Standard procedure

Here is a standard procedure based on pyXpcm. This will show you how to create a model, how to fit/train it, how to classify data and to visualise results.

Create a model

Let’s import the Profile Classification Model (PCM) constructor:

[2]:

from pyxpcm.models import pcm

A PCM can be created independently of any dataset using the class constructor.

To be created a PCM requires a number of classes (or clusters) and a dictionary to define the list of features and their vertical axis:

[3]:

z = np.arange(0.,-1000,-10.)
pcm_features = {'temperature': z, 'salinity':z}

We can now instantiate a PCM, say with 8 classes:

[4]:

m = pcm(K=8, features=pcm_features)
m

[4]:

<pcm 'gmm' (K: 8, F: 2)>
Number of class: 8
Number of feature: 2
Feature names: odict_keys(['temperature', 'salinity'])
Fitted: False
Feature: 'temperature'
         Interpoler: <class 'pyxpcm.utils.Vertical_Interpolator'>
         Scaler: 'normal', <class 'sklearn.preprocessing._data.StandardScaler'>
         Reducer: True, <class 'sklearn.decomposition._pca.PCA'>
Feature: 'salinity'
         Interpoler: <class 'pyxpcm.utils.Vertical_Interpolator'>
         Scaler: 'normal', <class 'sklearn.preprocessing._data.StandardScaler'>
         Reducer: True, <class 'sklearn.decomposition._pca.PCA'>
Classifier: 'gmm', <class 'sklearn.mixture._gaussian_mixture.GaussianMixture'>

Here we created a PCM with 8 classes (K=8) and 2 features (F=2) that are temperature and salinity profiles defined between the surface and 1000m depth.

We furthermore note the list of transform methods that will be used to preprocess each of the features (see the preprocessing documentation page for more details).

Note that the number of classes and features are PCM properties accessible at pyxpcm.pcm.K and pyxpcm.pcm.F.

Load training data

pyXpcm is able to work with both gridded datasets (eg: model outputs with longitude,latitude,time dimensions) and collection of profiles (eg: Argo, XBT, CTD section profiles).

In this example, let’s import a sample of North Atlantic Argo data that come with pyxpcm.pcm:

[5]:

import pyxpcm
ds = pyxpcm.tutorial.open_dataset('argo').load()
print(ds)

<xarray.Dataset>
Dimensions:    (DEPTH: 282, N_PROF: 7560)
Coordinates:
  * DEPTH      (DEPTH) float32 0.0 -5.0 -10.0 -15.0 ... -1395.0 -1400.0 -1405.0
Dimensions without coordinates: N_PROF
Data variables:
    LATITUDE   (N_PROF) float32 ...
    LONGITUDE  (N_PROF) float32 ...
    TIME       (N_PROF) datetime64[ns] ...
    DBINDEX    (N_PROF) float64 ...
    TEMP       (N_PROF, DEPTH) float32 ...
    PSAL       (N_PROF, DEPTH) float32 ...
    SIG0       (N_PROF, DEPTH) float32 ...
    BRV2       (N_PROF, DEPTH) float32 ...
Attributes:
    Sample test prepared by:  G. Maze
    Institution:              Ifremer/LOPS
    Data source DOI:          10.17882/42182

Fit the model on data

Fitting can be done on any dataset coherent with the PCM definition, in a sense that it must have the feature variables of the PCM.

To tell the PCM model how to identify features in any xarray.Dataset, we need to provide a dictionary of variable names mapping:

[6]:

features_in_ds = {'temperature': 'TEMP', 'salinity': 'PSAL'}

which means that the PCM feature temperature is to be found in the dataset variables TEMP.

We also need to specify what is the vertical dimension of the dataset variables:

[7]:

features_zdim='DEPTH'

Now we’re ready to fit the model on the this dataset:

[8]:

m.fit(ds, features=features_in_ds, dim=features_zdim)
m

[8]:

<pcm 'gmm' (K: 8, F: 2)>
Number of class: 8
Number of feature: 2
Feature names: odict_keys(['temperature', 'salinity'])
Fitted: True
Feature: 'temperature'
         Interpoler: <class 'pyxpcm.utils.Vertical_Interpolator'>
         Scaler: 'normal', <class 'sklearn.preprocessing._data.StandardScaler'>
         Reducer: True, <class 'sklearn.decomposition._pca.PCA'>
Feature: 'salinity'
         Interpoler: <class 'pyxpcm.utils.Vertical_Interpolator'>
         Scaler: 'normal', <class 'sklearn.preprocessing._data.StandardScaler'>
         Reducer: True, <class 'sklearn.decomposition._pca.PCA'>
Classifier: 'gmm', <class 'sklearn.mixture._gaussian_mixture.GaussianMixture'>
         log likelihood of the training set: 38.784750

Note

pyXpcm can also identify PCM features and axis within a xarray.DataSet with variable attributes. From the example above we can set:

ds['TEMP'].attrs['feature_name'] = 'temperature'
ds['PSAL'].attrs['feature_name'] = 'salinity'
ds['DEPTH'].attrs['axis'] = 'Z'

And then simply call the fit method without arguments:

m.fit(ds)

Note that if data follows the CF the vertical dimension axis attribute should already be set to Z.

Classify data

Now that the PCM is fitted, we can predict the classification results like:

[9]:

