diff --git a/.gitignore b/.gitignore
index 37e1281..ab7ea3e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
 .idea/*
 src/nemosis/__pycache__/*
 venv/*
+venv*/*
 build/*
 dist/*
 src/nemosis/build/*
diff --git a/README.md b/README.md
index 1cffd68..a6c9f1a 100644
--- a/README.md
+++ b/README.md
@@ -50,7 +50,7 @@ Choose the exe from the latest [release](https://github.com/UNSW-CEEM/NEMOSIS/re
 
 ## Contributing
 
-Interested in contributing? Check out the [contributing instructions](./CONTRIBUTING. md), which also includes steps to install `nemosis` for development.
+Interested in contributing? Check out the [contributing instructions](./CONTRIBUTING.md), which also includes steps to install `nemosis` for development.
 
 Please note that this project is released with a [Code of Conduct](./CONDUCT.md). By contributing to this project, you agree to abide by its terms.
 
@@ -100,9 +100,10 @@ Your workflow may determine how you use NEMOSIS. Because the GUI relies on data
 
 ```python
 from nemosis import dynamic_data_compiler
+from datetime import datetime
 
-start_time = '2017/01/01 00:00:00'
-end_time = '2017/01/01 00:05:00'
+start_time = datetime(2017, 1, 1, 0, 0)
+end_time = datetime(2017, 1, 1, 0, 5)
 table = 'DISPATCHPRICE'
 raw_data_cache = 'C:/Users/your_data_storage'
 
@@ -113,6 +114,8 @@ Using the default settings of `dynamic_data_compiler` will download CSV data fro
 
 A number of options are available to configure filtering (i.e. what data NEMOSIS returns as a pandas DataFrame) and caching.
 
+For `start_time` and `end_time` you can pass a datetime (timezone unaware), a `date`, or a string of the form "YYYY/MM/DD HH:MM:SS", e.g. `2017/01/01 00:00:00`.
+
 ###### Filter options
 
 `dynamic_data_compiler` can be used to filter data before returning results.
@@ -206,8 +209,8 @@ from nemosis import defaults
 
 defaults.table_columns['BIDPEROFFER_D'] += ['PASAAVAILABILITY']
 
-start_time = '2017/01/01 00:00:00'
-end_time = '2017/01/01 00:05:00'
+start_time = datetime(2017, 1, 1, 0, 0)
+end_time = datetime(2017, 1, 1, 0, 5)
 table = 'BIDPEROFFER_D'
 raw_data_cache = 'C:/Users/your_data_storage'
 
diff --git a/pyproject.toml b/pyproject.toml
index dbeb9c7..b82973e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,9 +14,10 @@ dependencies = [
     "xlrd>=2.0.1",
     "beautifulsoup4>=4.12.3",
     "openpyxl>=3.1.5",
+    "cachetools>=7",
 ]
 readme = "README.md"
-requires-python = ">= 3.9"
+requires-python = ">= 3.10"
 
 [build-system]
 requires = ["hatchling"]
@@ -24,7 +25,9 @@ build-backend = "hatchling.build"
 
 [tool.uv]
 managed = true
-dev-dependencies = [
+
+[dependency-groups]
+dev = [
     "parameterized>=0.9.0",
     "pyinstaller>=6.11.1",
     "pytest>=8.3.4",
diff --git a/src/nemosis/__init__.py b/src/nemosis/__init__.py
index 8631110..547f763 100644
--- a/src/nemosis/__init__.py
+++ b/src/nemosis/__init__.py
@@ -1,6 +1,6 @@
 import logging
 import sys
-from .value_parser import _parse_datetime, _parse_column, _infer_column_data_types
+from .value_parser import _parse_column, _infer_column_data_types
 from .data_fetch_methods import *
 
 name = "osdan"
diff --git a/src/nemosis/custom_tables.py b/src/nemosis/custom_tables.py
index 17e1b6c..82f7868 100644
--- a/src/nemosis/custom_tables.py
+++ b/src/nemosis/custom_tables.py
@@ -2,8 +2,10 @@
 from datetime import timedelta, datetime
 import math
 import numpy as np
-from nemosis import defaults, data_fetch_methods, filters
-
+from nemosis import defaults
+from nemosis.date_generators import parse_datetime_py
+from nemosis.filters import filter_on_column_value
+from nemosis.data_fetch_methods import dynamic_data_compiler, static_table
 
 def fcas4s_scada_match(
     start_time,
@@ -17,12 +19,12 @@ def fcas4s_scada_match(
 
     # Pull in the 4 second fcas data.
     table_name_fcas4s = "FCAS_4_SECOND"
-    fcas4s = data_fetch_methods.dynamic_data_compiler(
+    fcas4s = dynamic_data_compiler(
         start_time, end_time, table_name_fcas4s, raw_data_location
     )
     # Pull in the 4 second fcas variable types.
     table_name_variable_types = "VARIABLES_FCAS_4_SECOND"
-    fcas4s_variable_types = data_fetch_methods.static_table(
+    fcas4s_variable_types = static_table(
         table_name_variable_types, raw_data_location
     )
 
@@ -52,7 +54,7 @@ def fcas4s_scada_match(
 
     # Pull in the dispatch unit scada data.
     table_name_scada = "DISPATCH_UNIT_SCADA"
-    scada = data_fetch_methods.dynamic_data_compiler(
+    scada = dynamic_data_compiler(
         start_time, end_time, table_name_scada, raw_data_location
     )
     scada["SETTLEMENTDATE"] = scada["SETTLEMENTDATE"] - timedelta(minutes=5)
@@ -62,7 +64,7 @@ def fcas4s_scada_match(
 
     # Pull in the interconnector scada data and use the intervention records where the exist.
     table_name_inter_flow = "DISPATCHINTERCONNECTORRES"
-    inter_flows = data_fetch_methods.dynamic_data_compiler(
+    inter_flows = dynamic_data_compiler(
         start_time, end_time, table_name_inter_flow, raw_data_location
     )
     inter_flows["METEREDMWFLOW"] = pd.to_numeric(inter_flows["METEREDMWFLOW"])
@@ -144,7 +146,7 @@ def fcas4s_scada_match(
         best_matches_scada = best_matches_scada.loc[:, select_columns]
 
     if filter_cols is not None:
-        best_matches_scada = filters.filter_on_column_value(
+        best_matches_scada = filter_on_column_value(
             best_matches_scada, filter_cols, filter_values
         )
 
@@ -232,8 +234,7 @@ def stats_for_group(capacity_and_scada_grouped):
     peak_percentile = capacity_factor_over_90th_percentile_of_nodal_demand(
         capacity_and_scada_grouped
     )
-    month = list(capacity_and_scada_grouped["MONTH"])[0]
-    duid = list(capacity_and_scada_grouped["DUID"])[0]
+    month, duid = capacity_and_scada_grouped.name # the keys used for the grouping
     cf_df = pd.DataFrame(
         {
             "Month": [month],
@@ -260,7 +261,7 @@ def stats_by_month_and_plant(capacity_and_scada):
     )
     capacity_factors = capacity_and_scada.groupby(
         ["MONTH", "DUID"], as_index=False
-    ).apply(stats_for_group)
+    ).apply(stats_for_group, include_groups=False)
     return capacity_factors
 
 
@@ -409,15 +410,15 @@ def plant_stats(
 ):
 
     ix = pd.date_range(
-        start=datetime.strptime(start_time, "%Y/%m/%d %H:%M:%S"),
-        end=datetime.strptime(end_time, "%Y/%m/%d %H:%M:%S") - timedelta(minutes=5),
+        start=parse_datetime_py(start_time, midnight='start'),
+        end=parse_datetime_py(end_time, midnight='end') - timedelta(minutes=5),
         freq="5T",
     )
     timeseries_df = pd.DataFrame(index=ix)
     timeseries_df.reset_index(inplace=True)
     timeseries_df.columns = ["SETTLEMENTDATE"]
 
-    gen_max_cap = data_fetch_methods.dynamic_data_compiler(
+    gen_max_cap = dynamic_data_compiler(
         start_time,
         end_time,
         "DUDETAIL",
@@ -429,7 +430,7 @@ def plant_stats(
     gen_max_cap = select_highest_version_number(
         gen_max_cap, defaults.table_primary_keys["DUDETAIL"]
     )
-    gen_region = data_fetch_methods.dynamic_data_compiler(
+    gen_region = dynamic_data_compiler(
         start_time,
         end_time,
         "DUDETAILSUMMARY",
@@ -438,14 +439,14 @@ def plant_stats(
         filter_cols=filter_cols,
         filter_values=filter_values,
     )
-    scada = data_fetch_methods.dynamic_data_compiler(
+    scada = dynamic_data_compiler(
         start_time,
         end_time,
         "DISPATCH_UNIT_SCADA",
         raw_data_location,
         select_columns=["SETTLEMENTDATE", "DUID", "SCADAVALUE"],
     )
-    dispatch_price = data_fetch_methods.dynamic_data_compiler(
+    dispatch_price = dynamic_data_compiler(
         start_time,
         end_time,
         "DISPATCHPRICE",
@@ -455,7 +456,7 @@ def plant_stats(
     dispatch_price = select_intervention_if_present(
         dispatch_price, defaults.table_primary_keys["DISPATCHPRICE"]
     )
-    trading_price = data_fetch_methods.dynamic_data_compiler(
+    trading_price = dynamic_data_compiler(
         start_time,
         end_time,
         "TRADINGPRICE",
@@ -466,7 +467,7 @@ def plant_stats(
     trading_price["RRP"] = pd.to_numeric(trading_price["RRP"])
     # trading_price = calc_trading_price(dispatch_price)
 
-    region_summary = data_fetch_methods.dynamic_data_compiler(
+    region_summary = dynamic_data_compiler(
         start_time,
         end_time,
         "DISPATCHREGIONSUM",
@@ -518,14 +519,14 @@ def plant_stats(
 
 
 def trading_and_dispatch_cost():
-    gen_region = data_fetch_methods.dynamic_data_compiler(
+    gen_region = dynamic_data_compiler(
         "2017/01/01 00:05:00",
         "2018/01/01 00:05:00",
         "DUDETAILSUMMARY",
         defaults.raw_data_cache,
         select_columns=["START_DATE", "END_DATE", "DUID", "REGIONID"],
     )
-    scada = data_fetch_methods.dynamic_data_compiler(
+    scada = dynamic_data_compiler(
         "2017/01/01 00:05:00",
         "2018/01/01 00:05:00",
         "DISPATCH_UNIT_SCADA",
@@ -533,8 +534,8 @@ def trading_and_dispatch_cost():
     )
 
     ix = pd.date_range(
-        start=datetime.strptime("2017/01/01 00:00:00", "%Y/%m/%d %H:%M:%S"),
-        end=datetime.strptime("2018/01/01 00:00:00", "%Y/%m/%d %H:%M:%S"),
+        start=datetime(2017, 1, 1),
+        end=datetime(2018, 1, 1),
         freq="5T",
     )
     timeseries_df = pd.DataFrame(index=ix)
@@ -552,7 +553,7 @@ def trading_and_dispatch_cost():
 
     scada = pd.concat(scada_list)
 
-    dispatch_price = data_fetch_methods.dynamic_data_compiler(
+    dispatch_price = dynamic_data_compiler(
         "2017/01/01 00:00:00",
         "2018/01/01 00:05:00",
         "DISPATCHPRICE",
diff --git a/src/nemosis/data_fetch_methods.py b/src/nemosis/data_fetch_methods.py
index 73d1285..aa8941d 100644
--- a/src/nemosis/data_fetch_methods.py
+++ b/src/nemosis/data_fetch_methods.py
@@ -3,12 +3,12 @@
 import glob as _glob
 import pandas as _pd
 from datetime import datetime as _datetime
-from nemosis import filters as _filters
-from nemosis import downloader as _downloader
+from nemosis.filters import filter_on_column_value as _filter_on_column_value
+from nemosis. downloader import download_csv, download_elements_file, download_xml
 from nemosis import processing_info_maps as _processing_info_maps
 from nemosis import defaults as _defaults
-from nemosis import custom_tables as _custom_tables
-from nemosis import _infer_column_data_types
+from nemosis.value_parser import _infer_column_data_types
+from nemosis.date_generators import parse_datetime_py as _parse_datetime_py
 from nemosis.custom_errors import UserInputError, NoDataToReturn, DataMismatchError
 
 logger = logging.getLogger(__name__)
@@ -33,8 +33,12 @@ def dynamic_data_compiler(
     will save data typed as strings/objects. To save typed data (e.g.
