Download: https://osdyn.ifremer.fr/pyweb/notebooks/utils/plot/colorbar.ipynb

colorbars

How to specify the levels of a colorbar ?

import matplotlib as mpl
import matplotlib.pyplot as plt

from osdyn.config import data_sample
from osdyn.grcm.generic import moawi
from osdyn.utils.plot.colorbar import auto_scale, histo
from osdyn.utils.plot.colormap import get_cmap

mc = moawi(data_sample("mars_siggen.nc"), model="mars", varnames=["temp_sfc", "temp_bot"])
var = mc.ds.temp_bot

Found 1 files
[WARNING]:  cf.py in name2key - line: 1288
 No osdyn key for this variable : sc_w
[WARNING]:  cf.py in name2key - line: 1288
 No osdyn key for this variable : sc_t

mc.ds

<xarray.Dataset>
Dimensions:   (nz_g: 41, nz_c: 40, nt: 3, ny_c: 33, nx_c: 33, nx_g: 33, ny_g: 33)
Coordinates:
  * nz_g      (nz_g) float64 0.5 1.5 2.5 3.5 4.5 ... 36.5 37.5 38.5 39.5 40.5
  * nz_c      (nz_c) float64 1.0 2.0 3.0 4.0 5.0 ... 36.0 37.0 38.0 39.0 40.0
  * nt        (nt) int64 0 1 2
    lat_t     (ny_c, nx_c) float64 47.64 47.64 47.64 47.64 ... 49.08 49.08 49.08
    lon_t     (ny_c, nx_c) float64 -6.249 -6.182 -6.115 ... -4.237 -4.17 -4.102
  * nx_c      (nx_c) float32 1.0 11.0 21.0 31.0 41.0 ... 291.0 301.0 311.0 321.0
  * ny_c      (ny_c) float32 1.0 11.0 21.0 31.0 41.0 ... 291.0 301.0 311.0 321.0
    time      (nt) float64 3.524e+09 3.524e+09 3.524e+09
  * nx_g      (nx_g) float32 1.5 11.5 21.5 31.5 41.5 ... 291.5 301.5 311.5 321.5
  * ny_g      (ny_g) float32 1.5 11.5 21.5 31.5 41.5 ... 291.5 301.5 311.5 321.5
    sc_t      (nz_c) float32 -0.9875 -0.9625 -0.9375 ... -0.0625 -0.0375 -0.0125
    sc_w      (nz_g) float32 -1.0 -0.975 -0.95 -0.925 ... -0.05 -0.025 0.0
Data variables:
    h_t       (ny_c, nx_c) float32 -152.0 -159.6 -150.8 ... -95.97 -90.55 -92.11
    theta     float64 6.0
    b         float64 0.0
    sc_cs_t   (nz_c) float32 -0.9277 -0.7985 -0.6873 ... -0.001125 -0.0003722
    sc_cs_w   (nz_g) float32 -1.0 -0.8607 -0.7408 ... -0.00151 -0.0007464 0.0
    hc        (ny_c, nx_c) float32 9.0 9.0 9.0 9.0 9.0 ... 9.0 9.0 9.0 9.0 9.0
    temp_sfc  (nt, ny_c, nx_c) float32 nan nan nan nan ... 14.56 14.67 14.72
    temp_bot  (nt, ny_c, nx_c) float32 nan nan nan nan ... 14.54 14.63 14.71
Attributes:
    model:    Mars

xarray.Dataset

• Dimensions:
  • nz_g: 41
  • nz_c: 40
  • nt: 3
  • ny_c: 33
  • nx_c: 33
  • nx_g: 33
  • ny_g: 33
• Coordinates: (12)
  • nz_g
    (nz_g)
    float64
    0.5 1.5 2.5 3.5 ... 38.5 39.5 40.5

    axis :

    Z

    standard_name :

    z_grid_index_at_g_location

    long_name :

    z-dimension of the grid at G location

    positive :

    up

    array([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5,
           12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5,
           24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5,
           36.5, 37.5, 38.5, 39.5, 40.5])

  • nz_c
    (nz_c)
    float64
    1.0 2.0 3.0 4.0 ... 38.0 39.0 40.0

    axis :

    Z

    standard_name :

    z_grid_index_at_c_location

    long_name :

    z-dimension of the grid at C location

    positive :

    up

    array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13., 14.,
           15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28.,
           29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.])

