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-  <a href="../docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link">
+  <a href="../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Data Resampling</span></a>
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 </li>
diff --git a/R/comparing-r-and-python/HADs-reprojection/WIP-Comparing-HADs-grids.html b/R/comparing-r-and-python/HADs-reprojection/WIP-Comparing-HADs-grids.html
index 0c4efa2e..d92e3266 100644
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+++ b/R/comparing-r-and-python/HADs-reprojection/WIP-Comparing-HADs-grids.html
@@ -240,7 +240,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../../docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link">
+  <a href="../../../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Data Resampling</span></a>
   </div>
 </li>
diff --git a/R/misc/Identifying_Runs.html b/R/misc/Identifying_Runs.html
index f87eac27..7c436740 100644
--- a/R/misc/Identifying_Runs.html
+++ b/R/misc/Identifying_Runs.html
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 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link">
+  <a href="../../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Data Resampling</span></a>
   </div>
 </li>
diff --git a/README.html b/README.html
index 14c2e35e..9f8ff2ff 100644
--- a/README.html
+++ b/README.html
@@ -240,7 +240,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link">
+  <a href="./docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Data Resampling</span></a>
   </div>
 </li>
diff --git a/docs/contributing.html b/docs/contributing.html
index 71a2de12..6db26ef3 100644
--- a/docs/contributing.html
+++ b/docs/contributing.html
@@ -240,7 +240,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link">
+  <a href="../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Data Resampling</span></a>
   </div>
 </li>
diff --git a/docs/datasets.html b/docs/datasets.html
index b0ee4fa1..1bd2e730 100644
--- a/docs/datasets.html
+++ b/docs/datasets.html
@@ -206,7 +206,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link">
+  <a href="../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Data Resampling</span></a>
   </div>
 </li>
diff --git a/docs/docker-configurations.html b/docs/docker-configurations.html
index 2bbb8c5a..0350ead9 100644
--- a/docs/docker-configurations.html
+++ b/docs/docker-configurations.html
@@ -240,7 +240,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link">
+  <a href="../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Data Resampling</span></a>
   </div>
 </li>
diff --git a/docs/pipeline_guidance.html b/docs/pipeline_guidance.html
index 2d880331..9b306dae 100644
--- a/docs/pipeline_guidance.html
+++ b/docs/pipeline_guidance.html
@@ -240,7 +240,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link">
+  <a href="../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Data Resampling</span></a>
   </div>
 </li>
@@ -397,7 +397,7 @@ <h3 data-number="1.2" class="anchored" data-anchor-id="setting-up-your-python-en
 
 
 
-```ztaxvl
+```ymaxaf
 #| eval: false
 
 conda install -c conda-forge gdal
diff --git a/docs/reference/clim_recal.ceda_ftp_download.html b/docs/reference/clim_recal.ceda_ftp_download.html
index 548d9675..a09e06d3 100644
--- a/docs/reference/clim_recal.ceda_ftp_download.html
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@@ -206,7 +206,7 @@
 </li>
           <li class="sidebar-item">
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-  <a href="../../docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link">
+  <a href="../../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Data Resampling</span></a>
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 </li>
diff --git a/docs/reference/clim_recal.config.html b/docs/reference/clim_recal.config.html
index 08a42552..2885ce0c 100644
--- a/docs/reference/clim_recal.config.html
+++ b/docs/reference/clim_recal.config.html
@@ -240,7 +240,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link">
+  <a href="../../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Data Resampling</span></a>
   </div>
 </li>
diff --git a/docs/reference/clim_recal.resample.html b/docs/reference/clim_recal.convert.html
similarity index 82%
rename from docs/reference/clim_recal.resample.html
rename to docs/reference/clim_recal.convert.html
index b6aef4c3..f88e0826 100644
--- a/docs/reference/clim_recal.resample.html
+++ b/docs/reference/clim_recal.convert.html
@@ -7,7 +7,7 @@
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>clim_recal.resample – clim-recal</title>
+<title>clim_recal.convert – clim-recal</title>
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@@ -113,7 +113,7 @@
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+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../../python/README.html">python</a></li><li class="breadcrumb-item"><a href="../../docs/reference/index.html">Reference</a></li><li class="breadcrumb-item"><a href="../../docs/reference/clim_recal.convert.html">Data Resampling</a></li></ol></nav>
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@@ -240,7 +240,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../docs/reference/clim_recal.resample.html" class="sidebar-item-text sidebar-link active">
+  <a href="../../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link active">
  <span class="menu-text">Data Resampling</span></a>
   </div>
 </li>
@@ -322,21 +322,21 @@
     <h2 id="toc-title">On this page</h2>
    
   <ul>
-  <li><a href="#python.clim_recal.resample" id="toc-python.clim_recal.resample" class="nav-link active" data-scroll-target="#python.clim_recal.resample"><span class="header-section-number">1</span> clim_recal.resample</a>
+  <li><a href="#python.clim_recal.convert" id="toc-python.clim_recal.convert" class="nav-link active" data-scroll-target="#python.clim_recal.convert"><span class="header-section-number">1</span> clim_recal.convert</a>
   <ul class="collapse">
   <li><a href="#classes" id="toc-classes" class="nav-link" data-scroll-target="#classes"><span class="header-section-number">1.1</span> Classes</a>
   <ul class="collapse">
-  <li><a href="#python.clim_recal.resample.CPMResampler" id="toc-python.clim_recal.resample.CPMResampler" class="nav-link" data-scroll-target="#python.clim_recal.resample.CPMResampler"><span class="header-section-number">1.1.1</span> CPMResampler</a></li>
-  <li><a href="#python.clim_recal.resample.CPMResamplerManager" id="toc-python.clim_recal.resample.CPMResamplerManager" class="nav-link" data-scroll-target="#python.clim_recal.resample.CPMResamplerManager"><span class="header-section-number">1.1.2</span> CPMResamplerManager</a></li>
-  <li><a href="#python.clim_recal.resample.HADsResampler" id="toc-python.clim_recal.resample.HADsResampler" class="nav-link" data-scroll-target="#python.clim_recal.resample.HADsResampler"><span class="header-section-number">1.1.3</span> HADsResampler</a></li>
-  <li><a href="#python.clim_recal.resample.HADsResamplerManager" id="toc-python.clim_recal.resample.HADsResamplerManager" class="nav-link" data-scroll-target="#python.clim_recal.resample.HADsResamplerManager"><span class="header-section-number">1.1.4</span> HADsResamplerManager</a></li>