     appropriate cols are Float or Int), use cache_compiler.
     Args:
-        start_time (str): format 'yyyy/mm/dd HH:MM:SS'.
-        end_time (str): format 'yyyy/mm/dd HH:MM:SS'.
+        start_time (datetime): A native datetime. (Timezone unaware) 
+                               For legacy reasons, may be a string 
+                               of format 'yyyy/mm/dd HH:MM:SS'.
+        end_time (datetime):   A native datetime. (Timezone unaware) 
+                               For legacy reasons, may be a string 
+                               of format 'yyyy/mm/dd HH:MM:SS'.
         table_name (str): table as per Wiki.
         raw_data_location (str): directory to download and cache data to.
                                  existing data will be used if in this dir.
@@ -56,14 +60,19 @@ def dynamic_data_compiler(
         parse_data_types (bool): infers data types of columns when reading
                                  data. default True for API use.
         rebuild (bool): If True then cache files are rebuilt
+                        (redownload, re-unzip, re-convert)
                         even if they exist already. False by default.
         **kwargs: additional arguments passed to the pd.to_{fformat}() function
 
     Returns:
         all_data (pd.Dataframe): All data concatenated.
     """
+
+    if raw_data_location is None:
+        raise UserInputError("The raw_data_location provided is None.")
+
     if not _os.path.isdir(raw_data_location):
-        raise UserInputError("The raw_data_location provided does not exist.")
+        raise UserInputError(f"The raw_data_location {raw_data_location} provided does not exist.")
 
     if table_name not in _defaults.dynamic_tables:
         raise UserInputError("Table name provided is not a dynamic table.")
@@ -94,9 +103,10 @@ def dynamic_data_compiler(
 
     logger.info(f"Compiling data for table {table_name}")
 
-    start_time = _datetime.strptime(start_time, "%Y/%m/%d %H:%M:%S")
-    end_time = _datetime.strptime(end_time, "%Y/%m/%d %H:%M:%S")
-    start_search = _datetime.strptime(start_search, "%Y/%m/%d %H:%M:%S")
+    # cast from string to datetime, if not already datetime
+    start_time = _parse_datetime_py(start_time, midnight='start')
+    end_time = _parse_datetime_py(end_time, midnight='end')
+    start_search = _parse_datetime_py(start_search, midnight='start')
     data_tables = _dynamic_data_fetch_loop(
         start_search,
         start_time,
@@ -126,7 +136,7 @@ def dynamic_data_compiler(
                 ]
                 UserInputError(f"Filter columns {missing_columns} not in data.")
             else:
-                all_data = _filters.filter_on_column_value(
+                all_data = _filter_on_column_value(
                     all_data, filter_cols, filter_values
                 )
         logger.info(f"Returning {table_name}.")
@@ -151,7 +161,7 @@ def cache_compiler(
     select_columns=None,
     fformat="feather",
     rebuild=False,
-    keep_csv=False,
+    keep_csv=True,
     **kwargs,
 ):
     """
@@ -163,8 +173,12 @@ def cache_compiler(
     that is used to store csvs (such as the cache for the GUI).
 
     Args:
-        start_time (str): format 'yyyy/mm/dd HH:MM:SS'.
-        end_time (str): format 'yyyy/mm/dd HH:MM:SS'.
+        start_time (datetime): A native datetime. (Timezone unaware) 
+                               For legacy reasons, may be a string 
+                               of format 'yyyy/mm/dd HH:MM:SS'.
+        end_time (datetime):   A native datetime. (Timezone unaware) 
+                               For legacy reasons, may be a string 
+                               of format 'yyyy/mm/dd HH:MM:SS'.
         table_name (str): table as per Wiki.
         raw_data_location (str): directory to download and cache data to.
                                  existing data will be used if in this dir.
@@ -177,16 +191,24 @@ def cache_compiler(
                           as object type (compatbile with GUI use). For
                           type inference for a cache, use cache_compiler.
         rebuild (bool): If True then cache files are rebuilt
+                        (redownload, re-unzip, re-convert)
                         even if they exist already. False by default.
         keep_csv (bool): If True raw CSVs from AEMO are not deleted after
-                         the cache is built. False by default
+                         the cache is built. True by default
         **kwargs: additional arguments passed to the pd.to_{fformat}() function
 
     Returns:
         Nothing
     """
+    
+    if raw_data_location is None:
+        raise UserInputError("The raw_data_location provided is None.")
+
     if not _os.path.isdir(raw_data_location):
-        raise UserInputError("The raw_data_location provided does not exist.")
+        if _os.path.isfile(raw_data_location):
+            raise UserInputError(f"The raw_data_location {raw_data_location} provided exists as a file, not directory.")
+        else:
+            _os.makedirs(raw_data_location)
 
     if table_name not in _defaults.dynamic_tables:
         raise UserInputError("Table name provided is not a dynamic table.")
@@ -211,9 +233,9 @@ def cache_compiler(
         _,
         start_search,
     ) = _set_up_dynamic_compilers(table_name, start_time, end_time, select_columns)
-    start_time = _datetime.strptime(start_time, "%Y/%m/%d %H:%M:%S")
-    end_time = _datetime.strptime(end_time, "%Y/%m/%d %H:%M:%S")
-    start_search = _datetime.strptime(start_search, "%Y/%m/%d %H:%M:%S")
+    start_time = _parse_datetime_py(start_time, midnight='start')
+    end_time = _parse_datetime_py(end_time, midnight='end')
+    start_search = _parse_datetime_py(start_search, midnight='start')
 
     _dynamic_data_fetch_loop(
         start_search,
@@ -262,7 +284,7 @@ def static_table(
         data (pd.Dataframe)
     """
     if not _os.path.isdir(raw_data_location):
-        raise UserInputError("The raw_data_location provided does not exist.")
+        raise UserInputError(f"The raw_data_location {raw_data_location} provided does not exist.")
 
     if table_name not in _defaults.static_tables:
         raise UserInputError("Table name provided is not a static table.")
@@ -323,7 +345,7 @@ def static_table(
             missing_columns = [col for col in filter_cols if col not in table.columns]
             UserInputError(f"Filter columns {missing_columns} not in data.")
         else:
-            table = _filters.filter_on_column_value(table, filter_cols, filter_values)
+            table = _filter_on_column_value(table, filter_cols, filter_values)
 
     static_table_finalisers = static_data_finaliser_map[table_name]
     for finaliser in static_table_finalisers:
@@ -401,15 +423,15 @@ def _read_static_csv(path_and_name, table_name):
 
 
 def _read_excel(path_and_name, table_name):
-    xls = _pd.ExcelFile(path_and_name)
-    for tab_option in _defaults.reg_exemption_list_tabs[table_name]:
-        try:
-            return _pd.read_excel(xls, tab_option, dtype=str)
-        except ValueError as e:
-            pass
-    raise NoDataToReturn(f"""
-        The excel file did not have any of the expected tabs {_defaults.reg_exemption_list_tabs[table_name]}.
-        """)
+    with _pd.ExcelFile(path_and_name) as xls:
+        for tab_option in _defaults.reg_exemption_list_tabs[table_name]:
+            try:
+                return _pd.read_excel(xls, tab_option, dtype=str)
+            except ValueError as e:
+                pass
+        raise NoDataToReturn(f"""
+            The excel file did not have any of the expected tabs {_defaults.reg_exemption_list_tabs[table_name]}.
+            """)
 
 
 def _finalise_excel_data(data, table_name):
@@ -432,10 +454,10 @@ def _finalise_csv_data(data, table_name):
 
 
 static_downloader_map = {
-    "VARIABLES_FCAS_4_SECOND": _downloader.download_csv,
-    "ELEMENTS_FCAS_4_SECOND": _downloader.download_elements_file,
-    "Generators and Scheduled Loads": _downloader.download_xl,
-    "_downloader.download_xl": _downloader.download_xl,
+    "VARIABLES_FCAS_4_SECOND": download_csv,
+    "ELEMENTS_FCAS_4_SECOND": download_elements_file,
+    "Generators and Scheduled Loads": download_xml,
+    "_downloader.download_xml": download_xml,
 }
 
 static_file_reader_map = {
@@ -543,6 +565,8 @@ def _dynamic_data_fetch_loop(
     1. If it does, read the data in and write any required files
        (parquet or feather).
     2. If it does not, download data then do the same as 1.
+
+    Returns: None if caching_mode=False, else List[pd.Dataframe]
     """
     data_tables = []
 
@@ -630,7 +654,10 @@ def _dynamic_data_fetch_loop(
 
                 data_tables.append(data)
             elif not caching_mode and chunk == 1:
-                logger.warning(f"Loading data from {full_filename} failed.")
