import rastereasy

Read geoimage

In rastereasy, one of the main class to deal with georeferenced images is Geoimage. It contains the main functions (for resampling, reprojection, stack, plotting, …). Let’s start by opening and plotting an image, some bands or making color compositions

Open an image

Read basic information of the image, without open it (parameter meta_only=True)

name_im='./data/demo/sentinel.tif'
image=rastereasy.open(name_im,meta_only=True)

# Alternatively, we can give names to bands
names = {"NIR":8,"G":3,"CO" : 1,"SWIR2":11,"B": 2,"R":4,"RE1":5,"RE2":6,"RE3":7,"WA":9,"SWIR1":10,"SWIR3":12}
image_names=rastereasy.open(name_im,names=names)

General information

The function ìnfo() gives general informations on the opened image

image.info()
image_names.info()

- Size of the image:
   - Rows (height): 1000
   - Cols (width): 1000
   - Bands: 12
- Spatial resolution: 10.0  meters / degree (depending on projection system)
- Central point latitude - longitude coordinates: (7.04099599, 38.39058840)
- Driver: GTiff
- Data type: int16
- Projection system: EPSG:32637
- Nodata: -32768.0

- Given names for spectral bands: 
   {'1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9, '10': 10, '11': 11, '12': 12}


- Size of the image:
   - Rows (height): 1000
   - Cols (width): 1000
   - Bands: 12
- Spatial resolution: 10.0  meters / degree (depending on projection system)
- Central point latitude - longitude coordinates: (7.04099599, 38.39058840)
- Driver: GTiff
- Data type: int16
- Projection system: EPSG:32637
- Nodata: -32768.0

- Given names for spectral bands: 
   {'CO': 1, 'B': 2, 'G': 3, 'R': 4, 'RE1': 5, 'RE2': 6, 'RE3': 7, 'NIR': 8, 'WA': 9, 'SWIR1': 10, 'SWIR2': 11, 'SWIR3': 12}

Open an entire image

See help for initializing a Geoimage

help(rastereasy.open.__init__)

Help on method-wrapper:

__init__(*args, **kwargs) unbound builtins.object method
    Initialize self.  See help(type(self)) for accurate signature.

name_im='./data/demo/sentinel.tif'
image=rastereasy.open(name_im)

Make a color composition

See the dedicated example. By default, the three first bands are used

# By default, coordinates are lat/lon 
image.colorcomp()

# To have coordinates in pixel
image.colorcomp(extent='pixel')

# To remove coordinates 
image.colorcomp(extent='pixel', percentile=0.5)

Open a window of the image based on pixel coordinates

You must indicate

• the row/col coordinades of the north-west corner (deb_row,deb_col)

• the row/col coordinades of the south-east corner (end_row,end_col)

in a tuple ((deb_row,end_row),(deb_col,end_col))

deb_row=35
end_row=712
deb_col=40
end_col=450
area_pixel=((deb_row,end_row),(deb_col,end_col))
image=rastereasy.open(name_im,area=area_pixel)
image.info()
image.colorcomp(extent='pixel')

- Size of the image:
   - Rows (height): 677
   - Cols (width): 410
   - Bands: 12
- Spatial resolution: 10.0  meters / degree (depending on projection system)
- Central point latitude - longitude coordinates: (7.05240988, 38.36748819)
- Driver: GTiff
- Data type: int16
- Projection system: EPSG:32637
- Nodata: -32768.0

- Given names for spectral bands: 
   {'1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9, '10': 10, '11': 11, '12': 12}

Open a window of the image based on longitude / latitude coordinates

You must indicate

• the lat/lon coordinades of the north-west corner (lat1,lon1)

• the lat/lon coordinades of the south-east corner (lat2,lon2)

in a tuple ((lon1,lon2),(lat1,lat2))

lat1=7.06
lat2=7.02
lon1=38.36
lon2=38.41
area_ll=((lon1,lon2),(lat1,lat2))
image_window_latlon=rastereasy.open(name_im,area=area_ll,extent='latlon')
image_window_latlon.colorcomp()

Save an image

The function .save() enables to save an image

image_names.save('./data/results/save/save_image.tif')

Now we can reload it

image_reloaded=rastereasy.open('./data/results/save/save_image.tif')
image_reloaded.info()

- Size of the image:
   - Rows (height): 1000
   - Cols (width): 1000
   - Bands: 12
- Spatial resolution: 10.0  meters / degree (depending on projection system)
- Central point latitude - longitude coordinates: (7.04099599, 38.39058840)
- Driver: GTiff
- Data type: int16
- Projection system: EPSG:32637
- Nodata: -32768.0

