Hi I am currently trying to interpolate data. My data structure looks like this:
WMOCode,Country,Latitude,Longitude,Altitude,H2,O18,Date,Year,month,dates,a_diffH,H2a,a_diffO,O18a
7162800,CA,45.32,-75.67,114.0,-57.0,-8.85,1990-06-15,1990,6,1990-06-15,-1.71,-55.29,-0.228,-8.622
To make the interpolation better, I copy the data and paste it back to the actual data set in all sky directions (so I can interpolate data from N America with data from Japan, for example).
def extend_data(data):
# Copy data to the "left" and "right" to allow for interpolation to the edges of the map
return {
"lons": np.concatenate([np.array([lon-360 for lon in data["lons"]]), data["lons"], np.array([lon+360 for lon in data["lons"]])]),
"lats": np.concatenate([data["lats"], data["lats"], data["lats"]]),
"values": np.concatenate([data["values"], data["values"], data["values"]]),
}
To join the data together I use the concatenate command. However, if I execute it, I get the following error message: ValueError: operands could not be broadcast together with shapes (152,6) (1,2) (152,6)
I have already tried to work with the reharp command, but the error message does not change.
The big problem is, that this code workes yesterday but today (without any changes from my side) it shows this error message! So wath did Changed?
My code looks like this:
from traceback import print_tb
import numpy as np
from pykrige.ok import OrdinaryKriging
from pykrige.kriging_tools import write_asc_grid
import pykrige.kriging_tools as kt
def get_data(df):
return {
"lons": np.array([6.47, 4.8, 1.94]),
"lats": np.array([46.37, 43.9, 47.83]),
"values": np.array([-29.5, -27.6, -32.5]),
}
def extend_data(data):
return {
"lons": np.concatenate([np.array([lon-360 for lon in data["lons"]]), data["lons"], np.array([lon+360 for lon in data["lons"]])]),
"lats": np.concatenate([data["lats"], data["lats"], data["lats"]]),
"values": np.concatenate([data["values"], data["values"], data["values"]]),
}
def generate_grid(data, basemap, delta=1):
grid = {
'lon': np.arange(-180, 180, delta),
'lat': np.arange(np.amin(data["lats"]), np.amax(data["lats"]), delta)
}
grid["x"], grid["y"] = np.meshgrid(grid["lon"], grid["lat"])
grid["x"], grid["y"] = basemap(grid["x"], grid["y"])
return(grid)
def interpolate(data, grid):
Ok = OrdinaryKriging(
data["lons"],
data["lats"],
data["values"],
variogram_model='exponential',
# nlags=6, # TODO select plausible value
)
return Ok.execute("grid", grid["lon"], grid["lat"])
base_data = get_data()
grid = generate_grid(base_data, basemap, 1)
extended_data = extend_data(base_data)
interpolation, interpolation_error = interpolate(extended_data, grid)
This error only occours at the code sniped: interpolation, interpolation_error = interpolate(extended_data, grid)
so my extended_data looks like (each List repeats 3 Times):
[-435.67 -350.8 -353.52916667 -370.25 -343.64361111
-352.4 -350.0422 -380.56666667 -350.81 -351.635
-347.8981 -350.0572 -351.2942 -349.5536 -348.9378
-353.3419 -245.83333333 -423.9775 -340.15138889 -353.75
-346.5981 -352.56067943 -351.66728038 -353.45 -351.70729317
-348.1242 -351.21144395 -351.81321784 -346.32861111 -351.15
-347.57 -352.40555556 -191.68 -186.72 -344.55111111
-345.68166667 -351.2481 -327.12 -348.9892 -206.92
-519.8 -322.13 -537.37 -419.48972222 -345.403
-385.65 -324.7 -329.3 -253.17 -344.03
-219.28 -348.06666667 -329.37 -357.88 -320.18
-303.82 -432.93 -321.27 -348.41 -257.31666667
-277.1 -255.65 -245.58333333 -354.22 -428.28
-215.31 -272.38 -354.82 -367.55 -286.62
-273.81944444 -267.85 -368.6 -430.833333 -326.95
-329.7 -298.31666667 -259.57 -253.12 -454.98
-240.71666667 -338.47 -376.9 -251.07 -242.83333333
-255.98 -246.35 -253.28333333 -250.15 -465.11666667