+                if _os.path.exists(path_and_name):
+                    logger.warning(f"Loading data from {full_filename} failed.")
+                else:
+                    logger.warning(f"Loading data from {full_filename} failed, file does not exist locally.")
 
             if data is None or '#' not in filename_stub:
                 check_for_next_data_chunk = False
@@ -719,7 +746,7 @@ def _determine_columns_and_read_csv(
     - To preserve compatability with previous versions of NEMOSIS and
       thus any existing data caches, read in all columns as strings.
 
-    Returns: data, columns
+    Returns: data
     """
     if dtypes == "all":
         type = None
@@ -751,18 +778,24 @@ def _determine_columns_and_read_csv(
 def _write_to_format(data, fformat, full_filename, write_kwargs):
     """
     Used by read_data_and_create_file
-    Writes the DataFrame to a non-CSV format is a non_CSV format is specified.
+    Writes the DataFrame to a non-CSV format if a non_CSV format is specified.
     """
     write_function = {"feather": data.to_feather, "parquet": data.to_parquet}
     # Remove files of the same name - deals with case of corrupted files.
     if _os.path.isfile(full_filename) and fformat != "csv":
         _os.unlink(full_filename)
     # Write to required format
-    if fformat == "feather":
-        write_function[fformat](full_filename, **write_kwargs)
-    elif fformat == "parquet":
-        write_function[fformat](full_filename, index=False, **write_kwargs)
-    return
+    try:
+        if fformat == "feather":
+            write_function[fformat](full_filename, **write_kwargs)
+        elif fformat == "parquet":
+            write_function[fformat](full_filename, index=False, **write_kwargs)
+        return
+    except Exception:
+        # tidy up incomplete file
+        if os.path.exists(full_filename):
+            os.remove(full_filename)
+        raise
 
 
 def _download_data(
@@ -829,44 +862,3 @@ def _static_table_wrapper_for_gui(
     )
     return table
 
-
-_method_map = {
-    "DISPATCHLOAD": _dynamic_data_wrapper_for_gui,
-    "DISPATCHPRICE": _dynamic_data_wrapper_for_gui,
-    "TRADINGLOAD": _dynamic_data_wrapper_for_gui,
-    "TRADINGPRICE": _dynamic_data_wrapper_for_gui,
-    "TRADINGREGIONSUM": _dynamic_data_wrapper_for_gui,
-    "TRADINGINTERCONNECT": _dynamic_data_wrapper_for_gui,
-    "DISPATCH_UNIT_SCADA": _dynamic_data_wrapper_for_gui,
-    "DISPATCHCONSTRAINT": _dynamic_data_wrapper_for_gui,
-    "DUDETAILSUMMARY": _dynamic_data_wrapper_for_gui,
-    "PARTICIPANT": _dynamic_data_wrapper_for_gui,
-    "DUDETAIL": _dynamic_data_wrapper_for_gui,
-    "GENCONDATA": _dynamic_data_wrapper_for_gui,
-    "SPDREGIONCONSTRAINT": _dynamic_data_wrapper_for_gui,
-    "SPDCONNECTIONPOINTCONSTRAINT": _dynamic_data_wrapper_for_gui,
-    "SPDINTERCONNECTORCONSTRAINT": _dynamic_data_wrapper_for_gui,
-    "FCAS_4_SECOND": _dynamic_data_wrapper_for_gui,
-    "ELEMENTS_FCAS_4_SECOND": _static_table_wrapper_for_gui,
-    "VARIABLES_FCAS_4_SECOND": _static_table_wrapper_for_gui,
-    "Generators and Scheduled Loads": _static_table_wrapper_for_gui,
-    "FCAS Providers": _static_table_wrapper_for_gui,
-    "BIDDAYOFFER_D": _dynamic_data_wrapper_for_gui,
-    "BIDPEROFFER_D": _dynamic_data_wrapper_for_gui,
-    "FCAS_4s_SCADA_MAP": _custom_tables.fcas4s_scada_match,
-    "PLANTSTATS": _custom_tables.plant_stats,
-    "DISPATCHINTERCONNECTORRES": _dynamic_data_wrapper_for_gui,
-    "DISPATCHREGIONSUM": _dynamic_data_wrapper_for_gui,
-    "LOSSMODEL": _dynamic_data_wrapper_for_gui,
-    "LOSSFACTORMODEL": _dynamic_data_wrapper_for_gui,
-    "MNSP_DAYOFFER": _dynamic_data_wrapper_for_gui,
-    "MNSP_PEROFFER": _dynamic_data_wrapper_for_gui,
-    "MNSP_INTERCONNECTOR": _dynamic_data_wrapper_for_gui,
-    "INTERCONNECTOR": _dynamic_data_wrapper_for_gui,
-    "INTERCONNECTORCONSTRAINT": _dynamic_data_wrapper_for_gui,
-    "MARKET_PRICE_THRESHOLDS": _dynamic_data_wrapper_for_gui,
-    "DAILY_REGION_SUMMARY": _dynamic_data_wrapper_for_gui,
-    "NEXT_DAY_DISPATCHLOAD": _dynamic_data_wrapper_for_gui,
-    "INTERMITTENT_GEN_SCADA": _dynamic_data_wrapper_for_gui,
-    "ROOFTOP_PV_ACTUAL": _dynamic_data_wrapper_for_gui
-}
diff --git a/src/nemosis/date_generators.py b/src/nemosis/date_generators.py
index 2c986e7..06a26a7 100644
--- a/src/nemosis/date_generators.py
+++ b/src/nemosis/date_generators.py
@@ -1,7 +1,7 @@
 import logging
 from nemosis import defaults
 from calendar import monthrange
-from datetime import timedelta
+from datetime import timedelta, datetime, date
 
 logger = logging.getLogger(__name__)
 
@@ -155,3 +155,34 @@ def current_gen(start_time, end_time):
                 ):
                     continue
                 yield str(year), month, str(day).zfill(2), None
+
+def parse_datetime_py(t, midnight='start'):
+    """
+    Takes in a string of a datetime, native datetime or date
+    Returns a datetime.
+    If midnight='end', dates will be converted to the midnight at the end of the day.
+    If midnight='start', dates will be converted to the midnight at the start of the day.
+    If not a date, the midnight argument is ignored.
+    This is not intended to be used for conversions within Pandas/CSV/Parquet etc.
+    """
+    if isinstance(t, str):
+        return datetime.strptime(t, defaults.nemosis_date_format)
+    elif isinstance(t, datetime):
+        if t.utcoffset() is not None:
+            raise ValueError(f"Conversion between timezones not implemented. (Even if it's market time.) "
+                             f"For {t}, pass a timezone unaware version, which will be treated as market time.")
+        return t
+    elif isinstance(t, date):
+        if midnight not in ['start', 'end']:
+            raise ValueError(f"midnight must be 'start' or 'end', got {midnight}")
+
+        elif midnight == 'end':
+            # end of this day
+            # is the start of the next day
+            t = t + timedelta(days=1)
+            midnight = 'start'
+
+        return datetime.combine(t, datetime.min.time())
+        
+    else:
+        raise ValueError(f"Unsure how to handle datetime {t} of unexpected type {type(t)}")
\ No newline at end of file
diff --git a/src/nemosis/defaults.py b/src/nemosis/defaults.py
index 5a24442..7ef0e53 100644
--- a/src/nemosis/defaults.py
+++ b/src/nemosis/defaults.py
@@ -1,7 +1,7 @@
 import os
 
 names = {
-    "FCAS Providers": "NEM Registration and Exemption List.xls",
+    "FCAS Providers": "NEM Registration and Exemption List.xlsx",
     "DISPATCHLOAD": "PUBLIC_DVD_DISPATCHLOAD",
     "NEXT_DAY_DISPATCHLOAD": "PUBLIC_NEXT_DAY_DISPATCHLOAD",
     "DUDETAILSUMMARY": "PUBLIC_DVD_DUDETAILSUMMARY",
@@ -22,7 +22,7 @@
     "FCAS_4_SECOND": "FCAS",
     "ELEMENTS_FCAS_4_SECOND": "Elements_FCAS.csv",
     "VARIABLES_FCAS_4_SECOND": "Ancillary Services Market Causer Pays Variables File.csv",
-    "Generators and Scheduled Loads": "NEM Registration and Exemption List.xls",
+    "Generators and Scheduled Loads": "NEM Registration and Exemption List.xlsx",
     "MNSP_INTERCONNECTOR": "PUBLIC_DVD_MNSP_INTERCONNECTOR",
     "MNSP_PEROFFER": "PUBLIC_DVD_MNSP_PEROFFER",
     "INTERCONNECTOR": "PUBLIC_DVD_INTERCONNECTOR",
@@ -133,11 +133,11 @@
 static_table_url = {
     "ELEMENTS_FCAS_4_SECOND": "https://www.nemweb.com.au/Reports/Current/Causer_Pays_Elements/",
     "VARIABLES_FCAS_4_SECOND": "https://aemo.com.au/-/media/files/electricity/nem/settlements_and_payments/settlements/auction-reports/archive/ancillary-services-market-causer-pays-variables-file.csv",
-    "Generators and Scheduled Loads": "https://www.aemo.com.au/-/media/Files/Electricity/NEM/Participant_Information/NEM-Registration-and-Exemption-List.xls",
-    "_downloader.download_xl": "https://www.aemo.com.au/-/media/Files/Electricity/NEM/Participant_Information/NEM-Registration-and-Exemption-List.xls",
+    "Generators and Scheduled Loads": "https://www.aemo.com.au/-/media/Files/Electricity/NEM/Participant_Information/NEM-Registration-and-Exemption-List.xlsx",
+    "_downloader.download_xl": "https://www.aemo.com.au/-/media/Files/Electricity/NEM/Participant_Information/NEM-Registration-and-Exemption-List.xlsx",
 }
 
-aemo_mms_url = "http://www.nemweb.com.au/Data_Archive/Wholesale_Electricity/MMSDM/{}/MMSDM_{}_{}/MMSDM_Historical_Data_SQLLoader/DATA/{}.zip"
+aemo_mms_url = "https://www.nemweb.com.au/Data_Archive/Wholesale_Electricity/MMSDM/{}/MMSDM_{}_{}/MMSDM_Historical_Data_SQLLoader/DATA/{}.zip"
 
 current_data_page_urls = {
     "BIDDING": "Reports/Current/Bidmove_Complete/",
@@ -146,9 +146,9 @@
     "INTERMITTENT_GEN_SCADA": "/Reports/Current/Next_Day_Intermittent_Gen_Scada/"
 }
 
-fcas_4_url = "http://www.nemweb.com.au/Reports/Current/Causer_Pays/FCAS_{}{}{}{}.zip"
+fcas_4_url = "https://www.nemweb.com.au/Reports/Current/Causer_Pays/FCAS_{}{}{}{}.zip"
 
-fcas_4_url_hist = "http://www.nemweb.com.au/Data_Archive/Wholesale_Electricity/FCAS_Causer_Pays/{}/FCAS_Causer_Pays_{}_{}/FCAS_{}{}{}{}.zip"
+fcas_4_url_hist = "https://www.nemweb.com.au/Data_Archive/Wholesale_Electricity/FCAS_Causer_Pays/{}/FCAS_Causer_Pays_{}_{}/FCAS_{}{}{}{}.zip"
 
 data_url = {
     "DISPATCHLOAD": "aemo_data_url",
@@ -299,9 +299,11 @@
         "LOWER5MIN",
         "LOWER60SEC",
         "LOWER6SEC",
+        "LOWER1SEC",
         "RAISE5MIN",
         "RAISE60SEC",
         "RAISE6SEC",
+        "RAISE1SEC",
         "LOWERREG",
