From 1f1fa4505a3be243261dc8e2c220b66ab51b6a16 Mon Sep 17 00:00:00 2001
From: rabii-chaarani <rabii.chaarani@monash.edu>
Date: Fri, 23 Aug 2024 15:56:52 +0930
Subject: [PATCH] fix: converts correctly shapefiles

---
 .../loop_converter.cpython-311.pyc            | Bin 3805 -> 4027 bytes
 .../ntgs_converter.cpython-311.pyc            | Bin 6792 -> 7735 bytes
 .../converters/loop_converter.py              |  19 ++--
 .../converters/ntgs_converter.py              |  82 ++++++++++++------
 4 files changed, 68 insertions(+), 33 deletions(-)

diff --git a/LoopDataConverter/converters/__pycache__/loop_converter.cpython-311.pyc b/LoopDataConverter/converters/__pycache__/loop_converter.cpython-311.pyc
index e8ba4dd19e5541d5062860e8bdd9c83b7846bcec..6b79a1b4c903875fa19ca02f9db59f5e545332f8 100644
GIT binary patch
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diff --git a/LoopDataConverter/converters/__pycache__/ntgs_converter.cpython-311.pyc b/LoopDataConverter/converters/__pycache__/ntgs_converter.cpython-311.pyc
index afdbba7a86da1e5c6da01226070bd26ebcb9823c..322399e4a779aa6a71e69150c225f1ac861fbc90 100644
GIT binary patch
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diff --git a/LoopDataConverter/converters/loop_converter.py b/LoopDataConverter/converters/loop_converter.py
index fae1c0f..52f0ba2 100644
--- a/LoopDataConverter/converters/loop_converter.py
+++ b/LoopDataConverter/converters/loop_converter.py
@@ -25,9 +25,9 @@ def __init__(self, survey_name: SurveyName, data: InputData, layer: str = None):
             The `data` parameter in the `__init__` method is of type `InputData`. It seems to represent the
         data that will be used in the survey.
         layer : str
-            The `layer` parameter in the `__init__` method is a string that represents a specific layer
-        within the data. It is an optional parameter with a default value of `None`, which means it can
-        be omitted when creating an instance of the class. If provided, it specifies the layer to
+            The `layer` parameter is a string that represents a specific layer within a .GPKG file. 
+            It is an optional parameter with a default value of `None`, which means it can be omitted 
+            when creating an instance of the class. If provided, it specifies the layer to
 
         '''
         self._fileData = data
@@ -43,13 +43,15 @@ def __init__(self, survey_name: SurveyName, data: InputData, layer: str = None):
             SurveyName.GSNSW: "",
             SurveyName.MRT: "",
         }
+        self._used_converter = None
 
     def read_file(self):
         """
         read the file using the correct file reader
         """
-        file_reader = LoopGisReader(self._fileData)()
-        return file_reader.data
+        self.file_reader = LoopGisReader(self._fileData)
+        self.file_reader()
+        return self.file_reader._data
 
     def get_converter(self):
         '''
@@ -70,6 +72,7 @@ def convert(self):
 
         '''
         data = self.read_file()
-        converter = self.get_converter()
-        converter(data)
-        self.data = converter._data
+        self._used_converter = self.get_converter()
+        self._used_converter = self._used_converter(data)
+        self._used_converter.convert()
+        self.data = self._used_converter._data
diff --git a/LoopDataConverter/converters/ntgs_converter.py b/LoopDataConverter/converters/ntgs_converter.py
index 071def6..0e84cfb 100644
--- a/LoopDataConverter/converters/ntgs_converter.py
+++ b/LoopDataConverter/converters/ntgs_converter.py
@@ -17,6 +17,7 @@ class NTGSConverter(BaseConverter):
     # TODO: modify class to take fold, fault, and structure layers as arguments
     def __init__(self, data: pandas.DataFrame):
         self.raw_data = data.copy()
+        self.update_empty_rows()
         self._type_label = "NTGSConverter"
         self._data = None
 
@@ -30,6 +31,30 @@ def type(self):
 
         '''
         return self._type_label
+    
+    def update_empty_rows(self):
+        '''
+        The function `update_empty_rows` updates empty rows in the DataFrame with NaN values.
+
+        Parameters
+        ----------
+            None
+        
+        This method operates on the DataFrames stored in the class and replaces all empty values 
+        (e.g., empty strings, None, NaN) with NaN across the specified tables.
+        '''
+    
+        # List of tables (DataFrames) to update
+        tables_to_update = [Datatype.FOLD, 
+                            Datatype.FAULT, 
+                            Datatype.STRUCTURE]
+        
+        for table in tables_to_update:
+            # Replace empty strings, None, or NaN with np.nan in the entire table
+            self.raw_data[table] = self.raw_data[table].map(
+                lambda x: "NaN" if pandas.isna(x) or x == "" or x is None else x
+            )
+
 
     def convert_fold_map(self):
         '''
@@ -38,16 +63,16 @@ def convert_fold_map(self):
 
