From 5987636d9c9df9e696549d1a0bcb53cc5aa8dd19 Mon Sep 17 00:00:00 2001
From: Julieanna Bacon <julieannabacon2014@gmail.com>
Date: Tue, 12 Mar 2024 12:28:18 -0400
Subject: [PATCH 1/3] netcdf preprocess: Parallelized workflow to crop original
 IMS netcdf files to centered window at variable window size with luigi

---
 config/preprocess_netcdf.toml |   4 +
 icedyno/preprocess/crop.py    |  80 +++++++++
 pixi.lock                     | 307 ++++++++++++++++++++++++++++------
 pixi.toml                     |   2 +
 4 files changed, 344 insertions(+), 49 deletions(-)
 create mode 100644 config/preprocess_netcdf.toml
 create mode 100644 icedyno/preprocess/crop.py

diff --git a/config/preprocess_netcdf.toml b/config/preprocess_netcdf.toml
new file mode 100644
index 0000000..58216ce
--- /dev/null
+++ b/config/preprocess_netcdf.toml
@@ -0,0 +1,4 @@
+[CropFiles]
+input_dir = "ims_1km"
+output_dir = "ims_netcdf_1km_cropped_4_000_000km_window"
+window_size = 4000
diff --git a/icedyno/preprocess/crop.py b/icedyno/preprocess/crop.py
new file mode 100644
index 0000000..0a5ea5f
--- /dev/null
+++ b/icedyno/preprocess/crop.py
@@ -0,0 +1,80 @@
+""" 
+To run this as a Luigi DAG locally:
+``pixi run python icedyno/preprocess/crop.py``
+
+You may have to enable toml support with luigi by setting an variable in your terminal, like ``export LUIGI_CONFIG_PARSER=toml``
+"""
+import glob
+import os
+import pathlib
+
+import luigi
+import xarray as xr
+
+
+class CropFiles(luigi.Task):
+    """
+    Crop IMS and MASIE NetCDF files from the center of their grids (where x, y == 1/2*sie.shape) based on input window_size.
+
+    """
+
+    input_dir = luigi.Parameter()
+    output_dir = luigi.Parameter()
+
+    window_size = luigi.IntParameter(default=4000)
+    year = luigi.IntParameter()
+
+    def output(self) -> luigi.LocalTarget:
+        return luigi.LocalTarget(
+            os.path.join("data", self.output_dir, f"_SUCCESS_{self.year}")
+        )
+
+    def run(self) -> None:
+        year_output_dir = os.path.join("data", self.output_dir, str(self.year))
+        if not os.path.exists(year_output_dir):
+            os.makedirs(year_output_dir)
+
+        input_cdf_files = glob.glob(
+            os.path.join("data", self.input_dir, str(self.year), "*.nc")
+        )
+
+        for cdf_filepath in input_cdf_files:
+            output_filename = (
+                os.path.join(year_output_dir, pathlib.Path(cdf_filepath).stem)
+                + f"_grid{self.window_size}.nc"
+            )
+            if os.path.exists(output_filename):
+                print(cdf_filepath, "already on disk, skipping...")
+
+            # Open the original NetCDF file
+            ds = xr.open_dataset(cdf_filepath, engine="h5netcdf")
+
+            x_coord = ds["x"].shape[0] // 2
+            y_coord = ds["y"].shape[0] // 2
+            window = self.window_size * 1000
+
+            cropped_ds = ds.sel(
+                x=slice(x_coord - window, x_coord + window),
+                y=slice(y_coord - window, y_coord + window),
+            )
+
+            # Write the cropped data to a new NetCDF file
+            cropped_ds.to_netcdf(output_filename, engine="h5netcdf")
+
+
+if __name__ == "__main__":
+    os.environ["LUIGI_CONFIG_PARSER"] = "toml"
+
