Applying different distributions to collections and images



In [1]:

    
import ee
ee.Initialize()



In [2]:

    
from geetools import tools



In [3]:

    
import ipygee as ui



In [4]:

    
p = ee.Geometry.Point([0, 0])

Build a Test Collection



In [5]:

    
# values = ee.List([0, 2, 5, 8, 10, 12, 15, 18, 22, 25, 28, 31, 35, 39, 42, 48, 51, 55])
# values = ee.List([-10, -8, -7, -1, -2, 0, 1, 5, 7, 8, 10, 12, 14, 16, 18, 20, 22, 24])  # With negative values
values = ee.List([-20, -32, -15, -8, -5, 0, 2, 4, 6, 8, 9, 10, 12, 14, 16, 18, 20, 23])



In [6]:

    
def build_collection(n):
    n = ee.Number(n)
    return ee.Image.constant(n).rename('test').set('test', n).toFloat()
images = values.map(build_collection)



In [7]:

    
collection = ee.ImageCollection.fromImages(images)

Linear Function

Using a band



In [8]:

    
help(tools.imagecollection.linearFunctionBand)









    



Help on function linearFunctionBand in module geetools.tools.imagecollection:

linearFunctionBand(collection, band, range_min=None, range_max=None, mean=None, output_min=None, output_max=None, name='linear_function')
    Apply a linear function over the bands across every image of the
    ImageCollection using the following formula:
    
    - a = abs(val-mean)
    - b = output_max-output_min
    - c = abs(range_max-mean)
    - d = abs(range_min-mean)
    - e = max(c, d)
    
    f(x) = a*(-1)*(b/e)+output_max
    
    :param band: the band to process
    :param range_min: the minimum pixel value in the parsed band. If None, it
        will be computed reducing the collection
    :param range_max: the maximum pixel value in the parsed band. If None, it
        will be computed reducing the collection
    :param output_min: the minimum value that will take the resulting band.
    :param output_max: the minimum value that will take the resulting band.
    :param mean: the value on the given range that will take the `output_max`
        value
    :param name: the name of the resulting band
    :return: the parsed collection in which every image will have an extra band
        that results of applying the linear function over every pixel in the
        image
    :rtype: ee.ImageCollection



In [9]:

    
linear_band1 = tools.imagecollection.linearFunctionBand(collection, 'test')



In [10]:

    
linear_band1_chart = ui.chart.Image.series(linear_band1, p, scale=1000, xProperty='test', bands=['linear_function'], label_bands=['default values'])



In [11]:

    
linear_band2 = tools.imagecollection.linearFunctionBand(collection, 'test', mean=10)



In [12]:

    
linear_band2_chart = ui.chart.Image.series(linear_band2, p, scale=1000, xProperty='test', bands=['linear_function'], label_bands=['mean=10'])



In [13]:

    
linear_band3 = tools.imagecollection.linearFunctionBand(collection, 'test', mean=10, output_min=0)



In [14]:

    
linear_band3_chart = ui.chart.Image.series(linear_band3, p, scale=1000, xProperty='test', bands=['linear_function'], label_bands=['mean=10 min=0'])



In [15]:

    
linear_band4 = tools.imagecollection.linearFunctionBand(collection, 'test', mean=10, output_min=10, output_max=20)



In [16]:

    
linear_band4_chart = ui.chart.Image.series(linear_band4, p, scale=1000, xProperty='test', bands=['linear_function'], label_bands=['mean=10 min=0 max=12'])



In [17]:

    
linear_chart = linear_band1_chart.cat(linear_band2_chart, linear_band3_chart, linear_band4_chart)
linear_chart.renderWidget(width=800)

Using a Property



In [18]:

    
linear_prop1 = tools.imagecollection.linearFunctionProperty(collection, 'test')



In [19]:

    
linear_prop1_chart = ui.chart.Image.series(linear_prop1, p, scale=1000, xProperty='test', properties=['LINEAR_FUNCTION'], label_properties=['default values'])



In [20]:

    
linear_prop2 = tools.imagecollection.linearFunctionProperty(collection, 'test', mean=10)



In [21]:

    
linear_prop2_chart = ui.chart.Image.series(linear_prop2, p, scale=1000, xProperty='test', properties=['LINEAR_FUNCTION'], label_properties=['mean=10'])



