, 1]),4,axis=1)) ) )
-
- self.nn_wei = dist_wei/np.repeat(np.reshape(np.nansum(dist_wei, axis=1),[dst_lat.size, 1]),4,axis=1)
- self.nn_wei[ind | self.msk] = 0.
-
- # Need to identify missing points and throw a warning and set values to zero
-
- mv = np.sum(self.nn_wei,axis=1) == 0
- if np.sum(mv) > 1:
- print '##WARNING## There are', np.sum(mv), 'missing values, these will be set to ZERO'
- else:
- print '##WARNING## There is', np.sum(mv), 'missing value, this will be set to ZERO'
-
- def extract_con(self, con):
-
- if con in self.cons:
- con_ind = self.cons.index(con)
-
- # Extract the complex amplitude components
- nc = Dataset(self.fileID) # pass variable ids to nc
-
- if self.g_type == 't':
-
-
- vIm = np.ravel(np.concatenate([nc.variables[self.var_Im][con_ind,-2:,:],
- nc.variables[self.var_Im][con_ind,:,:],
- nc.variables[self.var_Im][con_ind,0:2,:]]))
- vRe = np.ravel(np.concatenate([nc.variables[self.var_Re][con_ind,-2:,:],
- nc.variables[self.var_Re][con_ind,:,:],
- nc.variables[self.var_Re][con_ind,0:2,:]]))
-
- self.harm_Im[con] = np.sum(vIm[self.nn_id]*self.nn_wei,axis=1)
- self.harm_Re[con] = np.sum(vRe[self.nn_id]*self.nn_wei,axis=1)
-
- else:
-
- uIm = np.concatenate([nc.variables[self.var_Im][con_ind,-2:,:],
- nc.variables[self.var_Im][con_ind,:,:],
- nc.variables[self.var_Im][con_ind,0:3,:]])
- uRe = np.concatenate([nc.variables[self.var_Re][con_ind,-2:,:],
- nc.variables[self.var_Re][con_ind,:,:],
- nc.variables[self.var_Re][con_ind,0:3,:]])
-
- vIm = np.concatenate([nc.variables[self.var_Im2][con_ind,-2:,:],
- nc.variables[self.var_Im2][con_ind,:,:],
- nc.variables[self.var_Im2][con_ind,0:2,:]])
- vRe = np.concatenate([nc.variables[self.var_Re2][con_ind,-2:,:],
- nc.variables[self.var_Re2][con_ind,:,:],
- nc.variables[self.var_Re2][con_ind,0:2,:]])
- # Deal with north pole. NB in TPXO7.2 data U and Z at 90N have different
- # values for each Longitude value! Plus there's something odd with the
- # hu and hv depths not being the min of surrounding T-grid depths
- # TODO remove hardwired 722 index point and make generic
- vIm = np.concatenate([vIm[:,:], np.concatenate([vIm[722:,-1],vIm[:722,-1]])[:,np.newaxis]],axis=1)
- vRe = np.concatenate([vRe[:,:], np.concatenate([vRe[722:,-1],vRe[:722,-1]])[:,np.newaxis]],axis=1)
-
- # Average U and V onto the T-grid
-
- uIm = np.ravel((uIm[:-1,:] + uIm[1:,:])/2)
- uRe = np.ravel((uRe[:-1,:] + uRe[1:,:])/2)
- vIm = np.ravel((vIm[:,:-1] + vIm[:,1:])/2)
- vRe = np.ravel((vRe[:,:-1] + vRe[:,1:])/2)
-
- if self.key_tr: # We convert to velocity using tidal model bathymetry
-
- harm_Im = np.sum(uIm[self.nn_id]*self.nn_wei,axis=1)/np.sum(self.bat*self.nn_wei,axis=1)
- harm_Re = np.sum(uRe[self.nn_id]*self.nn_wei,axis=1)/np.sum(self.bat*self.nn_wei,axis=1)
- harm_Im2 = np.sum(vIm[self.nn_id]*self.nn_wei,axis=1)/np.sum(self.bat*self.nn_wei,axis=1)
- harm_Re2 = np.sum(vRe[self.nn_id]*self.nn_wei,axis=1)/np.sum(self.bat*self.nn_wei,axis=1)
-
- else: # We convert to velocity using the regional model bathymetry
-
- harm_Im = np.sum(uIm[self.nn_id]*self.nn_wei,axis=1)/self.dst_dep
- harm_Re = np.sum(uRe[self.nn_id]*self.nn_wei,axis=1)/self.dst_dep
- harm_Im2 = np.sum(vIm[self.nn_id]*self.nn_wei,axis=1)/self.dst_dep
- harm_Re2 = np.sum(vRe[self.nn_id]*self.nn_wei,axis=1)/self.dst_dep
-
-
- # Rotate vectors
-
- self.harm_Im[con] = rot_rep(harm_Im, harm_Im2, self.g_type,
- 'en to %s' %self.rot_dir, self.gcos, self.gsin)
- self.harm_Re[con] = rot_rep(harm_Re, harm_Re2, self.g_type,
- 'en to %s' %self.rot_dir, self.gcos, self.gsin)
- self.harm_Im[con][self.msk]=0.
