Fixing conflicts

.coveragerc

@@ -1,5 +1,8 @@
 [run]
+source = metcalcpy
 relative_files = True
 omit = 
     config.py
     config-3.py
+    metcalcpy/contributed/*
+

docs/Users_Guide/logging.rst

@@ -20,7 +20,11 @@ configurations. This approach ensures consistency and simplifies the maintenance
 settings across all modules within METcalcpy.
 
 
+<<<<<<< HEAD
 * **Key Feature:** :code:`setup_logging` **Function**
+=======
+* Key Feature: :code:`setup_logging` function
+>>>>>>> develop
 
   * The :code:`setup_logging` function is the core of **logging_config.py**. It initializes 
     and configures the logger instance based on parameters specified in a YAML configuration 
@@ -51,18 +55,35 @@ YAML-Driven Configuration
 -------------------------
 
 METcalcpy now allows users to customize logging behavior directly within 
+<<<<<<< HEAD
 the user's YAML configuration files, eliminating the need for hardcoding 
+=======
+their YAML configuration files, eliminating the need for hardcoding 
+>>>>>>> develop
 logging settings in Python scripts.
 
 **Key Parameters in YAML Configuration:**
 
+<<<<<<< HEAD
 | :code:`log_dir:` Specifies the directory where log files are stored.
 | :code:`log_filename:` Defines the name of the log file.
 | :code:`log_level:` Determines the verbosity of the log output. 
   Available levels are DEBUG, INFO, WARNING, and ERROR.
 | :code:`log_level:` By setting the appropriate log level in the YAML configuration 
   file (e.g., log_level: WARNING), the user can control the verbosity of the log output, 
   ensuring that only the necessary information is recorded.
+=======
+:code:`log_dir:` Specifies the directory where log files are stored.
+
+:code:`log_filename:` Defines the name of the log file.
+
+:code:`log_level:` Determines the verbosity of the log output. 
+Available levels are :code:`DEBUG, INFO, WARNING, and ERROR:`.
+
+:code:`log_level:` By setting the appropriate log level in your YAML configuration 
+file (e.g., log_level: WARNING), you can control the verbosity of the log output, 
+ensuring that only the necessary information is recorded.
+>>>>>>> develop
 
 METcalcpy supports the following log levels:
 
@@ -98,6 +119,7 @@ Informative Log Formatting
 Log messages in METcalcpy are meticulously formatted to include detailed information, 
 improving readability and facilitating easier analysis of log data.
 
+<<<<<<< HEAD
 **Standard Log Format Includes:**
 
   * **Timestamp (UTC):** Each log message is tagged with a UTC timestamp 
@@ -173,6 +195,13 @@ specified settings.
 **Example Log Entry:**
 
 :code:`2023-12-19 18:20:00 UTC | user123 | INFO | Data loading completed successfully.`
+=======
+
+
+
+
+
+>>>>>>> develop
 
 
 

metcalcpy/util/mode_2d_arearat_statistics.py

@@ -545,7 +545,8 @@ def calculate_2d_arearat_fsa_osa(input_data, columns_names, logger=None):
             calculated statistic
             or None if some of the data values are missing or invalid
     """
-     try:
+
+    try:
         safe_log(logger, "debug", "Filtering data based on TWO_D_DATA_FILTER.")
         filtered_data = column_data_by_name_value(input_data, columns_names, TWO_D_DATA_FILTER)
 

metcalcpy/util/mode_3d_ratio_statistics.py

@@ -563,7 +563,8 @@ def calculate_3d_ratio_osa_fsa(input_data, columns_names, logger=None):
             calculated statistic as float
             or None if some of the data values are missing or invalid
     """
-     try:
+
+    try:
         safe_log(logger, "debug", "Filtering data based on THREE_D_DATA_FILTER.")
         filtered_data = column_data_by_name_value(input_data, columns_names, THREE_D_DATA_FILTER)
 

