add __array__ for pairs,loops& fix small bugs

faninsar/_core/pair_tools.py

@@ -2,7 +2,7 @@
 from collections.abc import Iterable
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Callable, Literal, Optional,  Union
+from typing import Any, Callable, Literal, Optional, Union
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -45,6 +45,9 @@ def __eq__(self, other: "Pair") -> bool:
     def __hash__(self) -> int:
         return hash(self._name)
 
+    def __array__(self) -> np.ndarray:
+        return self._values
+
     @property
     def values(self):
         """return the values of the pair.
@@ -195,22 +198,11 @@ def __init__(
         """
         if pairs is None or len(pairs) == 0:
             raise ValueError("pairs cannot be None.")
-        pairs_ls = []
-        for pair in pairs:
-            if isinstance(pair, Pair):
-                _pair = pair
-            elif isinstance(pair, Iterable):
-                _pair = Pair(pair)
-            else:
-                raise TypeError(
-                    f"pairs should be an Iterable containing Iterable or Pair object, but got {type(pair)}."
-                )
-            pairs_ls.append(_pair.values)
 
-        _values = np.array(pairs_ls)
+        _values = np.array(pairs, dtype="M8[D]")
 
         self._values = _values
-        self._dates = np.unique(pairs_ls)
+        self._dates = np.unique(_values.flatten())
         self._length = self._values.shape[0]
         self._edge_index = np.searchsorted(self._dates, self._values)
 
@@ -328,6 +320,9 @@ def __contains__(self, item):
 
         return np.any(np.all(item == self.values, axis=1))
 
+    def __array__(self) -> np.ndarray:
+        return self._values
+
     @property
     def values(self) -> np.ndarray:
         """return the pairs array in type of np.datetime64[D]"""
@@ -561,6 +556,9 @@ def __eq__(self, other) -> bool:
     def __hash__(self) -> int:
         return hash(self.name)
 
+    def __array__(self) -> np.ndarray:
+        return self._values
+
     @property
     def values(self) -> np.ndarray:
         """return the values array of the loop.
@@ -683,24 +681,13 @@ def __init__(
         """
         if loops is None or len(loops) == 0:
             raise ValueError("loops cannot be None.")
-        loops_ls = []
-        for loop in loops:
-            if isinstance(loop, Loop):
-                _loop = loop
-            elif isinstance(loop, Iterable):
-                _loop = Loop(loop)
-            else:
-                raise TypeError(
-                    f"loops should be an Iterable containing Iterable or Poop object, but got {type(loop)}."
-                )
-            loops_ls.append(_loop.values)
 
-        _values = np.array(loops_ls)
+        _values = np.array(loops, dtype="M8[D]")
 
         self._values = _values
-        self._dates = np.unique(loops_ls)
+        self._dates = np.unique(_values)
         self._length = self._values.shape[0]
-        
+
         if sort:
             self.sort(inplace=True)
 
@@ -814,6 +801,9 @@ def __contains__(self, item):
 
         return np.any(np.all(item == self.values, axis=1))
 
+    def __array__(self) -> np.ndarray:
+        return self._values
+
     @property
     def values(self) -> np.ndarray:
         """return the values of the loops.
@@ -1070,7 +1060,7 @@ def sort(
 
         Returns
         -------
-        None or (Loops, np.ndarray). if inplace is True, return the sorted loops 
+        None or (Loops, np.ndarray). if inplace is True, return the sorted loops
         and the index of the sorted loops in the original loops. Otherwise,
         return None.
         """
@@ -1085,10 +1075,10 @@ def sort(
             "days23": self.days23,
             "days13": self.days13,
             "date": self._values,
-            "pairs": zip(
+            "pairs": np.hstack(
                 [self.pairs12.values, self.pairs23.values, self.pairs13.values]
             ),
-            "days": zip([self.days12, self.days23, self.days13]),
+            "days": np.hstack([self.days12, self.days23, self.days13]),
         }
         if isinstance(order, str):
             order = [order]
@@ -1098,7 +1088,7 @@ def sort(
                 raise ValueError(
                     f"order should be one of {list(item_map.keys())}, but got {order}."
                 )
-            _values.append(item_map[i].reshape(self._length, -1))
+            _values.append(item_map[i])
         _values = np.hstack(_values)
         _, _index = np.unique(_values, axis=0, return_index=True)
         if not ascending:
@@ -1303,11 +1293,11 @@ def sort(
             Whether to sort ascending. Default is True.
         inplace: bool, optional
             Whether to sort the pairs and loops inplace. Default is True.
-            
+
         Returns
         -------
-        None or (SBASNetwork, np.ndarray). if inplace is True, return the sorted 
-        SBASNetwork and the index of the sorted pairs in the original pairs. 
+        None or (SBASNetwork, np.ndarray). if inplace is True, return the sorted
+        SBASNetwork and the index of the sorted pairs in the original pairs.
         Otherwise, return None.
         """
         pairs, _index = self._pairs.sort(order, ascending, inplace=False)
@@ -1358,7 +1348,7 @@ def __init__(self, dates: Iterable, **kwargs) -> None:
         date_args: dict, optional
             Keyword arguments for pd.to_datetime().
         """
-        self.dates = pd.to_datetime(dates, **kwargs)
+        self.dates = pd.to_datetime(dates, **kwargs).unique().sort_values()
 