In [22]:

    
linear_prop3 = tools.imagecollection.linearFunctionProperty(collection, 'test', mean=10, output_min=0)



In [23]:

    
linear_prop3_chart = ui.chart.Image.series(linear_prop3, p, scale=1000, xProperty='test', properties=['LINEAR_FUNCTION'], label_properties=['mean=10 min=0'])



In [24]:

    
linear_prop4 = tools.imagecollection.linearFunctionProperty(collection, 'test', mean=10, output_min=0, output_max=10)



In [25]:

    
linear_prop4_chart = ui.chart.Image.series(linear_prop4, p, scale=1000, xProperty='test', properties=['LINEAR_FUNCTION'], label_properties=['mean=10 min=0 max=10'])



In [26]:

    
linear_prop5 = tools.imagecollection.linearFunctionProperty(collection, 'test', mean=10, output_min=5, output_max=20)



In [27]:

    
linear_prop5_chart = ui.chart.Image.series(linear_prop5, p, scale=1000, xProperty='test', properties=['LINEAR_FUNCTION'], label_properties=['mean=10 min=5 max=20'])



In [28]:

    
linear_chart_prop = linear_prop1_chart.cat(linear_prop2_chart, linear_prop3_chart, linear_prop4_chart, linear_prop5_chart)
linear_chart_prop.renderWidget(width=800)



In [29]:

    
linear_chart_prop.dataframe









    Out[29]:







  
    
      
      default values
      mean=10
      mean=10 min=0
      mean=10 min=0 max=10
      mean=10 min=5 max=20
    
  
  
    
      -32
      -32
      -32.000000
      -3.552714e-15
      0.000000
      5.000000
    
    
      -20
      -20
      -16.285714
      6.571429e+00
      2.857143
      9.285714
    
    
      -15
      -15
      -9.738095
      9.309524e+00
      4.047619
      11.071429
    
    
      -8
      -8
      -0.571429
      1.314286e+01
      5.714286
      13.571429
    
    
      -5
      -5
      3.357143
      1.478571e+01
      6.428571
      14.642857
    
    
      0
      0
      9.904762
      1.752381e+01
      7.619048
      16.428571
    
    
      2
      2
      12.523810
      1.861905e+01
      8.095238
      17.142857
    
    
      4
      4
      15.142857
      1.971429e+01
      8.571429
      17.857143
    
    
      6
      6
      17.761905
      2.080952e+01
      9.047619
      18.571429
    
    
      8
      8
      20.380952
      2.190476e+01
      9.523810
      19.285714
    
    
      9
      9
      21.690476
      2.245238e+01
      9.761905
      19.642857
    
    
      10
      10
      23.000000
      2.300000e+01
      10.000000
      20.000000
    
    
      12
      12
      20.380952
      2.190476e+01
      9.523810
      19.285714
    
    
      14
      14
      17.761905
      2.080952e+01
      9.047619
      18.571429
    
    
      16
      16
      15.142857
      1.971429e+01
      8.571429
      17.857143
    
    
      18
      18
      12.523810
      1.861905e+01
      8.095238
      17.142857
    
    
      20
      20
      9.904762
      1.752381e+01
      7.619048
      16.428571
    
    
      23
      23
      5.976190
      1.588095e+01
      6.904762
      15.357143

Gauss Function

Over a band



In [30]:

    
gauss_band1 = tools.imagecollection.gaussFunctionBand(collection, 'test')



In [31]:

    
gauss_band1_chart = ui.chart.Image.series(gauss_band1, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', bands=['gauss'], label_bands=['defualt'])



In [32]:

    
gauss_band2 = tools.imagecollection.gaussFunctionBand(collection, 'test', mean=10)



In [33]:

    
gauss_band2_chart = ui.chart.Image.series(gauss_band2, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', bands=['gauss'], label_bands=['mean=10'])



In [34]:

    
gauss_band3 = tools.imagecollection.gaussFunctionBand(collection, 'test', mean=10, output_min=0.5)



In [35]:

    
gauss_band3_chart = ui.chart.Image.series(gauss_band3, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', bands=['gauss'], label_bands=['mean=10 min=0.5'])