  • nt
    (nt)
    int64
    0 1 2

    axis :

    T

    standard_name :

    t_index

    long_name :

    t-dimension of the grid

    array([0, 1, 2])

  • lat_t
    (ny_c, nx_c)
    float64
    ...

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    array([[47.639871, 47.639871, 47.639871, ..., 47.639871, 47.639871, 47.639871],
           [47.684887, 47.684887, 47.684887, ..., 47.684887, 47.684887, 47.684887],
           [47.729904, 47.729904, 47.729904, ..., 47.729904, 47.729904, 47.729904],
           ...,
           [48.990354, 48.990354, 48.990354, ..., 48.990354, 48.990354, 48.990354],
           [49.03537 , 49.03537 , 49.03537 , ..., 49.03537 , 49.03537 , 49.03537 ],
           [49.080386, 49.080386, 49.080386, ..., 49.080386, 49.080386, 49.080386]])

  • lon_t
    (ny_c, nx_c)
    float64
    ...

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    array([[-6.24878 , -6.181707, -6.114634, ..., -4.236585, -4.169512, -4.102439],
           [-6.24878 , -6.181707, -6.114634, ..., -4.236585, -4.169512, -4.102439],
           [-6.24878 , -6.181707, -6.114634, ..., -4.236585, -4.169512, -4.102439],
           ...,
           [-6.24878 , -6.181707, -6.114634, ..., -4.236585, -4.169512, -4.102439],
           [-6.24878 , -6.181707, -6.114634, ..., -4.236585, -4.169512, -4.102439],
           [-6.24878 , -6.181707, -6.114634, ..., -4.236585, -4.169512, -4.102439]])

  • nx_c
    (nx_c)
    float32
    1.0 11.0 21.0 ... 301.0 311.0 321.0

    axis :

    X

    standard_name :

    x_grid_index_at_c_location

    long_name :

    x-dimension of the grid at C location

    array([  1.,  11.,  21.,  31.,  41.,  51.,  61.,  71.,  81.,  91., 101., 111.,
           121., 131., 141., 151., 161., 171., 181., 191., 201., 211., 221., 231.,
           241., 251., 261., 271., 281., 291., 301., 311., 321.], dtype=float32)

  • ny_c
    (ny_c)
    float32
    1.0 11.0 21.0 ... 301.0 311.0 321.0

    axis :

    Y

    standard_name :

    y_grid_index_at_c_location

    long_name :

    y-dimension of the grid at C location

    array([  1.,  11.,  21.,  31.,  41.,  51.,  61.,  71.,  81.,  91., 101., 111.,
           121., 131., 141., 151., 161., 171., 181., 191., 201., 211., 221., 231.,
           241., 251., 261., 271., 281., 291., 301., 311., 321.], dtype=float32)

  • time
    (nt)
    float64
    3.524e+09 3.524e+09 3.524e+09

    long_name :

    Time

    standard_name :

    time

    units :

    seconds since 01-JAN-1900 00:00:00

    time_origin :

    01-JAN-1900 00:00:00

    conventions :

    relative number of seconds with no decimal part

    array([3.523824e+09, 3.523914e+09, 3.524004e+09])

  • nx_g
    (nx_g)
    float32
    1.5 11.5 21.5 ... 301.5 311.5 321.5

    axis :

    X

    standard_name :

    x_grid_index_at_g_location

    long_name :

    x-dimension of the grid at G location

    array([  1.5,  11.5,  21.5,  31.5,  41.5,  51.5,  61.5,  71.5,  81.5,  91.5,
           101.5, 111.5, 121.5, 131.5, 141.5, 151.5, 161.5, 171.5, 181.5, 191.5,
           201.5, 211.5, 221.5, 231.5, 241.5, 251.5, 261.5, 271.5, 281.5, 291.5,
           301.5, 311.5, 321.5], dtype=float32)

  • ny_g
    (ny_g)
    float32
    1.5 11.5 21.5 ... 301.5 311.5 321.5

    axis :

