Feature/issue 271 - Remove randomized value check

CHANGELOG.md

@@ -10,6 +10,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Catch "unsupported datatype" exception from netCDF library ([#268](https://github.com/nasa/ncompare/pull/268)) ([**@danielfromearth**](https://github.com/danielfromearth))
 
+### Removed
+
+- **Breaking:** remove randomized value check logic and from API ([#271](https://github.com/nasa/ncompare/pull/271)) ([**@danielfromearth**](https://github.com/danielfromearth))
+
 ## [1.11.0] - 2024-11-14
 
 ### Added

README.md

@@ -90,8 +90,6 @@ ncompare S001G01.nc S001G01_SUBSET.nc --file-text subset_comparison.txt
 - `--no-color` : Turn off all colorized output.
 - `--show-attributes` : Include variable attributes in the table that compares variables.
 - `--show-chunks` : Include chunk sizes in the table that compares variables.
-- `-v` (`--comparison_var_name`) [VAR_NAME]: Compare specific values for this variable.
-- `-g` (`--comparison_var_group`) [VAR_GROUP]: Group that contains the `comparison_var_name`.
 - `--column-widths` [WIDTH, WIDTH, WIDTH]: Width, in number of characters, of the three columns in the comparison report
 - `--version` : Show the current version and then exit.
 

ncompare/console.py

@@ -51,8 +51,6 @@ def _cli(args: Optional[Sequence[str]]) -> argparse.Namespace:
     )
     parser.add_argument("nc_a", help="First NetCDF file")
     parser.add_argument("nc_b", help="Second NetCDF file")
-    parser.add_argument("-v", "--comparison_var_name", help="Comparison variable name")
-    parser.add_argument("-g", "--comparison_var_group", help="Comparison variable group")
     parser.add_argument(
         "--only-diffs",
         action="store_true",

ncompare/core.py

@@ -29,17 +29,14 @@
 
 """Compare the structure of two NetCDF files."""
 
-import random
-import traceback
 from collections import namedtuple
 from collections.abc import Iterable, Iterator
 from pathlib import Path
 from typing import Optional, Union
 
 import netCDF4
-import numpy as np
 import xarray as xr
-from colorama import Fore, Style
+from colorama import Fore
 
 from ncompare.printing import Outputter
 from ncompare.sequence_operations import common_elements, count_diffs
@@ -57,8 +54,6 @@
 def compare(
     nc_a: Union[str, Path],
     nc_b: Union[str, Path],
-    comparison_var_group: Optional[str] = None,
-    comparison_var_name: Optional[str] = None,
     only_diffs: bool = False,
     no_color: bool = False,
     show_chunks: bool = False,
@@ -76,10 +71,6 @@ def compare(
         filepath to NetCDF4
     nc_b : str
         filepath to NetCDF4
-    comparison_var_group : str, optional
-        The name of a group which contains a desired comparison variable
-    comparison_var_name : str, optional
-        The name of a variable for which we want to compare values
     only_diffs : bool, optional
         Whether to show only the variables/attributes that are different between the two files
     no_color : bool, default False
@@ -127,8 +118,6 @@ def compare(
             out,
             nc_a,
             nc_b,
-            comparison_var_group=comparison_var_group,
-            comparison_var_name=comparison_var_name,
             show_chunks=show_chunks,
             show_attributes=show_attributes,
         )
@@ -146,8 +135,6 @@ def run_through_comparisons(
     out: Outputter,
     nc_a: Union[str, Path],
     nc_b: Union[str, Path],
-    comparison_var_group: Optional[str],
-    comparison_var_name: Optional[str],
     show_chunks: bool,
     show_attributes: bool,
 ) -> None:
@@ -158,8 +145,6 @@ def run_through_comparisons(
     out
     nc_a
     nc_b
-    comparison_var_group
-    comparison_var_name
     show_chunks
     show_attributes
     """
@@ -175,92 +160,12 @@ def run_through_comparisons(
     list_b = _get_groups(nc_b)
     _, _, _ = out.lists_diff(list_a, list_b)
 
-    if comparison_var_group:
-        # Show the variables within the selected group.
-        out.print(
-            Fore.LIGHTBLUE_EX + f"\nVariables within specified group <{comparison_var_group}>:",
-            add_to_history=True,
-        )
-        vlist_a = _get_vars(nc_a, comparison_var_group)
-        vlist_b = _get_vars(nc_b, comparison_var_group)
-        _, _, _ = out.lists_diff(vlist_a, vlist_b)
-
-        # TODO: Remove comparison variable/val?
-        if comparison_var_name:
-            try:
-                # Print the first part of the values array for the selected variable.
-                out.print(
-                    Fore.LIGHTBLUE_EX
-                    + f"\nSample values within specified variable <{comparison_var_name}>:"
-                )
-                _print_sample_values(out, nc_a, comparison_var_group, comparison_var_name)
-                _print_sample_values(out, nc_b, comparison_var_group, comparison_var_name)
-                # compare_sample_values(nc_a, nc_b, groupname=comparison_var_group, varname=comparison_var_name)
-
-                out.print(
-                    Fore.LIGHTBLUE_EX
-                    + f"\nChecking multiple random values within specified variable <{comparison_var_name}>:"
-                )
-                compare_multiple_random_values(
-                    out,
-                    nc_a,
-                    nc_b,
-                    groupname=comparison_var_group,
-                    varname=comparison_var_name,
-                )
-
-            except KeyError:
-                out.print(
-                    Style.BRIGHT
-                    + Fore.RED
-                    + f"\nError when comparing values for variable <{comparison_var_name}> "
-                    f"in group <{comparison_var_group}>."
-                )
-                out.print(traceback.format_exc())
-                out.print("\n")
-        else:
-            out.print(Fore.LIGHTBLACK_EX + "\nNo variable selected for comparison. Skipping..")
-    else:
-        out.print(Fore.LIGHTBLACK_EX + "\nNo variable group selected for comparison. Skipping..")
-
     out.print(Fore.LIGHTBLUE_EX + "\nAll variables:", add_to_history=True)
     _, _, _ = compare_two_nc_files(
         out, nc_a, nc_b, show_chunks=show_chunks, show_attributes=show_attributes
     )
 