- Given names for spectral bands: 
   {'CO': 1, 'B': 2, 'G': 3, 'R': 4, 'RE1': 5, 'RE2': 6, 'RE3': 7, 'NIR': 8, 'WA': 9, 'SWIR1': 10, 'SWIR2': 11, 'SWIR3': 12}

Read some informations

Apart from the .info() function, get_meta() outputs rasterio metadata

image_names.get_meta()

{'driver': 'GTiff',
 'dtype': 'int16',
 'nodata': -32768.0,
 'width': 1000,
 'height': 1000,
 'count': 12,
 'crs': CRS.from_wkt('PROJCS["WGS 84 / UTM zone 37N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",39],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32637"]]'),
 'transform': Affine(10.0, 0.0, 427690.0,
        0.0, -10.0, 783330.0)}

To get some more useful information

print('number of bands = ',image.nb_bands)
print('shape = ',image.shape)
print('spatial resolution = ',image.resolution)
print('lat / lon of central pixel = ',image.get_latlon_coordinates())

number of bands =  12
shape =  (677, 410)
spatial resolution =  10.0
lat / lon of central pixel =  (7.052409883832736, 38.367488186916184)

Values in the image

Values in some pixel locations

image=rastereasy.open(name_im)
# Extract the specrum in pixel 309,223
image[309,223]

array([ 867, 1144, 1456, 1960, 2246, 2324, 2444, 2380, 2566, 2597, 3252,
       3149], dtype=int16)

# Extract the specra in all cols of line 309
image[309,:]

array([[ 514,  514,  514, ...,  257,  257,  257],
       [ 751,  758,  596, ...,  269,  279,  281],
       [1084, 1112,  898, ...,  484,  483,  480],
       ...,
       [3079, 3079, 3079, ...,  187,  187,  187],
       [2775, 2555, 2555, ...,  147,  147,  147],
       [2399, 2000, 2000, ...,  115,  115,  117]],
      shape=(12, 1000), dtype=int16)

# Extract the image in band 3
image[:,:][3]

array([[ 567,  645,  690, ...,  324,  322,  335],
       [ 579,  590,  608, ...,  346,  342,  335],
       [ 558,  610,  645, ...,  338,  334,  327],
       ...,
       [1518, 1750, 1694, ...,  339,  348,  350],
       [1534, 1652, 1688, ...,  346,  342,  342],
       [1636, 1694, 1750, ...,  347,  339,  336]],
      shape=(1000, 1000), dtype=int16)

# all spectral values in a given pixel
row=20
col=10
print('spectral values in',row,',',col,' : ',image[row,col])
print('size of all spectral values in row',row,':', image[row,:].shape)
print('size of all spectral values in col',col,':', image[:,col].shape)
# check consistency
pixel_row = 15
pixel_col = 22
print('diff with table with values at row ',row,' in pixel col',pixel_col,':', image[row,:][:,pixel_col]-image[row,pixel_col])
print('diff with table with values at col ',col,' in pixel row',pixel_row,':', image[:,col][:,pixel_row]-image[pixel_row,col])

spectral values in 20 , 10  :  [ 499  542  869  813 1591 3169 3654 3717 3856 3704 2732 1716]
size of all spectral values in row 20 : (12, 1000)
size of all spectral values in col 10 : (12, 1000)
diff with table with values at row  20  in pixel col 22 : [0 0 0 0 0 0 0 0 0 0 0 0]
diff with table with values at col  10  in pixel row 15 : [0 0 0 0 0 0 0 0 0 0 0 0]

Min, Max, Sum, Mean, Std, Median, …

Global values

# Let's crop the image to have different shapes in row/col
image=rastereasy.open(name_im)
image.crop(area=((10,100),(50,70)),inplace=True)
print('Shape of the image (lig x col) : ',image.shape)
print('Number of channels of the image : ',image.nb_bands)

Shape of the image (lig x col) :  (90, 20)
Number of channels of the image :  12

### Global min, max, std, ...
print('Global minimum of the image', image.min())
print('Global minimum of the image', image.max())
print('Global std of the image', image.std())
print('Global mean of the image', image.mean())
print('Global median of the image', image.median())
print('Global sum of the image', image.sum())

Global minimum of the image 273
Global minimum of the image 3836
Global std of the image 890.1268204217575
Global mean of the image 1958.3739814814815
Global median of the image 2261.0
Global sum of the image 42300878

Values per row, col, band, pixel

help(image.min)

Help on method min in module rastereasy:

min(axis=None) method of rastereasy.Geoimage instance
    Calculate the minimum value along a specified axis.
    