    Y

    standard_name :

    y_grid_index_at_g_location

    long_name :

    y-dimension of the grid at G location

    array([  1.5,  11.5,  21.5,  31.5,  41.5,  51.5,  61.5,  71.5,  81.5,  91.5,
           101.5, 111.5, 121.5, 131.5, 141.5, 151.5, 161.5, 171.5, 181.5, 191.5,
           201.5, 211.5, 221.5, 231.5, 241.5, 251.5, 261.5, 271.5, 281.5, 291.5,
           301.5, 311.5, 321.5], dtype=float32)

  • sc_t
    (nz_c)
    float32
    -0.9875 -0.9625 ... -0.0375 -0.0125

    long_name :

    Sigma level

    c_grid_axis_shift :

    0.0

    positive :

    up

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_at_t_location

    formula_terms :

    s: level eta: XE depth: h_t a: sc_theta_sfc b: sc_theta_bot depth_c: sc_hc_t

    formula_definition :

    z(n,k,j,i) = eta(n,j,i)*(1+s(k)) + depth_c*s(k) + (depth(j,i)-depth_c)*C(k)

    units :

    array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375,
           -0.8125, -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625,
           -0.6375, -0.6125, -0.5875, -0.5625, -0.5375, -0.5125, -0.4875,
           -0.4625, -0.4375, -0.4125, -0.3875, -0.3625, -0.3375, -0.3125,
           -0.2875, -0.2625, -0.2375, -0.2125, -0.1875, -0.1625, -0.1375,
           -0.1125, -0.0875, -0.0625, -0.0375, -0.0125], dtype=float32)

  • sc_w
    (nz_g)
    float32
    -1.0 -0.975 -0.95 ... -0.025 0.0

    long_name :

    Sigma level

    c_grid_axis_shift :

    -0.5

    positive :

    up

    valid_min :

    -1.1

    valid_max :

    0.1

    standard_name :

    ocean_s_coordinate_at_w_location

    formula_terms :

    s: level_w eta: XE depth: h_t a: sc_theta_sfc b: sc_theta_bot depth_c: sc_hc_t

    formula_definition :

    z(n,k,j,i) = eta(n,j,i)*(1+s_w(k)) + depth_c*s_w(k) + (depth(j,i)-depth_c)*C_w(k)

    units :

    array([-1.        , -0.975     , -0.95      , -0.925     , -0.9       ,
           -0.875     , -0.85      , -0.825     , -0.8       , -0.775     ,
           -0.75      , -0.725     , -0.7       , -0.675     , -0.65      ,
           -0.625     , -0.6       , -0.575     , -0.55      , -0.525     ,
           -0.5       , -0.47500002, -0.45      , -0.425     , -0.39999998,
           -0.375     , -0.35000002, -0.325     , -0.3       , -0.27499998,
           -0.25      , -0.225     , -0.2       , -0.175     , -0.15      ,
           -0.125     , -0.09999999, -0.075     , -0.05      , -0.025     ,
            0.        ], dtype=float32)

• Data variables: (8)
  • h_t
    (ny_c, nx_c)
    float32
    -152.0 -159.6 ... -90.55 -92.11

    long_name :

    bathymetry relative to the mean level

    standard_name :

    model_sea_floor_depth_below_sea_level

    units :

    m

    valid_min :

    -10000.0

    valid_max :

    50.0

    array([[-152.04868 , -159.56192 , -150.7655  , ...,  -97.09141 ,  -93.74322 ,
             -89.59828 ],
           [-133.128   , -158.35814 , -154.55916 , ...,  -90.37972 ,  -82.5452  ,
             -57.282276],
           [-150.63385 , -154.5106  , -151.86385 , ...,  -51.368973,  -56.16341 ,
             -49.97957 ],
           ...,
           [-124.79165 , -122.99923 , -122.56928 , ...,  -95.41551 ,  -96.07728 ,
             -92.99572 ],
           [-122.30562 , -122.09388 , -119.7059  , ...,  -96.03376 ,  -95.36986 ,
             -94.08709 ],
           [-120.96553 , -119.506615, -118.628685, ...,  -95.97394 ,  -90.55442 ,
             -92.11184 ]], dtype=float32)

  • theta
    ()
    float32
    ...

    long_name :

    surface control parameter

    standard_name :

    ocean_s_coordinate_surface_control

    units :

    valid_min :

    0.0

    valid_max :

    20.0

    array(6.)