m.predict(ds, features=features_in_ds, inplace=True)
ds

[9]:
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • DEPTH: 282
    • N_PROF: 7560
    • N_PROF
      (N_PROF)
      int64
      0 1 2 3 4 ... 7556 7557 7558 7559
      array([   0,    1,    2, ..., 7557, 7558, 7559])
    • DEPTH
      (DEPTH)
      float32
      0.0 -5.0 -10.0 ... -1400.0 -1405.0
      axis :
      Z
      standard_name :
      depth
      long_name :
      Vertical Distance Below the Surface
      convention :
      Negative, downward oriented
      units :
      meters
      positive :
      up
      array([    0.,    -5.,   -10., ..., -1395., -1400., -1405.], dtype=float32)
    • LATITUDE
      (N_PROF)
      float32
      ...
      axis :
      Y
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      array([27.122, 27.818, 27.452, ...,  4.243,  4.15 ,  4.44 ], dtype=float32)
    • LONGITUDE
      (N_PROF)
      float32
      ...
      axis :
      X
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      array([-7.4860e+01, -7.5600e+01, -7.4949e+01, ..., -1.2630e+00, -8.2100e-01,
       -2.0000e-03], dtype=float32)
    • TIME
      (N_PROF)
      datetime64[ns]
      ...
      standard_name :
      time
      short_name :
      time
      long_name :
      Time of Measurement
      array(['2008-06-23T13:07:30.000000000', '2011-05-22T12:46:24.375000064',
       '2011-07-11T12:54:50.624999936', ..., '2014-03-17T05:44:31.875000064',
       '2014-03-27T03:05:37.500000000', '2013-03-09T14:52:58.124999936'],
      dtype='datetime64[ns]')
    • DBINDEX
      (N_PROF)
      float64
      ...
      short_name :
      index
      long_name :
      Profile index within the complete database
      array([14840., 16215., 16220., ...,  8556.,  8557., 10628.])
    • TEMP
      (N_PROF, DEPTH)
      float32
      27.422163 27.422163 ... 4.391791
      long_name :
      Sea Temperature In-Situ ITS-90 Scale
      standard_name :
      sea_water_temperature
      units :
      degree_Celsius
      valid_min :
      -2.5.f
      valid_max :
      40.f
      C_format :
      %9.3f
      FORTRAN_format :
      F9.3
      resolution :
      0.001f
      array([[27.422163, 27.422163, 27.29007 , ...,  4.436046,  4.423681,  4.411316],
       [25.129957, 25.129957, 24.970064, ...,  4.757417,  4.743126,  4.728835],
       [28.132914, 28.132914, 27.969038, ...,  4.371902,  4.356699,  4.341496],
       ...,
       [28.722956, 28.722956, 28.721252, ...,  4.275817,  4.270741,  4.266666],
       [28.643309, 28.643309, 28.64578 , ...,  4.292866,  4.285667,  4.278265],
       [29.249382, 29.249382, 29.13765 , ...,  4.412214,  4.403805,  4.391791]],
      dtype=float32)
    • PSAL
      (N_PROF, DEPTH)
      float32
      36.35267 36.35267 ... 34.910286
      long_name :
      Practical Salinity
      standard_name :
      sea_water_salinity
      units :
      psu
      valid_min :
      2.f
      valid_max :
      41.f
      C_format :
      %9.3f
      FORTRAN_format :
      F9.3
      resolution :
      0.001f
      array([[36.35267 , 36.35267 , 36.468353, ..., 35.01112 , 35.010513, 35.009903],
       [36.508953, 36.508953, 36.502014, ..., 35.029182, 35.02817 , 35.027157],
       [36.492146, 36.492146, 36.514534, ..., 34.995472, 34.994053, 34.992634],
       ...,
       [34.973022, 34.973022, 34.973873, ..., 34.92445 , 34.925774, 34.92674 ],
       [35.184   , 35.184   , 35.184   , ..., 34.931606, 34.933006, 34.934402],
       [35.05187 , 35.05187 , 35.05117 , ..., 34.9081  , 34.909523, 34.910286]],
      dtype=float32)
    • SIG0
      (N_PROF, DEPTH)
      float32
      ...
      standard_name :
      sea_water_density
      long_name :
      Potential Density Referenced to Surface
      units :
      kg/m^3
      [2131920 values with dtype=float32]
    • BRV2
      (N_PROF, DEPTH)
      float32
      ...
      standard_name :
      N2
      long_name :
      Brunt-Vaisala Frequency Squared
      units :
      1/s^2
      [2131920 values with dtype=float32]
    • PCM_LABELS
      (N_PROF)
      int64
      1 1 1 1 1 1 1 1 ... 3 3 3 3 3 3 3 3
      long_name :
      PCM labels
      units :
      valid_min :
      0
      valid_max :
      7
      llh :
      38.784750479707895
      _pyXpcm_cleanable :
      1
      array([1, 1, 1, ..., 3, 3, 3])
  • Sample test prepared by :
    G. Maze
    Institution :
    Ifremer/LOPS
    Data source DOI :
    10.17882/42182

Prediction labels are automatically added to the dataset as PCM_LABELS because the option inplace was set to True. We didn’t specify the dim option because our dataset is CF compliant.

pyXpcm use a GMM classifier by default, which is a fuzzy classifier. So we can also predict the probability of each classes for all profiles, the so-called posteriors:

[10]:

m.predict_proba(ds, features=features_in_ds, inplace=True)
ds

[10]:
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • DEPTH: 282
    • N_PROF: 7560
    • pcm_class: 8
    • N_PROF
      (N_PROF)
      int64
      0 1 2 3 4 ... 7556 7557 7558 7559
      array([   0,    1,    2, ..., 7557, 7558, 7559])
    • DEPTH
      (DEPTH)
      float32
      0.0 -5.0 -10.0 ... -1400.0 -1405.0
      axis :
      Z
      standard_name :
      depth
      long_name :
      Vertical Distance Below the Surface
      convention :
      Negative, downward oriented
      units :
      meters
      positive :
      up
      array([    0.,    -5.,   -10., ..., -1395., -1400., -1405.], dtype=float32)
    • LATITUDE
      (N_PROF)
      float32
      27.122 27.818 27.452 ... 4.15 4.44
      axis :
      Y
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      array([27.122, 27.818, 27.452, ...,  4.243,  4.15 ,  4.44 ], dtype=float32)
    • LONGITUDE
      (N_PROF)
      float32
      -74.86 -75.6 ... -0.821 -0.002
      axis :
      X
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      array([-7.4860e+01, -7.5600e+01, -7.4949e+01, ..., -1.2630e+00, -8.2100e-01,
       -2.0000e-03], dtype=float32)
    • TIME
      (N_PROF)
      datetime64[ns]
      2008-06-23T13:07:30 ... 2013-03-09T14:52:58.124999936
      standard_name :
      time
      short_name :
      time
      long_name :
      Time of Measurement
      array(['2008-06-23T13:07:30.000000000', '2011-05-22T12:46:24.375000064',
       '2011-07-11T12:54:50.624999936', ..., '2014-03-17T05:44:31.875000064',
       '2014-03-27T03:05:37.500000000', '2013-03-09T14:52:58.124999936'],
      dtype='datetime64[ns]')
    • DBINDEX
      (N_PROF)
      float64
      1.484e+04 1.622e+04 ... 1.063e+04
      short_name :
      index
      long_name :
      Profile index within the complete database
      array([14840., 16215., 16220., ...,  8556.,  8557., 10628.])
    • TEMP
      (N_PROF, DEPTH)
      float32
      27.422163 27.422163 ... 4.391791
      long_name :
      Sea Temperature In-Situ ITS-90 Scale
      standard_name :
      sea_water_temperature
      units :
      degree_Celsius
      valid_min :
      -2.5.f
      valid_max :
      40.f
      C_format :
      %9.3f
      FORTRAN_format :
      F9.3
      resolution :
      0.001f
      array([[27.422163, 27.422163, 27.29007 , ...,  4.436046,  4.423681,  4.411316],
       [25.129957, 25.129957, 24.970064, ...,  4.757417,  4.743126,  4.728835],
       [28.132914, 28.132914, 27.969038, ...,  4.371902,  4.356699,  4.341496],
       ...,
       [28.722956, 28.722956, 28.721252, ...,  4.275817,  4.270741,  4.266666],
       [28.643309, 28.643309, 28.64578 , ...,  4.292866,  4.285667,  4.278265],
       [29.249382, 29.249382, 29.13765 , ...,  4.412214,  4.403805,  4.391791]],
      dtype=float32)
    • PSAL
      (N_PROF, DEPTH)
      float32
      36.35267 36.35267 ... 34.910286
      long_name :
      Practical Salinity
      standard_name :
      sea_water_salinity
      units :
      psu
      valid_min :
      2.f
      valid_max :
      41.f
      C_format :
      %9.3f
      FORTRAN_format :
      F9.3
      resolution :
      0.001f
      array([[36.35267 , 36.35267 , 36.468353, ..., 35.01112 , 35.010513, 35.009903],
       [36.508953, 36.508953, 36.502014, ..., 35.029182, 35.02817 , 35.027157],
       [36.492146, 36.492146, 36.514534, ..., 34.995472, 34.994053, 34.992634],
       ...,
       [34.973022, 34.973022, 34.973873, ..., 34.92445 , 34.925774, 34.92674 ],
       [35.184   , 35.184   , 35.184   , ..., 34.931606, 34.933006, 34.934402],
       [35.05187 , 35.05187 , 35.05117 , ..., 34.9081  , 34.909523, 34.910286]],
      dtype=float32)
    • SIG0
      (N_PROF, DEPTH)
      float32
      ...
      standard_name :
      sea_water_density
      long_name :
      Potential Density Referenced to Surface
      units :
      kg/m^3
      [2131920 values with dtype=float32]
    • BRV2
      (N_PROF, DEPTH)
      float32
      ...
      standard_name :
      N2
      long_name :
      Brunt-Vaisala Frequency Squared
      units :
      1/s^2
      [2131920 values with dtype=float32]
    • PCM_LABELS
      (N_PROF)
      int64
      1 1 1 1 1 1 1 1 ... 3 3 3 3 3 3 3 3
      long_name :
      PCM labels
      units :
      valid_min :
      0
      valid_max :
      7
      llh :
      38.784750479707895
      _pyXpcm_cleanable :
      1
      array([1, 1, 1, ..., 3, 3, 3])
    • PCM_POST
      (pcm_class, N_PROF)
      float64
      0.0 0.0 0.0 ... 1.388e-19 6.625e-20
      long_name :
      PCM posteriors
      units :
      valid_min :
      0
      valid_max :
      1
      llh :
      38.784750479707895
      _pyXpcm_cleanable :
      1
      array([[0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
        0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
       [1.00000000e+000, 1.00000000e+000, 1.00000000e+000, ...,
        0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
       [2.75142938e-035, 1.71775866e-048, 1.47313441e-051, ...,
        9.86880534e-065, 3.81613835e-035, 1.17629638e-060],
       ...,
       [3.62174559e-041, 1.53234929e-041, 2.04866455e-011, ...,
        3.58739580e-156, 7.71107953e-076, 5.77624573e-087],
       [1.75054380e-043, 4.39827507e-058, 9.91303461e-043, ...,
        0.00000000e+000, 2.51971559e-279, 0.00000000e+000],
       [1.51181396e-037, 2.06372255e-057, 1.60024100e-030, ...,
        1.09259071e-008, 1.38768016e-019, 6.62479874e-020]])
  • Sample test prepared by :
    G. Maze
    Institution :
    Ifremer/LOPS
    Data source DOI :
    10.17882/42182

which are added to the dataset as the PCM_POST variables. The probability of classes for each profiles has a new dimension pcm_class by default that goes from 0 to K-1.

Note

You can delete variables added by pyXpcm to the xarray.DataSet with the pyxpcm.xarray.pyXpcmDataSetAccessor.drop_all() method:

ds.pyxpcm.drop_all()

Or you can split pyXpcm variables out of the original xarray.DataSet:

ds_pcm, ds = ds.pyxpcm.split()

It is important to note that once the PCM is fitted, you can predict labels for any dataset, as long as it has the PCM features.

For instance, let’s predict labels for a gridded dataset:

[12]:

ds_gridded = pyxpcm.tutorial.open_dataset('isas_snapshot').load()
ds_gridded

[12]:
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • depth: 152
    • latitude: 53
    • longitude: 61
    • latitude
      (latitude)
      float32
      30.023445 30.455408 ... 49.737103
      standard_name :
      latitude
      units :
      degree_north
      valid_min :
      -90.0
      valid_max :
      90.0
      axis :
      Y
      array([30.023445, 30.455408, 30.885464, 31.313599, 31.739796, 32.16404 ,
       32.58632 , 33.006615, 33.424923, 33.84122 , 34.2555  , 34.66775 ,
       35.07796 , 35.48612 , 35.892216, 36.296238, 36.698177, 37.09803 ,
       37.49578 , 37.891426, 38.284954, 38.67636 , 39.06564 , 39.452785,
       39.837788, 40.220642, 40.60135 , 40.979897, 41.35629 , 41.73051 ,
       42.10257 , 42.472458, 42.84017 , 43.20571 , 43.569073, 43.930252,
       44.289257, 44.646076, 45.000717, 45.353176, 45.703453, 46.051548,
       46.39746 , 46.7412  , 47.08276 , 47.422142, 47.75935 , 48.094387,
       48.427258, 48.757957, 49.0865  , 49.41288 , 49.737103], dtype=float32)
    • longitude
      (longitude)
      float32
      -70.0 -69.5 -69.0 ... -40.5 -40.0
      standard_name :
      longitude
      units :
      degree_east
      valid_min :
      -180.0
      valid_max :
      180.0
      axis :
      X
      array([-70. , -69.5, -69. , -68.5, -68. , -67.5, -67. , -66.5, -66. , -65.5,
       -65. , -64.5, -64. , -63.5, -63. , -62.5, -62. , -61.5, -61. , -60.5,
       -60. , -59.5, -59. , -58.5, -58. , -57.5, -57. , -56.5, -56. , -55.5,
       -55. , -54.5, -54. , -53.5, -53. , -52.5, -52. , -51.5, -51. , -50.5,
       -50. , -49.5, -49. , -48.5, -48. , -47.5, -47. , -46.5, -46. , -45.5,
       -45. , -44.5, -44. , -43.5, -43. , -42.5, -42. , -41.5, -41. , -40.5,
       -40. ], dtype=float32)
    • depth
      (depth)
      float32
      -1.0 -3.0 -5.0 ... -1980.0 -2000.0
      axis :
      Z
      units :
      meters
      positive :
      up
      array([-1.00e+00, -3.00e+00, -5.00e+00, -1.00e+01, -1.50e+01, -2.00e+01,
       -2.50e+01, -3.00e+01, -3.50e+01, -4.00e+01, -4.50e+01, -5.00e+01,
       -5.50e+01, -6.00e+01, -6.50e+01, -7.00e+01, -7.50e+01, -8.00e+01,
       -8.50e+01, -9.00e+01, -9.50e+01, -1.00e+02, -1.10e+02, -1.20e+02,
       -1.30e+02, -1.40e+02, -1.50e+02, -1.60e+02, -1.70e+02, -1.80e+02,
       -1.90e+02, -2.00e+02, -2.10e+02, -2.20e+02, -2.30e+02, -2.40e+02,
       -2.50e+02, -2.60e+02, -2.70e+02, -2.80e+02, -2.90e+02, -3.00e+02,
       -3.10e+02, -3.20e+02, -3.30e+02, -3.40e+02, -3.50e+02, -3.60e+02,
       -3.70e+02, -3.80e+02, -3.90e+02, -4.00e+02, -4.10e+02, -4.20e+02,
       -4.30e+02, -4.40e+02, -4.50e+02, -4.60e+02, -4.70e+02, -4.80e+02,
       -4.90e+02, -5.00e+02, -5.10e+02, -5.20e+02, -5.30e+02, -5.40e+02,
       -5.50e+02, -5.60e+02, -5.70e+02, -5.80e+02, -5.90e+02, -6.00e+02,
       -6.10e+02, -6.20e+02, -6.30e+02, -6.40e+02, -6.50e+02, -6.60e+02,
       -6.70e+02, -6.80e+02, -6.90e+02, -7.00e+02, -7.10e+02, -7.20e+02,
       -7.30e+02, -7.40e+02, -7.50e+02, -7.60e+02, -7.70e+02, -7.80e+02,
       -7.90e+02, -8.00e+02, -8.20e+02, -8.40e+02, -8.60e+02, -8.80e+02,
       -9.00e+02, -9.20e+02, -9.40e+02, -9.60e+02, -9.80e+02, -1.00e+03,
       -1.02e+03, -1.04e+03, -1.06e+03, -1.08e+03, -1.10e+03, -1.12e+03,
       -1.14e+03, -1.16e+03, -1.18e+03, -1.20e+03, -1.22e+03, -1.24e+03,
       -1.26e+03, -1.28e+03, -1.30e+03, -1.32e+03, -1.34e+03, -1.36e+03,
       -1.38e+03, -1.40e+03, -1.42e+03, -1.44e+03, -1.46e+03, -1.48e+03,
       -1.50e+03, -1.52e+03, -1.54e+03, -1.56e+03, -1.58e+03, -1.60e+03,
       -1.62e+03, -1.64e+03, -1.66e+03, -1.68e+03, -1.70e+03, -1.72e+03,
       -1.74e+03, -1.76e+03, -1.78e+03, -1.80e+03, -1.82e+03, -1.84e+03,
       -1.86e+03, -1.88e+03, -1.90e+03, -1.92e+03, -1.94e+03, -1.96e+03,
       -1.98e+03, -2.00e+03], dtype=float32)
    • TEMP
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Temperature
      standard_name :
      sea_water_temperature
      units :
      degree_Celsius
      valid_min :
      -23000
      valid_max :
      20000
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
  53
  152
    • TEMP_ERR
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Temperature Error
      standard_name :
      units :
      degree_Celsius
      valid_min :
      0
      valid_max :
      20000
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
  53
  152
    • TEMP_PCTVAR
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Error on TEMP variable (% variance)
      standard_name :
      units :
      %
      valid_min :
      0.0
      valid_max :
      100.0
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
  53
  152
    • PSAL
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Practical salinity
      standard_name :
      sea_water_salinity
      units :
      PSS-78
      valid_min :
      -26000
      valid_max :
      30000
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
  53
  152
    • PSAL_ERR
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Practical salinity Error
      standard_name :
      units :
      PSS-78
      valid_min :
      0
      valid_max :
      15000
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
  53
  152
    • PSAL_PCTVAR
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Error on PSAL variable (% variance)
      standard_name :
      units :
      %
      valid_min :
      0.0
      valid_max :
      100.0
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
  53
  152
    • SST
      (latitude, longitude)
      float32
      dask.array<chunksize=(53, 61), meta=np.ndarray>
      long_name :
      Temperature
      standard_name :
      sea_water_temperature
      units :
      degree_Celsius
      valid_min :
      -23000
      valid_max :
      20000
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  12.93 kB   12.93 kB 
      
    
       Shape  (53, 61)   (53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  61
  53



      

[13]:

m.predict(ds_gridded, features={'temperature':'TEMP','salinity':'PSAL'}, dim='depth', inplace=True)
ds_gridded