+    config_path = os.path.join("config", "preprocess_netcdf.toml")
+
+    config = luigi.configuration.get_config(parser="toml")
+    config.read(config_path)
+
+    luigi.configuration.add_config_path(config_path)
+
+    ## Change acording to your number of cores
+    n_workers = 10
+    years = range(2014, 2025)
+
+    tasks = [CropFiles(year=year) for year in years]
+    luigi.build(tasks, workers=n_workers, local_scheduler=True)
diff --git a/pixi.lock b/pixi.lock
index ec709b6..a22ff3f 100644
--- a/pixi.lock
+++ b/pixi.lock
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.1-py312h30efb56_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/docutils-0.20.1-py312h7900ff3_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.1-h59595ed_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/kealib-1.5.3-h2f55d51_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/h5netcdf-1.3.0-pyhd8ed1ab_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.0-pyhd8ed1ab_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.9.0-pyhd8ed1ab_0.conda
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/h5netcdf-1.3.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.0.0-pyh9f0ad1d_0.tar.bz2
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-  md5: 5db7505ee6fbb00f060782a8d93f974a
+  url: https://conda.anaconda.org/conda-forge/osx-64/jupyter_core-5.7.2-py312hb401068_0.conda
+  sha256: 3e57d1eaf22c793711367335f9f8b647c011b64a95bfc796b50967a4b2ae27c2
+  md5: a205e28ce7ab71773dcaaf94f6418612
   depends:
   - platformdirs >=2.5
   - python >=3.12,<3.13.0a0
@@ -6194,8 +6276,8 @@ packages:
   - traitlets >=5.3
   license: BSD-3-Clause
   license_family: BSD
-  size: 92773
-  timestamp: 1704812314487
+  size: 92679
+  timestamp: 1710257658978
 - kind: conda
   name: jupyter_events
   version: 0.9.0
@@ -10496,6 +10578,114 @@ packages:
   license_family: APACHE
   size: 274631
   timestamp: 1701222947083
+- kind: conda
+  name: lockfile
+  version: 0.12.2
+  build: py_1
+  build_number: 1
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/lockfile-0.12.2-py_1.tar.bz2
+  sha256: d3a68045ef74a2a7b8c8a55b242fdbc875d362e37adcf793613cf0d8c8e4fbf7
+  md5: c104d98e09c47519950cffb8dd5b4f10
+  depends:
+  - python
+  license: MIT
+  license_family: MIT
+  size: 10856
+  timestamp: 1531372274693
+- kind: conda
+  name: luigi
+  version: 3.5.0
+  build: py312h2e8e312_0
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/luigi-3.5.0-py312h2e8e312_0.conda
+  sha256: ef58be732d7aaf5cd57e4f23d99f43a77967c8e8966bdc9549f3d3906841c0f5
+  md5: 9f658de27da5c64db9c7670c9f80beb6
+  depends:
+  - jsonschema
+  - prometheus_client >=0.5.0,<0.15
+  - python >=3.12,<3.13.0a0
+  - python-dateutil >=2.7.5,<3
+  - python_abi 3.12.* *_cp312
+  - setuptools
+  - tenacity >=8,<9
+  - toml <2.0.0
+  - tornado >=5.0,<7
+  license: Apache-2.0
+  license_family: Apache
+  size: 1317712
+  timestamp: 1705340569066
+- kind: conda
+  name: luigi
+  version: 3.5.0
+  build: py312h7900ff3_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/luigi-3.5.0-py312h7900ff3_0.conda
+  sha256: 6ee26282116f16035d674502b28124810444e74cffee240c173269cba3fc6ac2
+  md5: 58cd3cea6fbceebe970da7f050689b86
+  depends:
+  - jsonschema
+  - prometheus_client >=0.5.0,<0.15
+  - python >=3.12,<3.13.0a0