    Parameters
    ----------
    axis : {'band', 'row', 'col', None}, optional
        The axis along which to compute the minimum:
        - 'band': Minimum across spectral bands for each pixel
        - 'row': Minimum across rows (lines) for each band and column
        - 'col': Minimum across columns for each band and row
        - 'pixel': Minimum across pixels for each bands
        - None: Global minimum of the entire image
        Default is None.
    
    Returns
    -------
    float or numpy.ndarray
        - If axis=None: A single value representing the global minimum
        - If axis='band': Array with shape (nb_rows,nb_cols) containing  mins along bands
        - If axis='row': Array with shape (nb_bands,nb_cols) containing mins along rows
        - If axis='col': Array with shape (nb_bands,nb_rows) containing  mins along cols
        - If axis='pixel': Array with shape (nb_bands) containing  mins along all pixels for each band
    
    Raises
    ------
    ValueError
        If an invalid axis is specified
    
    Examples
    --------
    >>> min_value = image.min()  # Global minimum value
    >>> print(f"Minimum pixel value: {min_value}")
    >>>
    >>> band_mins = image.min(axis='pixel')  # Minimum along all pixels for each band

for axis in ['band','row','col','pixel']:
    mini=image.min(axis=axis)
    print('Minimum on axis ',axis, 'gives a numpy table of size',mini.shape)

Minimum on axis  band gives a numpy table of size (90, 20)
Minimum on axis  row gives a numpy table of size (12, 20)
Minimum on axis  col gives a numpy table of size (12, 90)
Minimum on axis  pixel gives a numpy table of size (12,)

As we can see in the help,

• If axis=None: A single value representing the global minimum

• If axis='band': Array with shape (nb_rows,nb_cols) containing mins along bands

• If axis='row': Array with shape (nb_bands,nb_cols) containing mins along rows

• If axis='col': Array with shape (nb_bands,nb_rows) containing mins along cols

• If axis='pixel': Array with shape (nb_bands,) containing mins along all pixels for each band

The same holds for max, sum, mean, median

print('Overall min  of the image along the channels : ',image.min(axis='pixel'),'\n')
# As we can see, we have 12 values corresponding to the minimum in each image'

Overall min  of the image along the channels :  [ 372  273  557  497 1066 1878 1953 1830 2143 2456 2106 1329] 

help(image.max)
print('Maximum  of the image for each band: ', image.max(axis = 'band'),'\n')

Help on method max in module rastereasy:

max(axis=None) method of rastereasy.Geoimage instance
    Calculate the maximum value along a specified axis.
    
    Parameters
    ----------
    axis : {'band', 'row', 'col', None}, optional
        The axis along which to compute the maximum:
        - 'band': Maximum across spectral bands for each pixel
        - 'row': Maximum across rows (lines) for each band and column
        - 'col': Maximum across columns for each band and row
        - 'pixel': Maximum across pixels for each bands
        - None: Global maximum of the entire image
        Default is None.
    
    Returns
    -------
    float or numpy.ndarray
        - If axis=None: A single value representing the global maximum
        - If axis='band': Array with shape (nb_rows,nb_cols) containing  max along bands
        - If axis='row': Array with shape (nb_bands,nb_cols) containing max along rows
        - If axis='col': Array with shape (nb_bands,nb_rows) containing  max along cols
        - If axis='pixel': Array with shape (nb_bands) containing  maxs along all pixels for each band
    
    Raises
    ------
    ValueError
        If an invalid axis is specified
    
    Examples
    --------
    >>> max_value = image.max()  # Global maximum value
    >>> print(f"Maximum pixel value: {max_value}")
    >>>
    >>> band_maxs = image.max(axis='pixel')  # Maximum along all pixels for each band

Maximum  of the image for each band:  [[3225 3290 3290 ... 2807 2807 3135]
 [3304 3403 3403 ... 2625 2625 2849]
 [3304 3403 3403 ... 2625 2625 2849]
 ...
 [3135 2960 2960 ... 2922 2922 2997]
 [3135 2960 2960 ... 2922 2922 2997]
 [3062 3097 3097 ... 2895 2895 2954]] 

Plot image (colorcomp or bands)

Note on axes (pixel vs latlong)

By default, rastereasy plots images using latitude and longitude coordinates. If you want to plot with pixel coordinates, put the parameter extent = 'pixel'

Color composition via colorcomp function

On the entire image

image=rastereasy.open(name_im)
help(image.colorcomp)

Help on method colorcomp in module rastereasy:

colorcomp(bands=None, dest_name='', percentile=0.5, fig_size=(5, 5), title='', extent='latlon', zoom=None, pixel=True) method of rastereasy.Geoimage instance
    Create and display a color composite image from selected bands.
    