         "RAISEREG",
         "SEMIDISPATCHCAP",
@@ -311,10 +313,12 @@
         "SETTLEMENTDATE",
         "REGIONID",
         "RRP",
+        "RAISE1SECRRP",
         "RAISE6SECRRP",
         "RAISE60SECRRP",
         "RAISE5MINRRP",
         "RAISEREGRRP",
+        "LOWER1SECRRP",
         "LOWER6SECRRP",
         "LOWER60SECRRP",
         "LOWER5MINRRP",
@@ -335,9 +339,11 @@
         "LOWER5MINLOCALDISPATCH",
         "LOWER60SECLOCALDISPATCH",
         "LOWER6SECLOCALDISPATCH",
+        "LOWER1SECLOCALDISPATCH",
         "RAISE5MINLOCALDISPATCH",
         "RAISE60SECLOCALDISPATCH",
         "RAISE6SECLOCALDISPATCH",
+        "RAISE1SECLOCALDISPATCH",
         "LOWERREGLOCALDISPATCH",
         "RAISEREGLOCALDISPATCH",
         "INITIALSUPPLY",
@@ -428,10 +434,12 @@
         "REGIONID",
         "INTERVENTION",
         "RRP",
+        "RAISE1SECRRP",
         "RAISE6SECRRP",
         "RAISE60SECRRP",
         "RAISE5MINRRP",
         "RAISEREGRRP",
+        "LOWER1SECRRP",
         "LOWER6SECRRP",
         "LOWER60SECRRP",
         "LOWER5MINRRP",
@@ -557,9 +565,11 @@
         "LOWER5MINLOCALDISPATCH",
         "LOWER60SECLOCALDISPATCH",
         "LOWER6SECLOCALDISPATCH",
+        "LOWER1SECLOCALDISPATCH",
         "RAISE5MINLOCALDISPATCH",
         "RAISE60SECLOCALDISPATCH",
         "RAISE6SECLOCALDISPATCH",
+        "RAISE1SECLOCALDISPATCH",
         "LOWERREGLOCALDISPATCH",
         "RAISEREGLOCALDISPATCH",
         "INITIALSUPPLY",
@@ -883,7 +893,15 @@
     '%Y-%m-%d %H:%M:%S'
 ]
 
-nem_data_model_start_time = "2009/07/01 00:00:00"
+# If a user passes a datetime to us, as a string,
+# it should be in this format.
+nemosis_date_format = "%Y/%m/%d %H:%M:%S"
+
+# data is available from 2009
+# however for 2009-2014, the structure in MMSDM is different.
+# It's one zip per month, with all tables combined.
+# 2015 is when it's back to the per-table files.
+nem_data_model_start_time = "2015/01/01 00:00:00"
 
 
 # GUI settings.
diff --git a/src/nemosis/downloader.py b/src/nemosis/downloader.py
index dac391c..1a38a85 100644
--- a/src/nemosis/downloader.py
+++ b/src/nemosis/downloader.py
@@ -5,19 +5,23 @@
 import zipfile
 import io
 import pandas as pd
+from cachetools import cached, TTLCache
+from urllib.parse import urlparse, urljoin, urldefrag
 
 from . import defaults, custom_errors
 
 logger = logging.getLogger(__name__)
 
+session = requests.Session()
+
 # Windows Chrome for User-Agent request headers
-USR_AGENT_HEADER = {
+session.headers.update({
     "User-Agent": (
         "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"
         + " AppleWebKit/537.36 (KHTML, like Gecko) "
         + "Chrome/80.0.3987.87 Safari/537.36"
     )
-}
+})
 
 
 def run(year, month, day, chunk, index, filename_stub, down_load_to):
@@ -30,9 +34,11 @@ def run(year, month, day, chunk, index, filename_stub, down_load_to):
     # Perform the download, unzipping saving of the file
     try:
         download_unzip_csv(url_formatted, down_load_to)
-    except Exception:
+    except KeyboardInterrupt:
+        raise
+    except Exception as e:
         if chunk == 1:
-            logger.warning(f"{filename_stub} not downloaded")
+            logger.warning(f"{filename_stub} not downloaded ({e})")
 
 
 def run_bid_tables(year, month, day, chunk, index, filename_stub, down_load_to):
@@ -47,8 +53,10 @@ def run_bid_tables(year, month, day, chunk, index, filename_stub, down_load_to):
             _download_and_unpack_bid_move_complete_files(
                 download_url, down_load_to
             )
-        except Exception:
-            logger.warning(f"{filename_stub} not downloaded")
+        except KeyboardInterrupt:
+            raise
+        except Exception as e:
+            logger.warning(f"{filename_stub} not downloaded ({e})")
 
 
 def run_next_day_region_tables(year, month, day, chunk, index, filename_stub, down_load_to):
@@ -60,8 +68,10 @@ def run_next_day_region_tables(year, month, day, chunk, index, filename_stub, do
         _download_and_unpack_next_region_tables(
             download_url, down_load_to
         )
-    except Exception:
-        logger.warning(f"{filename_stub} not downloaded")
+    except KeyboardInterrupt:
+        raise
+    except Exception as e:
+        logger.warning(f"{filename_stub} not downloaded ({e})")
 
 
 def run_next_dispatch_tables(year, month, day, chunk, index, filename_stub, down_load_to):
@@ -71,8 +81,10 @@ def run_next_dispatch_tables(year, month, day, chunk, index, filename_stub, down
             filename_stub,
             defaults.current_data_page_urls["NEXT_DAY_DISPATCHLOAD"])
         _download_and_unpack_next_dispatch_load_files_complete_files(download_url, down_load_to)
-    except Exception:
-        logger.warning(f"{filename_stub} not downloaded")
+    except KeyboardInterrupt:
+        raise
+    except Exception as e:
+        logger.warning(f"{filename_stub} not downloaded ({e})")
 
 
 def run_intermittent_gen_scada(year, month, day, chunk, index, filename_stub, down_load_to):
@@ -81,8 +93,10 @@ def run_intermittent_gen_scada(year, month, day, chunk, index, filename_stub, do
             filename_stub,
             defaults.current_data_page_urls["INTERMITTENT_GEN_SCADA"])
         _download_and_unpack_intermittent_gen_scada_file(download_url, down_load_to)
-    except Exception:
-        logger.warning(f"{filename_stub} not downloaded")
+    except KeyboardInterrupt:
+        raise
+    except Exception as e:
+        logger.warning(f"{filename_stub} not downloaded ({e})")
 
 
 def _get_current_url(filename_stub, current_page_url):
@@ -95,116 +109,116 @@ def _get_current_url(filename_stub, current_page_url):
 def _download_and_unpack_bid_move_complete_files(
     download_url, down_load_to
 ):
-    r = requests.get(download_url, headers=USR_AGENT_HEADER)
-    zipped_file = zipfile.ZipFile(io.BytesIO(r.content))
-
-    file_name = zipped_file.namelist()[
-        0
-    ]  # Just one file so we can pull it out of the list using 0
-    start_row_second_table = _find_start_row_nth_table(
-        zipped_file, file_name, 2
-    )
-    csv_file = zipped_file.open(file_name)
-    BIDDAYOFFER_D = pd.read_csv(
-        csv_file, header=1, nrows=start_row_second_table - 3, dtype=str
-    )
-    BIDDAYOFFER_D.to_csv(
-        os.path.join(
-            down_load_to,
-            "PUBLIC_DVD_BIDDAYOFFER_D_" + file_name[24:32] + ".csv",
-        ),
-        index=False,
-    )
-    csv_file = zipped_file.open(file_name)
-    BIDPEROFFER_D = pd.read_csv(
-        csv_file, header=start_row_second_table - 1, dtype=str
-    )[:-1]
-    BIDPEROFFER_D.to_csv(
-        os.path.join(
-            down_load_to,
-            "PUBLIC_DVD_BIDPEROFFER_D_" + file_name[24:32] + ".csv",
-        ),
-        index=False,
-    )
+    zip_local_path = download_to_dir(download_url, down_load_to)
+    with zipfile.ZipFile(zip_local_path) as zipped_file:
+        
+        file_name = zipped_file.namelist()[
+            0
+        ]  # Just one file so we can pull it out of the list using 0
+        start_row_second_table = _find_start_row_nth_table(
+            zipped_file, file_name, 2
+        )
+        csv_file = zipped_file.open(file_name)
+        BIDDAYOFFER_D = pd.read_csv(
+            csv_file, header=1, nrows=start_row_second_table - 3, dtype=str
+        )
+        BIDDAYOFFER_D.to_csv(
+            os.path.join(
+                down_load_to,
+                "PUBLIC_DVD_BIDDAYOFFER_D_" + file_name[24:32] + ".csv",
+            ),
+            index=False,
+        )
+        csv_file = zipped_file.open(file_name)
+        BIDPEROFFER_D = pd.read_csv(
+            csv_file, header=start_row_second_table - 1, dtype=str
+        )[:-1]
+        BIDPEROFFER_D.to_csv(
+            os.path.join(
+                down_load_to,
+                "PUBLIC_DVD_BIDPEROFFER_D_" + file_name[24:32] + ".csv",
+            ),
+            index=False,
+        )
 
 
 def _download_and_unpack_next_region_tables(
     download_url, down_load_to
 ):
-    r = requests.get(download_url, headers=USR_AGENT_HEADER)
-    zipped_file = zipfile.ZipFile(io.BytesIO(r.content))
-
-    file_name = zipped_file.namelist()[
-        0
-    ]  # Just one file so we can pull it out of the list using 0
-    start_row_second_table = _find_start_row_nth_table(
-        zipped_file, file_name, 2
-    )
-    start_row_third_table = _find_start_row_nth_table(
-        zipped_file, file_name, 3
-    )
-    csv_file = zipped_file.open(file_name)
-    DAILY_REGION_SUMMARY = pd.read_csv(
-        csv_file, header=start_row_second_table - 1,
-        nrows=start_row_third_table - start_row_second_table - 1, dtype=str
-    )
-    DAILY_REGION_SUMMARY.to_csv(
-        os.path.join(
-            down_load_to,
-            "PUBLIC_DAILY_REGION_SUMMARY_" + file_name[13:21] + ".csv",
-        ),
-        index=False,
-    )
+    zip_local_path = download_to_dir(download_url, down_load_to)
+    with zipfile.ZipFile(zip_local_path) as zipped_file:
+
+        file_name = zipped_file.namelist()[
+            0
+        ]  # Just one file so we can pull it out of the list using 0
+        start_row_second_table = _find_start_row_nth_table(
+            zipped_file, file_name, 2
+        )
+        start_row_third_table = _find_start_row_nth_table(
+            zipped_file, file_name, 3
+        )
+        csv_file = zipped_file.open(file_name)
