Merge branch 'refactor_24' into definition_summary_fix

sasdata/data_backing.py

@@ -1,126 +1,126 @@
-from typing import TypeVar, Self
-from dataclasses import dataclass
-from enum import Enum
-
-from sasdata.quantities.quantity import NamedQuantity
-
-DataType = TypeVar("DataType")
-
-""" Sasdata metadata tree """
-
-def shorten_string(string):
-    lines = string.split("\n")
-    if len(lines) <= 1:
-        return string
-    else:
-        return lines[0][:30] + " ... " + lines[-1][-30:]
-
-@dataclass
-class Dataset[DataType]:
-    name: str
-    data: DataType
-    attributes: dict[str, Self | str]
-
-    def summary(self, indent_amount: int = 0, indent: str = "  ") -> str:
-
-        s = f"{indent*indent_amount}{self.name.split("/")[-1]}:\n"
-        s += f"{indent*(indent_amount+1)}{shorten_string(str(self.data))}\n"
-        for key in self.attributes:
-            value = self.attributes[key]
-            if isinstance(value, (Group, Dataset)):
-                value_string = value.summary(indent_amount+1, indent)
-            else:
-                value_string = f"{indent * (indent_amount+1)}{key}: {shorten_string(repr(value))}\n"
-
-            s += value_string
-
-        return s
-
-@dataclass
-class Group:
-    name: str
-    children: dict[str, Self | Dataset]
-
-    def summary(self, indent_amount: int=0, indent="  "):
-        s = f"{indent*indent_amount}{self.name.split("/")[-1]}:\n"
-        for key in self.children:
-            s += self.children[key].summary(indent_amount+1, indent)
-
-        return s
-
-class Function:
-    """ Representation of a (data driven) function, such as I vs Q """
-
-    def __init__(self, abscissae: list[NamedQuantity], ordinate: NamedQuantity):
-        self.abscissae = abscissae
-        self.ordinate = ordinate
-
-
-class FunctionType(Enum):
-    """ What kind of function is this, should not be relied upon to be perfectly descriptive
-
-    The functions might be parametrised by more variables than the specification
-    """
-    UNKNOWN = 0
-    SCATTERING_INTENSITY_VS_Q = 1
-    SCATTERING_INTENSITY_VS_Q_2D = 2
-    SCATTERING_INTENSITY_VS_Q_3D = 3
-    SCATTERING_INTENSITY_VS_ANGLE = 4
-    UNKNOWN_METADATA = 20
-    TRANSMISSION = 21
-    POLARISATION_EFFICIENCY = 22
-    UNKNOWN_REALSPACE = 30
-    SESANS = 31
-    CORRELATION_FUNCTION_1D = 32
-    CORRELATION_FUNCTION_2D = 33
-    CORRELATION_FUNCTION_3D = 34
-    INTERFACE_DISTRIBUTION_FUNCTION = 35
-    PROBABILITY_DISTRIBUTION = 40
-    PROBABILITY_DENSITY = 41
-
-def function_type_identification_key(names):
-    """ Create a key from the names of data objects that can be used to assign a function type"""
-    return ":".join([s.lower() for s in sorted(names)])
-
-function_fields_to_type = [
-    (["Q"], "I", FunctionType.SCATTERING_INTENSITY_VS_Q),
-    (["Qx", "Qy"], "I", FunctionType.SCATTERING_INTENSITY_VS_Q_2D),
-    (["Qx", "Qy", "Qz"], "I", FunctionType.SCATTERING_INTENSITY_VS_Q_3D),
-    (["Z"], "G", FunctionType.SESANS),
-    (["lambda"], "T", FunctionType.TRANSMISSION)
-]
-
-function_fields_lookup = {
-    function_type_identification_key(inputs + [output]): function_type for inputs, output, function_type in function_fields_to_type
-}
-
-def build_main_data(data: list[NamedQuantity]) -> Function:
-    names = [datum.name for datum in data]
-    identifier = function_type_identification_key(names)
-
-    if identifier in function_fields_lookup:
-        function_type = function_fields_lookup[identifier]
-    else:
-        function_type = FunctionType.UNKNOWN
-
-    match function_type:
-        case FunctionType.UNKNOWN:
-            pass
-        case _:
-            raise NotImplementedError("Unknown ")
-
-def key_tree(data: Group | Dataset, indent_amount=0, indent: str = "  ") -> str:
-    """ Show a metadata tree, showing the names of they keys used to access them"""
-    s = ""
-    if isinstance(data, Group):
-        for key in data.children:
-            s += indent*indent_amount + key + "\n"
-            s += key_tree(data.children[key], indent_amount=indent_amount+1, indent=indent)
-
-    if isinstance(data, Dataset):
-        s += indent*indent_amount + "[data]\n"
-        for key in data.attributes:
-            s += indent*indent_amount + key + "\n"
-            s += key_tree(data.attributes[key], indent_amount=indent_amount+1, indent=indent)