test/test_mode_2d_statistics.py

@@ -0,0 +1,157 @@
+import pytest
+import numpy as np
+import pandas as pd
+
+import metcalcpy.util.mode_2d_ratio_statistics as m2rs
+import metcalcpy.util.mode_2d_arearat_statistics as m2as
+
+def prepare_data(obj_type = "2d"):
+    """
+    Prepare some data for testing mode ara rations.
+    This uses an existing file in test/data. A more 
+    robust approach would be to use a real MODE export
+    from METviewer.
+    """
+    file_path = "test/data/ee_av_input.data"
+    df = pd.read_csv(file_path, sep="\t")
+
+    df["object_type"] = obj_type
+
+    cols = np.array(df.columns)
+    data = df.to_numpy()
+    return cols, data
+
+
+# Expected values
+# I suspect these could be simplified
+# as ratios of each other.
+FSA_ASA = 0.5858572
+ASM_ASA = 0.8955360
+FSM_FSA = 0.9069445
+OSM_OSA = 0.8793973
+FSM_ASM = 0.0930555
+OSM_ASM = 0.4066794
+OSU_ASU = 0.4781241
+OSA_AAA = 0.2184832
+FSA_AAA = 0.3090720
+FSA_FAA = 0.5243991
+OSA_OAA = 0.5320855
+OCA_ACA = 0.4066794
+
+OBJ_HITS = 362487
+OBJ_OSU = 20217
+OBJ_FSU = 22067
+
+@pytest.mark.parametrize(
+        "pair, expected",
+        [
+            ("fsa_asa", FSA_ASA),
+            ("osa_asa", 1 - FSA_ASA),
+            ("asm_asa", ASM_ASA),
+            ("asu_asa", 1 - ASM_ASA),
+            ("fsm_fsa", FSM_FSA),
+            ("fsu_fsa", 1- FSM_FSA),
+            ("osm_osa", OSM_OSA),
+            ("osu_osa", 1 - OSM_OSA),
+            ("fsm_asm", FSM_ASM),
+            ("osm_asm", OSM_ASM),
+            ("osa_aaa", OSA_AAA),
+            ("osu_asu", OSU_ASU),
+            ("fsa_aaa", FSA_AAA),
+            ("fsa_faa", FSA_FAA),
+            ("fca_faa", 1 - FSA_FAA),
+            ("osa_oaa", OSA_OAA),
+            ("oca_oaa", 1 - OSA_OAA),
+            ("fca_aca", 1 - OSM_ASM),
+            ("oca_aca", OCA_ACA),
+            ("fsa_osa", FSA_AAA / OSA_AAA),
+            ("osa_fsa", 1), # Can this be right?
+            ("aca_asa", ASM_ASA),
+            ("asa_aca", 1/ASM_ASA),
+            ("fca_fsa", (1-FSA_FAA)/FSA_FAA),
+            ("fsa_fca", FSA_FAA/(1-FSA_FAA)),
+            ("oca_osa", OSM_OSA),
+            ("osa_oca", 1 / OSM_OSA),
+            ("objahits", OBJ_HITS / 2),
+            ("objamisses", OBJ_OSU),
+            ("objafas", OBJ_FSU),
+            # The values below should be derived from
+            # the FSU, OSU, and HITS. However, the
+            # formulas described in the doc strings
+            # do not appear to give these answers.
+            ("objacsi", 0.8108331),
+            ("objapody", 0.8996478),
+            ("objafar", 0.0295392),
+        ]
+)
+def test_m2as(pair, expected):
+    col_names, data = prepare_data()
+    if pair.startswith("obj"):
+        func_str = "calculate_2d_{}".format(pair)
+    else:
+         func_str = "calculate_2d_arearat_{}".format(pair)
+
+    func = getattr(m2as, func_str)
+    actual = func(data, col_names)
+    np.testing.assert_almost_equal(actual, expected)
+
+
+# Expected values for rations
+rFSA_ASA = 0.4399575
+rASM_ASA = 0.5855473
+rFSM_FSA = 0.9541063
+rFSU_FSA = 0.5458937
+rOSM_OSA = 0.6166983
+rFSM_ASM = 0.3411978
+rOSM_ASM = 0.3666062
+rFSU_ASU = 0.4820513
+rFSA_AAA = 0.3236904
+rOSA_AAA = 0.4120407
+rFSA_FAA = 0.7101201
+rOSA_OAA = 0.7571839
+rFCA_ACA = 0.5
+rACA_ASA = 0.3591923
+
+@pytest.mark.parametrize(
+        "pair, expected",
+        [
+            ("fsa_asa", rFSA_ASA),
+            ("osa_asa", 1 - rFSA_ASA),
+            ("asm_asa", rASM_ASA),
+            ("asu_asa", 1 - rASM_ASA),
+            ("fsm_fsa", rFSM_FSA),
+            ("fsu_fsa", rFSU_FSA),
+            ("osm_osa", rOSM_OSA),
+            ("osu_osa", 1 - rOSM_OSA),
+            ("fsm_asm", rFSM_ASM),
+            ("osm_asm", rOSM_ASM),
+            ("fsu_asu", rFSU_ASU),
+            ("osu_asu", 1 - rFSU_ASU),
+            ("fsa_aaa", rFSA_AAA),
+            ("osa_aaa", rOSA_AAA),
+            ("fsa_faa", rFSA_FAA),
+            ("fca_faa", 1- rFSA_FAA),
+            ("osa_oaa", rOSA_OAA),
+            ("oca_oaa", rOSA_OAA),
+            ("fca_aca", rFCA_ACA),
+            ("oca_aca", 1 - rFCA_ACA),
+            ("fsa_osa", rFSA_AAA/rOSA_AAA),
+            ("osa_fsa", rOSA_AAA/rFSA_AAA),
+            ("aca_asa", rACA_ASA),
+            ("asa_aca", 1 / rACA_ASA),
+            ("fca_fsa", (1- rFSA_FAA)/rFSA_FAA),
+            ("fsa_fca", rFSA_FAA / (1- rFSA_FAA)),
+            ("oca_osa", (1 - rOSA_OAA) / rOSA_OAA),
+            ("osa_oca", rOSA_OAA / (1 - rOSA_OAA)),
+        ]
+)
+def test_m2rs(pair, expected):
+    col_names, data = prepare_data()
+    if pair.startswith("obj"):
+        func_str = "calculate_2d_{}".format(pair)
+    else:
+         func_str = "calculate_2d_ratio_{}".format(pair)
+
+    func = getattr(m2rs, func_str)
+    actual = func(data, col_names)
+    np.testing.assert_almost_equal(actual, expected, 6)