Exponential function using the gauss function

Use as mean value the range_max



In [36]:

    
gauss_band4 = tools.imagecollection.gaussFunctionBand(collection, 'test', mean=23, output_min=0.5)



In [37]:

    
gauss_band4_chart = ui.chart.Image.series(gauss_band4, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', bands=['gauss'], label_bands=['Exponential'])



In [38]:

    
gauss_band_chart = gauss_band1_chart.cat(gauss_band2_chart, gauss_band3_chart, gauss_band4_chart)
gauss_band_chart.renderWidget(width=800)



In [39]:

    
gauss_band_chart.dataframe









    Out[39]:







  
    
      
      defualt
      mean=10
      mean=10 min=0.5
      Exponential
    
  
  
    
      -32
      0.066663
      0.009418
      0.500000
      0.500000
    
    
      -20
      0.347201
      0.092535
      0.541954
      0.503594
    
    
      -15
      0.551540
      0.191495
      0.591904
      0.510812
    
    
      -8
      0.844293
      0.424493
      0.709511
      0.539220
    
    
      -5
      0.936023
      0.551540
      0.773638
      0.562733
    
    
      0
      1.000000
      0.767618
      0.882704
      0.623294
    
    
      2
      0.989477
      0.844293
      0.921406
      0.655646
    
    
      4
      0.958569
      0.909185
      0.954161
      0.692359
    
    
      6
      0.909185
      0.958569
      0.979087
      0.732741
    
    
      8
      0.844293
      0.989477
      0.994689
      0.775695
    
    
      9
      0.807175
      0.997359
      0.998667
      0.797684
    
    
      10
      0.767618
      1.000000
      1.000000
      0.819730
    
    
      12
      0.683296
      0.989477
      0.994689
      0.863029
    
    
      14
      0.595504
      0.958569
      0.979087
      0.903555
    
    
      16
      0.508127
      0.909185
      0.954161
      0.939209
    
    
      18
      0.424493
      0.844293
      0.921406
      0.968001
    
    
      20
      0.347201
      0.767618
      0.882704
      0.988236
    
    
      23
      0.246842
      0.639581
      0.818077
      1.000000

Over a Property



In [40]:

    
gauss_prop1 = tools.imagecollection.gaussFunctionProperty(collection, 'test')



In [41]:

    
gauss_prop1_chart = ui.chart.Image.series(gauss_prop1, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', properties=['GAUSS'], label_properties=['defualt'])



In [42]:

    
gauss_prop2 = tools.imagecollection.gaussFunctionProperty(collection, 'test', mean=10)



In [43]:

    
gauss_prop2_chart = ui.chart.Image.series(gauss_prop2, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', properties=['GAUSS'], label_properties=['mean=10'])



In [44]:

    
gauss_prop3 = tools.imagecollection.gaussFunctionProperty(collection, 'test', mean=10, output_min=0.5)



In [45]:

    
gauss_prop3_chart = ui.chart.Image.series(gauss_prop3, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', properties=['GAUSS'], label_properties=['mean=10 min=0.5'])



In [46]:

    
gauss_prop = gauss_prop1_chart.cat(gauss_prop2_chart, gauss_prop3_chart)



In [47]:

    
gauss_prop.renderWidget(width=800)



In [48]:

    
gauss_prop.dataframe









    Out[48]:







  
    
      
      defualt
      mean=10
      mean=10 min=0.5
    
  
  
    
      -32
      0.066663
      0.009418
      0.500000
    
    
      -20
      0.347201
      0.092535
      0.541954
    
    
      -15
      0.551540
      0.191495
      0.591904
    
    
      -8
      0.844293
      0.424493
      0.709511
    
    
      -5
      0.936023
      0.551540
      0.773638
    
    
      0
      1.000000
      0.767618
      0.882704
    
    
      2
      0.989477
      0.844293
      0.921406
    
    
      4
      0.958569
      0.909185
      0.954161
    
    
      6
      0.909185
      0.958569
      0.979087
    
    
      8
      0.844293
      0.989477
      0.994689
    
    
      9
      0.807175
      0.997359
      0.998667
    
    
      10
      0.767618
      1.000000
      1.000000
    
    
      12
      0.683296
      0.989477
      0.994689
    
    
      14
      0.595504
      0.958569
      0.979087
    
    
      16
      0.508127
      0.909185
      0.954161
    
    
      18
      0.424493
      0.844293
      0.921406
    
    
      20
      0.347201
      0.767618
      0.882704
    
    
      23
      0.246842
      0.639581
      0.818077

Normal Distribution (applies the Gauss function using a computed stdDev and mean)