-  <li><a href="#python.clim_recal.resample.ResamplerBase" id="toc-python.clim_recal.resample.ResamplerBase" class="nav-link" data-scroll-target="#python.clim_recal.resample.ResamplerBase"><span class="header-section-number">1.1.5</span> ResamplerBase</a></li>
-  <li><a href="#python.clim_recal.resample.ResamplerManagerBase" id="toc-python.clim_recal.resample.ResamplerManagerBase" class="nav-link" data-scroll-target="#python.clim_recal.resample.ResamplerManagerBase"><span class="header-section-number">1.1.6</span> ResamplerManagerBase</a></li>
+  <li><a href="#python.clim_recal.convert.CPMResampler" id="toc-python.clim_recal.convert.CPMResampler" class="nav-link" data-scroll-target="#python.clim_recal.convert.CPMResampler"><span class="header-section-number">1.1.1</span> CPMResampler</a></li>
+  <li><a href="#python.clim_recal.convert.CPMResamplerManager" id="toc-python.clim_recal.convert.CPMResamplerManager" class="nav-link" data-scroll-target="#python.clim_recal.convert.CPMResamplerManager"><span class="header-section-number">1.1.2</span> CPMResamplerManager</a></li>
+  <li><a href="#python.clim_recal.convert.HADsResampler" id="toc-python.clim_recal.convert.HADsResampler" class="nav-link" data-scroll-target="#python.clim_recal.convert.HADsResampler"><span class="header-section-number">1.1.3</span> HADsResampler</a></li>
+  <li><a href="#python.clim_recal.convert.HADsResamplerManager" id="toc-python.clim_recal.convert.HADsResamplerManager" class="nav-link" data-scroll-target="#python.clim_recal.convert.HADsResamplerManager"><span class="header-section-number">1.1.4</span> HADsResamplerManager</a></li>
+  <li><a href="#python.clim_recal.convert.IterCalcBase" id="toc-python.clim_recal.convert.IterCalcBase" class="nav-link" data-scroll-target="#python.clim_recal.convert.IterCalcBase"><span class="header-section-number">1.1.5</span> IterCalcBase</a></li>
+  <li><a href="#python.clim_recal.convert.IterCalcManagerBase" id="toc-python.clim_recal.convert.IterCalcManagerBase" class="nav-link" data-scroll-target="#python.clim_recal.convert.IterCalcManagerBase"><span class="header-section-number">1.1.6</span> IterCalcManagerBase</a></li>
   </ul></li>
   <li><a href="#functions" id="toc-functions" class="nav-link" data-scroll-target="#functions"><span class="header-section-number">1.2</span> Functions</a>
   <ul class="collapse">
-  <li><a href="#python.clim_recal.resample.reproject_2_2km_file_name" id="toc-python.clim_recal.resample.reproject_2_2km_file_name" class="nav-link" data-scroll-target="#python.clim_recal.resample.reproject_2_2km_file_name"><span class="header-section-number">1.2.1</span> reproject_2_2km_file_name</a></li>
-  <li><a href="#python.clim_recal.resample.reproject_standard_calendar_file_name" id="toc-python.clim_recal.resample.reproject_standard_calendar_file_name" class="nav-link" data-scroll-target="#python.clim_recal.resample.reproject_standard_calendar_file_name"><span class="header-section-number">1.2.2</span> reproject_standard_calendar_file_name</a></li>
+  <li><a href="#python.clim_recal.convert.reproject_2_2km_file_name" id="toc-python.clim_recal.convert.reproject_2_2km_file_name" class="nav-link" data-scroll-target="#python.clim_recal.convert.reproject_2_2km_file_name"><span class="header-section-number">1.2.1</span> reproject_2_2km_file_name</a></li>
+  <li><a href="#python.clim_recal.convert.reproject_standard_calendar_file_name" id="toc-python.clim_recal.convert.reproject_standard_calendar_file_name" class="nav-link" data-scroll-target="#python.clim_recal.convert.reproject_standard_calendar_file_name"><span class="header-section-number">1.2.2</span> reproject_standard_calendar_file_name</a></li>
   </ul></li>
   </ul></li>
   </ul>
@@ -348,9 +348,9 @@ <h2 id="toc-title">On this page</h2>
 
 
 
-<section id="python.clim_recal.resample" class="level1" data-number="1">
-<h1 data-number="1"><span class="header-section-number">1</span> clim_recal.resample</h1>
-<p><code>clim_recal.resample</code></p>
+<section id="python.clim_recal.convert" class="level1" data-number="1">
+<h1 data-number="1"><span class="header-section-number">1</span> clim_recal.convert</h1>
+<p><code>clim_recal.convert</code></p>
 <p>Resample UKHADS data and UKCP18 data.</p>
 <ul>
 <li>UKCP18 is resampled temporally from a 360 day calendar to a standard (365/366 day) calendar and projected to British National Grid (BNG) EPSG:27700 from its original rotated polar grid.</li>
@@ -367,34 +367,34 @@ <h2 data-number="1.1" class="anchored" data-anchor-id="classes"><span class="hea
 </thead>
 <tbody>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.CPMResampler">CPMResampler</a></td>
+<td><a href="#python.clim_recal.convert.CPMResampler">CPMResampler</a></td>
 <td>CPM specific changes to HADsResampler.</td>
 </tr>
 <tr class="even">
-<td><a href="#python.clim_recal.resample.CPMResamplerManager">CPMResamplerManager</a></td>
+<td><a href="#python.clim_recal.convert.CPMResamplerManager">CPMResamplerManager</a></td>
 <td>Class to manage processing CPM resampling.</td>
 </tr>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.HADsResampler">HADsResampler</a></td>
+<td><a href="#python.clim_recal.convert.HADsResampler">HADsResampler</a></td>
 <td>Manage resampling HADs datafiles for modelling.</td>
 </tr>
 <tr class="even">
-<td><a href="#python.clim_recal.resample.HADsResamplerManager">HADsResamplerManager</a></td>
+<td><a href="#python.clim_recal.convert.HADsResamplerManager">HADsResamplerManager</a></td>
 <td>Class to manage processing HADs resampling.</td>
 </tr>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.ResamplerBase">ResamplerBase</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcBase">IterCalcBase</a></td>
 <td>Base class to inherit for <code>HADs</code> and <code>CPM</code>.</td>
 </tr>
 <tr class="even">
-<td><a href="#python.clim_recal.resample.ResamplerManagerBase">ResamplerManagerBase</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcManagerBase">IterCalcManagerBase</a></td>
 <td>Base class to inherit for <code>HADs</code> and <code>CPM</code> resampler managers.</td>
 </tr>
 </tbody>
 </table>
-<section id="python.clim_recal.resample.CPMResampler" class="level3" data-number="1.1.1">
-<h3 data-number="1.1.1" class="anchored" data-anchor-id="python.clim_recal.resample.CPMResampler"><span class="header-section-number">1.1.1</span> CPMResampler</h3>
-<p><code>clim_recal.resample.CPMResampler(self, *, input_path=RAW_CPM_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', prior_time_series=None)</code></p>
+<section id="python.clim_recal.convert.CPMResampler" class="level3" data-number="1.1.1">
+<h3 data-number="1.1.1" class="anchored" data-anchor-id="python.clim_recal.convert.CPMResampler"><span class="header-section-number">1.1.1</span> CPMResampler</h3>
+<p><code>clim_recal.convert.CPMResampler(self, *, input_path=RAW_CPM_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', prior_time_series=None)</code></p>
 <p>CPM specific changes to HADsResampler.</p>