-
+from typing import TypeVar, Self
+from dataclasses import dataclass
+from enum import Enum
+
+from sasdata.quantities.quantity import NamedQuantity
+
+DataType = TypeVar("DataType")
+
+""" Sasdata metadata tree """
+
+def shorten_string(string):
+    lines = string.split("\n")
+    if len(lines) <= 1:
+        return string
+    else:
+        return lines[0][:30] + " ... " + lines[-1][-30:]
+
+@dataclass
+class Dataset[DataType]:
+    name: str
+    data: DataType
+    attributes: dict[str, Self | str]
+
+    def summary(self, indent_amount: int = 0, indent: str = "  ") -> str:
+
+        s = f"{indent*indent_amount}{self.name.split("/")[-1]}:\n"
+        s += f"{indent*(indent_amount+1)}{shorten_string(str(self.data))}\n"
+        for key in self.attributes:
+            value = self.attributes[key]
+            if isinstance(value, (Group, Dataset)):
+                value_string = value.summary(indent_amount+1, indent)
+            else:
+                value_string = f"{indent * (indent_amount+1)}{key}: {shorten_string(repr(value))}\n"
+
+            s += value_string
+
+        return s
+
+@dataclass
+class Group:
+    name: str
+    children: dict[str, Self | Dataset]
+
+    def summary(self, indent_amount: int=0, indent="  "):
+        s = f"{indent*indent_amount}{self.name.split("/")[-1]}:\n"
+        for key in self.children:
+            s += self.children[key].summary(indent_amount+1, indent)
+
+        return s
+
+class Function:
+    """ Representation of a (data driven) function, such as I vs Q """
+
+    def __init__(self, abscissae: list[NamedQuantity], ordinate: NamedQuantity):
+        self.abscissae = abscissae
+        self.ordinate = ordinate
+
+
+class FunctionType(Enum):
+    """ What kind of function is this, should not be relied upon to be perfectly descriptive
+
+    The functions might be parametrised by more variables than the specification
+    """
+    UNKNOWN = 0
+    SCATTERING_INTENSITY_VS_Q = 1
+    SCATTERING_INTENSITY_VS_Q_2D = 2
+    SCATTERING_INTENSITY_VS_Q_3D = 3
+    SCATTERING_INTENSITY_VS_ANGLE = 4
+    UNKNOWN_METADATA = 20
+    TRANSMISSION = 21
+    POLARISATION_EFFICIENCY = 22
+    UNKNOWN_REALSPACE = 30
+    SESANS = 31
+    CORRELATION_FUNCTION_1D = 32
+    CORRELATION_FUNCTION_2D = 33
+    CORRELATION_FUNCTION_3D = 34
+    INTERFACE_DISTRIBUTION_FUNCTION = 35
+    PROBABILITY_DISTRIBUTION = 40
+    PROBABILITY_DENSITY = 41
+
+def function_type_identification_key(names):
+    """ Create a key from the names of data objects that can be used to assign a function type"""
+    return ":".join([s.lower() for s in sorted(names)])
+
+function_fields_to_type = [
+    (["Q"], "I", FunctionType.SCATTERING_INTENSITY_VS_Q),
+    (["Qx", "Qy"], "I", FunctionType.SCATTERING_INTENSITY_VS_Q_2D),
+    (["Qx", "Qy", "Qz"], "I", FunctionType.SCATTERING_INTENSITY_VS_Q_3D),
+    (["Z"], "G", FunctionType.SESANS),
+    (["lambda"], "T", FunctionType.TRANSMISSION)
+]
+
+function_fields_lookup = {
+    function_type_identification_key(inputs + [output]): function_type for inputs, output, function_type in function_fields_to_type
+}
+
+def build_main_data(data: list[NamedQuantity]) -> Function:
+    names = [datum.name for datum in data]
+    identifier = function_type_identification_key(names)
+
+    if identifier in function_fields_lookup:
+        function_type = function_fields_lookup[identifier]
+    else:
+        function_type = FunctionType.UNKNOWN
+
+    match function_type:
+        case FunctionType.UNKNOWN:
+            pass
+        case _:
+            raise NotImplementedError("Unknown ")
+
+def key_tree(data: Group | Dataset, indent_amount=0, indent: str = "  ") -> str:
+    """ Show a metadata tree, showing the names of they keys used to access them"""
+    s = ""
+    if isinstance(data, Group):
+        for key in data.children:
+            s += indent*indent_amount + key + "\n"
+            s += key_tree(data.children[key], indent_amount=indent_amount+1, indent=indent)
+
+    if isinstance(data, Dataset):
+        s += indent*indent_amount + "[data]\n"
+        for key in data.attributes:
+            s += indent*indent_amount + key + "\n"
+            s += key_tree(data.attributes[key], indent_amount=indent_amount+1, indent=indent)
+
     return s