+        DAILY_REGION_SUMMARY = pd.read_csv(
+            csv_file, header=start_row_second_table - 1,
+            nrows=start_row_third_table - start_row_second_table - 1, dtype=str
+        )
+        DAILY_REGION_SUMMARY.to_csv(
+            os.path.join(
+                down_load_to,
+                "PUBLIC_DAILY_REGION_SUMMARY_" + file_name[13:21] + ".csv",
+            ),
+            index=False,
+        )
     
 
 def _download_and_unpack_next_dispatch_load_files_complete_files(
     download_url, down_load_to
 ):
-    r = requests.get(download_url, headers=USR_AGENT_HEADER)
-    zipped_file = zipfile.ZipFile(io.BytesIO(r.content))
-
-    file_name = zipped_file.namelist()[
-        0
-    ]  # Just one file so we can pull it out of the list using 0
-    start_row_second_table = _find_start_row_nth_table(
-        zipped_file, file_name, 2
-    )
-    csv_file = zipped_file.open(file_name)
-    NEXT_DAY_DISPATCHLOAD = pd.read_csv(
-        csv_file, header=1, nrows=start_row_second_table - 3, dtype=str
-    )
-    NEXT_DAY_DISPATCHLOAD.to_csv(
-        os.path.join(
-            down_load_to,
-            "PUBLIC_NEXT_DAY_DISPATCHLOAD_" + file_name[25:33] + ".csv",
-        ),
-        index=False,
-    )
+    zip_local_path = download_to_dir(download_url, down_load_to)
+    with zipfile.ZipFile(zip_local_path) as zipped_file:
+
+        file_name = zipped_file.namelist()[
+            0
+        ]  # Just one file so we can pull it out of the list using 0
+        start_row_second_table = _find_start_row_nth_table(
+            zipped_file, file_name, 2
+        )
+        csv_file = zipped_file.open(file_name)
+        NEXT_DAY_DISPATCHLOAD = pd.read_csv(
+            csv_file, header=1, nrows=start_row_second_table - 3, dtype=str
+        )
+        NEXT_DAY_DISPATCHLOAD.to_csv(
+            os.path.join(
+                down_load_to,
+                "PUBLIC_NEXT_DAY_DISPATCHLOAD_" + file_name[25:33] + ".csv",
+            ),
+            index=False,
+        )
 
 
 def _download_and_unpack_intermittent_gen_scada_file(
     download_url, down_load_to
 ):
-    r = requests.get(download_url, headers=USR_AGENT_HEADER)
-    zipped_file = zipfile.ZipFile(io.BytesIO(r.content))
-
-    file_name = zipped_file.namelist()[
-        0
-    ]  # Just one file so we can pull it out of the list using 0
-    start_row_second_table = _find_start_row_nth_table(
-        zipped_file, file_name, 1
-    )
-    csv_file = zipped_file.open(file_name)
-    data = pd.read_csv(
-        csv_file, header=1, dtype=str
-    )[:-1]
-    data.to_csv(
-        os.path.join(
-            down_load_to,
-            "PUBLIC_NEXT_DAY_INTERMITTENT_GEN_SCADA_" + file_name[39:47] + ".csv",
-        ),
-        index=False,
-    )
+    zip_local_path = download_to_dir(download_url, down_load_to)
+    with zipfile.ZipFile(zip_local_path) as zipped_file:
+
+        file_name = zipped_file.namelist()[
+            0
+        ]  # Just one file so we can pull it out of the list using 0
+        start_row_second_table = _find_start_row_nth_table(
+            zipped_file, file_name, 1
+        )
+        csv_file = zipped_file.open(file_name)
+        data = pd.read_csv(
+            csv_file, header=1, dtype=str
+        )[:-1]
+        data.to_csv(
+            os.path.join(
+                down_load_to,
+                "PUBLIC_NEXT_DAY_INTERMITTENT_GEN_SCADA_" + file_name[39:47] + ".csv",
+            ),
+            index=False,
+        )
 
 
 def _find_start_row_nth_table(sub_folder_zipfile, file_name, n):
@@ -236,58 +250,120 @@ def run_fcas4s(year, month, day, chunk, index, filename_stub, down_load_to):
     # Perform the download, unzipping saving of the file
     try:
         download_unzip_csv(url_formatted_latest, down_load_to)
+    except KeyboardInterrupt:
+        raise
     except Exception:
         try:
             download_unzip_csv(url_formatted_hist, down_load_to)
+        except KeyboardInterrupt:
+            raise
         except Exception as e:
             # FCAS csvs are bundled in 30 minute bundles
             # Check if the csv exists before warning
             file_check = os.path.join(down_load_to, filename_stub + ".csv")
             if not os.path.isfile(file_check):
-                logger.warning(f"{filename_stub} not downloaded")
+                logger.warning(f"{filename_stub} not downloaded {(e)}")
 
+def download_to_dir(url, down_load_to, force_redo=False):
+    """
+    This function downloads a file from a url to a folder.
+    It streams it, so that large files do not fill up memory.
+    An exception is thrown if the url does not exist.
+    """
+
+    url = url.replace('#', '%23')
+    filename = url.split('/')[-1].split('?')[0]
+    path = os.path.join(down_load_to, filename)
+    download_to_path(url, path)
+    return path
+
+def download_to_path(url, path_and_name, force_redo=False):
+    """
+    This function downloads a file from a url to a specific path.
+    It streams it, so that large files do not fill up memory.
+    An exception is thrown if the url does not exist.
+    """
+    url = url.replace('#', '%23')
+    filename = url.split('/')[-1].split('?')[0]
+    if (not os.path.exists(path_and_name)) or force_redo:
+
+        # Most headers should be set via the session
+        # This is just for per-request variation
+        headers = {}
+
+        # For the few files that are hosted outside the usual dataset,
+        # AEMO discriminates based on user agent, blocking scrapers.
+        # So let's pretend we're a normal web browser.
+        # AEMO, if you see this and don't like it,
+        # then just move the participant registration spreadsheet
+        # into the usual MMS nemweb dataset, 
+        # at https://nemweb.com.au/ instead of https://www.aemo.com.au
+        # ideally as the same CSV format everything else is.
+        # That would make life easier for both of us.
+        # https://github.com/UNSW-CEEM/NEMOSIS/issues/60
+        domain = urlparse(url).netloc
+        if domain.endswith("aemo.com.au"):
+            chrome_user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
+            headers["User-Agent"] = chrome_user_agent
+
+        if _pre_check_file_is_missing(url):
+            # Since we cache the parent HTML, this should be faster than doing a real request and getting 404
+            raise ValueError(f"File {url} is missing (according to parent HTML)")
+
+        with session.get(url, stream=True, headers=headers) as response:
+            if response.status_code not in [200, 404]:
+                logger.debug(f"URL {url} gave status {response.status_code}: {response.text[:1000]}")
+            response.raise_for_status()
+            try:
+                with open(path_and_name, 'wb') as file:
+                    for chunk in response.iter_content(chunk_size=2**13):
+                        file.write(chunk)
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                logger.error(f"Failed to write file to {path_and_name}: {e}")
+                if os.path.exists(path_and_name):
+                    os.remove(path_and_name)
+                raise
+
+        # temporary debugging
+        if url.lower().endswith('.zip'):
+            logger.info(f"Zip downloaded to {path_and_name}")
 
 def download_unzip_csv(url, down_load_to):
     """
     This function downloads a zipped csv using a url,
     extracts the csv and saves it a specified location
     """
-    url = url.replace('#', '%23')
-    r = requests.get(url, headers=USR_AGENT_HEADER)
-    z = zipfile.ZipFile(io.BytesIO(r.content))
-    z.extractall(down_load_to)
+    zip_local_path = download_to_dir(url, down_load_to)
+    with zipfile.ZipFile(zip_local_path) as z:
+        z.extractall(down_load_to)
 
 
 def download_csv(url, path_and_name):
     """
-    This function downloads a zipped csv using a url,
-    extracts the csv and saves it a specified location
+    This function downloads a csv using a url,
+    and saves it a specified location
     """
-    r = requests.get(url, headers=USR_AGENT_HEADER)
-    with open(path_and_name, "wb") as f:
-        f.write(r.content)
+    download_to_path(url, path_and_name)
 
 
 def download_elements_file(url, path_and_name):
-    page = requests.get(url)
-    text = page.text
+    text = download_html(url)
     soup = BeautifulSoup(text, "html.parser")
     links = soup.find_all("a")
     last_file_name = links[-1].text
     link = url + last_file_name
-    r = requests.get(link, headers=USR_AGENT_HEADER)
-    with open(path_and_name, "wb") as f:
-        f.write(r.content)
 
+    download_to_path(link, path_and_name)
+    
 
-def download_xl(url, path_and_name):
+def download_xml(url, path_and_name):
     """
-    This function downloads a zipped csv using a url, extracts the csv and
+    This function downloads a XML using a url, and
     saves it a specified location
     """
-    r = requests.get(url, headers=USR_AGENT_HEADER)
-    with open(path_and_name, "wb") as f:
-        f.write(r.content)
+    download_to_path(url, path_and_name)
 
 
 def format_aemo_url(url, year, month, filename_stub):
@@ -299,17 +375,67 @@ def format_aemo_url(url, year, month, filename_stub):
     year = str(year)
     return url.format(year, year, month, filename_stub)
 
+# cache 5 minutes for operational data
+# however we only look at monthly data,
+# so an hourly cache is probably fine.
