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The following commit(s) were added to refs/heads/master by this push:
new 9ab3ec9 SDAP-436 - Added special case for uniform time arrays in
multi-var swath tiles (#227)
9ab3ec9 is described below
commit 9ab3ec966a1c42db5c1e1ad932cf0ffdc484e324
Author: Riley Kuttruff <[email protected]>
AuthorDate: Tue Feb 21 09:42:15 2023 -0800
SDAP-436 - Added special case for uniform time arrays in multi-var swath
tiles (#227)
* Don't simplify time_data arrays, this seems to cause issues down the line
* Restored simplification, added case for desired_shape[0] == 1 and
multi_var
* Changelog
* Simplify _to_standard_index (#7)
Co-authored-by: skorper <[email protected]>
---------
Co-authored-by: rileykk <[email protected]>
Co-authored-by: skorper <[email protected]>
---
CHANGELOG.md | 1 +
data-access/nexustiles/dao/CassandraProxy.py | 49 +++++++++++++---------------
2 files changed, 24 insertions(+), 26 deletions(-)
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4ed345f..ae01564 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,6 +13,7 @@ and this project adheres to [Semantic
Versioning](https://semver.org/spec/v2.0.0
### Fixed
- Made `platforms` param optional in `/cdmssubset`, and removed int requirement
- Updated OpenAPI specification for `/cdmssubset` to accurately reflect
`platforms` and `parameter` field options.
+- SDAP-436: Added special case for handling Cassandra SwathMulti tiles with
uniform time arrays
### Security
## [1.0.0] - 2022-12-05
diff --git a/data-access/nexustiles/dao/CassandraProxy.py
b/data-access/nexustiles/dao/CassandraProxy.py
index 4ce684a..96f7c4c 100644
--- a/data-access/nexustiles/dao/CassandraProxy.py
+++ b/data-access/nexustiles/dao/CassandraProxy.py
@@ -226,40 +226,37 @@ class NexusTileData(Model):
:rtype: np.array
"""
- if desired_shape[0] == 1:
- reshaped_array = np.ma.masked_all((desired_shape[1],
desired_shape[2]))
- row, col = np.indices(data_array.shape)
-
- reshaped_array[np.diag_indices(desired_shape[1],
len(reshaped_array.shape))] = data_array[
- row.flat, col.flat]
- reshaped_array.mask[np.diag_indices(desired_shape[1],
len(reshaped_array.shape))] = data_array.mask[
- row.flat, col.flat]
- reshaped_array = reshaped_array[np.newaxis, :]
- elif is_multi_var == True:
- # Break the array up by variable. Translate shape from
- # len(times) x len(latitudes) x len(longitudes) x num_vars,
- # to
- # num_vars x len(times) x len(latitudes) x len(longitudes)
+ reshaped_array = []
+ if is_multi_var:
reshaped_data_array = np.moveaxis(data_array, -1, 0)
- reshaped_array = []
+ else:
+ reshaped_data_array = [data_array]
- for variable_data_array in reshaped_data_array:
+ for variable_data_array in reshaped_data_array:
+ if desired_shape[0] == 1:
+ variable_reshaped_array = np.ma.masked_all((desired_shape[1],
desired_shape[2]))
+ else:
variable_reshaped_array = np.ma.masked_all(desired_shape)
- row, col = np.indices(variable_data_array.shape)
- variable_reshaped_array[np.diag_indices(desired_shape[1],
len(variable_reshaped_array.shape))] = variable_data_array[
+ row, col = np.indices(variable_data_array.shape)
+
+ variable_reshaped_array[
+ np.diag_indices(desired_shape[1],
len(variable_reshaped_array.shape))] = \
+ variable_data_array[
row.flat, col.flat]
- variable_reshaped_array.mask[np.diag_indices(desired_shape[1],
len(variable_reshaped_array.shape))] = variable_data_array.mask[
+ variable_reshaped_array.mask[
+ np.diag_indices(desired_shape[1],
len(variable_reshaped_array.shape))] = \
+ variable_data_array.mask[
row.flat, col.flat]
+
+ if desired_shape[0] == 1:
+ reshaped_array.append(variable_reshaped_array[np.newaxis, :])
+ else:
reshaped_array.append(variable_reshaped_array)
- else:
- reshaped_array = np.ma.masked_all(desired_shape)
- row, col = np.indices(data_array.shape)
- reshaped_array[np.diag_indices(desired_shape[1],
len(reshaped_array.shape))] = data_array[
- row.flat, col.flat]
- reshaped_array.mask[np.diag_indices(desired_shape[1],
len(reshaped_array.shape))] = data_array.mask[
- row.flat, col.flat]
+ if not is_multi_var:
+ # If single var, squeeze extra dim out of array
+ reshaped_array = reshaped_array[0]
return reshaped_array