+  - python-daemon
+  - python-dateutil >=2.7.5,<3
+  - python_abi 3.12.* *_cp312
+  - setuptools
+  - tenacity >=8,<9
+  - toml <2.0.0
+  - tornado >=5.0,<7
+  license: Apache-2.0
+  license_family: Apache
+  size: 1301655
+  timestamp: 1705340017029
+- kind: conda
+  name: luigi
+  version: 3.5.0
+  build: py312h81bd7bf_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/luigi-3.5.0-py312h81bd7bf_0.conda
+  sha256: c962430b9fe81ce1ec8d19be2b2ccb58601d6a00fe77fb8d80a3560bd568a940
+  md5: 08cf3dcd12261e802c890463ec229a1d
+  depends:
+  - jsonschema
+  - prometheus_client >=0.5.0,<0.15
+  - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
+  - python-daemon
+  - python-dateutil >=2.7.5,<3
+  - python_abi 3.12.* *_cp312
+  - setuptools
+  - tenacity >=8,<9
+  - toml <2.0.0
+  - tornado >=5.0,<7
+  license: Apache-2.0
+  license_family: Apache
+  size: 1305078
+  timestamp: 1705340376949
+- kind: conda
+  name: luigi
+  version: 3.5.0
+  build: py312hb401068_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/luigi-3.5.0-py312hb401068_0.conda
+  sha256: 4375efc5c5c9dd5b23a733e2df82e33e0b5030b5333c90e623911bcf77dd2a64
+  md5: 29a91174a844f96ac5bbdf186ffed0e0
+  depends:
+  - jsonschema
+  - prometheus_client >=0.5.0,<0.15
+  - python >=3.12,<3.13.0a0
+  - python-daemon
+  - python-dateutil >=2.7.5,<3
+  - python_abi 3.12.* *_cp312
+  - setuptools
+  - tenacity >=8,<9
+  - toml <2.0.0
+  - tornado >=5.0,<7
+  license: Apache-2.0
+  license_family: Apache
+  size: 1301543
+  timestamp: 1705340364904
 - kind: conda
   name: lz4-c
   version: 1.9.4
@@ -12726,19 +12916,19 @@ packages:
   timestamp: 1701485332654
 - kind: conda
   name: prometheus_client
-  version: 0.20.0
+  version: 0.14.1
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.20.0-pyhd8ed1ab_0.conda
-  sha256: 757cd91d01c2e0b64fadf6bc9a11f558cf7638d897dfbaf7415ddf324d5405c9
-  md5: 9a19b94034dd3abb2b348c8b93388035
+  url: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.14.1-pyhd8ed1ab_0.tar.bz2
+  sha256: 66d41fd720b39faad502ea4f67cf70c797ff4a4c5bc01fef536a0880e882713e
+  md5: b7fa7d86530b8de805268e48988eb483
   depends:
-  - python >=3.8
+  - python >=3.6
   license: Apache-2.0
   license_family: Apache
-  size: 48913
-  timestamp: 1707932844383
+  size: 50172
+  timestamp: 1649447244565
 - kind: conda
   name: prompt-toolkit
   version: 3.0.42
@@ -13401,6 +13591,25 @@ packages:
   license: Python-2.0
   size: 13085901
   timestamp: 1708117361381
+- kind: conda
+  name: python-daemon
+  version: 3.0.1
+  build: pyhd8ed1ab_2
+  build_number: 2
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/python-daemon-3.0.1-pyhd8ed1ab_2.conda
+  sha256: 0918c11fab3c3d40739047dcabd107379dd44f0c839e1eb89eb59cc24c3357c2
+  md5: ce98ed304e32a8075f7ff5e53c405046
+  depends:
+  - docutils
+  - lockfile >=0.10
+  - python >=3.0
+  - setuptools >=62.4.0
+  license: Apache-2.0
+  license_family: APACHE
+  size: 33618
+  timestamp: 1700732416187
 - kind: conda
   name: python-dateutil
   version: 2.9.0
diff --git a/pixi.toml b/pixi.toml
index 9ef24a0..66446f5 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -25,3 +25,5 @@ plotly = ">=5.19.0,<5.20"
 seaborn = ">=0.13.2,<0.14"
 pyproj = ">=3.6.1,<3.7"
 pip = ">=24.0,<25"
+h5netcdf = ">=1.3.0,<1.4"
+luigi = ">=3.5.0,<3.6"