    This method creates an RGB color composite by assigning three bands to the red,
    green, and blue channels. It's useful for creating false color compositions,
    natural color images, or any three-band visualization.
    
    Parameters
    ----------
    bands : list of str, optional
        List of three band identifiers to use for the RGB composite (in order: R, G, B).
        Can be band names (e.g., ["NIR", "R", "G"]) or indices (e.g., ["4", "3", "2"]).
        If None, uses the first three bands in the image.
        Default is None.
    
    dest_name : str, optional
        Path to save the color composite image. If empty, the image is not saved.
        Default is ''.
    
    percentile : int, optional
        Percentile value for contrast stretching (e.g., 2 for a 2-98% stretch).
        This enhances the visual contrast of the image.
        Default is 0.5
    
    fig_size : tuple, optional
        Size of the figure in inches as (width, height).
        Default is DEF_FIG_SIZE.
    
    title : str, optional
        Title for the visualization.
        Default is ''.
    
    extent : {'latlon', 'pixel', None}, optional
        Type of extent to use for the plot:
        - 'latlon': Use latitude/longitude coordinates (default)
        - 'pixel': Use pixel coordinates
        - None: Don't show coordinate axes
    
    zoom : tuple, optional
        To plot  only a window of the image
            If based on pixel coordinates, you must indicate
            - the row/col coordinades of
                    the north-west corner (deb_row,deb_col)
            - the row/col coordinades of
                    the south-east corner (end_row,end_col)
            in a tuple  `zoom = ((deb_row,end_row),(deb_col,end_col))`
    
            If based on latitude/longitude coordinates, you must indicate
            - the lat/lon coordinades of the north-west corner (lat1,lon1)
            - the lat/lon coordinades of the south-east corner (lat2,lon2)
            `zoom = ((lon1,lon2),(lat1,lat2))`
        If not provide, perform on the entire image
    
    pixel : bool, optional
        Coordinate system flag, if zoom is given:
        - If True: Coordinates are interpreted as pixel indices
        - If False: Coordinates are interpreted as geographic coordinates
        Default is True.
    
    Returns
    -------
    None
        This method displays and/or saves the color composite but doesn't return any values.
    
    Raises
    ------
    ValueError
        If the image has only 2 bands, which is not enough for an RGB composite.
        If an invalid extent value is provided.
    
    Examples
    --------
    >>> # Create a natural color composite (for Landsat/Sentinel-2 style ordering)
    >>> image.colorcomp(bands=["R", "G", "B"])
    >>>
    >>> # Create a color-infrared composite (vegetation appears red)
    >>> image.colorcomp(bands=["NIR", "R", "G"], title="Color-Infrared Composite")
    >>>
    >>> # Zoom and save a false color composite
    >>> image.colorcomp(bands=["SWIR1", "NIR", "G"], dest_name="false_color.tif",zoom=((100,300),(200,400)))
    >>>
    >>> # Change the contrast stretch
    >>> image.colorcomp(bands=["R", "G", "B"], percentile=5)  # More aggressive stretch
    
    Notes
    -----
    Common band combinations for satellite imagery include:
    - Natural color: R, G, B (shows the scene as human eyes would see it)
    - Color-infrared: NIR, R, G (vegetation appears red, useful for vegetation analysis)
    - Agriculture: SWIR, NIR, B (highlights crop health and soil moisture)
    - Urban: SWIR, NIR, R (emphasizes urban areas and bare soil)

# colorcomp with 3 first bands and show pixel coordinates
image.colorcomp(extent='pixel')

On a zoom on the image

By specifying pixel coordinates

image.colorcomp(bands=['4','3','6'], zoom = ((200,400),(400,800)), extent='pixel')

By specifying lat/lon coordinates

image.colorcomp(bands=[8,8,3], zoom=((38.36,38.40),(7.07,7.04)), pixel=False)

Saving a color composition

image_names.colorcomp(bands=["R","G","B"],dest_name='./data/results/colorcomp/S2_colorcomp_R_G_B.tif')

image_names.colorcomp(bands=["R","G","NIR"], zoom=((38.36,38.40),(7.07,7.04)), pixel=False,dest_name='./data/results/colorcomp/test_colorcomp_R_G_NIR_zoom.tif')

By bands

help(image.visu)

Help on method visu in module rastereasy:

visu(bands=None, title='', percentile=0, fig_size=(5, 5), cmap=None, colorbar=False, extent='latlon', zoom=None, pixel=True) method of rastereasy.Geoimage instance
    Visualize one or more bands of the image.
    