[13]:
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • depth: 152
    • latitude: 53
    • longitude: 61
    • latitude
      (latitude)
      float64
      30.02 30.46 30.89 ... 49.41 49.74
      array([30.023445, 30.455408, 30.885464, 31.313599, 31.739796, 32.16404 ,
       32.586319, 33.006615, 33.424923, 33.841221, 34.255501, 34.667751,
       35.077961, 35.486118, 35.892216, 36.296238, 36.698177, 37.09803 ,
       37.495781, 37.891426, 38.284954, 38.676361, 39.065639, 39.452785,
       39.837788, 40.220642, 40.601349, 40.979897, 41.356289, 41.730511,
       42.10257 , 42.472458, 42.840172, 43.205711, 43.569073, 43.930252,
       44.289257, 44.646076, 45.000717, 45.353176, 45.703453, 46.051548,
       46.397461, 46.741199, 47.08276 , 47.422142, 47.75935 , 48.094387,
       48.427258, 48.757957, 49.086498, 49.41288 , 49.737103])
    • longitude
      (longitude)
      float64
      -70.0 -69.5 -69.0 ... -40.5 -40.0
      array([-70. , -69.5, -69. , -68.5, -68. , -67.5, -67. , -66.5, -66. , -65.5,
       -65. , -64.5, -64. , -63.5, -63. , -62.5, -62. , -61.5, -61. , -60.5,
       -60. , -59.5, -59. , -58.5, -58. , -57.5, -57. , -56.5, -56. , -55.5,
       -55. , -54.5, -54. , -53.5, -53. , -52.5, -52. , -51.5, -51. , -50.5,
       -50. , -49.5, -49. , -48.5, -48. , -47.5, -47. , -46.5, -46. , -45.5,
       -45. , -44.5, -44. , -43.5, -43. , -42.5, -42. , -41.5, -41. , -40.5,
       -40. ])
    • depth
      (depth)
      float32
      -1.0 -3.0 -5.0 ... -1980.0 -2000.0
      axis :
      Z
      units :
      meters
      positive :
      up
      array([-1.00e+00, -3.00e+00, -5.00e+00, -1.00e+01, -1.50e+01, -2.00e+01,
       -2.50e+01, -3.00e+01, -3.50e+01, -4.00e+01, -4.50e+01, -5.00e+01,
       -5.50e+01, -6.00e+01, -6.50e+01, -7.00e+01, -7.50e+01, -8.00e+01,
       -8.50e+01, -9.00e+01, -9.50e+01, -1.00e+02, -1.10e+02, -1.20e+02,
       -1.30e+02, -1.40e+02, -1.50e+02, -1.60e+02, -1.70e+02, -1.80e+02,
       -1.90e+02, -2.00e+02, -2.10e+02, -2.20e+02, -2.30e+02, -2.40e+02,
       -2.50e+02, -2.60e+02, -2.70e+02, -2.80e+02, -2.90e+02, -3.00e+02,
       -3.10e+02, -3.20e+02, -3.30e+02, -3.40e+02, -3.50e+02, -3.60e+02,
       -3.70e+02, -3.80e+02, -3.90e+02, -4.00e+02, -4.10e+02, -4.20e+02,
       -4.30e+02, -4.40e+02, -4.50e+02, -4.60e+02, -4.70e+02, -4.80e+02,
       -4.90e+02, -5.00e+02, -5.10e+02, -5.20e+02, -5.30e+02, -5.40e+02,
       -5.50e+02, -5.60e+02, -5.70e+02, -5.80e+02, -5.90e+02, -6.00e+02,
       -6.10e+02, -6.20e+02, -6.30e+02, -6.40e+02, -6.50e+02, -6.60e+02,
       -6.70e+02, -6.80e+02, -6.90e+02, -7.00e+02, -7.10e+02, -7.20e+02,
       -7.30e+02, -7.40e+02, -7.50e+02, -7.60e+02, -7.70e+02, -7.80e+02,
       -7.90e+02, -8.00e+02, -8.20e+02, -8.40e+02, -8.60e+02, -8.80e+02,
       -9.00e+02, -9.20e+02, -9.40e+02, -9.60e+02, -9.80e+02, -1.00e+03,
       -1.02e+03, -1.04e+03, -1.06e+03, -1.08e+03, -1.10e+03, -1.12e+03,
       -1.14e+03, -1.16e+03, -1.18e+03, -1.20e+03, -1.22e+03, -1.24e+03,
       -1.26e+03, -1.28e+03, -1.30e+03, -1.32e+03, -1.34e+03, -1.36e+03,
       -1.38e+03, -1.40e+03, -1.42e+03, -1.44e+03, -1.46e+03, -1.48e+03,
       -1.50e+03, -1.52e+03, -1.54e+03, -1.56e+03, -1.58e+03, -1.60e+03,
       -1.62e+03, -1.64e+03, -1.66e+03, -1.68e+03, -1.70e+03, -1.72e+03,
       -1.74e+03, -1.76e+03, -1.78e+03, -1.80e+03, -1.82e+03, -1.84e+03,
       -1.86e+03, -1.88e+03, -1.90e+03, -1.92e+03, -1.94e+03, -1.96e+03,
       -1.98e+03, -2.00e+03], dtype=float32)
    • TEMP
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Temperature
      standard_name :
      sea_water_temperature
      units :
      degree_Celsius
      valid_min :
      -23000
      valid_max :
      20000
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
  53
  152
    • TEMP_ERR
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Temperature Error
      standard_name :
      units :
      degree_Celsius
      valid_min :
      0
      valid_max :
      20000
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
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  152
    • TEMP_PCTVAR
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Error on TEMP variable (% variance)
      standard_name :
      units :
      %
      valid_min :
      0.0
      valid_max :
      100.0
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
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  152
    • PSAL
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Practical salinity
      standard_name :
      sea_water_salinity
      units :
      PSS-78
      valid_min :
      -26000
      valid_max :
      30000
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
  53
  152
    • PSAL_ERR
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Practical salinity Error
      standard_name :
      units :
      PSS-78
      valid_min :
      0
      valid_max :
      15000
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
  53
  152
    • PSAL_PCTVAR
      (depth, latitude, longitude)
      float32
      dask.array<chunksize=(152, 53, 61), meta=np.ndarray>
      long_name :
      Error on PSAL variable (% variance)
      standard_name :
      units :
      %
      valid_min :
      0.0
      valid_max :
      100.0
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  1.97 MB   1.97 MB 
      
