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tausiaj merged 1 commit into from
Feb 20, 2025
Merged

[JTH] pcs df option and binwaves kp extraction working #55

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92 changes: 59 additions & 33 deletions bluemath_tk/datamining/pca.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

import cartopy.crs as ccrs
import numpy as np
import pandas as pd
import xarray as xr
from sklearn.decomposition import PCA as PCA_
from sklearn.decomposition import IncrementalPCA as IncrementalPCA_
Expand Down Expand Up @@ -75,6 +76,8 @@ class PCA(BaseReduction):
The explained variance ratio.
cumulative_explained_variance_ratio : np.ndarray
The cumulative explained variance ratio.
pcs_df : pd.DataFrame
The fitting PCs (self.pcs) as a pandas DataFrame.

Methods
-------
Expand All @@ -97,33 +100,41 @@ class PCA(BaseReduction):

Examples
--------
>>> from bluemath_tk.core.data.sample_data import get_2d_dataset
>>> from bluemath_tk.datamining.pca import PCA
>>> ds = get_2d_dataset()
>>> pca = PCA(
... n_components=5,
... is_incremental=False,
... debug=True,
... )
>>> pca.fit(
... data=ds,
... vars_to_stack=["X", "Y"],
... coords_to_stack=["coord1", "coord2"],
... pca_dim_for_rows="coord3",
... windows_in_pca_dim_for_rows={"X": [1, 2, 3]},
... value_to_replace_nans={"X": 0.0},
... nan_threshold_to_drop={"X": 0.95},
... )
>>> pcs = pca.transform(
... data=ds,
... )
>>> reconstructed_ds = pca.inverse_transform(PCs=pcs)
>>> eofs = pca.eofs
>>> explained_variance = pca.explained_variance
>>> explained_variance_ratio = pca.explained_variance_ratio
>>> cumulative_explained_variance_ratio = pca.cumulative_explained_variance_ratio
>>> # Save the full class in a pickle file
>>> pca.save_model("pca_model.pkl")
.. jupyter-execute::

from bluemath_tk.core.data.sample_data import get_2d_dataset
from bluemath_tk.datamining.pca import PCA

ds = get_2d_dataset()

pca = PCA(
n_components=5,
is_incremental=False,
debug=True,
)
pca.fit(
data=ds,
vars_to_stack=["X", "Y"],
coords_to_stack=["coord1", "coord2"],
pca_dim_for_rows="coord3",
windows_in_pca_dim_for_rows={"X": [1, 2, 3]},
value_to_replace_nans={"X": 0.0},
nan_threshold_to_drop={"X": 0.95},
)
pcs = pca.transform(
data=ds,
)
reconstructed_ds = pca.inverse_transform(PCs=pcs)
eofs = pca.eofs
explained_variance = pca.explained_variance
explained_variance_ratio = pca.explained_variance_ratio
cumulative_explained_variance_ratio = pca.cumulative_explained_variance_ratio

# Save the full class in a pickle file
pca.save_model("pca_model.pkl")

# Plot the calculated EOFs
pca.plot_eofs(vars_to_plot=["X", "Y"], num_eofs=3)

References
----------
Expand Down Expand Up @@ -212,10 +223,10 @@ def __init__(

# Exclude attributes from beign saved with pca.save_model()
self._exclude_attributes = [
# "_data",
"_data",
"_window_processed_data",
# "_stacked_data_matrix",
# "_standarized_stacked_data_matrix",
"_stacked_data_matrix",
"_standarized_stacked_data_matrix",
]

@property
Expand Down Expand Up @@ -254,6 +265,19 @@ def explained_variance_ratio(self) -> np.ndarray:
def cumulative_explained_variance_ratio(self) -> np.ndarray:
return np.cumsum(self.explained_variance_ratio)

@property
def pcs_df(self) -> pd.DataFrame:
if self.pcs is not None:
return pd.DataFrame(
data=self.pcs["PCs"].values,
columns=[f"PC{i + 1}" for i in range(self.pca.n_components_)],
index=self.pcs[self.pca_dim_for_rows].values,
)
else:
raise PCAError(
"PCA model must be fitted and transformed before calling pcs_df"
)

def _generate_stacked_data(self, data: xr.Dataset) -> np.ndarray:
"""
Generate stacked data matrix.
Expand Down Expand Up @@ -606,7 +630,7 @@ def transform(self, data: xr.Dataset, after_fitting: bool = False) -> xr.Dataset
transformed_data = self.pca.transform(X=processed_data)

# Save the Principal Components (PCs) in an xr.Dataset
self.pcs = xr.Dataset(
pcs = xr.Dataset(
{
"PCs": ((self.pca_dim_for_rows, "n_component"), transformed_data),
"stds": (("n_component",), np.std(transformed_data, axis=0)),
Expand All @@ -616,8 +640,10 @@ def transform(self, data: xr.Dataset, after_fitting: bool = False) -> xr.Dataset
"n_component": np.arange(self.pca.n_components_),
},
)
if after_fitting:
self.pcs = pcs.copy()

return self.pcs.copy()
return pcs

def fit_transform(
self,
Expand Down Expand Up @@ -736,7 +762,7 @@ def plot_pcs(self, num_pcs: int, pcs: xr.Dataset = None) -> None:
"No Principal Components (PCs) found. Please transform some data first."
)
self.logger.info("Using the Principal Components (PCs) from the class")
pcs = self.pcs
pcs = self.pcs.copy()