 <section id="attributes" class="level4" data-number="1.1.1.1">
 <h4 data-number="1.1.1.1" class="anchored" data-anchor-id="attributes"><span class="header-section-number">1.1.1.1</span> Attributes</h4>
@@ -476,9 +476,9 @@ <h4 data-number="1.1.1.2" class="anchored" data-anchor-id="examples"><span class
 <span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a> ...Path(<span class="st">'.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_20791201-20801130.nc'</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </section>
 </section>
-<section id="python.clim_recal.resample.CPMResamplerManager" class="level3" data-number="1.1.2">
-<h3 data-number="1.1.2" class="anchored" data-anchor-id="python.clim_recal.resample.CPMResamplerManager"><span class="header-section-number">1.1.2</span> CPMResamplerManager</h3>
-<p><code>clim_recal.resample.CPMResamplerManager(self, *, input_paths=RAW_CPM_PATH, output_paths=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=CPM_SUB_PATH, start_index=0, stop_index=None, start_date=CPM_START_DATE, end_date=CPM_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=CPMResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, runs=RunOptions.preferred())</code></p>
+<section id="python.clim_recal.convert.CPMResamplerManager" class="level3" data-number="1.1.2">
+<h3 data-number="1.1.2" class="anchored" data-anchor-id="python.clim_recal.convert.CPMResamplerManager"><span class="header-section-number">1.1.2</span> CPMResamplerManager</h3>
+<p><code>clim_recal.convert.CPMResamplerManager(self, *, input_paths=RAW_CPM_PATH, output_paths=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=CPM_SUB_PATH, start_index=0, stop_index=None, start_date=CPM_START_DATE, end_date=CPM_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=CPMResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, runs=RunOptions.preferred())</code></p>
 <p>Class to manage processing CPM resampling.</p>
 <section id="attributes-1" class="level4" data-number="1.1.2.1">
 <h4 data-number="1.1.2.1" class="anchored" data-anchor-id="attributes-1"><span class="header-section-number">1.1.2.1</span> Attributes</h4>
@@ -604,9 +604,9 @@ <h4 data-number="1.1.2.2" class="anchored" data-anchor-id="examples-1"><span cla
 <span id="cb2-26"><a href="#cb2-26" aria-hidden="true" tabindex="-1"></a>               output_path<span class="op">=</span><span class="st">'.../cpm/tasmax/08'</span>)<span class="op">&gt;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </section>
 </section>
-<section id="python.clim_recal.resample.HADsResampler" class="level3" data-number="1.1.3">
-<h3 data-number="1.1.3" class="anchored" data-anchor-id="python.clim_recal.resample.HADsResampler"><span class="header-section-number">1.1.3</span> HADsResampler</h3>
-<p><code>clim_recal.resample.HADsResampler(self, *, input_path=RAW_HADS_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', input_file_x_column_name=HADS_XDIM, input_file_y_column_name=HADS_YDIM, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)</code></p>
+<section id="python.clim_recal.convert.HADsResampler" class="level3" data-number="1.1.3">
+<h3 data-number="1.1.3" class="anchored" data-anchor-id="python.clim_recal.convert.HADsResampler"><span class="header-section-number">1.1.3</span> HADsResampler</h3>
+<p><code>clim_recal.convert.HADsResampler(self, *, input_path=RAW_HADS_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', input_file_x_column_name=HADS_XDIM, input_file_y_column_name=HADS_YDIM, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)</code></p>
 <p>Manage resampling HADs datafiles for modelling.</p>
 <section id="attributes-2" class="level4" data-number="1.1.3.1">
 <h4 data-number="1.1.3.1" class="anchored" data-anchor-id="attributes-2"><span class="header-section-number">1.1.3.1</span> Attributes</h4>
@@ -722,21 +722,21 @@ <h4 data-number="1.1.3.3" class="anchored" data-anchor-id="methods"><span class=
 </thead>
 <tbody>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.HADsResampler.set_cpm_for_coord_alignment">set_cpm_for_coord_alignment</a></td>
+<td><a href="#python.clim_recal.convert.HADsResampler.set_cpm_for_coord_alignment">set_cpm_for_coord_alignment</a></td>
 <td>Check if <code>cpm_for_coord_alignment</code> is a <code>Dataset</code>, process if a <code>Path</code>.</td>
 </tr>
 </tbody>
 </table>
-<section id="python.clim_recal.resample.HADsResampler.set_cpm_for_coord_alignment" class="level5" data-number="1.1.3.3.1">
-<h5 data-number="1.1.3.3.1" class="anchored" data-anchor-id="python.clim_recal.resample.HADsResampler.set_cpm_for_coord_alignment"><span class="header-section-number">1.1.3.3.1</span> set_cpm_for_coord_alignment</h5>
-<p><code>clim_recal.resample.HADsResampler.set_cpm_for_coord_alignment()</code></p>
+<section id="python.clim_recal.convert.HADsResampler.set_cpm_for_coord_alignment" class="level5" data-number="1.1.3.3.1">
+<h5 data-number="1.1.3.3.1" class="anchored" data-anchor-id="python.clim_recal.convert.HADsResampler.set_cpm_for_coord_alignment"><span class="header-section-number">1.1.3.3.1</span> set_cpm_for_coord_alignment</h5>
+<p><code>clim_recal.convert.HADsResampler.set_cpm_for_coord_alignment()</code></p>
 <p>Check if <code>cpm_for_coord_alignment</code> is a <code>Dataset</code>, process if a <code>Path</code>.</p>
 </section>
 </section>
 </section>
-<section id="python.clim_recal.resample.HADsResamplerManager" class="level3" data-number="1.1.4">
-<h3 data-number="1.1.4" class="anchored" data-anchor-id="python.clim_recal.resample.HADsResamplerManager"><span class="header-section-number">1.1.4</span> HADsResamplerManager</h3>
-<p><code>clim_recal.resample.HADsResamplerManager(self, *, input_paths=RAW_HADS_PATH, output_paths=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=HADS_SUB_PATH, start_index=0, stop_index=None, start_date=HADS_START_DATE, end_date=HADS_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=HADsResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)</code></p>
+<section id="python.clim_recal.convert.HADsResamplerManager" class="level3" data-number="1.1.4">
+<h3 data-number="1.1.4" class="anchored" data-anchor-id="python.clim_recal.convert.HADsResamplerManager"><span class="header-section-number">1.1.4</span> HADsResamplerManager</h3>
+<p><code>clim_recal.convert.HADsResamplerManager(self, *, input_paths=RAW_HADS_PATH, output_paths=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=HADS_SUB_PATH, start_index=0, stop_index=None, start_date=HADS_START_DATE, end_date=HADS_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=HADsResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)</code></p>
 <p>Class to manage processing HADs resampling.</p>
 <section id="attributes-3" class="level4" data-number="1.1.4.1">
 <h4 data-number="1.1.4.1" class="anchored" data-anchor-id="attributes-3"><span class="header-section-number">1.1.4.1</span> Attributes</h4>
@@ -861,30 +861,30 @@ <h4 data-number="1.1.4.3" class="anchored" data-anchor-id="methods-1"><span clas
 </thead>
 <tbody>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.HADsResamplerManager.set_cpm_for_coord_alignment">set_cpm_for_coord_alignment</a></td>
