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384 | @pd.api.extensions.register_dataframe_accessor("qtm")
class QTMPandas:
def __init__(self, df: DataFrame):
self._df = df
# QTM API
# These methods simply mirror the Vgrid qtm API and apply QTM functions to all rows
def latlon2qtm(
self,
resolution: int,
lat_col: str = "lat",
lon_col: str = "lon",
set_index: bool = True,
) -> AnyDataFrame:
"""Adds qtm ID to (Geo)DataFrame.
pd.DataFrame: uses `lat_col` and `lon_col` (default `lat` and `lon`)
gpd.GeoDataFrame: uses `geometry`
Assumes coordinates in epsg=4326.
Parameters
----------
resolution : int
QTM resolution
lat_col : str
Name of the latitude column (if used), default 'lat'
lon_col : str
Name of the longitude column (if used), default 'lon'
set_index : bool
If True, the columns with QTM ID is set as index, default 'True'
Returns
-------
(Geo)DataFrame with QTM IDs added
"""
resolution = validate_qtm_resolution(resolution)
if isinstance(self._df, gpd.GeoDataFrame):
lons = self._df.geometry.x
lats = self._df.geometry.y
else:
lons = self._df[lon_col]
lats = self._df[lat_col]
qtm_ids = [
qtm.latlon_to_qtm_id(lat, lon, resolution) for lat, lon in zip(lats, lons)
]
# qtm_column = self._format_resolution(resolution)
qtm_column = "qtm"
assign_arg = {qtm_column: qtm_ids, "qtm_res": resolution}
df = self._df.assign(**assign_arg)
if set_index:
return df.set_index(qtm_column)
return df
def qtm2geo(self, qtm_column: str = None) -> GeoDataFrame:
"""Add geometry with QTM geometry to the DataFrame. Assumes QTM ID.
Parameters
----------
qtm_column : str, optional
Name of the column containing QTM. If None, assumes qtm qtm_ids are in the index.
Returns
-------
GeoDataFrame with QTM geometry
Raises
------
ValueError
When an invalid QTM ID is encountered
"""
if qtm_column is not None:
# qtm qtm_ids are in the specified column
if qtm_column not in self._df.columns:
raise ValueError(f"Column '{qtm_column}' not found in DataFrame")
qtm_ids = self._df[qtm_column]
# Handle both single 1_ids and lists of 1_ids
geometries = []
for q_ids in qtm_ids:
try:
if pd.isna(q_ids):
# Handle NaN values - create empty geometry
geometries.append(Polygon())
elif isinstance(q_ids, list):
# Handle list of 1_ids - create a MultiPolygon
if len(q_ids) == 0:
# Handle empty list - create empty geometry
geometries.append(Polygon())
else:
cell_geometries = [qtm_to_geo(e_id) for e_id in q_ids]
geometries.append(MultiPolygon(cell_geometries))
else:
# Handle single id
geometries.append(qtm_to_geo(q_ids))
except (ValueError, TypeError):
if isinstance(q_ids, list):
if len(q_ids) == 0:
geometries.append(Polygon())
else:
cell_geometries = [qtm_to_geo(q_id) for q_id in q_ids]
geometries.append(MultiPolygon(cell_geometries))
else:
# Try to handle as single id
try:
geometries.append(qtm_to_geo(q_ids))
except Exception:
# If all else fails, create empty geometry
geometries.append(Polygon())
result_df = self._df.copy()
result_df['geometry'] = geometries
return gpd.GeoDataFrame(result_df, crs="epsg:4326")
else:
# QTM IDs are in the index
return self._apply_index_assign(
wrapped_partial(qtm_to_geo),
"geometry",
finalizer=lambda x: gpd.GeoDataFrame(x, crs="epsg:4326"),
)
def polyfill(self, resolution: int, predicate: str = None, compact: bool = False, explode: bool = False) -> AnyDataFrame:
"""
Parameters
----------
resolution : int
QTM resolution
predicate : str, optional
Spatial predicate to apply ('intersect', 'within', 'centroid_within', 'largest_overlap')
compact : bool, optional
Whether to compact the QTM IDs
explode : bool
If True, will explode the resulting list vertically.
All other columns' values are copied.
Default: False
"""
resolution = validate_qtm_resolution(resolution)
def func(row):
return list(polyfill(row.geometry, resolution, predicate, compact))
result = self._df.apply(func, axis=1)
if not explode:
assign_args = {COLUMN_QTM_POLYFILL: result}
return self._df.assign(**assign_args)
result = result.explode().to_frame(COLUMN_QTM_POLYFILL)
return self._df.join(result)
def qtmbin(
self,
resolution: int,
stats: str = "count",
numeric_column: str = None,
category_column: str = None,
lat_col: str = "lat",
lon_col: str = "lon",
return_geometry: bool = True,
) -> DataFrame:
"""
Bin points into QTM cells and compute statistics, optionally grouped by a category column.
Supports both GeoDataFrame (with point geometry) and DataFrame (with lat/lon columns).