_ = (
pcs["PCs"]
Expand Down
2 changes: 1 addition & 1 deletion bluemath_tk/wrappers/_base_wrappers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -422,7 +422,7 @@ def run_cases(
cases_dir_to_run = copy.deepcopy(self.cases_dirs)

if parallel:
num_threads = self.get_num_processors_available()
num_threads = 5 # self.get_num_processors_available()
self.logger.debug(
f"Running cases in parallel with launcher={launcher}. Number of threads: {num_threads}."
)
Expand Down
177 changes: 140 additions & 37 deletions bluemath_tk/wrappers/swan/swan_example.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -12,69 +12,173 @@
from bluemath_tk.wrappers.swan.swan_wrapper import SwanModelWrapper

example_directions = [
1.5,
4.5,
7.5,
10.5,
13.5,
16.5,
19.5,
22.5,
25.5,
28.5,
31.5,
34.5,
37.5,
40.5,
43.5,
46.5,
49.5,
52.5,
55.5,
58.5,
61.5,
64.5,
67.5,
70.5,
73.5,
76.5,
79.5,
82.5,
85.5,
88.5,
91.5,
94.5,
97.5,
100.5,
103.5,
106.5,
109.5,
112.5,
115.5,
118.5,
121.5,
124.5,
127.5,
130.5,
133.5,
136.5,
139.5,
142.5,
145.5,
148.5,
151.5,
154.5,
157.5,
160.5,
163.5,
166.5,
169.5,
172.5,
175.5,
178.5,
181.5,
184.5,
187.5,
190.5,
193.5,
196.5,
199.5,
202.5,
205.5,
208.5,
211.5,
214.5,
217.5,
220.5,
223.5,
226.5,
229.5,
232.5,
235.5,
238.5,
241.5,
244.5,
247.5,
250.5,
253.5,
256.5,
259.5,
262.5,
265.5,
268.5,
271.5,
274.5,
277.5,
280.5,
283.5,
286.5,
289.5,
292.5,
295.5,
298.5,
301.5,
304.5,
307.5,
310.5,
313.5,
316.5,
319.5,
322.5,
325.5,
328.5,
331.5,
334.5,
337.5,
340.5,
343.5,
346.5,
349.5,
352.5,
355.5,
358.5,
]
example_frequencies = [
0.035,
0.0385,
0.042349998,
0.046585,
0.051243503,
0.05636785,
0.062004633,
0.068205096,
0.07502561,
0.082528174,
0.090780996,
0.099859096,
0.10984501,
0.120829515,
0.13291247,
0.14620373,
0.1608241,
0.17690653,
0.19459718,
0.21405691,
0.2354626,
0.25900885,
0.28490975,
0.31340075,
0.3447408,
0.37921488,
0.4171364,
0.45885003,
0.50473505,
0.03,
0.033,
0.0363,
0.0399,
0.0438,
0.0482,
0.053,
0.0582,
0.064,
0.0704,
0.0774,
0.0851,
0.0935,
0.1028,
0.1131,
0.1243,
0.1367,
0.1503,
0.1652,
0.1816,
0.1997,
0.2195,
0.2413,
0.2653,
0.2917,
0.3207,
0.3526,
0.3876,
0.4262,
0.4685,
0.5151,
0.5663,
0.6226,
0.6845,
0.7525,
0.8273,
0.9096,
1.0,
]


class BinWavesWrapper(SwanModelWrapper):
""" """

def build_case(self, case_dir: str, case_context: dict):
self.logger.info(f"Saving spectrum for {case_dir}")
input_spectrum = construct_partition(
freq_name="jonswap",
freq_kwargs={
Expand Down Expand Up @@ -105,13 +209,12 @@ def build_cases(self, mode="one_by_one"):
templates_dir = (
"/home/tausiaj/GitHub-GeoOcean/BlueMath/bluemath_tk/wrappers/swan/templates/"
)
templates_name = ["input.swn", "depth_main.dat", "buoys.loc"]
output_dir = "/home/tausiaj/GitHub-GeoOcean/BlueMath/test_cases/swan/javi/"
templates_name = ["input.swn", "depth_main_cantabria.dat", "buoys.loc"]
output_dir = "/home/tausiaj/GitHub-GeoOcean/BlueMath/test_cases/swan/CAN/"
# Load swan model parameters
model_parameters = (
xr.open_dataset("/home/tausiaj/GitHub-GeoOcean/BlueMath/test_data/subset.nc")
.to_dataframe()
.iloc[::60]
.to_dict(orient="list")
)
# Create an instance of the SWAN model wrapper
Expand All @@ -126,11 +229,11 @@ def build_cases(self, mode="one_by_one"):
# List available launchers
print(swan_wrapper.list_available_launchers())
# Run the model
swan_wrapper.run_cases(launcher="docker", parallel=True)
# swan_wrapper.run_cases(launcher="docker", parallel=True)
# Post-process the output files
postprocessed_ds = swan_wrapper.postprocess_cases()
postprocessed_ds.to_netcdf(op.join(swan_wrapper.output_dir, "waves_part.nc"))
print(postprocessed_ds)
# postprocessed_ds = swan_wrapper.postprocess_cases()
# postprocessed_ds.to_netcdf(op.join(swan_wrapper.output_dir, "waves_part.nc"))
# print(postprocessed_ds)
# # Load spectra example
# spectra = xr.open_dataset(
# "/home/tausiaj/GitHub-GeoOcean/BlueMath/test_data/Waves_Cantabria_356.08_43.82.nc"
Expand Down
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