+<td><a href="#python.clim_recal.convert.HADsResamplerManager.set_cpm_for_coord_alignment">set_cpm_for_coord_alignment</a></td>
 <td>Check if <code>cpm_for_coord_alignment</code> is a <code>Dataset</code>, process if a <code>Path</code>.</td>
 </tr>
 <tr class="even">
-<td><a href="#python.clim_recal.resample.HADsResamplerManager.yield_configs">yield_configs</a></td>
+<td><a href="#python.clim_recal.convert.HADsResamplerManager.yield_configs">yield_configs</a></td>
 <td>Generate a <code>CPMResampler</code> or <code>HADsResampler</code> for <code>self.input_paths</code>.</td>
 </tr>
 </tbody>
 </table>
-<section id="python.clim_recal.resample.HADsResamplerManager.set_cpm_for_coord_alignment" class="level5" data-number="1.1.4.3.1">
-<h5 data-number="1.1.4.3.1" class="anchored" data-anchor-id="python.clim_recal.resample.HADsResamplerManager.set_cpm_for_coord_alignment"><span class="header-section-number">1.1.4.3.1</span> set_cpm_for_coord_alignment</h5>
-<p><code>clim_recal.resample.HADsResamplerManager.set_cpm_for_coord_alignment()</code></p>
+<section id="python.clim_recal.convert.HADsResamplerManager.set_cpm_for_coord_alignment" class="level5" data-number="1.1.4.3.1">
+<h5 data-number="1.1.4.3.1" class="anchored" data-anchor-id="python.clim_recal.convert.HADsResamplerManager.set_cpm_for_coord_alignment"><span class="header-section-number">1.1.4.3.1</span> set_cpm_for_coord_alignment</h5>
+<p><code>clim_recal.convert.HADsResamplerManager.set_cpm_for_coord_alignment()</code></p>
 <p>Check if <code>cpm_for_coord_alignment</code> is a <code>Dataset</code>, process if a <code>Path</code>.</p>
 </section>
-<section id="python.clim_recal.resample.HADsResamplerManager.yield_configs" class="level5" data-number="1.1.4.3.2">
-<h5 data-number="1.1.4.3.2" class="anchored" data-anchor-id="python.clim_recal.resample.HADsResamplerManager.yield_configs"><span class="header-section-number">1.1.4.3.2</span> yield_configs</h5>
-<p><code>clim_recal.resample.HADsResamplerManager.yield_configs()</code></p>
+<section id="python.clim_recal.convert.HADsResamplerManager.yield_configs" class="level5" data-number="1.1.4.3.2">
+<h5 data-number="1.1.4.3.2" class="anchored" data-anchor-id="python.clim_recal.convert.HADsResamplerManager.yield_configs"><span class="header-section-number">1.1.4.3.2</span> yield_configs</h5>
+<p><code>clim_recal.convert.HADsResamplerManager.yield_configs()</code></p>
 <p>Generate a <code>CPMResampler</code> or <code>HADsResampler</code> for <code>self.input_paths</code>.</p>
 </section>
 </section>
 </section>
-<section id="python.clim_recal.resample.ResamplerBase" class="level3" data-number="1.1.5">
-<h3 data-number="1.1.5" class="anchored" data-anchor-id="python.clim_recal.resample.ResamplerBase"><span class="header-section-number">1.1.5</span> ResamplerBase</h3>
-<p><code>clim_recal.resample.ResamplerBase(self, *, input_path=Path(), output_path=RESAMPLE_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection')</code></p>
+<section id="python.clim_recal.convert.IterCalcBase" class="level3" data-number="1.1.5">
+<h3 data-number="1.1.5" class="anchored" data-anchor-id="python.clim_recal.convert.IterCalcBase"><span class="header-section-number">1.1.5</span> IterCalcBase</h3>
+<p><code>clim_recal.convert.IterCalcBase(self, *, input_path=Path(), output_path=RESAMPLE_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection')</code></p>
 <p>Base class to inherit for <code>HADs</code> and <code>CPM</code>.</p>
 <section id="attributes-4" class="level4" data-number="1.1.5.1">
 <h4 data-number="1.1.5.1" class="anchored" data-anchor-id="attributes-4"><span class="header-section-number">1.1.5.1</span> Attributes</h4>
@@ -897,7 +897,7 @@ <h4 data-number="1.1.5.1" class="anchored" data-anchor-id="attributes-4"><span c
 </thead>
 <tbody>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.ResamplerBase.max_count">max_count</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcBase.max_count">max_count</a></td>
 <td>Maximum length of <code>self.input_files</code> ignoring <code>start_index</code> and <code>start_index</code>.</td>
 </tr>
 </tbody>
@@ -914,30 +914,30 @@ <h4 data-number="1.1.5.2" class="anchored" data-anchor-id="methods-2"><span clas
 </thead>
 <tbody>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.ResamplerBase.execute">execute</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcBase.execute">execute</a></td>
 <td>Run all steps for processing</td>
 </tr>
 <tr class="even">
-<td><a href="#python.clim_recal.resample.ResamplerBase.set_input_files">set_input_files</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcBase.set_input_files">set_input_files</a></td>
 <td>Replace <code>self.input</code> and process <code>self.input_files</code>.</td>
 </tr>
 </tbody>
 </table>
-<section id="python.clim_recal.resample.ResamplerBase.execute" class="level5" data-number="1.1.5.2.1">
-<h5 data-number="1.1.5.2.1" class="anchored" data-anchor-id="python.clim_recal.resample.ResamplerBase.execute"><span class="header-section-number">1.1.5.2.1</span> execute</h5>
-<p><code>clim_recal.resample.ResamplerBase.execute(**kwargs)</code></p>
+<section id="python.clim_recal.convert.IterCalcBase.execute" class="level5" data-number="1.1.5.2.1">
+<h5 data-number="1.1.5.2.1" class="anchored" data-anchor-id="python.clim_recal.convert.IterCalcBase.execute"><span class="header-section-number">1.1.5.2.1</span> execute</h5>
+<p><code>clim_recal.convert.IterCalcBase.execute(**kwargs)</code></p>
 <p>Run all steps for processing</p>
 </section>
-<section id="python.clim_recal.resample.ResamplerBase.set_input_files" class="level5" data-number="1.1.5.2.2">
-<h5 data-number="1.1.5.2.2" class="anchored" data-anchor-id="python.clim_recal.resample.ResamplerBase.set_input_files"><span class="header-section-number">1.1.5.2.2</span> set_input_files</h5>
-<p><code>clim_recal.resample.ResamplerBase.set_input_files(new_input_path=None)</code></p>
+<section id="python.clim_recal.convert.IterCalcBase.set_input_files" class="level5" data-number="1.1.5.2.2">
+<h5 data-number="1.1.5.2.2" class="anchored" data-anchor-id="python.clim_recal.convert.IterCalcBase.set_input_files"><span class="header-section-number">1.1.5.2.2</span> set_input_files</h5>
+<p><code>clim_recal.convert.IterCalcBase.set_input_files(new_input_path=None)</code></p>
 <p>Replace <code>self.input</code> and process <code>self.input_files</code>.</p>
 </section>
 </section>
 </section>
-<section id="python.clim_recal.resample.ResamplerManagerBase" class="level3" data-number="1.1.6">
-<h3 data-number="1.1.6" class="anchored" data-anchor-id="python.clim_recal.resample.ResamplerManagerBase"><span class="header-section-number">1.1.6</span> ResamplerManagerBase</h3>
-<p><code>clim_recal.resample.ResamplerManagerBase(self, *, input_paths=Path(), output_paths=Path(), variables=(VariableOptions.default()), sub_path=Path(), start_index=0, stop_index=None, start_date=None, end_date=None, configs=list(), config_default_kwargs=dict(), calc_class=None, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False)</code></p>