    This method provides a flexible way to display individual bands or multiple bands
    as separate figures. Unlike colorcomp, which creates RGB composites, this method
    displays each band in grayscale or with a specified colormap.
    
    Parameters
    ----------
    bands : str, list of str, or None, optional
        The bands to visualize:
        - If None: Displays all bands separately
        - If a string: Displays a single specified band
        - If a list: Displays each specified band separately
        Default is None.
    
    title : str, optional
        Base title for the visualization. Band names will be appended.
        Default is ''.
    
    percentile : int, optional
        Percentile value for contrast stretching (e.g., 2 for a 2-98% stretch).
        Default is 2.
    
    fig_size : tuple, optional
        Size of the figure in inches as (width, height).
        Default is DEF_FIG_SIZE.
    
    cmap : str, optional
        Matplotlib colormap name to use for display.
        Examples: 'viridis', 'plasma', 'gray', 'RdYlGn'
        Default is None (uses matplotlib default).
    
    colorbar : bool, optional
        Whether to display a colorbar next to each image.
        Default is False.
    
    extent : {'latlon', 'pixel', None}, optional
        Type of extent to use for the plot:
        - 'latlon': Use latitude/longitude coordinates (default)
        - 'pixel': Use pixel coordinates
        - None: Don't show coordinate axes
    
    zoom : tuple, optional
        To visualize  only a window of the image
            If based on pixel coordinates, you must indicate
            - the row/col coordinades of
                    the north-west corner (deb_row,deb_col)
            - the row/col coordinades of
                    the south-east corner (end_row,end_col)
            in a tuple  `zoom = ((deb_row,end_row),(deb_col,end_col))`
    
            If based on latitude/longitude coordinates, you must indicate
            - the lat/lon coordinades of the north-west corner (lat1,lon1)
            - the lat/lon coordinades of the south-east corner (lat2,lon2)
            `zoom = ((lon1,lon2),(lat1,lat2))`
        If not provide, visualize the entire image
    
    pixel : bool, optional
        Coordinate system flag, if zoom is given:
        - If True: Coordinates are interpreted as pixel indices
        - If False: Coordinates are interpreted as geographic coordinates
        Default is True.
    
    
    Examples
    --------
    >>> # Visualize all bands
    >>> image.visu()
    >>>
    >>> # Visualize a single band with a colormap and colorbar
    >>> image.visu("NIR", cmap='plasma', colorbar=True, title="Near Infrared Band")
    >>>
    >>> # Visualize selected bands
    >>> image.visu(["Red", "NIR", "NDVI"], fig_size=(10, 8))
    >>>
    >>> # Visualize selected bands on a zoom
    >>> image.visu(["Red", "NIR", "NDVI"], fig_size=(10, 8), zoom = ((100,200),(450,600)))
    
    Notes
    -----
    This method is useful for:
    - Examining individual spectral bands in detail
    - Comparing several derived indices side by side
    - Applying different colormaps to highlight specific features
    - Visualizing single-band thematic data (e.g., elevation, classification results)

Visu af all bands

image.visu(fig_size=(5,5))

image.visu(bands=8)

image_names.visu(["SWIR1","NIR"])

Visu af som specific bands with pixel extent

image.visu(bands=['4','7','1'], extent = 'pixel')

Visu af som specific bands and zoom

image.visu(bands=['4','7','1'], extent = 'pixel', zoom=((600,800),(450,850)))

Histograms

help(image.hist)

Help on method hist in module rastereasy:

hist(**args) method of rastereasy.Geoimage instance
    Display histograms of the image data.
    
    This method provides a flexible way to visualize the distribution of pixel values
    in one or more bands of the image. It supports various customization options for
    the histogram display.
    