+@cached(cache=TTLCache(maxsize=2**10, ttl=60*60))
+def download_html(url):
+    r = session.get(url)
+    r.raise_for_status()
+    return r.text
+
+
+@cached(cache=TTLCache(maxsize=2**10, ttl=60*60))
+def download_html_as_soup(url):
+    return BeautifulSoup(download_html(url), "html.parser")
+
+
+def _pre_check_file_is_missing(file_url):
+    """
+    When we check for files in folders earlier than when they exist,
+    we get many 404 errors. These are slow to iterate through.
+    If we check the HTML parent page, with many files,
+    we can check if a file exists or not.
+    By caching this HTML, which applies to many ZIP/CSV files,
+    we can spot missing files more quickly.
+    This function returns True if missing, False if present, 
+    None if it's not a URL with a suitable HTML parent.
+    """
+
+    if (file_url.startswith("https://www.nemweb.com.au/Data_Archive/") or file_url.startswith("https://www.nemweb.com.au/Reports/")) \
+        and (file_url.upper().endswith(".ZIP") or file_url.upper().endswith(".CSV")):
+        parent_url = urljoin(file_url, "./")
+        try:
+            soup = download_html_as_soup(parent_url)
+        except KeyboardInterrupt:
+            raise
+        except Exception:
+            # The parent HTML can't be loaded
+            return None
+
+        matches: list[str] = []
+        for a_tag in soup.find_all("a", href=True):
+            # Resolve relative URLs against the parent page URL
+            absolute_href = urljoin(parent_url, a_tag["href"])
+            # Normalise (remove fragments, query ordering etc.) for a fair comparison
+            normalized_href = urldefrag(absolute_href)[0]
+            normalized_original = urldefrag(file_url)[0]
+            if normalized_href == normalized_original:
+                return False # link found, not missing
+
+        return True
+
+    else:
+        assert not file_url.startswith("http://"), f"URLs should use encryption: {file_url}"
+        assert file_url.startswith("https://"), f"URLs should use encryption: {file_url}"
 
-def status_code_return(url):
-    r = requests.get(url, headers=USR_AGENT_HEADER)
-    return r.status_code
 
 
 def _get_matching_link(url, stub_link):
-    r = requests.get(url, headers=USR_AGENT_HEADER)
-    soup = BeautifulSoup(r.content, "html.parser")
+    html = download_html(url)
+    soup = BeautifulSoup(html, "html.parser")
     links = [link.get("href") for link in soup.find_all("a")]
     for link in links:
         if stub_link in link:
             return link
-    logger.warning(f"{stub_link} not downloaded")
+    logger.warning(f"{stub_link} not downloaded because no match for {stub_link} was found on {url}")
diff --git a/src/nemosis/filters.py b/src/nemosis/filters.py
index f4a0247..4926024 100644
--- a/src/nemosis/filters.py
+++ b/src/nemosis/filters.py
@@ -2,30 +2,30 @@
 from datetime import datetime, timedelta
 import numpy as np
 
-from nemosis.value_parser import _parse_datetime
+from nemosis.value_parser import _parse_datetime_np
 
 logger = logging.getLogger(__name__)
 
 def filter_on_start_and_end_date(data, start_time, end_time):
-    data["START_DATE"] = _parse_datetime(data["START_DATE"])
+    data["START_DATE"] = _parse_datetime_np(data["START_DATE"])
     data["END_DATE"] = np.where(
         data["END_DATE"] == "2999/12/31 00:00:00",
         "2100/12/31 00:00:00",
         data["END_DATE"],
     )
-    data["END_DATE"] = _parse_datetime(data["END_DATE"])
+    data["END_DATE"] = _parse_datetime_np(data["END_DATE"])
     data = data[(data["START_DATE"] < end_time) & (data["END_DATE"] > start_time)]
     return data
 
 
 def filter_on_effective_date(data, start_time, end_time):
-    data["EFFECTIVEDATE"] = _parse_datetime(data["EFFECTIVEDATE"])
+    data["EFFECTIVEDATE"] = _parse_datetime_np(data["EFFECTIVEDATE"])
     data = data[data["EFFECTIVEDATE"] < end_time]
     return data
 
 
 def filter_on_settlementdate(data, start_time, end_time):
-    data["SETTLEMENTDATE"] = _parse_datetime(data["SETTLEMENTDATE"])
+    data["SETTLEMENTDATE"] = _parse_datetime_np(data["SETTLEMENTDATE"])
     data = data[
         (data["SETTLEMENTDATE"] > start_time) & (data["SETTLEMENTDATE"] <= end_time)
     ]
@@ -33,7 +33,7 @@ def filter_on_settlementdate(data, start_time, end_time):
 
 
 def filter_on_run_datetime(data, start_time, end_time):
-    data["RUN_DATETIME"] = _parse_datetime(data["RUN_DATETIME"])
+    data["RUN_DATETIME"] = _parse_datetime_np(data["RUN_DATETIME"])
     data = data[
         (data["RUN_DATETIME"] > start_time) & (data["RUN_DATETIME"] <= end_time)
     ]
@@ -42,14 +42,14 @@ def filter_on_run_datetime(data, start_time, end_time):
 
 def filter_on_timestamp(data, start_time, end_time):
     try:
-        data["TIMESTAMP"] = _parse_datetime(data["TIMESTAMP"])
+        data["TIMESTAMP"] = _parse_datetime_np(data["TIMESTAMP"])
     except ValueError as e:
         logger.error(e)
         # if date format is wrong, str may be too short
         med_str_len = np.median(data["TIMESTAMP"].str.len())
         not_data = data.loc[data["TIMESTAMP"].str.len() < med_str_len, :]
         data = data.loc[data["TIMESTAMP"].str.len() >= med_str_len, :]
-        data["TIMESTAMP"] = _parse_datetime(data["TIMESTAMP"])
+        data["TIMESTAMP"] = _parse_datetime_np(data["TIMESTAMP"])
         logger.warning("Rows with incorrect data formats omitted")
         logger.warning(not_data.head())
     finally:
@@ -58,7 +58,7 @@ def filter_on_timestamp(data, start_time, end_time):
 
 
 def filter_on_interval_datetime(data, start_time, end_time):
-    data["INTERVAL_DATETIME"] = _parse_datetime(data["INTERVAL_DATETIME"])
+    data["INTERVAL_DATETIME"] = _parse_datetime_np(data["INTERVAL_DATETIME"])
     data = data[
         (data["INTERVAL_DATETIME"] > start_time)
         & (data["INTERVAL_DATETIME"] <= end_time)
@@ -78,7 +78,7 @@ def filter_on_date_and_peroid(data, start_time, end_time):
 
 # Not tested, just for nemlite integration.
 def filter_on_date_and_interval(data, start_time, end_time):
-    data["SETTLEMENTDATE"] = _parse_datetime(data["SETTLEMENTDATE"])
+    data["SETTLEMENTDATE"] = _parse_datetime_np(data["SETTLEMENTDATE"])
     data = data[
         (data["SETTLEMENTDATE"] > start_time) & (data["SETTLEMENTDATE"] <= end_time)
     ]
@@ -87,7 +87,7 @@ def filter_on_date_and_interval(data, start_time, end_time):
 
 # Not tested, just for nemlite integration.
 def filter_on_last_changed(data, start_time, end_time):
-    data["LASTCHANGED"] = _parse_datetime(data["LASTCHANGED"])
+    data["LASTCHANGED"] = _parse_datetime_np(data["LASTCHANGED"])
     data = data[data["LASTCHANGED"] < end_time]
     return data
 
diff --git a/src/nemosis/gui.py b/src/nemosis/gui.py
index 2adfb42..a1aa275 100644
--- a/src/nemosis/gui.py
+++ b/src/nemosis/gui.py
@@ -1,4 +1,5 @@
-from nemosis import rows, defaults, data_fetch_methods
+from nemosis import rows, defaults
+from nemosis.method_map import _method_map
 import pandas as pd
 import tkinter as tk
 import tkinter.ttk as ttk
@@ -323,6 +324,8 @@ def run_queries(self):
                     date_format="%Y/%m/%d %H:%M:%S",
                 )
             messagebox.showinfo("Finished", "Your query has finished!")
+        except KeyboardInterrupt:
+            raise
         except Exception:
             traceback.print_exc()
             messagebox.showerror(
@@ -362,7 +365,7 @@ def run_query(self, row, raw_data_location):
         start_time = row.start_time.get()
         end_time = row.end_time.get()