+<section id="python.clim_recal.convert.IterCalcManagerBase" class="level3" data-number="1.1.6">
+<h3 data-number="1.1.6" class="anchored" data-anchor-id="python.clim_recal.convert.IterCalcManagerBase"><span class="header-section-number">1.1.6</span> IterCalcManagerBase</h3>
+<p><code>clim_recal.convert.IterCalcManagerBase(self, *, input_paths=Path(), output_paths=Path(), variables=(VariableOptions.default()), sub_path=Path(), start_index=0, stop_index=None, start_date=None, end_date=None, configs=list(), config_default_kwargs=dict(), calc_class=None, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False)</code></p>
 <p>Base class to inherit for <code>HADs</code> and <code>CPM</code> resampler managers.</p>
 <section id="attributes-5" class="level4" data-number="1.1.6.1">
 <h4 data-number="1.1.6.1" class="anchored" data-anchor-id="attributes-5"><span class="header-section-number">1.1.6.1</span> Attributes</h4>
@@ -950,15 +950,15 @@ <h4 data-number="1.1.6.1" class="anchored" data-anchor-id="attributes-5"><span c
 </thead>
 <tbody>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.ResamplerManagerBase.input_folder">input_folder</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcManagerBase.input_folder">input_folder</a></td>
 <td>Return <code>self._input_path</code> set by <code>set_input_paths()</code>.</td>
 </tr>
 <tr class="even">
-<td><a href="#python.clim_recal.resample.ResamplerManagerBase.max_count">max_count</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcManagerBase.max_count">max_count</a></td>
 <td>Maximum length of <code>self.input_files</code> ignoring <code>start_index</code> and <code>start_index</code>.</td>
 </tr>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.ResamplerManagerBase.output_folder">output_folder</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcManagerBase.output_folder">output_folder</a></td>
 <td>Return <code>self._output_path</code> set by <code>set_output_paths()</code>.</td>
 </tr>
 </tbody>
@@ -975,14 +975,14 @@ <h4 data-number="1.1.6.2" class="anchored" data-anchor-id="classes-1"><span clas
 </thead>
 <tbody>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.ResamplerManagerBase.VarirableInBaseImportPathError">VarirableInBaseImportPathError</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcManagerBase.VarirableInBaseImportPathError">VarirableInBaseImportPathError</a></td>
 <td>Checking import path validity for <code>self.variables</code>.</td>
 </tr>
 </tbody>
 </table>
-<section id="python.clim_recal.resample.ResamplerManagerBase.VarirableInBaseImportPathError" class="level5" data-number="1.1.6.2.1">
-<h5 data-number="1.1.6.2.1" class="anchored" data-anchor-id="python.clim_recal.resample.ResamplerManagerBase.VarirableInBaseImportPathError"><span class="header-section-number">1.1.6.2.1</span> VarirableInBaseImportPathError</h5>
-<p><code>clim_recal.resample.ResamplerManagerBase.VarirableInBaseImportPathError()</code></p>
+<section id="python.clim_recal.convert.IterCalcManagerBase.VarirableInBaseImportPathError" class="level5" data-number="1.1.6.2.1">
+<h5 data-number="1.1.6.2.1" class="anchored" data-anchor-id="python.clim_recal.convert.IterCalcManagerBase.VarirableInBaseImportPathError"><span class="header-section-number">1.1.6.2.1</span> VarirableInBaseImportPathError</h5>
+<p><code>clim_recal.convert.IterCalcManagerBase.VarirableInBaseImportPathError()</code></p>
 <p>Checking import path validity for <code>self.variables</code>.</p>
 </section>
 </section>
@@ -997,35 +997,35 @@ <h4 data-number="1.1.6.3" class="anchored" data-anchor-id="methods-3"><span clas
 </thead>
 <tbody>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.ResamplerManagerBase.check_paths">check_paths</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcManagerBase.check_paths">check_paths</a></td>
 <td>Check and set <code>input</code>, <code>resample</code> and <code>crop</code> paths.</td>
 </tr>
 <tr class="even">
-<td><a href="#python.clim_recal.resample.ResamplerManagerBase.execute_configs">execute_configs</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcManagerBase.execute_configs">execute_configs</a></td>
 <td>Run all resampler configurations</td>
 </tr>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.ResamplerManagerBase.set_input_paths">set_input_paths</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcManagerBase.set_input_paths">set_input_paths</a></td>
 <td>Propagate <code>self.input_paths</code> if needed.</td>
 </tr>
 <tr class="even">
-<td><a href="#python.clim_recal.resample.ResamplerManagerBase.set_output_paths">set_output_paths</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcManagerBase.set_output_paths">set_output_paths</a></td>
 <td>Propagate <code>self.output_paths</code> if needed.</td>
 </tr>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.ResamplerManagerBase.yield_configs">yield_configs</a></td>
+<td><a href="#python.clim_recal.convert.IterCalcManagerBase.yield_configs">yield_configs</a></td>
 <td>Generate a <code>CPMResampler</code> or <code>HADsResampler</code> for <code>self.input_paths</code>.</td>
 </tr>
 </tbody>
 </table>
-<section id="python.clim_recal.resample.ResamplerManagerBase.check_paths" class="level5" data-number="1.1.6.3.1">
-<h5 data-number="1.1.6.3.1" class="anchored" data-anchor-id="python.clim_recal.resample.ResamplerManagerBase.check_paths"><span class="header-section-number">1.1.6.3.1</span> check_paths</h5>
-<p><code>clim_recal.resample.ResamplerManagerBase.check_paths(run_set_data_paths=True)</code></p>
+<section id="python.clim_recal.convert.IterCalcManagerBase.check_paths" class="level5" data-number="1.1.6.3.1">
+<h5 data-number="1.1.6.3.1" class="anchored" data-anchor-id="python.clim_recal.convert.IterCalcManagerBase.check_paths"><span class="header-section-number">1.1.6.3.1</span> check_paths</h5>
+<p><code>clim_recal.convert.IterCalcManagerBase.check_paths(run_set_data_paths=True)</code></p>
 <p>Check and set <code>input</code>, <code>resample</code> and <code>crop</code> paths.</p>
 </section>
-<section id="python.clim_recal.resample.ResamplerManagerBase.execute_configs" class="level5" data-number="1.1.6.3.2">
-<h5 data-number="1.1.6.3.2" class="anchored" data-anchor-id="python.clim_recal.resample.ResamplerManagerBase.execute_configs"><span class="header-section-number">1.1.6.3.2</span> execute_configs</h5>
-<p><code>clim_recal.resample.ResamplerManagerBase.execute_configs(multiprocess=False, cpus=None, return_resamplers=False, return_path=True, **kwargs)</code></p>
+<section id="python.clim_recal.convert.IterCalcManagerBase.execute_configs" class="level5" data-number="1.1.6.3.2">
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 <p>Run all resampler configurations</p>
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+<h5 data-number="1.1.6.3.3" class="anchored" data-anchor-id="python.clim_recal.convert.IterCalcManagerBase.set_input_paths"><span class="header-section-number">1.1.6.3.3</span> set_input_paths</h5>
+<p><code>clim_recal.convert.IterCalcManagerBase.set_input_paths()</code></p>
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 </section>