    
       Shape  (152, 53, 61)   (152, 53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  61
  53
  152
    • SST
      (latitude, longitude)
      float32
      dask.array<chunksize=(53, 61), meta=np.ndarray>
      long_name :
      Temperature
      standard_name :
      sea_water_temperature
      units :
      degree_Celsius
      valid_min :
      -23000
      valid_max :
      20000
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  12.93 kB   12.93 kB 
      
    
       Shape  (53, 61)   (53, 61) 
      
    
       Count  2 Tasks  1 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  

  
  

  
  61
  53
    • PCM_LABELS
      (latitude, longitude)
      float64
      1.0 1.0 1.0 1.0 ... 7.0 7.0 7.0 7.0
      long_name :
      PCM labels
      units :
      valid_min :
      0
      valid_max :
      7
      llh :
      37.003444293299125
      _pyXpcm_cleanable :
      1
      array([[ 1.,  1.,  1., ...,  6.,  6.,  6.],
       [ 1.,  1.,  1., ...,  6.,  6.,  6.],
       [ 1.,  1.,  1., ...,  6.,  6.,  6.],
       ...,
       [nan, nan, nan, ...,  7.,  7.,  7.],
       [nan, nan, nan, ...,  7.,  7.,  7.],
       [nan, nan, nan, ...,  7.,  7.,  7.]])

where you can see the adition of the PCM_LABELS variable.

Vertical structure of classes

One key outcome of the PCM analysis if the vertical structure of each classes. This can be computed using the :meth:pyxpcm.stat.quantile method.

Below we compute the 5, 50 and 95% quantiles for temperature and salinity of each classes:

[14]:

for vname in ['TEMP', 'PSAL']:
    ds = ds.pyxpcm.quantile(m, q=[0.05, 0.5, 0.95], of=vname, outname=vname + '_Q', keep_attrs=True, inplace=True)
ds