         # Call the query using the tables predefined wraper function.
-        result = data_fetch_methods._method_map[table](
+        result = _method_map[table](
             start_time,
             end_time,
             table,
@@ -575,6 +578,8 @@ def resource_path(relative_path):
     try:
         # PyInstaller creates a temp folder and stores path in _MEIPASS
         base_path = sys._MEIPASS
+    except KeyboardInterrupt:
+        raise
     except Exception:
         base_path = os.path.abspath("")
 
diff --git a/src/nemosis/method_map.py b/src/nemosis/method_map.py
new file mode 100644
index 0000000..cafd613
--- /dev/null
+++ b/src/nemosis/method_map.py
@@ -0,0 +1,43 @@
+from nemosis.custom_tables import fcas4s_scada_match, plant_stats
+from nemosis.data_fetch_methods import _dynamic_data_wrapper_for_gui, _static_table_wrapper_for_gui
+
+_method_map = {
+    "DISPATCHLOAD": _dynamic_data_wrapper_for_gui,
+    "DISPATCHPRICE": _dynamic_data_wrapper_for_gui,
+    "TRADINGLOAD": _dynamic_data_wrapper_for_gui,
+    "TRADINGPRICE": _dynamic_data_wrapper_for_gui,
+    "TRADINGREGIONSUM": _dynamic_data_wrapper_for_gui,
+    "TRADINGINTERCONNECT": _dynamic_data_wrapper_for_gui,
+    "DISPATCH_UNIT_SCADA": _dynamic_data_wrapper_for_gui,
+    "DISPATCHCONSTRAINT": _dynamic_data_wrapper_for_gui,
+    "DUDETAILSUMMARY": _dynamic_data_wrapper_for_gui,
+    "PARTICIPANT": _dynamic_data_wrapper_for_gui,
+    "DUDETAIL": _dynamic_data_wrapper_for_gui,
+    "GENCONDATA": _dynamic_data_wrapper_for_gui,
+    "SPDREGIONCONSTRAINT": _dynamic_data_wrapper_for_gui,
+    "SPDCONNECTIONPOINTCONSTRAINT": _dynamic_data_wrapper_for_gui,
+    "SPDINTERCONNECTORCONSTRAINT": _dynamic_data_wrapper_for_gui,
+    "FCAS_4_SECOND": _dynamic_data_wrapper_for_gui,
+    "ELEMENTS_FCAS_4_SECOND": _static_table_wrapper_for_gui,
+    "VARIABLES_FCAS_4_SECOND": _static_table_wrapper_for_gui,
+    "Generators and Scheduled Loads": _static_table_wrapper_for_gui,
+    "FCAS Providers": _static_table_wrapper_for_gui,
+    "BIDDAYOFFER_D": _dynamic_data_wrapper_for_gui,
+    "BIDPEROFFER_D": _dynamic_data_wrapper_for_gui,
+    "FCAS_4s_SCADA_MAP": fcas4s_scada_match,
+    "PLANTSTATS": plant_stats,
+    "DISPATCHINTERCONNECTORRES": _dynamic_data_wrapper_for_gui,
+    "DISPATCHREGIONSUM": _dynamic_data_wrapper_for_gui,
+    "LOSSMODEL": _dynamic_data_wrapper_for_gui,
+    "LOSSFACTORMODEL": _dynamic_data_wrapper_for_gui,
+    "MNSP_DAYOFFER": _dynamic_data_wrapper_for_gui,
+    "MNSP_PEROFFER": _dynamic_data_wrapper_for_gui,
+    "MNSP_INTERCONNECTOR": _dynamic_data_wrapper_for_gui,
+    "INTERCONNECTOR": _dynamic_data_wrapper_for_gui,
+    "INTERCONNECTORCONSTRAINT": _dynamic_data_wrapper_for_gui,
+    "MARKET_PRICE_THRESHOLDS": _dynamic_data_wrapper_for_gui,
+    "DAILY_REGION_SUMMARY": _dynamic_data_wrapper_for_gui,
+    "NEXT_DAY_DISPATCHLOAD": _dynamic_data_wrapper_for_gui,
+    "INTERMITTENT_GEN_SCADA": _dynamic_data_wrapper_for_gui,
+    "ROOFTOP_PV_ACTUAL": _dynamic_data_wrapper_for_gui
+}
diff --git a/src/nemosis/query_wrappers.py b/src/nemosis/query_wrappers.py
index 34506c2..93c1e09 100644
--- a/src/nemosis/query_wrappers.py
+++ b/src/nemosis/query_wrappers.py
@@ -1,6 +1,7 @@
 import pandas as pd
 from datetime import datetime, timedelta
-from nemosis import defaults, _parse_datetime
+from nemosis import defaults
+from nemosis.value_parser import _parse_datetime_np
 
 
 def dispatch_date_setup(start_time, end_time):
@@ -70,5 +71,5 @@ def drop_duplicates_by_primary_key(data, start_time, table_name):
 
 def convert_genconid_effectivedate_to_datetime_format(data, start_time, table_name):
     if "GENCONID_EFFECTIVEDATE" in data.columns:
-        data["GENCONID_EFFECTIVEDATE"] = _parse_datetime(data["GENCONID_EFFECTIVEDATE"])
+        data["GENCONID_EFFECTIVEDATE"] = _parse_datetime_np(data["GENCONID_EFFECTIVEDATE"])
     return data
diff --git a/src/nemosis/value_parser.py b/src/nemosis/value_parser.py
index b6dc92f..7dbcb4e 100644
--- a/src/nemosis/value_parser.py
+++ b/src/nemosis/value_parser.py
@@ -3,7 +3,7 @@
 from nemosis import defaults as _defaults
 
 
-def _parse_datetime(series):
+def _parse_datetime_np(series):
     """
     Attempts to parse a column into a datetime
     If unable to (because the data is not a datetime), will raise a ValueError
@@ -38,7 +38,7 @@ def _parse_column(series):
     """
 
     try:
-        return _parse_datetime(series)
+        return _parse_datetime_np(series)
     except ValueError:
         try:
             col_new = pd.to_numeric(series)
diff --git a/tests/test_00_test_setup.py b/tests/test_00_test_setup.py
new file mode 100644
index 0000000..90af03f
--- /dev/null
+++ b/tests/test_00_test_setup.py
@@ -0,0 +1,10 @@
+# This file is named 00 so that it runs first
+# to help debug, because it this test fails, all others will fail
+
+import unittest
+
+from nemosis import defaults
+
+class TestDefault(unittest.TestCase):
+    def test_cache_path(self):
+        self.assertIsNotNone(defaults.raw_data_cache, "raw_data_cache not set. Try setting NEMOSIS_TEST_CACHE env var.")
diff --git a/tests/test_data_fetch_methods.py b/tests/test_data_fetch_methods.py
index b5c6278..7a55c3a 100644
--- a/tests/test_data_fetch_methods.py
+++ b/tests/test_data_fetch_methods.py
@@ -770,6 +770,23 @@ def test_caching_with_select_columns_works(self):
         self.assertSequenceEqual(got_columns, expected_columns)
         print("Passed")
 
+    
+    def test_dt_not_string(self):
+        start_time_dt = datetime(2018, 2, 20, 23)
+        start_time_s = "2018/02/20 23:00:00"
+        end_time_dt = datetime(2018, 2, 20, 23, 30)
+        end_time_s = "2018/02/20 23:30:00"
+        table = "DISPATCHPRICE"
+        data_fetch_methods.cache_compiler(
+            start_time_s, end_time_s, table, defaults.raw_data_cache
+        )
+        # just check that it doesn't throw an exception
+        # (since there's no return value to test)
+        data_fetch_methods.cache_compiler(
+            start_time_dt, end_time_dt, table, defaults.raw_data_cache
+        )
+
+
 
 class TestDynamicDataCompilerWithStartDateFiltering(unittest.TestCase):
     def setUp(self):
@@ -868,7 +885,37 @@ def test_filter_of_raw_data(self):
             groupby(['PARTICIPANTID'], as_index=False).count().sort_values('PARTICIPANTID').reset_index(drop=True)
         assert_frame_equal(raw_data, data)
 
+class TestDynamicDataCompiler(unittest.TestCase):
+    def test_1s_cols(self):
+        start_time = f"{recent_test_month.year}/{recent_test_month.month}/01 00:00:00"
+        end_time = f"{recent_test_month.year}/{recent_test_month.month}/01 00:05:00"
+        table = "DISPATCHPRICE"
+        data = data_fetch_methods.dynamic_data_compiler(
+            start_time,
+            end_time,
+            table,
+            defaults.raw_data_cache,
+        )
+        self.assertIn("RAISE1SECRRP", data.columns, "1sec price missing from DISPATCHPRICE")
+        self.assertIn("LOWER1SECRRP", data.columns, "1sec price missing from DISPATCHPRICE")
+        
+    def test_dt_not_string(self):
+        start_time_dt = datetime(2018, 2, 20, 23)
+        start_time_s = "2018/02/20 23:00:00"
+        end_time_dt = datetime(2018, 2, 20, 23, 30)
+        end_time_s = "2018/02/20 23:30:00"
+        table = "DISPATCHPRICE"
+        data_s = data_fetch_methods.dynamic_data_compiler(
+            start_time_s, end_time_s, table, defaults.raw_data_cache
+        )
+        data_dt = data_fetch_methods.dynamic_data_compiler(
+            start_time_dt, end_time_dt, table, defaults.raw_data_cache
+        )
+
+        assert_frame_equal(data_s, data_dt)
+        
 
+@unittest.skip(f"Nemweb changed, so 4sec data no longer works. Issue #64")
 class TestFCAS4SecondData(unittest.TestCase):
     def setUp(self):
         self.start_day = (datetime.now() - timedelta(30)).replace(
@@ -1059,6 +1106,7 @@ class TestCustomTables(unittest.TestCase):
     def setUp(self):
         pass
 
+    @unittest.skip(f"Nemweb changed, so 4sec data no longer works. Issue #64")
     def test_works_on_recent_data(self):
         table = "FCAS_4_SECOND"
         minute_offset = 5
diff --git a/tests/test_date_generators.py b/tests/test_date_generators.py
index 741a870..1aece21 100644
--- a/tests/test_date_generators.py
+++ b/tests/test_date_generators.py
@@ -1,6 +1,9 @@
 import unittest
-from datetime import datetime
-from nemosis import date_generators
+from datetime import datetime, date
+
+import pytz
+
+from nemosis import defaults, date_generators
 
 
 class TestYearAndMonthGen(unittest.TestCase):
@@ -411,3 +414,50 @@ def test_no_missing_values_in_a_week(self):
         self.assertEqual(times[-1][2], "07")
         self.assertEqual(times[-1][3], "0000")
         self.assertEqual(len(times), 1740)
+
+class TestParseDatetimePy(unittest.TestCase):
+    def test_dt_to_dt(self):
+        # if we pass in a dt (no timezone) we get the same thing back
+        dt = datetime(2023, 1, 1, 12, 0, 0)
+        result = date_generators.parse_datetime_py(dt)
+        self.assertEqual(result, dt)
+        self.assertIsNone(result.tzinfo)
+
+    def test_dt_to_dt_with_tz(self):
+        # if we pass in a dt with a timezone specified, we get an exception thrown
+        tz = pytz.timezone('Australia/Brisbane')
+        dt_with_tz = tz.localize(datetime(2023, 1, 1, 12, 0, 0))
+        with self.assertRaises((ValueError, TypeError, AssertionError)):
+            date_generators.parse_datetime_py(dt_with_tz)
+
+    def test_valid_s_to_dt(self):
+        # if we pass in a string of right format, we get the corresponding datetime back
+        dt_string = "2023/01/01 12:00:00"
+        expected = datetime(2023, 1, 1, 12, 0, 0)
+        result = date_generators.parse_datetime_py(dt_string)
+        self.assertEqual(result, expected)
+        self.assertIsNone(result.tzinfo)
+
+    def test_invalid_s_to_dt(self):
+        # if we pass in a string in the wrong format,
+        # we get an exception thrown
+        # ("T" is the wrong part)
+        dt_string_invalid = "2023/01/01T12:00:00"
+        with self.assertRaises((ValueError, TypeError, AssertionError)):
+            date_generators.parse_datetime_py(dt_string_invalid)
+
+    def test_date_to_dt(self):
+        d = date(2026, 1, 2)
+        expected_t_start = datetime(2026, 1, 2)
+        expected_t_end = datetime(2026, 1, 3)
+        
+        actual_t_start = date_generators.parse_datetime_py(d, midnight='start')
+        actual_t_end = date_generators.parse_datetime_py(d, midnight='end')
+
+        self.assertEqual(expected_t_start, actual_t_start)
+        self.assertEqual(expected_t_end, actual_t_end)
+
+        with self.assertRaises(ValueError):
+            actual_t_start = date_generators.parse_datetime_py(d, midnight='Start')
+
+        self.assertEqual(date_generators.parse_datetime_py(d, midnight='start'), date_generators.parse_datetime_py(d))
diff --git a/tests/test_downloader.py b/tests/test_downloader.py
new file mode 100644
index 0000000..1597d99
--- /dev/null
+++ b/tests/test_downloader.py
@@ -0,0 +1,19 @@
+import unittest
+import os
+
+from nemosis import downloader
+
+class TestDownloader(unittest.TestCase):
+    def test_pre_check_file_is_missing(self):
+        html_url = "https://www.nemweb.com.au/Data_Archive/Wholesale_Electricity/MMSDM/2026/MMSDM_2026_02/MMSDM_Historical_Data_SQLLoader/DATA/"
+        child_link = "PUBLIC_ARCHIVE%23BIDDAYOFFER_D%23FILE01%23202602010000.zip"
+        child_url = os.path.join(html_url, child_link)
+        bad_child_link = "not_here.zip"
+        bad_url = os.path.join(html_url, bad_child_link)
+        self.assertFalse(downloader._pre_check_file_is_missing(child_url))
+        self.assertTrue(downloader._pre_check_file_is_missing(bad_url))
+
+        # This is aemo.com.au not nemweb.com.au
+        # so don't do the check
+        registration_url = "https://www.aemo.com.au/-/media/files/electricity/nem/participant_information/nem-registration-and-exemption-list.xlsx?rev=144b01b07674458b875e7a87a8293d92&sc_lang=en"
+        self.assertIsNone(downloader._pre_check_file_is_missing(registration_url))
diff --git a/tests/test_errors_and_warnings.py b/tests/test_errors_and_warnings.py
index 562e404..4e24dff 100644
--- a/tests/test_errors_and_warnings.py
+++ b/tests/test_errors_and_warnings.py
@@ -101,7 +101,7 @@ def test_raise_error_if_select_columns_not_in_data(self):
             )
         self.assertIn(
             (
-                f"None of columns ['NOTACOLUMN'] are in D:\\nemosis_test_cache\\PUBLIC_DVD_DISPATCHPRICE_201812010000.feather. "
+                f"None of columns ['NOTACOLUMN'] are in {defaults.raw_data_cache}\\PUBLIC_DVD_DISPATCHPRICE_201812010000.feather. "
                 "This may be caused by user input if the 'select_columns' "
                 "argument is being used, or by changed AEMO data formats. "
                 "This error can be avoided by using the argument select_columns='all'."