-<section id="python.clim_recal.resample.ResamplerManagerBase.set_output_paths" class="level5" data-number="1.1.6.3.4">
-<h5 data-number="1.1.6.3.4" class="anchored" data-anchor-id="python.clim_recal.resample.ResamplerManagerBase.set_output_paths"><span class="header-section-number">1.1.6.3.4</span> set_output_paths</h5>
-<p><code>clim_recal.resample.ResamplerManagerBase.set_output_paths()</code></p>
+<section id="python.clim_recal.convert.IterCalcManagerBase.set_output_paths" class="level5" data-number="1.1.6.3.4">
+<h5 data-number="1.1.6.3.4" class="anchored" data-anchor-id="python.clim_recal.convert.IterCalcManagerBase.set_output_paths"><span class="header-section-number">1.1.6.3.4</span> set_output_paths</h5>
+<p><code>clim_recal.convert.IterCalcManagerBase.set_output_paths()</code></p>
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 </section>
-<section id="python.clim_recal.resample.ResamplerManagerBase.yield_configs" class="level5" data-number="1.1.6.3.5">
-<h5 data-number="1.1.6.3.5" class="anchored" data-anchor-id="python.clim_recal.resample.ResamplerManagerBase.yield_configs"><span class="header-section-number">1.1.6.3.5</span> yield_configs</h5>
-<p><code>clim_recal.resample.ResamplerManagerBase.yield_configs()</code></p>
+<section id="python.clim_recal.convert.IterCalcManagerBase.yield_configs" class="level5" data-number="1.1.6.3.5">
+<h5 data-number="1.1.6.3.5" class="anchored" data-anchor-id="python.clim_recal.convert.IterCalcManagerBase.yield_configs"><span class="header-section-number">1.1.6.3.5</span> yield_configs</h5>
+<p><code>clim_recal.convert.IterCalcManagerBase.yield_configs()</code></p>
 <p>Generate a <code>CPMResampler</code> or <code>HADsResampler</code> for <code>self.input_paths</code>.</p>
 </section>
 </section>
@@ -1108,23 +1108,23 @@ <h2 data-number="1.2" class="anchored" data-anchor-id="functions"><span class="h
 </thead>
 <tbody>
 <tr class="odd">
-<td><a href="#python.clim_recal.resample.reproject_2_2km_file_name">reproject_2_2km_file_name</a></td>
+<td><a href="#python.clim_recal.convert.reproject_2_2km_file_name">reproject_2_2km_file_name</a></td>
 <td>Return tweaked <code>path</code> to indicate standard day projection.</td>
 </tr>
 <tr class="even">
-<td><a href="#python.clim_recal.resample.reproject_standard_calendar_file_name">reproject_standard_calendar_file_name</a></td>
+<td><a href="#python.clim_recal.convert.reproject_standard_calendar_file_name">reproject_standard_calendar_file_name</a></td>
 <td>Return tweaked <code>path</code> to indicate standard day projection.</td>
 </tr>
 </tbody>
 </table>
-<section id="python.clim_recal.resample.reproject_2_2km_file_name" class="level3" data-number="1.2.1">
-<h3 data-number="1.2.1" class="anchored" data-anchor-id="python.clim_recal.resample.reproject_2_2km_file_name"><span class="header-section-number">1.2.1</span> reproject_2_2km_file_name</h3>
-<p><code>clim_recal.resample.reproject_2_2km_file_name(path)</code></p>
+<section id="python.clim_recal.convert.reproject_2_2km_file_name" class="level3" data-number="1.2.1">
+<h3 data-number="1.2.1" class="anchored" data-anchor-id="python.clim_recal.convert.reproject_2_2km_file_name"><span class="header-section-number">1.2.1</span> reproject_2_2km_file_name</h3>
+<p><code>clim_recal.convert.reproject_2_2km_file_name(path)</code></p>
 <p>Return tweaked <code>path</code> to indicate standard day projection.</p>
 </section>
-<section id="python.clim_recal.resample.reproject_standard_calendar_file_name" class="level3" data-number="1.2.2">
-<h3 data-number="1.2.2" class="anchored" data-anchor-id="python.clim_recal.resample.reproject_standard_calendar_file_name"><span class="header-section-number">1.2.2</span> reproject_standard_calendar_file_name</h3>
-<p><code>clim_recal.resample.reproject_standard_calendar_file_name(path)</code></p>
+<section id="python.clim_recal.convert.reproject_standard_calendar_file_name" class="level3" data-number="1.2.2">
+<h3 data-number="1.2.2" class="anchored" data-anchor-id="python.clim_recal.convert.reproject_standard_calendar_file_name"><span class="header-section-number">1.2.2</span> reproject_standard_calendar_file_name</h3>
+<p><code>clim_recal.convert.reproject_standard_calendar_file_name(path)</code></p>
 <p>Return tweaked <code>path</code> to indicate standard day projection.</p>
 
 
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index 2ef505aa..44f7a900 100644
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+  <a href="../../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
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+  <a href="../../docs/reference/clim_recal.convert.html" class="sidebar-item-text sidebar-link">
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diff --git a/docs/reference/clim_recal.utils.core.html b/docs/reference/clim_recal.utils.core.html
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 <tr class="odd">
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 <td>Resample UKHADS data and UKCP18 data.</td>
 </tr>
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-    "text": "clim_recal.pipeline\nWrappers to automate the entire pipeline.\nFollowing Andy’s very helpful excel file, this manages a reproduction of all steps of the debiasing pipeline.",
+    "text": "clim_recal.ceda_ftp_download\n\n\n\n\n\nName\nDescription\n\n\n\n\ndownload_ftp\nFunction to connect to the CEDA archive and download data.\n\n\n\n\n\nclim_recal.ceda_ftp_download.download_ftp(input, output, username, password, order)\nFunction to connect to the CEDA archive and download data.\nYou need to have a user account and provide your username and FTP password.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ninput\nstr\nPath where the CEDA data to download is located (e.g /badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/1km/tasmin/day/v20220310 or top level folder like /badc/ukcp18/data/land-cpm/uk/2.2km/rcp85 if you want to download all files in all sub-directories).\nrequired\n\n\noutput\nstr\nPath to save the downloaded data - sub-directories will be created automatically under the output directory.\nrequired\n\n\nusername\nstr\nCEDA registered username\nrequired\n\n\npassword\nstr\nCEDA FPT password (obtained as explained in https://help.ceda.ac.uk/article/280-ftp)\nrequired\n\n\norder\nint\nOrder in which to run download 0: default order of file from FTP server 1: reverse order 2: shuffle. This functionality allows to run several downloads in parallel without rewriting files that are being downloaded.\nrequired",
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-    "section": "4.1 Todo",