 
-def compare_multiple_random_values(
-    out: Outputter,
-    nc_a: Union[str, Path],
-    nc_b: Union[str, Path],
-    groupname: str,
-    varname: str,
-    num_comparisons: int = 100,
-):
-    """Iterate through N random samples, and evaluate whether the differences exceed a threshold."""
-    # Open a variable from each NetCDF
-    nc_var_a = xr.open_dataset(nc_a, backend_kwargs={"group": groupname}).variables[varname]
-    nc_var_b = xr.open_dataset(nc_b, backend_kwargs={"group": groupname}).variables[varname]
-
-    num_mismatches = 0
-    for _ in range(num_comparisons):
-        match_result = _match_random_value(out, nc_var_a, nc_var_b)
-        if match_result is True:
-            out.print(".", colors=False, end="")
-        elif match_result is None:
-            out.print("n", colors=False, end="")
-            num_mismatches += 1
-        else:
-            out.print("x", colors=False, end="")
-            num_mismatches += 1
-
-    if num_mismatches > 0:
-        out.print(Fore.RED + f" {num_mismatches} mismatches, out of {num_comparisons} samples.")
-    else:
-        out.print(Fore.CYAN + " No mismatches.")
-    out.print("Done.", colors=False)
-
-
 def walk_common_groups_tree(
     top_a_name: str,
     top_a: Union[netCDF4.Dataset, netCDF4.Group],
@@ -587,59 +492,6 @@ def _var_properties(group: Union[netCDF4.Dataset, netCDF4.Group], varname: str)
     return VarProperties(varname, the_variable, v_dtype, v_shape, v_chunking, v_attributes)
 
 
-def _match_random_value(
-    out: Outputter, nc_var_a: xr.Variable, nc_var_b: xr.Variable, thresh: float = 1e-6
-) -> Union[bool, None]:
-    """Check whether a randomly selected data point matches between two variables.
-
-    Returns
-    -------
-    None or bool
-        None if data point is null for one and only one of the variables
-        True if values match
-        False if the difference exceeds the given threshold
-    """
-    # Get a random indexer
-    rand_index = []
-    for dim_length in nc_var_a.shape:
-        rand_index.append(random.randint(0, dim_length - 1))
-    rand_index_tuple = tuple(rand_index)
-
-    # Get the values from each variable
-    value_a = nc_var_a.values[rand_index_tuple]
-    value_b = nc_var_b.values[rand_index_tuple]
-
-    # Check whether null
-    if np.isnan(value_a) and np.isnan(value_b):
-        return True
-    elif np.isnan(value_a) or np.isnan(value_b):
-        return None
-
-    # Evaluate difference between values
-    diff = value_b - value_a
-    if abs(diff) > thresh:
-        out.print()
-        out.print(Fore.RED + f"Difference exceeded threshold (diff == {diff}")
-        out.print(f"var shape: {nc_var_a.shape}", colors=False)
-        out.print(f"indices:   {rand_index_tuple}", colors=False)
-        out.print(f"value a: {value_a}", colors=False)
-        out.print(f"value b: {value_b}", colors=False, end="\n\n")
-        return False
-
-    return True
-
-
-def _print_sample_values(out: Outputter, nc_filepath, groupname: str, varname: str) -> None:
-    comparison_variable = xr.open_dataset(nc_filepath, backend_kwargs={"group": groupname})[varname]
-    vector_of_values = comparison_variable.values.flatten()
-    n_values = len(vector_of_values)
-    if n_values > 100:
-        sample_length = 100
-    else:
-        sample_length = n_values
-    out.print(str(vector_of_values[:sample_length]), colors=False)
-
-
 def _get_attribute_value_as_str(varprops: VarProperties, attribute_key: str) -> str:
     if attribute_key and (attribute_key in varprops.attributes):
         attr = varprops.attributes[attribute_key]

tests/data/a-b_test_golden_file.txt

@@ -9,8 +9,6 @@ Root-level Groups:
 	Are all items the same? ---> True.
 	['Data', 'Position', 'Statistics']
 
-No variable group selected for comparison. Skipping..
-
 All variables:
                                                                              File A                                           File B
                      All Variables