[14]:
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • DEPTH: 282
    • N_PROF: 7560
    • pcm_class: 8
    • quantile: 3
    • pcm_class
      (pcm_class)
      int64
      0 1 2 3 4 5 6 7
      array([0, 1, 2, 3, 4, 5, 6, 7])
    • N_PROF
      (N_PROF)
      int64
      0 1 2 3 4 ... 7556 7557 7558 7559
      array([   0,    1,    2, ..., 7557, 7558, 7559])
    • DEPTH
      (DEPTH)
      float32
      0.0 -5.0 -10.0 ... -1400.0 -1405.0
      axis :
      Z
      standard_name :
      depth
      long_name :
      Vertical Distance Below the Surface
      convention :
      Negative, downward oriented
      units :
      meters
      positive :
      up
      array([    0.,    -5.,   -10., ..., -1395., -1400., -1405.], dtype=float32)
    • quantile
      (quantile)
      float64
      0.05 0.5 0.95
      array([0.05, 0.5 , 0.95])
    • LATITUDE
      (N_PROF)
      float32
      27.122 27.818 27.452 ... 4.15 4.44
      axis :
      Y
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      array([27.122, 27.818, 27.452, ...,  4.243,  4.15 ,  4.44 ], dtype=float32)
    • LONGITUDE
      (N_PROF)
      float32
      -74.86 -75.6 ... -0.821 -0.002
      axis :
      X
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      array([-7.4860e+01, -7.5600e+01, -7.4949e+01, ..., -1.2630e+00, -8.2100e-01,
       -2.0000e-03], dtype=float32)
    • TIME
      (N_PROF)
      datetime64[ns]
      2008-06-23T13:07:30 ... 2013-03-09T14:52:58.124999936
      standard_name :
      time
      short_name :
      time
      long_name :
      Time of Measurement
      array(['2008-06-23T13:07:30.000000000', '2011-05-22T12:46:24.375000064',
       '2011-07-11T12:54:50.624999936', ..., '2014-03-17T05:44:31.875000064',
       '2014-03-27T03:05:37.500000000', '2013-03-09T14:52:58.124999936'],
      dtype='datetime64[ns]')
    • DBINDEX
      (N_PROF)
      float64
      1.484e+04 1.622e+04 ... 1.063e+04
      short_name :
      index
      long_name :
      Profile index within the complete database
      array([14840., 16215., 16220., ...,  8556.,  8557., 10628.])
    • TEMP
      (N_PROF, DEPTH)
      float32
      27.422163 27.422163 ... 4.391791
      long_name :
      Sea Temperature In-Situ ITS-90 Scale
      standard_name :
      sea_water_temperature
      units :
      degree_Celsius
      valid_min :
      -2.5.f
      valid_max :
      40.f
      C_format :
      %9.3f
      FORTRAN_format :
      F9.3
      resolution :
      0.001f
      array([[27.422163, 27.422163, 27.29007 , ...,  4.436046,  4.423681,  4.411316],
       [25.129957, 25.129957, 24.970064, ...,  4.757417,  4.743126,  4.728835],
       [28.132914, 28.132914, 27.969038, ...,  4.371902,  4.356699,  4.341496],
       ...,
       [28.722956, 28.722956, 28.721252, ...,  4.275817,  4.270741,  4.266666],
       [28.643309, 28.643309, 28.64578 , ...,  4.292866,  4.285667,  4.278265],
       [29.249382, 29.249382, 29.13765 , ...,  4.412214,  4.403805,  4.391791]],
      dtype=float32)
    • PSAL
      (N_PROF, DEPTH)
      float32
      36.35267 36.35267 ... 34.910286
      long_name :
      Practical Salinity
      standard_name :
      sea_water_salinity
      units :
      psu
      valid_min :
      2.f
      valid_max :
      41.f
      C_format :
      %9.3f
      FORTRAN_format :
      F9.3
      resolution :
      0.001f
      array([[36.35267 , 36.35267 , 36.468353, ..., 35.01112 , 35.010513, 35.009903],
       [36.508953, 36.508953, 36.502014, ..., 35.029182, 35.02817 , 35.027157],
       [36.492146, 36.492146, 36.514534, ..., 34.995472, 34.994053, 34.992634],
       ...,
       [34.973022, 34.973022, 34.973873, ..., 34.92445 , 34.925774, 34.92674 ],
       [35.184   , 35.184   , 35.184   , ..., 34.931606, 34.933006, 34.934402],
       [35.05187 , 35.05187 , 35.05117 , ..., 34.9081  , 34.909523, 34.910286]],
      dtype=float32)
    • SIG0
      (N_PROF, DEPTH)
      float32
      23.601229 23.601229 ... 27.685583
      standard_name :
      sea_water_density
      long_name :
      Potential Density Referenced to Surface
      units :
      kg/m^3
      array([[23.601229, 23.601229, 23.731516, ..., 27.760933, 27.761826, 27.762716],
       [24.442646, 24.442646, 24.48671 , ..., 27.740076, 27.74091 , 27.741743],
       [23.47391 , 23.47391 , 23.545067, ..., 27.755363, 27.7559  , 27.756437],
       ...,
       [22.136534, 22.136534, 22.13814 , ..., 27.709084, 27.710722, 27.71197 ],
       [22.321472, 22.321472, 22.321053, ..., 27.712975, 27.714897, 27.716837],
       [22.019827, 22.019827, 22.057219, ..., 27.681578, 27.683651, 27.685583]],
      dtype=float32)
    • BRV2
      (N_PROF, DEPTH)
      float32
      0.00029447526 ... 4.500769e-06
      standard_name :
      N2
      long_name :
      Brunt-Vaisala Frequency Squared
      units :
      1/s^2
      array([[2.944753e-04, 2.944753e-04, 2.944753e-04, ..., 2.669623e-06,
        2.621662e-06, 2.573702e-06],
       [1.496874e-04, 1.496874e-04, 1.496874e-04, ..., 2.985381e-06,
        2.966512e-06, 2.947643e-06],
       [1.443552e-04, 1.443552e-04, 1.443552e-04, ..., 2.068531e-06,
        2.030830e-06, 1.993128e-06],
       ...,
       [1.353956e-04, 1.353956e-04, 1.353956e-04, ..., 4.763126e-06,
        4.643939e-06, 4.543649e-06],
       [3.694623e-05, 3.694623e-05, 3.694623e-05, ..., 4.115821e-06,
        4.053568e-06, 3.997502e-06],
       [3.564891e-04, 3.564891e-04, 3.564891e-04, ..., 4.519470e-06,
        4.508477e-06, 4.500769e-06]], dtype=float32)
    • PCM_LABELS
      (N_PROF)
      int64
      1 1 1 1 1 1 1 1 ... 3 3 3 3 3 3 3 3
      long_name :
      PCM labels
      units :
      valid_min :
      0
      valid_max :
      7
      llh :
      38.784750479707895
      _pyXpcm_cleanable :
      1
      array([1, 1, 1, ..., 3, 3, 3])
    • PCM_POST
      (pcm_class, N_PROF)
      float64
      0.0 0.0 0.0 ... 1.388e-19 6.625e-20
      long_name :
      PCM posteriors
      units :
      valid_min :
      0
      valid_max :
      1
      llh :
      38.784750479707895
      _pyXpcm_cleanable :
      1
      array([[0.00000000e+000, 0.00000000e+000, 0.00000000e+000, ...,
        0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
       [1.00000000e+000, 1.00000000e+000, 1.00000000e+000, ...,
        0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
       [2.75142938e-035, 1.71775866e-048, 1.47313441e-051, ...,
        9.86880534e-065, 3.81613835e-035, 1.17629638e-060],
       ...,
       [3.62174559e-041, 1.53234929e-041, 2.04866455e-011, ...,
        3.58739580e-156, 7.71107953e-076, 5.77624573e-087],
       [1.75054380e-043, 4.39827507e-058, 9.91303461e-043, ...,
        0.00000000e+000, 2.51971559e-279, 0.00000000e+000],
       [1.51181396e-037, 2.06372255e-057, 1.60024100e-030, ...,
        1.09259071e-008, 1.38768016e-019, 6.62479874e-020]])
    • TEMP_Q
      (pcm_class, quantile, DEPTH)
      float64
      3.07 3.07 3.07 ... 4.844 4.83 4.814
      long_name :
      Sea Temperature In-Situ ITS-90 Scale
      standard_name :
      sea_water_temperature
      units :
      degree_Celsius
      valid_min :
      -2.5.f
      valid_max :
      40.f
      C_format :
      %9.3f
      FORTRAN_format :
      F9.3
      resolution :
      0.001f
      _pyXpcm_cleanable :
      1
      array([[[ 3.0700623 ,  3.0700623 ,  3.0700623 , ...,  3.35431061,
          3.3517427 ,  3.34991541],
        [ 7.28403378,  7.28403378,  7.28452396, ...,  3.60048938,
          3.60041142,  3.5968492 ],
        [12.47129002, 12.47129002, 12.44652042, ...,  3.80719414,
          3.80425549,  3.80177546]],

       [[20.68908138, 20.68908138, 20.68477373, ...,  4.42709074,
          4.4156461 ,  4.40264764],
        [25.28415775, 25.28415775, 25.23971653, ...,  4.78096437,
          4.76742649,  4.75472689],
        [28.65389462, 28.65389462, 28.64907074, ...,  5.28582361,
          5.26761687,  5.24142032]],

       [[22.10998592, 22.1114954 , 22.11068916, ...,  4.61464977,
          4.60262957,  4.59209461],
        [26.21299934, 26.21311951, 26.21419525, ...,  4.8479557 ,
          4.83457565,  4.82184219],
        [28.55138512, 28.55138512, 28.5267458 , ...,  5.32599645,
          5.31082473,  5.29089804]],

       ...,

       [[11.23751669, 11.23751669, 11.22832422, ...,  3.77324021,
          3.76979504,  3.76486225],
        [17.56500053, 17.56500053, 17.55599976, ...,  5.02459002,
          5.00341415,  4.96935749],
        [24.89213123, 24.89213123, 24.83814144, ...,  8.82467117,
          8.78297062,  8.7307991 ]],

       [[18.3668314 , 18.3668314 , 18.36513605, ...,  5.00285695,
          4.98958879,  4.9757998 ],
        [23.00972939, 23.00972939, 22.99462128, ...,  5.90254211,
          5.87882209,  5.85720801],
        [27.31159039, 27.31159039, 27.30601883, ...,  7.54496312,
          7.52574463,  7.49450197]],