  • b
    ()
    float32
    ...

    long_name :

    bottom control parameter

    standard_name :

    ocean_s_coordinate_bottom_control

    units :

    valid_min :

    0.0

    valid_max :

    1.0

    array(0.)

  • sc_cs_t
    (nz_c)
    float32
    ...

    long_name :

    C(s) law at mid layer

    standard_name :

    ocean_s_coordinate_function_at_midlayer

    units :

    valid_min :

    -2.0

    valid_max :

    2.0

    formula_definition :

    C(k) = (1-b)*sinh(a*s(k))/sinh(a) + b*[tanh(a*(s(k)+0.5))-tanh(0.5*a)]/(2*tanh(0.5*a))

    array([-9.277428e-01, -7.985132e-01, -6.872846e-01, -5.915489e-01,
           -5.091474e-01, -4.382237e-01, -3.771782e-01, -3.246356e-01,
           -2.794108e-01, -2.404844e-01, -2.069792e-01, -1.781396e-01,
           -1.533159e-01, -1.319481e-01, -1.135548e-01, -9.772123e-02,
           -8.409049e-02, -7.235538e-02, -6.225132e-02, -5.355052e-02,
           -4.605690e-02, -3.960148e-02, -3.403877e-02, -2.924336e-02,
           -2.510718e-02, -2.153697e-02, -1.845224e-02, -1.578346e-02,
           -1.347048e-02, -1.146116e-02, -9.710197e-03, -8.178125e-03,
           -6.830404e-03, -5.636654e-03, -4.569970e-03, -3.606300e-03,
           -2.723926e-03, -1.902955e-03, -1.124881e-03, -3.721638e-04],
          dtype=float32)

  • sc_cs_w
    (nz_g)
    float32
    ...

    long_name :

    C(s) law at interface

    standard_name :

    ocean_s_coordinate_function_at_interface

    units :

    valid_min :

    -2.0

    valid_max :

    2.0

    formula_definition :

    C_w(k) = (1-b)*sinh(a*s_w(k))/sinh(a) + b*[tanh(a*(s_w(k)+0.5))-tanh(0.5*a)]/(2*tanh(0.5*a))

    array([-1.000000e+00, -8.607064e-01, -7.408143e-01, -6.376225e-01,
           -5.488036e-01, -4.723565e-01, -4.065572e-01, -3.499223e-01,
           -3.011757e-01, -2.592181e-01, -2.231040e-01, -1.920192e-01,
           -1.652627e-01, -1.422318e-01, -1.224070e-01, -1.053416e-01,
           -9.065080e-02, -7.800344e-02, -6.711452e-02, -5.773845e-02,
           -4.966396e-02, -4.270902e-02, -3.671680e-02, -3.155229e-02,
           -2.709902e-02, -2.325663e-02, -1.993850e-02, -1.706982e-02,
           -1.458593e-02, -1.243084e-02, -1.055598e-02, -8.919064e-03,
           -7.483208e-03, -6.216038e-03, -5.088993e-03, -4.076664e-03,
           -3.156232e-03, -2.306950e-03, -1.509670e-03, -7.464220e-04,
            0.000000e+00], dtype=float32)

  • hc
    (ny_c, nx_c)
    float32
    ...

    long_name :

    depth over which the resolution is increased

    standard_name :

    ocean_s_coordinate_thick_of_surface_resolution

    units :

    m

    valid_min :

    -20.0

    valid_max :

    500.0

    array([[9., 9., 9., ..., 9., 9., 9.],
           [9., 9., 9., ..., 9., 9., 9.],
           [9., 9., 9., ..., 9., 9., 9.],
           ...,
           [9., 9., 9., ..., 9., 9., 9.],
           [9., 9., 9., ..., 9., 9., 9.],
           [9., 9., 9., ..., 9., 9., 9.]], dtype=float32)

  • temp_sfc
    (nt, ny_c, nx_c)
    float32
    ...

    long_name :

    temperature

    standard_name :

    sea_water_potential_temperature

    units :

    degrees_Celsius

    valid_min :

    -5.0

    valid_max :

    50.0

    array([[[      nan,       nan, ...,       nan,       nan],
            [      nan, 16.999102, ..., 17.718267, 17.389494],
            ...,
            [      nan, 16.597242, ..., 14.719355, 14.742514],
            [      nan, 16.643784, ..., 14.672859, 14.654667]],
    