    Parameters
    ----------
    bands : str, int, list, optional
        The bands to visualize. If not specified, all bands are included.
        This can be band names (e.g., ["NIR", "R", "G"]) or indices (e.g., [4, 3, 2]).
    
    superpose : bool, optional
        If True, all histograms are plotted on the same figure. If False (default),
        each band gets its own separate histogram figure.
    
    bins : int, optional
        The number of bins for computing the histogram. Default is 100.
    
    xmin : float, optional
        The minimum value to plot on the x-axis. Values lower than this won't be displayed.
    
    xmax : float, optional
        The maximum value to plot on the x-axis. Values higher than this won't be displayed.
    
    title : str, optional
        The title for the histogram figure.
    
    histtype : str, optional
        The type of histogram to draw. Default is 'stepfilled'.
        Other options include 'bar', 'step', 'barstacked', etc.
    
    alpha : float, optional
        The transparency of the histogram bars (0.0 to 1.0). Default is 0.6.
    
    fig_size : tuple, optional
        The size of the figure in inches as (width, height). Default is DEF_FIG_SIZE.
    
    label : str or list of str, optional
        The labels for the histogram. If not provided, default labels will be created.
    
    zoom : tuple, optional
        To plot hist  only on a window of the image
            If based on pixel coordinates, you must indicate
            - the row/col coordinades of
                    the north-west corner (deb_row,deb_col)
            - the row/col coordinades of
                    the south-east corner (end_row,end_col)
            in a tuple  `zoom = ((deb_row,end_row),(deb_col,end_col))`
    
            If based on latitude/longitude coordinates, you must indicate
            - the lat/lon coordinades of the north-west corner (lat1,lon1)
            - the lat/lon coordinades of the south-east corner (lat2,lon2)
            `zoom = ((lon1,lon2),(lat1,lat2))`
        If not provide, plot hist of the entire image
    
    pixel : bool, optional
        Coordinate system flag, if zoom is given:
        - If True: Coordinates are interpreted as pixel indices
        - If False: Coordinates are interpreted as geographic coordinates
        Default is True.
    
    
    **args : dict, optional
        Additional keyword arguments passed to matplotlib's hist function.
    
    Returns
    -------
    None
        This method displays the histogram(s) but doesn't return any values.
    
    Examples
    --------
    >>> # Display histograms for all bands
    >>> image.hist(bins=100)
    >>>
    >>> # Display histogram for a single band with customization
    >>> image.hist(bands="NIR", bins=150, histtype='stepfilled',
    >>>            title="NIR Band Distribution", xmin=0, xmax=10000)
    >>>
    >>> # Superpose histograms from multiple bands
    >>> image.hist(bands=["NIR", "R", "G"], bins=100, superpose=True,
    >>>            alpha=0.7, fig_size=(10, 6))
    >>>
    >>> # Superpose histograms on a zoom from multiple bands
    >>> image.hist(bands=["NIR", "R", "G"], bins=100, superpose=True,
    >>>            alpha=0.7, fig_size=(10, 6), zoom = ((40,150),(100,300)))
    
    Notes
    -----
    This method is based on rasterio's show_hist function and supports most
    of matplotlib's histogram customization options. It's useful for understanding
    the distribution of values in your image and identifying potential issues like
    saturation, quantization, or outliers.

By bands

# Show all bands
image.hist(bins=150,histtype='stepfilled',fig_size=(5,5))

Superposition of bands

image_names.hist(bins=150,histtype='stepfilled',superpose=True, xmax=4500)

Show only some bands

Without superposition

image.hist(bands=["2","10"],bins=150,histtype='stepfilled',fig_size=(4,4),xmax=1500)
image.hist(bands=8,bins=150,histtype='stepfilled',label='Example of band')

With superposition

image.hist(bands=[8,4],bins=150,histtype='stepfilled',label='Example of band',superpose=True,xmin=0,xmax=4000)

image_names.hist(bands=["R","G","NIR"],bins=150,histtype='stepfilled',superpose = True,label=["R","band swir","bleu"],xmax=4000)

Histogram on a part of the image

Use the parameter zoom

Without superposition

image.hist(bands=["2","3"],zoom=((200,500),(300,400)), bins=150,histtype='stepfilled',fig_size=(4,4),xmax=1500)
image.hist(bands=8,zoom=((200,500),(300,400)), bins=150,histtype='stepfilled',label='Example of band')

With superposition

image.hist(bands=[8,4],zoom=((200,500),(300,400)),bins=150,histtype='stepfilled',label='Example of band',superpose=True,xmin=0,xmax=4000)

image_names.hist(bands=["R","G","NIR"],zoom=((200,500),(300,400)),bins=150,histtype='stepfilled',superpose = True,label=["R","band swir","bleu"],xmax=4000)