       [[ 4.38251553,  4.38251553,  4.35822134, ...,  3.57857409,
          3.57432342,  3.5740941 ],
        [17.41753769, 17.41753769, 17.31687546, ...,  3.95726109,
          3.95336437,  3.94936728],
        [27.90592651, 27.90592651, 27.89972496, ...,  4.84449224,
          4.83016787,  4.81395521]]])
    • PSAL_Q
      (pcm_class, quantile, DEPTH)
      float64
      34.03 34.03 34.03 ... 35.01 35.01
      long_name :
      Practical Salinity
      standard_name :
      sea_water_salinity
      units :
      psu
      valid_min :
      2.f
      valid_max :
      41.f
      C_format :
      %9.3f
      FORTRAN_format :
      F9.3
      resolution :
      0.001f
      _pyXpcm_cleanable :
      1
      array([[[34.02629547, 34.02629547, 34.02854004, ..., 34.8774765 ,
         34.87806854, 34.87847443],
        [34.75354385, 34.75354385, 34.75354385, ..., 34.90897751,
         34.90917969, 34.90939331],
        [35.05836639, 35.05836639, 35.05836639, ..., 34.94658127,
         34.94690933, 34.94753036]],

       [[36.14413528, 36.14738312, 36.14889565, ..., 34.98531075,
         34.98470345, 34.98376789],
        [36.59150124, 36.59150124, 36.59508324, ..., 35.03655815,
         35.03551102, 35.03469086],
        [36.99061012, 36.99440384, 36.99430428, ..., 35.12143936,
         35.12091942, 35.12045898]],

       [[34.7966114 , 34.7966114 , 34.79847603, ..., 34.97176018,
         34.97247543, 34.97301025],
        [36.4469986 , 36.4469986 , 36.44810867, ..., 35.00600052,
         35.00595856, 35.00588608],
        [37.18030014, 37.18030014, 37.18030357, ..., 35.10130501,
         35.10105553, 35.10047226]],

       ...,

       [[35.13364563, 35.13364563, 35.13353119, ..., 34.90844994,
         34.90806541, 34.90796356],
        [35.95100021, 35.95100021, 35.95291901, ..., 35.08145142,
         35.07888412, 35.07514954],
        [36.60344238, 36.60344238, 36.6132122 , ..., 35.85812607,
         35.85144691, 35.84058685]],

       [[36.49176941, 36.49176941, 36.49402256, ..., 35.04038658,
         35.04073067, 35.04102249],
        [37.13119698, 37.13119698, 37.13119698, ..., 35.23085976,
         35.22877693, 35.22781181],
        [37.57555389, 37.57555389, 37.57554474, ..., 35.58191986,
         35.57843609, 35.57495441]],

       [[32.63421402, 32.63421402, 32.65712433, ..., 34.89247131,
         34.89298935, 34.89305954],
        [35.338871  , 35.338871  , 35.34162903, ..., 34.94197083,
         34.94210052, 34.94210052],
        [36.35235672, 36.35235672, 36.35383072, ..., 35.01437607,
         35.01456451, 35.01464539]]])
  • Sample test prepared by :
    G. Maze
    Institution :
    Ifremer/LOPS
    Data source DOI :
    10.17882/42182

Quantiles can be plotted using the :func:pyxpcm.plot.quantile method.

[15]:

fig, ax = m.plot.quantile(ds['TEMP_Q'], maxcols=4, figsize=(10, 8), sharey=True)
_images/example_38_0.png

Geographic distribution of classes

Warning

To follow this section you’ll need to have Cartopy installed and working.

A map of labels can now easily be plotted:

[16]:

proj = ccrs.PlateCarree()
subplot_kw={'projection': proj, 'extent': np.array([-80,1,-1,66]) + np.array([-0.1,+0.1,-0.1,+0.1])}
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5,5), dpi=120, facecolor='w', edgecolor='k', subplot_kw=subplot_kw)

kmap = m.plot.cmap()
sc = ax.scatter(ds['LONGITUDE'], ds['LATITUDE'], s=3, c=ds['PCM_LABELS'], cmap=kmap, transform=proj, vmin=0, vmax=m.K)
cl = m.plot.colorbar(ax=ax)

gl = m.plot.latlongrid(ax, dx=10)
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.COASTLINE)
ax.set_title('LABELS of the training set')
plt.show()
_images/example_42_0.png

Since we predicted labels for 2 datasets, we can superimpose them

[17]:

proj = ccrs.PlateCarree()
subplot_kw={'projection': proj, 'extent': np.array([-75,-35,25,55]) + np.array([-0.1,+0.1,-0.1,+0.1])}
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5,5), dpi=120, facecolor='w', edgecolor='k', subplot_kw=subplot_kw)

kmap = m.plot.cmap()
sc = ax.pcolor(ds_gridded['longitude'], ds_gridded['latitude'], ds_gridded['PCM_LABELS'], cmap=kmap, transform=proj, vmin=0, vmax=m.K)
sc = ax.scatter(ds['LONGITUDE'], ds['LATITUDE'], s=10, c=ds['PCM_LABELS'], cmap=kmap, transform=proj, vmin=0, vmax=m.K, edgecolors=[0.3]*3, linewidths=0.3)
cl = m.plot.colorbar(ax=ax)

gl = m.plot.latlongrid(ax, dx=10)
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.COASTLINE)
ax.set_title('LABELS of the training set (dots) and another product (shade)')
plt.show()
_images/example_44_0.png

Posteriors are defined for each data point and give the probability of that point to belong to any of the classes. It can be plotted this way:

[18]:

cmap = sns.light_palette("blue", as_cmap=True)
proj = ccrs.PlateCarree()
subplot_kw={'projection': proj, 'extent': np.array([-80,1,-1,66]) + np.array([-0.1,+0.1,-0.1,+0.1])}
fig, ax = m.plot.subplots(figsize=(10,22), maxcols=2, subplot_kw=subplot_kw)

for k in m:
    sc = ax[k].scatter(ds['LONGITUDE'], ds['LATITUDE'], s=3, c=ds['PCM_POST'].sel(pcm_class=k),
                       cmap=cmap, transform=proj, vmin=0, vmax=1)
    cl = plt.colorbar(sc, ax=ax[k], fraction=0.03)
    gl = m.plot.latlongrid(ax[k], fontsize=8, dx=20, dy=10)
    ax[k].add_feature(cfeature.LAND)
    ax[k].add_feature(cfeature.COASTLINE)
    ax[k].set_title('PCM Posteriors k=%i' % k)
_images/example_46_0.png