Over a Property



In [49]:

    
normal_prop = tools.imagecollection.normalDistributionProperty(collection, 'test')



In [50]:

    
normal_prop_chart = ui.chart.Image.series(normal_prop, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', properties=['NORMAL_DISTRIBUTION'], label_properties=['defualt'])



In [51]:

    
normal_prop_chart.renderWidget(width=800)



In [52]:

    
normal_prop_chart.dataframe

Over a Band



In [53]:

    
normal_band = tools.imagecollection.normalDistributionBand(collection, 'test')



In [54]:

    
normal_band_chart = ui.chart.Image.series(normal_band, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', bands=['normal_distribution'], label_bands=['defualt'])



In [55]:

    
normal_band_chart.renderWidget(width=800)



In [56]:

    
normal_band_chart.dataframe



In [ ]:

	defualt
-32	0.001287
-20	0.007266
-15	0.012136
-8	0.020259
-5	0.023448
0	0.027123
2	0.027780
4	0.027901
6	0.027479
8	0.026538
9	0.025889
10	0.025133
12	0.023339
14	0.021253
16	0.018978
18	0.016618
20	0.014269
23	0.010943

	defualt
-32	0.001287
-20	0.007266
-15	0.012136
-8	0.020259
-5	0.023448
0	0.027123
2	0.027780
4	0.027901
6	0.027479
8	0.026538
9	0.025889
10	0.025133
12	0.023339
14	0.021253
16	0.018978
18	0.016618
20	0.014269
23	0.010943

	default values	mean=10	mean=10 min=0	mean=10 min=0 max=10	mean=10 min=5 max=20
-32	-32	-32.000000	-3.552714e-15	0.000000	5.000000
-20	-20	-16.285714	6.571429e+00	2.857143	9.285714
-15	-15	-9.738095	9.309524e+00	4.047619	11.071429
-8	-8	-0.571429	1.314286e+01	5.714286	13.571429
-5	-5	3.357143	1.478571e+01	6.428571	14.642857
0	0	9.904762	1.752381e+01	7.619048	16.428571
2	2	12.523810	1.861905e+01	8.095238	17.142857
4	4	15.142857	1.971429e+01	8.571429	17.857143
6	6	17.761905	2.080952e+01	9.047619	18.571429
8	8	20.380952	2.190476e+01	9.523810	19.285714
9	9	21.690476	2.245238e+01	9.761905	19.642857
10	10	23.000000	2.300000e+01	10.000000	20.000000
12	12	20.380952	2.190476e+01	9.523810	19.285714
14	14	17.761905	2.080952e+01	9.047619	18.571429
16	16	15.142857	1.971429e+01	8.571429	17.857143
18	18	12.523810	1.861905e+01	8.095238	17.142857
20	20	9.904762	1.752381e+01	7.619048	16.428571
23	23	5.976190	1.588095e+01	6.904762	15.357143

	defualt	mean=10	mean=10 min=0.5	Exponential
-32	0.066663	0.009418	0.500000	0.500000
-20	0.347201	0.092535	0.541954	0.503594
-15	0.551540	0.191495	0.591904	0.510812
-8	0.844293	0.424493	0.709511	0.539220
-5	0.936023	0.551540	0.773638	0.562733
0	1.000000	0.767618	0.882704	0.623294
2	0.989477	0.844293	0.921406	0.655646
4	0.958569	0.909185	0.954161	0.692359
6	0.909185	0.958569	0.979087	0.732741
8	0.844293	0.989477	0.994689	0.775695
9	0.807175	0.997359	0.998667	0.797684
10	0.767618	1.000000	1.000000	0.819730
12	0.683296	0.989477	0.994689	0.863029
14	0.595504	0.958569	0.979087	0.903555
16	0.508127	0.909185	0.954161	0.939209
18	0.424493	0.844293	0.921406	0.968001
20	0.347201	0.767618	0.882704	0.988236
23	0.246842	0.639581	0.818077	1.000000