-    "text": "4.1 Todo\n\nUpdate the example here\nRemove out of date elements\nHardcode the process below\nRefactor to facilitate testing\nEnsure HADs still works\nAdd function for UKCP\nCheck convert_xr_calendar doctest examples\nFix order of UKCP changes\n\nTo run this step in the pipeline the following should work for the default combindations of variables: tasmax, tasmin, and rainfall and the default set of runs: 05, 06, 07 and 08, assuming the necessary data is mounted.\nIf installed via pipx/pip etc. on your local path (or within Docker) clim-recal should be a command line function\n$ clim-recal --all-variables --default-runs --output-path /where/results/should/be/written\nclim-recal pipeline configurations:\n&lt;ClimRecalConfig(variables_count=3, runs_count=4, regions_count=1, methods_count=1,\n                 cpm_folders_count=12, hads_folders_count=3, resample_start_index=0,\n                 resample_stop_index=None, cpus=2)&gt;\n&lt;CPMResamplerManager(variables_count=3, runs_count=4, input_paths_count=12)&gt;\n&lt;HADsResamplerManager(variables_count=3, input_paths_count=3)&gt;\nOtherwise, you can install locally and either run via pdm from the python folder\n$ cd clim-recal/python\n$ pdm run clim-recal --all-variables --default-runs --output-path /where/results/should/be/written\n# Skipping output for brevity\nOr within an ipython or jupyter instance (truncated below for brevity)\n&gt;&gt;&gt; from clim_recal.pipeline import main\n&gt;&gt;&gt; main(all_variables=True, default_runs=True)  # doctest: +SKIP\nclim-recal pipeline configurations:\n&lt;ClimRecalConfig(variables_count=3, runs_count=4, ...&gt;\nRegardless of your route, once you’re confident with the configuration, add the --execute parameter to run. For example, assuming a local install:\n$ clim-recal --all-variables --default-runs --output-path /where/results/should/be/written --execute\nclim-recal pipeline configurations:\n&lt;ClimRecalConfig(variables_count=3, runs_count=4, regions_count=1, methods_count=1,\n                 cpm_folders_count=12, hads_folders_count=3, resample_start_index=0,\n                 resample_stop_index=None, cpus=2)&gt;\n&lt;CPMResamplerManager(variables_count=3, runs_count=4, input_paths_count=12)&gt;\n&lt;HADsResamplerManager(variables_count=3, input_paths_count=3)&gt;\nRunning CPM Standard Calendar projection...\n&lt;CPMResampler(count=100, max_count=100,\n              input_path='/mnt/vmfileshare/ClimateData/Raw/UKCP2.2/tasmax/05/latest',\n              output_path='/mnt/vmfileshare/ClimateData/CPM-365/test-run-3-may/resample/\n              cpm/tasmax/05/latest')&gt;\n 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100/100  [ 0:38:27 &lt; 0:00:00 , 0 it/s ]\n  87% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 87/100  [ 0:17:42 &lt; 0:03:07 , 0 it/s ]",
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+    "text": "Name\nDescription\n\n\n\n\ndownload_ftp\nFunction to connect to the CEDA archive and download data.\n\n\n\n\n\nclim_recal.ceda_ftp_download.download_ftp(input, output, username, password, order)\nFunction to connect to the CEDA archive and download data.\nYou need to have a user account and provide your username and FTP password.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ninput\nstr\nPath where the CEDA data to download is located (e.g /badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/1km/tasmin/day/v20220310 or top level folder like /badc/ukcp18/data/land-cpm/uk/2.2km/rcp85 if you want to download all files in all sub-directories).\nrequired\n\n\noutput\nstr\nPath to save the downloaded data - sub-directories will be created automatically under the output directory.\nrequired\n\n\nusername\nstr\nCEDA registered username\nrequired\n\n\npassword\nstr\nCEDA FPT password (obtained as explained in https://help.ceda.ac.uk/article/280-ftp)\nrequired\n\n\norder\nint\nOrder in which to run download 0: default order of file from FTP server 1: reverse order 2: shuffle. This functionality allows to run several downloads in parallel without rewriting files that are being downloaded.\nrequired",
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-    "text": "8.1 Functions\n\n\n\nName\nDescription\n\n\n\n\nmain\nRun all elements of the pipeline.\n\n\n\n\n8.1.1 main\nclim_recal.pipeline.main(execute=False, hads_input_path=RAW_HADS_PATH, cpm_input_path=RAW_CPM_PATH, output_path=DEFAULT_OUTPUT_PATH, variables=(VariableOptions.default()), regions=(RegionOptions.default()), runs=(RunOptions.default()), methods=(MethodOptions.default()), all_variables=False, all_regions=False, default_runs=False, all_runs=False, all_methods=False, hads_projection=False, cpm_projection=False, crop_hads=True, crop_cpm=True, cpus=None, multiprocess=False, resample_start_index=0, resample_stop_index=None, crop_start_index=0, crop_stop_index=None, total=None, print_range_length=5, **kwargs)\nRun all elements of the pipeline.\n\n8.1.1.1 Parameters\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nvariables\nSequence[VariableOptions | str]\nVariables to include in the model, eg. tasmax, tasmin.\n(VariableOptions.default())\n\n\nruns\nSequence[RunOptions | str]\nWhich model runs to include, eg. “01”, “08”, “11”.\n(RunOptions.default())\n\n\nregions\nSequence[RegionOptions | str] | None\nWhich regions to crop data to. Future plans facilitate skipping to run for entire UK.\n(RegionOptions.default())\n\n\nmethods\nSequence[MethodOptions | str]\nWhich debiasing methods to apply.\n(MethodOptions.default())\n\n\noutput_path\nPathLike\nPath to save intermediate and final results to.\nDEFAULT_OUTPUT_PATH\n\n\ncpus\nint | None\nNumber of cpus to use when multiprocessing.\nNone\n\n\nmultiprocess\nbool\nWhether to use multiprocessing.\nFalse\n\n\nresample_start_index\nint\nIndex to start all iterations from.\n0\n\n\nresample_stop_index\nint | None\nNumber of files from resample_start_index to resample.\nNone\n\n\ncrop_start_index\nint\nIndex to start all crop iterations from.\n0\n\n\ncrop_stop_index\nint | None\nNumber of files iterating from crop_start_index to crop.\nNone\n\n\ntotal\nint | None\nTotal number of records to iterate over. 0 and None indicate all values from resample_start_index.\nNone\n\n\n**kwargs\n\nAdditional parameters to pass to a ClimRecalConfig.\n{}\n\n\n\n\n\n8.1.1.2 Notes\nThe default parameters here are meant to reflect the entire workflow process to ease reproducibility.\n\n\n8.1.1.3 Examples\nNote the _allow_check_fail parameters support running the examples without data mounted from a server.\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; main(variables=(\"rainfall\", \"tasmin\"),\n...      output_path=test_runs_output_path,\n... )\n'set_cpm_for_coord_alignment' for 'HADs' not speficied.\nDefaulting to 'self.cpm_input_path': '...'\nclim-recal pipeline configurations:\n&lt;ClimRecalConfig(variables_count=2, runs_count=1,\n                 regions_count=1, methods_count=1,\n                 cpm_folders_count=2, hads_folders_count=2,\n                 resample_start_index=0, resample_stop_index=None,\n                 crop_star_index=0, cpus=...)&gt;\n&lt;CPMResamplerManager(variables_count=2, runs_count=1,\n                     input_paths_count=2)&gt;\n&lt;HADsResamplerManager(variables_count=2, input_paths_count=2)&gt;\nNo steps run. Add '--execute' to run steps.",
