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

updates metadata model #64

Merged
merged 1 commit into from
Jul 26, 2024
Merged

updates metadata model #64

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
7 changes: 4 additions & 3 deletions api/app/models/grid.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -46,9 +46,10 @@ class CategoricalLegend(BaseModel):
class DatasetMeta(BaseModel):
var_name: str = Field(description="Column name.")
var_dtype: str = Field(description="Column dtype.")
nodata: str
description: str
aggregation_method: str = Field(description="Aggregation method used to compute the overview levels.")
label: str = Field(description="Human readable name.")
nodata: str | None = Field(default=None, description="Nodata value used in grid")
description: str = Field(description="Human readable indicator description.")
unit: str | None = Field(description="Unit of the measurement")
lineage: list[str] | None = Field(default=None, description="Source data used to compute this dataset.")
legend: CategoricalLegend | NumericalLegend = Field(discriminator="legend_type")

Expand Down
97 changes: 97 additions & 0 deletions science/notebooks/check_combine_results.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,97 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e84373c7-a5e7-47c8-95a3-d2db7ade2e29",
"metadata": {},
"outputs": [],
"source": [
"import polars as pl\n",
"import polars.selectors as cs"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "750642ad-f9fc-434e-86f8-783cc41d533c",
"metadata": {},
"outputs": [],
"source": [
"df = pl.read_ipc(\"../data/processed/grid/0/8057fffffffffff.arrow\")\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5012a5d-ea51-4b01-8ccf-055db2feb3ec",
"metadata": {},
"outputs": [],
"source": [
"schema = {\"cell\":pl.UInt64, \"b\":pl.Float32, \"c\":pl.String}\n",
"df = pl.DataFrame(schema=schema)\n",
"\n",
"join1 = pl.DataFrame({\"cell\": [1,2,3], \"b\": [9., 9., 9.]}, schema_overrides=schema)\n",
"join2 = pl.DataFrame({\"cell\": [1,2,3], \"c\": [\"a\", \"b\", \"c\"]}, schema_overrides=schema)\n",
"join3 = pl.DataFrame({\"cell\": [4,5,6], \"c\": [\"a\", \"b\", \"c\"]}, schema_overrides=schema)\n",
"\n",
"tojoin = [join1, join2, join3]\n",
"\n",
"res = pl.concat(tojoin, how=\"diagonal\", rechunk=True)\n",
"res = res.group_by(\"cell\", maintain_order=True).agg(pl.all().max())\n",
"print(res)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af677cc8-34c5-4259-84c2-1d90a5bf3040",
"metadata": {},
"outputs": [],
"source": [
"import polars as pl\n",
"\n",
"# Define the initial DataFrame\n",
"df = pl.DataFrame({\n",
" \"cell\": [1, 2, 3, 1, 2, 3, 4, 5, 6],\n",
" \"b\": [9.0, 9.0, 9.0, None, None, None, None, None, None],\n",
" \"c\": [None, None, None, \"a\", \"b\", \"c\", \"a\", \"b\", \"c\"]\n",
"})\n",
"\n",
"# Perform the group by and aggregation\n",
"agg_df = df.groupby(\"cell\").agg([\n",
" pl.col(\"b\").max().alias(\"b\"),\n",
" pl.col(\"c\").max().alias(\"c\")\n",
"])\n",
"\n",
"# Sort the resulting DataFrame by the 'cell' column\n",
"result_df = agg_df.sort(\"cell\")\n",
"\n",
"# Print the resulting DataFrame\n",
"print(result_df)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
166 changes: 166 additions & 0 deletions science/notebooks/merge_entrega_roberto.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,166 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import polars as pl\n",
"from pathlib import Path\n",
"import h3ronpy.polars"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"csvs = list(Path(\"../raw/ENTREGA UNO MUESTRAS HEXA CSV 18072024/\").glob(\"*.CSV\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dfs = [pl.read_csv(f, separator=\";\", decimal_comma=True) for f in csvs]\n",
"df = pl.concat(dfs, how=\"align\", rechunk=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = df.with_columns(pl.col(\"GRID_ID\").h3.cells_parse())\n",
"df = df.drop(\"GRID_ID\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.select(pl.col(\"cell\").h3.cells_resolution()).unique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"CELLS_RES = 6\n",
"OVERVIEW_LEVEL = CELLS_RES - 5\n",
"\n",
"df = df.with_columns(\n",
" pl.col(\"cell\").h3.change_resolution(OVERVIEW_LEVEL).alias(\"tile_id\") # type: ignore[attr-defined]\n",
")\n",
"partition_dfs = df.partition_by([\"tile_id\"], as_dict=True, include_key=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Write tiles to IPC files"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"seen_tiles = set()\n",
"n_cells = 0\n",
"\n",
"for tile_group, tile_df in partition_dfs.items():\n",
" if tile_df.shape[0] == 0: # todo: skip empty tiles ?\n",
" continue\n",
" tile_id = hex(tile_group[0])[2:]\n",
" filename = Path(\"grid/1\") / (tile_id + \".arrow\")\n",
" if tile_id in seen_tiles:\n",
" tile_df = pl.concat(\n",
" [pl.read_ipc(filename), tile_df], how=\"vertical_relaxed\"\n",
" ).unique(subset=[\"cell\"])\n",
" tile_df.write_parquet(filename)\n",
" n_cells += len(tile_df)\n",
" else:\n",
" seen_tiles.add(tile_id)\n",
" tile_df.write_ipc(filename)\n",
" n_cells += len(tile_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make the metadata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.select(pl.all().min())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.select(pl.all().max())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.dtypes"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Loading