From 0ca9855b8de1bd88018630a96e55cdee3e5bda4a Mon Sep 17 00:00:00 2001
From: Julieanna Bacon <julieannabacon2014@gmail.com>
Date: Tue, 12 Mar 2024 15:01:54 -0400
Subject: [PATCH 2/3] preprocess: Support centering netcdf crop window
 arbitrarily and added additional documentation to config file and docstrings.
 Added project to grid function

---
 config/preprocess_netcdf.toml | 12 ++++++--
 icedyno/preprocess/crop.py    | 55 +++++++++++++++++++++++++++++++----
 2 files changed, 59 insertions(+), 8 deletions(-)

diff --git a/config/preprocess_netcdf.toml b/config/preprocess_netcdf.toml
index 58216ce..b3b34c9 100644
--- a/config/preprocess_netcdf.toml
+++ b/config/preprocess_netcdf.toml
@@ -1,4 +1,12 @@
 [CropFiles]
+# Full relative directory is data/input_dir, data/output_dir
 input_dir = "ims_1km"
-output_dir = "ims_netcdf_1km_cropped_4_000_000km_window"
-window_size = 4000
+# Change this output_dir name to reflect the window size and center coordinates selected
+output_dir = "ims_netcdf_1km_cropped_4_000_000m_window"
+
+# Final shape will be (window_size*2, window_size*2)
+window_size = 4000 #km
+
+# Expect str format in x,y coordinates if provided. Example: "1000, 2000"
+# Defaults to center of current grid system if "None"
+center_coordinates = "1000, 1500"#None"
diff --git a/icedyno/preprocess/crop.py b/icedyno/preprocess/crop.py
index 0a5ea5f..1c8d42b 100644
--- a/icedyno/preprocess/crop.py
+++ b/icedyno/preprocess/crop.py
@@ -9,6 +9,7 @@
 import pathlib
 
 import luigi
+import numpy as np
 import xarray as xr
 
 
@@ -16,13 +17,17 @@ class CropFiles(luigi.Task):
     """
     Crop IMS and MASIE NetCDF files from the center of their grids (where x, y == 1/2*sie.shape) based on input window_size.
 
+    Supports centering the window of the cropped files at different locations.
+    See config/preprocess_netcdf.toml for configuration settings.
     """
 
     input_dir = luigi.Parameter()
     output_dir = luigi.Parameter()
 
+    center_coordinates = luigi.Parameter()
+
     window_size = luigi.IntParameter(default=4000)
-    year = luigi.IntParameter()
+    year = luigi.IntParameter()  # Determined at runtime
 
     def output(self) -> luigi.LocalTarget:
         return luigi.LocalTarget(
@@ -39,19 +44,35 @@ def run(self) -> None:
         )
 
         for cdf_filepath in input_cdf_files:
+            # Set output file name and check if the output file already exists on disk.
             output_filename = (
                 os.path.join(year_output_dir, pathlib.Path(cdf_filepath).stem)
-                + f"_grid{self.window_size}.nc"
+                + f"_grid{self.window_size}"
             )
+            if self.center_coordinates != "None":
+                output_filename += (
+                    f"_{self.center_coordinates.replace(' ', '')}center.nc"
+                )
+            else:
+                output_filename += ".nc"
+
             if os.path.exists(output_filename):
                 print(cdf_filepath, "already on disk, skipping...")
 
