1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
|
import numpy as np
# from memory_profiler import profile
from pandas import DataFrame, Series
class BasicSatelliteInterpolator(object):
"""
Handles interpolation of geolocation data from a grid of tie points.
Currently, it is assumed, that tie-points reach out til the edges if the
tiepoint grid. Methods for extrapolation need to be added if needed.
Uses numpy and pandas.
The constructor gets the tiepointed lat/lon data as *lat_data* and
*lon_data*.
"""
def __init__(self, cols, rows, lat_data, lon_data):
self.row_indices = rows
self.col_indices = cols
self.lon_tiepoint = lon_data
self.lat_tiepoint = lat_data
self.longitudes = None
self.latitudes = None
def _interp(self, data):
"""The interpolation method implemented here is a kind of a billinear
interpolation. The input *data* field is first interpolated along the
rows and subsequently along its columns.
The final size of the interpolated *data* field is determined by the
last indices in self.row_indices and self.col_indices.
"""
row_interpol_data = self._interp_axis(data, 0)
interpol_data = self._interp_axis(row_interpol_data, 1)
return interpol_data
# @profile
def _interp_axis(self, data, axis):
"""The *data* field contains the data to be interpolated. It is
expected that values reach out to the *data* boundaries.
With *axis*=0 this method interpolates along rows and *axis*=1 it
interpolates along colums.
For column mode the *data* input is transposed before interpolation
and subsequently transposed back.
"""
if axis == 0:
return self._pandas_interp(data, self.row_indices)
if axis == 1:
data_transposed = data.as_matrix().T
data_interpol_transposed = self._pandas_interp(data_transposed,
self.col_indices)
data_interpol = data_interpol_transposed.as_matrix().T
return data_interpol
def _pandas_interp(self, data, indices):
"""The actual transformation based on the following stackoverflow
entry: http://stackoverflow.com/a/10465162
"""
new_index = np.arange(indices[-1] + 1)
data_frame = DataFrame(data, index=indices)
data_frame_reindexed = data_frame.reindex(new_index)
data_interpol = data_frame_reindexed.apply(Series.interpolate)
del new_index
del data_frame
del data_frame_reindexed
return data_interpol
def interpolate(self):
"""Do the interpolation and return resulting longitudes and latitudes.
"""
self.latitude = self._interp(self.lat_tiepoint)
self.longitude = self._interp(self.lon_tiepoint)
return self.latitude, self.longitude
|
