Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Refactor process parquet #124

Open
wants to merge 6 commits into
base: croptype
Choose a base branch
from
Open

Refactor process parquet #124

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
97ebb7d
avoid pandas to_datetime for 12-timesteps case
Dec 11, 2024
3293e75
more generic handling of ts indexing
Dec 11, 2024
2d2aa2d
refactoring process_parquet
Dec 11, 2024
b6cd500
using all non-feature columns as index; formatting
Dec 11, 2024
aac145e
removing lat-lon from required columns
Dec 11, 2024
d88124a
formatting
Dec 11, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 2 additions & 1 deletion presto/dataset.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -143,14 +143,15 @@ def row_to_arrays(
timestep_positions = cls.get_timestep_positions(row_d, augment=augment, is_ssl=is_ssl)

if cls.NUM_TIMESTEPS == 12:
initial_start_date_position = pd.to_datetime(row_d["start_date"]).month
initial_start_date_position = datetime.strptime(row_d["start_date"], "%Y-%m-%d").month
elif cls.NUM_TIMESTEPS > 12:
# get the correct index of the start_date based on NUM_TIMESTEPS`
# e.g. if NUM_TIMESTEPS is 36 (dekadal setup), we should take the correct
# 10-day interval that the start_date falls into
# TODO: 1) this needs to go into a separate function
# 2) definition of valid_position and timestep_ind
# should also be changed accordingly

year = pd.to_datetime(row_d["start_date"]).year
year_dates = pd.date_range(start=f"{year}-01-01", end=f"{year}-12-31")
bins = pd.cut(year_dates, bins=cls.NUM_TIMESTEPS, labels=False)
Expand Down
Loading
Loading