-422.33333333 -246.93333333 -474.27 -479.28333333 -241.82
-454.06 -249.65 -441.25 -236.08333333 -250.6
-259.2 -445.56 -280.06666667 -356.25 -354.95
-238.6 -268.86666667 -438.83 -233.38 -387.22009444
-366.73333333 -442.33333333 -329.45 -367.73333333 -253.01666667
-325.91666667 -430.7 -249.92 -316.14166667 -236.08
-244.22 -445.66666667 -444.05 -443.46 -445.345
-444.12208333 -445.32 -443.71666667 -346.99027778 -361.08333333
-361.1 -342.88809167 -340.417 -340.32 -338.27
-341.52428889 -339.69353056 -342.13799444 -346.13673611 -445.11
-443.28333333 -443.16666667 -445.53 -444.63333333 -443.85
-445.62 -432.09198611 -433.03333333 -432.07 -432.58922222
-466.6244 -418.56 ]
The Output of Lats:
[ 45.32 48.8281 46.37222222 51.93 48.24861111
50.3381 49.7703 -75.58333333 47.6772 49.0392
49.0422 50.4972 53.8713 52.2914 47.4828
49.7478 22.31666667 -65.07944444 50.06166667 51.83
52.4672 46.95222563 46.57248471 53.23 46.65772518
50.3119 46.17378583 46.72738634 46.60305556 44.42
51.35 47.59583333 -46.42 -35.07 47.07777778
46.64833333 52.1042 39.95 47.8008 -27.43
-21.2 -46.88 28.22 13.07176111 46.095
37.77 36.98 36.88 -6.18 45.80666667
-2.53 78.91666667 46.48 41.38 47.25
58.01 -41.47 9. 48.22 25.01666667
55.03 52.27 38.03333333 50.92 -54.78
-40.68 43.78 52.1 40.41666667 55.01
69.4075 58.45 41.13333333 -53. 68.96666667
59.96666667 69.76666667 38.93 27.7 74.72
26.08333333 68.68333333 32.63333333 34.3 39.1
30.67 34.72 26.58333333 40.67 69.1
82.5 28.2 62.28 76.23333333 32.18
58.75 20.03333333 68.78666667 47.38333333 24.35
13.17 80. 37.13333333 51.3 52.8
37.53 29.7 -33.62 45.68 38.66038889
41.8 23.16666667 37.75 41.31666667 47.93333333
36.79444444 -33.45 25.07 39.66666667 13.03
-31.95 9.96666667 9.8 8.95 10.31
10.10808333 10.76666667 9.78333333 46.52083333 52.88333333
51.6 48.16905278 48.933 49.36 48.65833333
48.28460556 49.13615278 47.96013056 45.641775 9.69
10.06666667 8.64 10.5 9.66666667 9.98333333
10.59 -36.6395 -36.79744444 -45.35 -38.73263889
52.141 -62.12 ]
The Output for Values:
[[ -8.85 -5.26 -6.4 -5.43 -6.37 -4.67 -5.48 -18.73 -6.45 -4.19
-8.23 -7.36 -7.12 -5.05 -6.61 -3.08 -5.98 -13.37 -5.52 -6.55
-8.16 -6.45 -11.47 -5.9 -9.59 -7.47 -5.83 -8.37 -8.08 -2.33
-8.8 -4.45 -6.63 -4.13 -5.43 -8.33 -5.13 -2.9 -9.83 -4.32
-1.74 -5.85 -2.23 -2.06 -5.87 -0.19 -2.66 -5.15 -4.28 -4.76
-8.77 -11.01 -6.9 -2.84 -5.1 -12.6 -5.8 2.57 -10.77 -11.85
-11.1 -14.5 -6.47 -5.4 -11.4 -6.77 -9.33 -4.83 -3.46 -6.7
-11.4 -8.5 -1.77 -13.9 -9.6 -9. -9.2 -3.83 -6.95 -25.25
-5.66 -12.55 -2.64 -8.69 -4.61 -5.27 -12.04 -8.45 -5.94 -23.17
-21.48 -9.8 -14.33 -23.49 -10.78 -15.73 -11.37 -22.81 -10.25 -6.16
-3.49 -25.94 2.23 -5.14 -5.98 -9.64 -3.33 -2.6 -12.31 -0.12
-3.84 -3.54 -6.07 -2.63 -9.17 -3.98 -6.1 -5.58 -6.95 -9.77
-3.53 -7.48 -8.96 -8.55 -9.32 -11.76 -5.51 -8.63 -7.16 -7.5
-5.6 -5.9 -8.55 -6.03 -4.23 -6. -5.18 -7.15 -5.54 -7.3
-4.69 -9.54 -7.06 -9.54 -11.04 -8.15 -6.2 -2.8 -15. -4.8
-9.6 -17.3 ]
and my grid looks like:
print(grid)
{'lon': array([-180, -179, -178, -177, -176, -175, -174, -173, -172, -171, -170,
-169, -168, -167, -166, -165, -164, -163, -162, -161, -160, -159,
-158, -157, -156, -155, -154, -153, -152, -151, -150, -149, -148,
-147, -146, -145, -144, -143, -142, -141, -140, -139, -138, -137,
-136, -135, -134, -133, -132, -131, -130, -129, -128, -127, -126,
-125, -124, -123, -122, -121, -120, -119, -118, -117, -116, -115,
...
-37, -36, -35, -34, -33, -32, -31, -30, -29, -28, -27,
-26, -25, -24, -23, -22, -21, -20, -19, -18, -17, -16,
-15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5,
...
172, 173, 174, 175, 176, 177, 178, 179]), 'lat': array([-75.58333333, -74.58333333, -73.58333333, -72.58333333,
-71.58333333, -70.58333333, -69.58333333, -68.58333333,
...])