Parameters
----------
resolution : int
QTM resolution
stats : str
Statistic to compute: count, sum, min, max, mean, median, std, var, range, minority, majority, variety
numeric_column : str, optional
Name of the numeric column to aggregate (for sum, min, max, etc.) or the value column for minority/majority/variety stats
category_column : str, optional
Name of the category column to group by. Required for minority, majority, and variety stats when grouping by category.
lat_col : str, optional
Name of the latitude column (only used for DataFrame input, ignored for GeoDataFrame)
lon_col : str, optional
Name of the longitude column (only used for DataFrame input, ignored for GeoDataFrame)
return_geometry : bool
If True, return a GeoDataFrame with qtm cell geometry
"""
# Validate inputs and prepare data
resolution = validate_qtm_resolution(resolution)
# qtm_column = self._format_resolution(resolution)
qtm_column = "qtm"
df = self.latlon2qtm(resolution, lat_col, lon_col, False)
# Filter to keep only QTM IDs at the requested resolution
df = df[df[qtm_column].astype(str).str.len() == resolution]
# Validate column existence
if category_column is not None and category_column not in df.columns:
raise ValueError(f"Category column '{category_column}' not found in DataFrame")
if numeric_column is not None and numeric_column not in df.columns:
raise ValueError(f"Numeric column '{numeric_column}' not found in DataFrame")
# Prepare grouping columns
group_cols = [qtm_column]
if category_column:
df[category_column] = df[category_column].fillna("NaN_category")
group_cols.append(category_column)
# Perform aggregation based on stats type
if stats == "count":
result = df.groupby(group_cols).size().reset_index(name=stats)
elif stats in ["sum", "min", "max", "mean", "median", "std", "var"]:
if not numeric_column:
raise ValueError(f"numeric_column must be provided for stats='{stats}'")
result = df.groupby(group_cols)[numeric_column].agg(stats).reset_index()
elif stats == "range":
if not numeric_column:
raise ValueError(f"numeric_column must be provided for stats='{stats}'")
result = df.groupby(group_cols)[numeric_column].agg(['min', 'max']).reset_index()
result[stats] = result['max'] - result['min']
result = result.drop(['min', 'max'], axis=1)
elif stats in ["minority", "majority", "variety"]:
if not numeric_column:
raise ValueError(f"numeric_column must be provided for stats='{stats}'")
# Define categorical aggregation function
def cat_agg_func(x):
values = x[numeric_column].dropna()
freq = Counter(values)
if not freq:
return None
if stats == "minority":
return min(freq.items(), key=lambda y: y[1])[0]
elif stats == "majority":
return max(freq.items(), key=lambda y: y[1])[0]
elif stats == "variety":
return values.nunique()
if category_column:
# Handle categorical aggregation with category grouping
all_categories = sorted([str(cat) for cat in df[category_column].unique()])
result = df.groupby([qtm_column, category_column]).apply(cat_agg_func, include_groups=False).reset_index(name=stats)
result = result.pivot(index=qtm_column, columns=category_column, values=stats)
result = result.reindex(columns=all_categories, fill_value=0 if stats == "variety" else None)
result = result.reset_index()
result.columns = [qtm_column] + [f"{cat}_{stats}" for cat in all_categories]
else:
# Handle categorical aggregation without category grouping
result = df.groupby([qtm_column]).apply(cat_agg_func, include_groups=False).reset_index(name=stats)
else:
raise ValueError(f"Unknown stats: {stats}")
# Handle column renaming for non-categorical stats
if len(result.columns) > len(group_cols) and not (category_column and stats in ["minority", "majority", "variety"]):
result = result.rename(columns={result.columns[-1]: stats})
# Handle category pivoting for non-categorical stats
if category_column and stats not in ["minority", "majority", "variety"]:
if len(result) == 0:
result = pd.DataFrame(columns=[qtm_column, category_column, stats])
else:
try:
# Pivot categories to columns
result = result.pivot(index=qtm_column, columns=category_column, values=stats)
result = result.fillna(0)
result = result.reset_index()
# Rename columns with category prefixes
new_columns = [qtm_column]
for col in sorted(result.columns[1:]):
if col == "NaN_category":
new_columns.append(f"NaN_{stats}")
else:
new_columns.append(f"{col}_{stats}")
result.columns = new_columns
except Exception:
# Fallback to simple count if pivot fails
result = df.groupby(qtm_column).size().reset_index(name=stats)
# Add geometry if requested
result = result.set_index(qtm_column)
if return_geometry:
result = result.qtm.qtm2geo()
return result.reset_index()
def _apply_index_assign(
self,
func: Callable,
column_name: str,
processor: Callable = lambda x: x,
finalizer: Callable = lambda x: x,
) -> Any:
"""Helper method. Applies `func` to index and assigns the result to `column`.
Parameters
----------
func : Callable
single-argument function to be applied to each S2 Token
column_name : str
name of the resulting column
processor : Callable
(Optional) further processes the result of func. Default: identity
finalizer : Callable
(Optional) further processes the resulting dataframe. Default: identity
Returns
-------
Dataframe with column `column` containing the result of `func`.
If using `finalizer`, can return anything the `finalizer` returns.
"""
func = catch_invalid_dggs_id(func)
result = [processor(func(qtm_id)) for qtm_id in self._df.index]
assign_args = {column_name: result}
return finalizer(self._df.assign(**assign_args))
def _apply_index_explode(
self,
func: Callable,
column_name: str,
processor: Callable = lambda x: x,
finalizer: Callable = lambda x: x,
) -> Any:
"""Helper method. Applies a list-making `func` to index and performs
a vertical explode.
Any additional values are simply copied to all the rows.
Parameters
----------
func : Callable
single-argument function to be applied to each S2 Token
column_name : str
name of the resulting column
processor : Callable
(Optional) further processes the result of func. Default: identity
finalizer : Callable
(Optional) further processes the resulting dataframe. Default: identity
Returns
-------
Dataframe with column `column` containing the result of `func`.
If using `finalizer`, can return anything the `finalizer` returns.
"""
func = catch_invalid_dggs_id(func)
result = (
pd.DataFrame.from_dict(
{qtm_id: processor(func(qtm_id)) for qtm_id in self._df.index},
orient="index",
)
.stack()
.to_frame(column_name)
.reset_index(level=1, drop=True)
)
result = self._df.join(result)
return finalizer(result)
@staticmethod
def _format_resolution(resolution: int) -> str:
return f"qtm_{str(resolution).zfill(2)}"
|