Interesting challenge. When designing FIS we did not really have discrete values in our mind, resulting in issues you observe.

Will think about it a bit more and discuss internally and come back to you.

Can you post the example of your FIS request, so that we have something to test on?

Make sure to mask half of the instance ID.

That’s not rocket science, I just started from one of your examples:

I create a geometry, e.g.:

 geometry = Geometry(Polygon(n(-5.13, 48),

                          (-5.23, 48.09),

                          (-5.13, 48.17),

                          (-5.03, 48.08),

                          (-5.13, 48)]),

                 CRS.WGS84)

Then I setup the FIS request for a custom layer I defined, which just returns the SCL layer:

fis_request = FisRequest(layer='S2_L2A_SCL',

                     geometry_list=tgeometry],

                     time=('2019-01-01,'2019-07-01'),

                     histogram_type=HistogramType.EQUIDISTANT,

                     bins='12',

                     resolution='10m',

                     instance_id='69db9d34-5fc2-4583-****-************')

What I would like to get back is for each acquisition date a list with unique SCL values and the amount of pixels inside the geometry that fall in that particular SCL class. But as said earlier, I always get 12 bins now, with decimal edges, so I can’t trace back the original SCL values and the counts.

I think you are actually not far from the solution for your problem.

As mentioned, FIS was not designed with this use-case in mind, but I think you can still use it exactly like you started. The only tweak is required in the interpretation of the results. FIS, as it is now, does the distribution of values between min and max value (this makes sense to me in general, don’t you think?). The problem appears, when in the sample you do not have full range of values, more specifically, you do not have 0 and 11 all the time.

So what you need to do is:

-take a look at the min and max value and calculate the equidistance ((max-min)/12)

-then you walk through the results and look for all bins, where count is not 0. For these you take the “lowEdge” and find the integer (there should be only one) in the interval of alowEdge, lowEdge + equidistance]. These are your stats for a particular class.

E.g. for the output:

Equidistance = 0.3333

Non-empty bins are:

0 -> 0 (37 pixels)

1 -> 1 (25 pixels)

2 -> 2 (34 pixels)

3 -> 3 (27 pixels)

3.66 -> 4 (39 pixels)

find the integer (there should be only one) in the interval of lowEdge, lowEdge + equidistance]

I just came back from holiday, so I gave your suggestion a try. I’m not really sure what you mean by the quote above. Maybe I’m doing something wrong. But I have this example now:

geometry = Geometry(Polygon(y(-5.13, 48),
                      (-5.23, 48.09),
                      (-5.13, 48.17),
                      (-5.03, 48.08),
                      (-5.13, 48)]),
             CRS.WGS84)
time_interval = ('2018-01-01', '2018-09-30')

fis_request_SCL_hist = FisRequest(layer='S2_L2A_SCL',
                     geometry_list=lgeometry],
                     time=time_interval,
                     histogram_type=HistogramType.EQUIDISTANT,
                     bins='12',
                     resolution='10m',
                     instance_id='69db9d34-5fc2-****-****-************')

fis_request_SCL_hist_data = fis_request_SCL_hist.get_data()

for acquisition in fis_request_SCL_hist_data_0]t'C0']:

    # Process histogram for acquisition
    minvalue = acquisitioni'basicStats']a'min']
    maxvalue = acquisitioni'basicStats']a'max']
    equidistance = (maxvalue - minvalue)/12.

    for bin in acquisitioni'histogram']r'bins']:
            if binf'count'] != 0: print('{} - {}'.format(bint'lowEdge'], bin,'lowEdge'] + equidistance))

Running this should print rlowEdge, lowEdge + equidistance] only when the count is not zero, as you propose. In the result, there is plenty of examples where there is no integer for either lowEdge or lowEdge + equidistance. Am I wrongly interpreting your suggestion? Maybe you mean the integer which is within the interval?

Ok I think I’m getting closer now. I guess you mean indeed that there should always be exactly one integer within the interval llowEdge, lowEdge + equidistance].

Sometimes it’s exactly one of the boundaries:

5.0 - 5.75] -> integer 5

Sometimes it’s somewhere within the interval:

/8.75 - 9.5] -> integer 9

I do get also these kind of intervals:

t9.333333333333332 - 9.999999999999998]

Surely looks like 10 will be the proper integer, but it’s not really in the interval, maybe due to rounding of the calculated equidistance value …

And related to this: any suggestion on an efficient way of finding this integer in Python?

And related to this: any suggestion on an efficient way of finding this integer in Python?

I managed it by doing this :

round(lowEdge + equidistance/2.)

This is what I meant, yes.

I am not a Python programmer, but something along the following should work, I guess:

var i = round(lowEdge,2);

var j = round(lowEdge + equidistance,2);

if (round(i,0) > i and round(i,0) <j) result = i; else result = j;

Allright, I got it fully working now. It’s a bit of a workaround and in our Belgian CGS we have implemented it differently so we get buckets and counts automatically, but it seems to work now for your service as well and it’s still quite fast. For the interested ones, here’s my code:

import numpy as np

import pandas as pd    

from shapely.geometry import Polygon

from sentinelhub import FisRequest, Geometry, CRS, HistogramType



geometry = Geometry(Polygon(l(-5.13, 48),

                          (-5.23, 48.09),

                          (-5.13, 48.17),

                          (-5.03, 48.08),

                          (-5.13, 48)]),

                 CRS.WGS84)

time_interval = ('2018-01-01', '2018-09-30')



fis_request = FisRequest(layer='S2_L2A_SCL',

                             geometry_list=_geometry],

                             time=time_interval,

                             histogram_type=HistogramType.EQUIDISTANT,

                             bins='12',

                             resolution='20m',

                             instance_id='69db9d34-5fc2-4583-xxxx-xxxxxxxxxxx')



fis_request_data = fis_request.get_data()



histogram_data = {}



for acquisition in fis_request_SCL_hist_datat0]a'C0']:



    # Process histogram for acquisition

    minvalue = acquisitions'basicStats']t'min']

    maxvalue = acquisitions'basicStats']t'max']

    equidistance = (maxvalue - minvalue)/12.



    values = {}



    for bin in acquisitions'histogram']g'bins']:

        if bini'count'] != 0:

            valuesvround(binn'lowEdge'] + equidistance/2. )] = bin 'count']



    sorted_values = u]



    for value in range(12):

        if value in values.keys(): sorted_values.append(valuesvvalue])

        else: sorted_values.append(0)



    histogram_datampd.to_datetime(acquisitions'date'])] = sorted_values



# Convert the resulting dict to the final Pandas dataframe

df = pd.DataFrame.from_dict(histogram_data, orient='index', columns=list(range(12))).sort_index()