     def from_interval(self, max_interval: int = 2, max_day: int = 180) -> Pairs:
         """generate interferometric pairs by SAR acquisition interval. SAR
@@ -1406,38 +1396,44 @@ def from_period(
         period_start: str = "1201",
         period_end: str = "0331",
         n_per_period: str = 3,
-        n_primary_period: str = 2,
+        n_primary_period: Optional[str] = None,
+        primary_years: Optional[list[int]] = None,
     ) -> Pairs:
-        """generate interferometric pairs between periods for all years. period is defined by month
-        and day for each year. For example, period_start='1201', period_end='0331' means the period
-        is from Dec 1 to Mar 31 for each year in the time series. This function will randomly select
-        n_per_period dates in each period and generate interferometric pairs between those dates. This
-        will be useful to mitigate the temporal cumulative bias.
+        """generate interferometric pairs between periods for all years.
+        period is defined by month and day for each year. For example,
+        period_start='1201', period_end='0331' means the period is from Dec 1
+        to Mar 31 for each year in the time series. This function will randomly
+        select n_per_period dates in each period and generate interferometric
+        pairs between those dates. This will be useful to mitigate the temporal
+        cumulative bias.
 
         Parameters
         ----------
         period_start, period_end:  str
-            start and end date for the period which expressed as month and day with format '%m%d'
+            start and end date for the period which expressed as month and day
+            with format '%m%d'
         n_per_period: int
-            how many dates will be used for each period. Those dates will be selected randomly
-            in each period. Default is 3
-        n_primary_period: int
-            how many periods used as primary date of ifg. Default is 2. For example, if n_primary_period=2,
-            then the interferometric pairs will be generated between the first two periods and the rest
-            periods.
+            how many dates will be used for each period. Those dates will be
+            selected randomly in each period. Default is 3
+        n_primary_period: int, optional
+            how many periods/years used as primary date of ifg. For example, if
+            n_primary_period=2, then the interferometric pairs will be generated
+            between the first two periods and the rest periods. If None, all
+            periods will be used. Default is None.
+        primary_years: list, optional
+            years used as primary date of ifg. If None, all years in the time
+            series will be used. Default is None.
 
         Returns
         -------
         pairs: Pairs object
         """
         years = sorted(set(self.dates.year))
-        df_dates = pd.Series(self.dates.strftime("%Y%m%d"), index=self.dates)
+        df_dates = pd.Series(self.dates, index=self.dates)
 
-        # check if period_start and period_end are in the same year. If not, the period_end should be
-        # in the next year
-        same_year = False
-        if int(period_start) < int(period_end):
-            same_year = True
+        # check if period_start and period_end are in the same year. If not,
+        # the period_end should be in the next year
+        same_year = True if int(period_start) < int(period_end) else False
 
         # randomly select n_per_period dates in each period/year
         date_years = []
@@ -1457,8 +1453,10 @@ def from_period(
         _pairs = []
         for i, date_year in enumerate(date_years):
             # only generate pairs for n_primary_period
-            if i + 1 > n_primary_period:
+            if n_primary_period is not None and i + 1 > n_primary_period:
                 break
+            if primary_years is not None and years[i] not in primary_years:
+                continue
             for date_primary in date_year:
                 # all rest periods
                 for date_year1 in date_years[i + 1 :]:
@@ -1489,10 +1487,10 @@ def from_summer_winter(
 
         Returns
         -------
-        Pairs object 
+        Pairs object
         """
         years = sorted(set(self.dates.year))
-        df_dates = pd.Series(self.dates.strftime('%Y%m%d'), index=self.dates)
+        df_dates = pd.Series(self.dates.strftime("%Y%m%d"), index=self.dates)
 
         _pairs = []
         for year in years:

tests/sampler.py

@@ -1,9 +1,11 @@
 from pathlib import Path
 
-from faninsar import samplers
-from faninsar.datasets import RasterDataset,BoundingBox,Points
 from tqdm import tqdm
 
+from faninsar import samplers
+from faninsar.datasets import RasterDataset
+from faninsar.query import BoundingBox, Points
+
 home_dir = Path("/home/fancy/work/data/test")
 files = list(home_dir.rglob("*unw_phase_clip.tif"))