- self.harm_Re[con][self.msk]=0.
-
- nc.close()
-
- else:
-
- # throw some warning
- print '##WARNING## Missing constituent values will be set to ZERO'
-
- self.harm_Im[con] = np.zeros(self.nn_id[:,0].size)
- self.harm_Re[con] = np.zeros(self.nn_id[:,0].size)
-
-
-
-
-
+Non
\ No newline at end of file
diff --git a/pynemo/tide/nemo_bdy_tide3.py b/pynemo/tide/nemo_bdy_tide3.py
index 3dc5951..7c9a148 100644
--- a/pynemo/tide/nemo_bdy_tide3.py
+++ b/pynemo/tide/nemo_bdy_tide3.py
@@ -7,7 +7,7 @@ Module to extract constituents for the input grid mapped onto output grid
# pylint: disable=E1103
# pylint: disable=no-name-in-module
import copy
-import tpxo_extract_HC
+from . import tpxo_extract_HC
import numpy as np
from netCDF4 import Dataset
from pynemo import nemo_bdy_grid_angle
@@ -263,7 +263,7 @@ def constituents_index(constituents, inputcons):
"""
retindx = np.zeros(len(inputcons))
count = 0
- for value in inputcons.values():
+ for value in list(inputcons.values()):
const_name = value.replace("'", "").lower() # force inputcons entries to lowercase
retindx[count] = [x.lower() for x in constituents].index(const_name) # force constituents to lowercase
count = count+1
diff --git a/pynemo/tide/tpxo_extract_HC.py b/pynemo/tide/tpxo_extract_HC.py
index c73b48b..7a4ad21 100644
--- a/pynemo/tide/tpxo_extract_HC.py
+++ b/pynemo/tide/tpxo_extract_HC.py
@@ -58,12 +58,12 @@ class TpxoExtract(object):
elif tide_model == 'FES':
constituents = ['2N2','EPS2','J1','K1','K2','L2','LA2','M2','M3','M4','M6','M8','MF','MKS2','MM','MN4','MS4','MSF','MSQM','MTM','MU2','N2','N4','NU2','O1','P1','Q1','R2','S1','S2','S4','SA','SSA','T2']
- print 'did not actually code stuff for FES in this routine. Though that would be ideal. Instead put it in fes_extract_HC.py'
+ print('did not actually code stuff for FES in this routine. Though that would be ideal. Instead put it in fes_extract_HC.py')
else:
- print 'Don''t know that tide model'
+ print('Don''t know that tide model')
# Wrap coordinates in longitude if the domain is global
glob = 0
@@ -130,7 +130,7 @@ class TpxoExtract(object):
VRe_name, VIm_name, lon_v_name, lat_v_name,
lon, lat, depth, maskname=mv_name)
else:
- print 'Unknown grid_type'
+ print('Unknown grid_type')
return
def interpolate_constituents(self, nc_dataset, real_var_name, img_var_name, lon_var_name,
@@ -221,7 +221,7 @@ def bilinear_interpolation(lon, lat, data, lon_new, lat_new):
# n = lon.size
# m = lat.size
if lon.size != data.shape[0] or lat.size != data.shape[1]:
- print 'Check Dimensions'
+ print('Check Dimensions')
return np.NaN
if glob == 1:
lon = np.concatenate(([lon[0] - 2 * lon_resolution, lon[0] - lon_resolution, ],
--
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