Use of datetime timedelta with numpy 3d array - python-3.x

Question

I have a 3D array with the count of number of days past a benchmark date (e.g., 01.01.2000). I am interested in the actual day-of-year (DOY: 1-365/366)rather than the total number of days past a given date.
For a single value, the below syntax works. For e.g.,
import numpy as np
import datetime
data = 1595
date = datetime.datetime(2000,1,1,0,0) + datetime.timedelta(data -1)
date.timetuple().tm_yday
134
However, I am having issues with using a 3D array.
import numpy as np
import datetime
data = np.random.randint(5, size = (2,2,2))
data = data + 1595
data
array([[[1596, 1595],
[1599, 1599]],
[[1596, 1599],
[1595, 1595]]])
#Function
def Int_to_DOY(int_array):
date_ = datetime.datetime(2000,1,1,0,0) + datetime.timedelta(int_array - 1)
return date_.timetuple().tm_yday
doy_data = data * 0 #Empty array
for i in range(2):
doy_data[:, :, i] = Int_to_DOY(data[:, :, i])
Here is the error message and I am not able to figure this out.
TypeError: unsupported type for timedelta days component: numpy.ndarray
Thanks for your help.

Answer 1

import numpy as np
import datetime
data = np.random.randint(5, size = (2,2,2))
data = data + 1595
#Function
def Int_to_DOY(int_array):
date_ = datetime.datetime(2000,1,1,0,0) + datetime.timedelta(int(int_array) -1)
return date_.timetuple().tm_yday
doy_data = data.flatten()
for i in range(len(doy_data)):
doy_data[i] = Int_to_DOY(doy_data[i])
doy_data = doy_data.reshape((2,2,2))

Answer 2

Since you tagged pandas:
data = np.array([[[1596, 1595],
[1599, 1599]],
[[1596, 1599],
[1595, 1595]]])
s = pd.to_datetime('2000-01-01') + pd.to_timedelta(data.ravel(), unit='D')
s.dayofyear.values.reshape(data.shape) - 1
Output:
array([[[135, 134],
[138, 138]],
[[135, 138],
[134, 134]]], dtype=int64)