         '''
         # convert dip direction terms to degrees
-        self.raw_data[Datatype.FOLD]["AxialPlaneDipDir"] = self.raw_data[Datatype.FOLD][
-            "AxialPlaneDipDir"
+        self.raw_data[Datatype.FOLD]["AxPlaneDD"] = self.raw_data[Datatype.FOLD][
+            "AxPlaneDD"
         ].apply(lambda x: convert_dipdir_terms(x))
         # convert dip terms to degrees
-        self.raw_data[Datatype.FOLD]["AxialPlaneDip"] = self.raw_data[Datatype.FOLD][
-            "AxialPlaneDip"
+        self.raw_data[Datatype.FOLD]["AxPlaneDip"] = self.raw_data[Datatype.FOLD][
+            "AxPlaneDip"
         ].apply(lambda x: convert_dip_terms(x, type="fold"))
         # convert tightness terms to degrees
-        self.raw_data[Datatype.FOLD]["InterlimbAngle"] = self.raw_data[Datatype.FOLD][
-            "InterlimbAngle"
+        self.raw_data[Datatype.FOLD]["Interlimb"] = self.raw_data[Datatype.FOLD][
+            "Interlimb"
         ].apply(lambda x: convert_tightness_terms(x))
 
     def convert_fault_map(self):
@@ -58,16 +83,18 @@ def convert_fault_map(self):
         '''
 
         # convert dip direction terms to degrees
-        self.raw_data[Datatype.FAULT]["DipDirection"] = self.raw_data[Datatype.FAULT][
-            "DipDirection"
-        ].apply(lambda x: convert_dipdir_terms(x))
+        
+        self.raw_data[Datatype.FAULT]["DipDirectn"] = self.raw_data[Datatype.FAULT][
+                "DipDirectn"
+            ].apply(lambda x: convert_dipdir_terms(x))
         # convert dip terms to degrees
         self.raw_data[Datatype.FAULT]["Dip"] = self.raw_data[Datatype.FAULT]["Dip"].apply(
-            lambda x: convert_dip_terms(x, type="fault")
-        )
-        self.raw_data[Datatype.FAULT]["Displacement"] = self.raw_data[Datatype.FAULT][
-            "Displacement"
-        ].apply(lambda x: convert_displacement_terms(x))
+                lambda x: convert_dip_terms(x, type="fault")
+            )
+        # convert displacement terms to meters
+        self.raw_data[Datatype.FAULT]["Displace"] = self.raw_data[Datatype.FAULT][
+                "Displace"
+            ].apply(lambda x: convert_displacement_terms(x))
 
     def convert_structure_map(self):
         '''
@@ -75,21 +102,26 @@ def convert_structure_map(self):
         dip estimates to floats by averaging the range.
 
         '''
-        # discard any rows that has a dip value of -99 and does not have any estimated dip value
-        condition = (self.raw_data[Datatype.STRUCTURE]["Dip"] != -99) & (
-            self.raw_data[Datatype.STRUCTURE]["DipEstimate"] != numpy.nan
+        # select any rows that has a dip value of -99 and have any estimated dip value
+        condition = (self.raw_data[Datatype.STRUCTURE]["Dip"] == -99) & (
+            self.raw_data[Datatype.STRUCTURE]["DipEstimte"] != "NaN"
         )
-        self.raw_data = self.raw_data[Datatype.STRUCTURE][condition]
+        
         # convert dip estimate to float (average of the range)
-        condition = self.raw_data[Datatype.STRUCTURE]["Dip"] == -99
-        self.raw_data[Datatype.STRUCTURE].loc[condition, "DipEstimate"] = (
-            self.raw_data[Datatype.STRUCTURE]
-            .loc[condition, "DipEstimate"]
-            .apply(lambda x: sum(map(float, x.split("-"))) / 2)
+        self.raw_data[Datatype.STRUCTURE].loc[condition, "Dip"] = (
+        self.raw_data[Datatype.STRUCTURE]
+        .loc[condition, "DipEstimte"]
+        .apply(lambda x: convert_dip_terms(x, type="structure"))
+        )
+        
+        # discard any rows that has a dip value of -99 and does not have any estimated dip value
+        condition = (self.raw_data[Datatype.STRUCTURE]["Dip"] == -99) & (
+            self.raw_data[Datatype.STRUCTURE]["DipEstimte"] == "NaN"
         )
-        self.raw_data[Datatype.STRUCTURE][condition, "Dip"] = self.raw_data[Datatype.STRUCTURE][
-            condition, "DipEstimate"
+        self.raw_data[Datatype.STRUCTURE] = self.raw_data[Datatype.STRUCTURE][
+            ~condition
         ]
+        
 
     def convert(self):
         '''