@@ -312,9 +312,10 @@ def test_raise_error_if_select_columns_not_in_data(self):
                 defaults.raw_data_cache,
                 select_columns=["NOTACOLUMN"],
             )
+
         self.assertIn(
             (
-                f"None of columns ['NOTACOLUMN'] are in D:\\nemosis_test_cache\\Ancillary Services Market Causer Pays Variables File.csv. "
+                f"None of columns ['NOTACOLUMN'] are in {defaults.raw_data_cache}\\Ancillary Services Market Causer Pays Variables File.csv. "
                 "This may be caused by user input if the 'select_columns' "
                 "argument is being used, or by changed AEMO data formats. "
                 "This error can be avoided by using the argument select_columns='all'."
diff --git a/tests/test_format_options.py b/tests/test_format_options.py
index b6fbe77..864b1db 100644
--- a/tests/test_format_options.py
+++ b/tests/test_format_options.py
@@ -33,6 +33,7 @@ def setUp(self):
             "DUID-BIDTYPE": (["AGLHAL", "ENERGY"],),
         }
 
+    @unittest.skip("Purges cache") # TODO: just choose a different folder
     def test_dispatch_tables_start_of_month_just_csv_format_dont_keep(self):
         # Empty cache.
         for f in os.listdir(defaults.raw_data_cache):
@@ -104,6 +105,7 @@ def test_dispatch_tables_start_of_month_just_csv_format_dont_keep(self):
             self.assertEqual(len(os.listdir(defaults.raw_data_cache)), 0)
             print("Passed")
 
+    @unittest.skip("Purges cache") # TODO: just choose a different folder
     def test_dispatch_tables_start_of_month_just_csv_format(self):
         # Empty cache.
         for f in os.listdir(defaults.raw_data_cache):
diff --git a/tests/test_performance_stats.py b/tests/test_performance_stats.py
index 4c5bb1e..5dbef51 100644
--- a/tests/test_performance_stats.py
+++ b/tests/test_performance_stats.py
@@ -469,7 +469,7 @@ def setUp(self):
             start=datetime.strptime("2015/01/01 00:00:00", "%Y/%m/%d %H:%M:%S"),
             end=datetime.strptime("2015/01/01 00:10:00", "%Y/%m/%d %H:%M:%S")
             - timedelta(minutes=5),
-            freq="5T",
+            freq="5min",
         )
         self.timeseries_df = pd.DataFrame(index=ix)
         self.timeseries_df.reset_index(inplace=True)
diff --git a/tests/test_processing_info_maps.py b/tests/test_processing_info_maps.py
index 6c2e4c1..d3e7d44 100644
--- a/tests/test_processing_info_maps.py
+++ b/tests/test_processing_info_maps.py
@@ -12,6 +12,7 @@ def setUp(self):
         )
         pass
 
+    @unittest.skip("Long test")
     def test_start_to_end_no_duplication_between_batches(self):
         for table_name in processing_info_maps.search_type.keys():
             if processing_info_maps.search_type[table_name] == "start_to_end":
@@ -43,6 +44,7 @@ def test_start_to_end_no_duplication_between_batches(self):
                 )
                 print("Type valid, no duplicates found.")
 
+    @unittest.skip("Long test")
     def test_start_to_end_has_settlement_or_interval_col(self):
         for table_name in processing_info_maps.search_type.keys():
             if processing_info_maps.search_type[table_name] == "start_to_end":
@@ -72,6 +74,7 @@ def test_start_to_end_has_settlement_or_interval_col(self):
                     )
                 )
 
+    @unittest.skip("Long test")
     def test_all_no_duplication_between_batches(self):
         for table_name in processing_info_maps.search_type.keys():
             if processing_info_maps.search_type[table_name] == "all":
@@ -120,6 +123,7 @@ def test_all_no_duplication_between_batches(self):
                 )
                 print("Type valid, no duplicates found.")
 
+    @unittest.skip("Long test")
     def test_all_no_duplication_between_batches_with_finalise_step(self):
         for table_name in processing_info_maps.search_type.keys():
             if processing_info_maps.search_type[table_name] == "all":
diff --git a/uv.lock b/uv.lock
index c26c9d0..3b94a95 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,18 +1,19 @@
 version = 1
-requires-python = ">=3.9"
+revision = 3
+requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version < '3.11'",
-    "python_full_version == '3.11.*'",
     "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
 ]
 
 [[package]]
 name = "altgraph"
 version = "0.17.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/de/a8/7145824cf0b9e3c28046520480f207df47e927df83aa9555fb47f8505922/altgraph-0.17.4.tar.gz", hash = "sha256:1b5afbb98f6c4dcadb2e2ae6ab9fa994bbb8c1d75f4fa96d340f9437ae454406", size = 48418 }
+sdist = { url = "https://files.pythonhosted.org/packages/de/a8/7145824cf0b9e3c28046520480f207df47e927df83aa9555fb47f8505922/altgraph-0.17.4.tar.gz", hash = "sha256:1b5afbb98f6c4dcadb2e2ae6ab9fa994bbb8c1d75f4fa96d340f9437ae454406", size = 48418, upload-time = "2023-09-25T09:04:52.164Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/4d/3f/3bc3f1d83f6e4a7fcb834d3720544ca597590425be5ba9db032b2bf322a2/altgraph-0.17.4-py2.py3-none-any.whl", hash = "sha256:642743b4750de17e655e6711601b077bc6598dbfa3ba5fa2b2a35ce12b508dff", size = 21212, upload-time = "2023-09-25T09:04:50.691Z" },
 ]
 
 [[package]]
@@ -22,119 +23,115 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "soupsieve" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b3/ca/824b1195773ce6166d388573fc106ce56d4a805bd7427b624e063596ec58/beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051", size = 581181 }
+sdist = { url = "https://files.pythonhosted.org/packages/b3/ca/824b1195773ce6166d388573fc106ce56d4a805bd7427b624e063596ec58/beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051", size = 581181, upload-time = "2024-01-17T16:53:17.902Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b1/fe/e8c672695b37eecc5cbf43e1d0638d88d66ba3a44c4d321c796f4e59167f/beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed", size = 147925 },
+    { url = "https://files.pythonhosted.org/packages/b1/fe/e8c672695b37eecc5cbf43e1d0638d88d66ba3a44c4d321c796f4e59167f/beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed", size = 147925, upload-time = "2024-01-17T16:53:12.779Z" },
+]
+
+[[package]]
+name = "cachetools"
+version = "7.0.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/af/dd/57fe3fdb6e65b25a5987fd2cdc7e22db0aef508b91634d2e57d22928d41b/cachetools-7.0.5.tar.gz", hash = "sha256:0cd042c24377200c1dcd225f8b7b12b0ca53cc2c961b43757e774ebe190fd990", size = 37367, upload-time = "2026-03-09T20:51:29.451Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/06/f3/39cf3367b8107baa44f861dc802cbf16263c945b62d8265d36034fc07bea/cachetools-7.0.5-py3-none-any.whl", hash = "sha256:46bc8ebefbe485407621d0a4264b23c080cedd913921bad7ac3ed2f26c183114", size = 13918, upload-time = "2026-03-09T20:51:27.33Z" },
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+    { url = "https://files.pythonhosted.org/packages/a5/32/8f6669fc4798494966bf446c8c4a162e0b5d893dff088afddf76414f70e1/certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56", size = 164927, upload-time = "2024-12-14T13:52:36.114Z" },
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