           [[      nan,       nan, ...,       nan,       nan],
            [      nan, 17.300663, ..., 17.770378, 17.724146],
            ...,
            [      nan, 16.892508, ..., 14.70472 , 14.757037],
            [      nan, 16.918776, ..., 14.685861, 14.698566]],
    
           [[      nan,       nan, ...,       nan,       nan],
            [      nan, 16.844296, ..., 17.98171 , 17.862837],
            ...,
            [      nan, 17.003305, ..., 14.701832, 14.764549],
            [      nan, 17.045088, ..., 14.672614, 14.719799]]], dtype=float32)

  • temp_bot
    (nt, ny_c, nx_c)
    float32
    ...

    long_name :

    temperature

    standard_name :

    sea_water_potential_temperature

    units :

    degrees_Celsius

    valid_min :

    -5.0

    valid_max :

    50.0

    array([[[      nan,       nan, ...,       nan,       nan],
            [      nan, 12.603567, ..., 12.539548, 14.696376],
            ...,
            [      nan, 11.343596, ..., 14.674865, 14.750368],
            [      nan, 11.280701, ..., 14.672324, 14.66119 ]],
    
           [[      nan,       nan, ...,       nan,       nan],
            [      nan, 12.607613, ..., 12.655465, 15.811017],
            ...,
            [      nan, 11.382615, ..., 14.673049, 14.76417 ],
            [      nan, 11.327298, ..., 14.665243, 14.707743]],
    
           [[      nan,       nan, ...,       nan,       nan],
            [      nan, 12.606469, ..., 12.908276, 15.580681],
            ...,
            [      nan, 11.421063, ..., 14.657363, 14.74859 ],
            [      nan, 11.378323, ..., 14.629726, 14.712269]]], dtype=float32)

• Attributes: (1)

  model :

  Mars

Classic quick search

The data are used to get the minimum and maximum values

scale = auto_scale(var)
scale

array([11., 12., 13., 14., 15., 16., 17., 18.])

def plot(scale):
    cmap = get_cmap("cmo.thermal")
    norm = mpl.colors.BoundaryNorm(scale, ncolors=cmap.N, clip=True)
    plt.figure()
    c = plt.imshow(var[-1,...], cmap="cmo.thermal", norm=norm)
    # plt.contour(mc.ds.temp_sfc.values[-1,...], cmap='cmo.thermal', levels=scale)
    plt.colorbar(c)
    plt.show()

plot(scale)

scale = auto_scale(var, nmax=11)
scale

array([11., 12., 13., 14., 15., 16., 17., 18.])

scale_n19 = auto_scale(var, nmax=19)
scale_n19

array([11. , 11.5, 12. , 12.5, 13. , 13.5, 14. , 14.5, 15. , 15.5, 16. ,
       16.5, 17. , 17.5])

plot(scale_n19)

The colorbar separators flatten the histogram of the data

Get the range

scalehist = auto_scale(var, nmax=12, flathisto=True, **{"show": False})
scalehist

array([11., 12., 13., 14., 15., 16., 17.])

scalehist = auto_scale(var, nmax=20, flathisto=True, **{"show": True})
scalehist

array([11.  , 11.5 , 11.75, 12.  , 12.25, 12.5 , 12.75, 13.  , 13.25,
       13.5 , 13.75, 14.  , 14.25, 14.5 , 14.75, 15.  , 15.5 , 16.  ,
       17.  ])

len(scalehist)

19

plot(scalehist)

The scale without flattening the histogram differs although we ask for the same number of separators and the same bounds

scale_n19 = auto_scale(var, nmax=19)
scale_n19

array([11. , 11.5, 12. , 12.5, 13. , 13.5, 14. , 14.5, 15. , 15.5, 16. ,
       16.5, 17. , 17.5])

scale_n19 = auto_scale(var, nmax=19, vmin=11, vmax=17)
scale_n19

array([11. , 11.5, 12. , 12.5, 13. , 13.5, 14. , 14.5, 15. , 15.5, 16. ,
       16.5, 17. ])

Check the repartition of the values

hist, bins = histo(var, bins=scalehist, show=True)

hist, bins = histo(var, bins=scale_n19, show=True)