             # Open the original NetCDF file
             ds = xr.open_dataset(cdf_filepath, engine="h5netcdf")
 
-            x_coord = ds["x"].shape[0] // 2
-            y_coord = ds["y"].shape[0] // 2
-            window = self.window_size * 1000
+            if self.center_coordinates != "None":
+                # Expects self.center_coordinates x,y to be "x, y" float values.
+                x, y = [float(coord) for coord in self.center_coordinates.split(",")]
+
+                # Project x, y to the defined grid and find the index in the grid corresponding to that pair.
+                x_coord = find_closest_index_in_grid(x, ds.x)
+                y_coord = find_closest_index_in_grid(y, ds.y)
+            else:
+                x_coord = ds["x"].shape[0] // 2
+                y_coord = ds["y"].shape[0] // 2
+            window = self.window_size * 1000  # from km to meters
 
             cropped_ds = ds.sel(
                 x=slice(x_coord - window, x_coord + window),
@@ -62,6 +83,28 @@ def run(self) -> None:
             cropped_ds.to_netcdf(output_filename, engine="h5netcdf")
 
 
+def find_closest_index_in_grid(target: float, coordinates: np.array) -> int:
+    """
+    Given a target coordinate in projected coordinates (x or y) and the list of coordinates, return the index of the closest number in the coordinates.
+    Assumes a 1km grid.
+    """
+    assert np.allclose(coordinates % 1000, 500)
+    start = coordinates[
+        0
+    ]  # Assume that the first element of coordinates is the minimum number in the list
+    # -12287500.0 for IMS 1km data.
+
+    # Define the step size
+    grid_resolution = 1000  # meters
+
+    # Calculate the index of the closest number
+    index = int((target - start) // grid_resolution)
+
+    # If the solution is correct, the target and the found value should never be more than the grid_resolution apart.
+    assert abs(coordinates[index] - target) < grid_resolution
+    return index
+
+
 if __name__ == "__main__":
     os.environ["LUIGI_CONFIG_PARSER"] = "toml"
 
@@ -74,7 +117,7 @@ def run(self) -> None:
 
     ## Change acording to your number of cores
     n_workers = 10
-    years = range(2014, 2025)
+    years = range(2015, 2025)
 
     tasks = [CropFiles(year=year) for year in years]
     luigi.build(tasks, workers=n_workers, local_scheduler=True)

From 6925889d908397d9f76b73ed609b7db7650396af Mon Sep 17 00:00:00 2001
From: Julieanna Bacon <julieannabacon2014@gmail.com>
Date: Tue, 12 Mar 2024 16:03:52 -0400
Subject: [PATCH 3/3] centering crop preprocess: Updated descriptions in the
 configuration file and tested process more thoroughly

---
 config/preprocess_netcdf.toml | 6 ++++--
 icedyno/preprocess/crop.py    | 1 -
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/config/preprocess_netcdf.toml b/config/preprocess_netcdf.toml
index b3b34c9..49961d5 100644
--- a/config/preprocess_netcdf.toml
+++ b/config/preprocess_netcdf.toml
@@ -1,12 +1,14 @@
 [CropFiles]
 # Full relative directory is data/input_dir, data/output_dir
 input_dir = "ims_1km"
+
 # Change this output_dir name to reflect the window size and center coordinates selected
-output_dir = "ims_netcdf_1km_cropped_4_000_000m_window"
+# Example: _50000x_15000y
+output_dir = "ims_netcdf_1km_cropped_4_000_000m_window
 
 # Final shape will be (window_size*2, window_size*2)
 window_size = 4000 #km
 
 # Expect str format in x,y coordinates if provided. Example: "1000, 2000"
 # Defaults to center of current grid system if "None"
-center_coordinates = "1000, 1500"#None"
+center_coordinates = "None"
diff --git a/icedyno/preprocess/crop.py b/icedyno/preprocess/crop.py
index 1c8d42b..12420cb 100644
--- a/icedyno/preprocess/crop.py
+++ b/icedyno/preprocess/crop.py
@@ -78,7 +78,6 @@ def run(self) -> None:
                 x=slice(x_coord - window, x_coord + window),
                 y=slice(y_coord - window, y_coord + window),
             )
-
             # Write the cropped data to a new NetCDF file
             cropped_ds.to_netcdf(output_filename, engine="h5netcdf")