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+    "text": "clim_recal.convert\nResample UKHADS data and UKCP18 data.\n\nUKCP18 is resampled temporally from a 360 day calendar to a standard (365/366 day) calendar and projected to British National Grid (BNG) EPSG:27700 from its original rotated polar grid.\nUKHADS is resampled spatially from 1km to 2.2km in BNG aligned with the projected UKCP18\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nCPMResampler\nCPM specific changes to HADsResampler.\n\n\nCPMResamplerManager\nClass to manage processing CPM resampling.\n\n\nHADsResampler\nManage resampling HADs datafiles for modelling.\n\n\nHADsResamplerManager\nClass to manage processing HADs resampling.\n\n\nIterCalcBase\nBase class to inherit for HADs and CPM.\n\n\nIterCalcManagerBase\nBase class to inherit for HADs and CPM resampler managers.\n\n\n\n\n\nclim_recal.convert.CPMResampler(self, *, input_path=RAW_CPM_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', prior_time_series=None)\nCPM specific changes to HADsResampler.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_path\nPathLike | None\nPath to CPM files to process.\n\n\noutput\n\nPath to save processed CPM files.\n\n\ninput_files\n\nPath or Paths of NCF files to reproject.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\nresampling_func\n\nFunction to call on self.input_files.\n\n\ncrop\n\nPath or file to spatially crop input_files with.\n\n\nfinal_crs\n\nCoordinate Reference System (CRS) to return final format in.\n\n\ninput_file_extension\n\nFile extensions to glob input_files with.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_cpm_output_path: Path = getfixture(\n...         'resample_test_cpm_output_path')\n&gt;&gt;&gt; cpm_resampler: CPMResampler = CPMResampler(\n...     input_path=RAW_CPM_TASMAX_PATH,\n...     output_path=resample_test_cpm_output_path,\n... )\n&gt;&gt;&gt; cpm_resampler\n&lt;CPMResampler(count=..., max_count=...,...\n    ...input_path='.../tasmax/01/latest',...\n    ...output_path='.../test-run-results_..._.../cpm')&gt;\n&gt;&gt;&gt; pprint(cpm_resampler.input_files)\n(...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_19801201-19811130.nc'),\n ...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_19811201-19821130.nc'),\n ...\n ...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_20791201-20801130.nc'))\n\n\n\n\nclim_recal.convert.CPMResamplerManager(self, *, input_paths=RAW_CPM_PATH, output_paths=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=CPM_SUB_PATH, start_index=0, stop_index=None, start_date=CPM_START_DATE, end_date=CPM_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=CPMResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, runs=RunOptions.preferred())\nClass to manage processing CPM resampling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to CPM files to process. If Path, will be propegated with files matching\n\n\noutput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to to save processed CPM files to. If Path will be propagated to match input_paths.\n\n\nvariables\n\nWhich VariableOptions to include.\n\n\nruns\nSequence[RunOptions | str]\nWhich RunOptions to include.\n\n\ncrop_regions\n\nRegionOptions (like Manchester, Scotland etc.) to crop results to.\n\n\ncrop_paths\n\nWhere to save region crop files.\n\n\nsub_path\nPath\nPath to include at the stem of input_paths.\n\n\nstart_index\n\nIndex to begin iterating input files for resampling or cropping.\n\n\nstop_index\n\nIndex to to run from start_index to when resampling or cropping. If None, iterate full list of paths.\n\n\nstart_date\ndate\nNot yet implemented, but in future from what date to generate start index from.\n\n\nend_date\ndate\nNot yet implemented, but in future from what date to generate stop index from.\n\n\nconfigs\nlist[CPMResampler]\nList of HADsResampler instances to iterate resampling or cropping.\n\n\nconfig_default_kwargs\n\nParameters passed to all running self.configs.\n\n\ncalc_class\ntype[CPMResampler]\nclass to construct all self.configs instances with.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_cpm_output_path: Path = getfixture(\n...         'resample_test_cpm_output_path')\n&gt;&gt;&gt; cpm_resampler_manager: CPMResamplerManager = CPMResamplerManager(\n...     stop_index=9,\n...     output_paths=resample_test_cpm_output_path,\n...     )\n&gt;&gt;&gt; cpm_resampler_manager\n&lt;CPMResamplerManager(variables_count=1, runs_count=4,\n                     input_paths_count=4)&gt;\n&gt;&gt;&gt; configs: tuple[CPMResampler, ...] = tuple(\n...     cpm_resampler_manager.yield_configs())\n&gt;&gt;&gt; pprint(configs)\n(&lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/05/latest',\n               output_path='.../cpm/tasmax/05')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/06/latest',\n               output_path='.../cpm/tasmax/06')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/07/latest',\n               output_path='.../cpm/tasmax/07')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/08/latest',\n               output_path='.../cpm/tasmax/08')&gt;)\n\n\n\n\nclim_recal.convert.HADsResampler(self, *, input_path=RAW_HADS_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', input_file_x_column_name=HADS_XDIM, input_file_y_column_name=HADS_YDIM, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)\nManage resampling HADs datafiles for modelling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_path\nPathLike | None\nPath to HADs files to process.\n\n\noutput\n\nPath to save processed HADS files.\n\n\ninput_files\nIterable[PathLike] | None\nPath or Paths of NCF files to resample.\n\n\nresampling_func\n\nFunction to call on self.input_files.\n\n\ncrop\n\nPath or file to spatially crop input_files with.\n\n\nfinal_crs\nstr\nCoordinate Reference System (CRS) to return final format in.\n\n\ninput_file_x_column_name\nstr\nColumn name in input_files or input for x coordinates.\n\n\ninput_file_y_column_name\nstr\nColumn name in input_files or input for y coordinates.\n\n\ninput_file_extension\nNETCDF_OR_TIF\nFile extensions to glob input_files with.\n\n\nstart_index\n\nFirst index of file to iterate processing from.\n\n\nstop_index\n\nLast index of files to iterate processing from as a count from start_index. If None, this will simply iterate over all available files.\n\n\ncpm_for_coord_alignment\nT_Dataset | PathLike | None\nCPM Path or Dataset to match alignment with.\n\n\ncpm_for_coord_alignment_path_converted\nbool\nWhether a Path passed to cpm_for_coord_alignment should be processed.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_hads_output_path: Path = getfixture(\n...         'resample_test_hads_output_path')\n&gt;&gt;&gt; hads_resampler: HADsResampler = HADsResampler(\n...     output_path=resample_test_hads_output_path,\n... )\n&gt;&gt;&gt; hads_resampler\n&lt;HADsResampler(...count=504,...\n    ...input_path='.../tasmax/day',...\n    ...output_path='...run-results_..._.../hads')&gt;\n&gt;&gt;&gt; pprint(hads_resampler.input_files)\n(...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_19800101-19800131.nc'),\n ...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_19800201-19800229.nc'),\n ...,\n ...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_20211201-20211231.nc'))\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nset_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\n\n\nclim_recal.convert.HADsResampler.set_cpm_for_coord_alignment()\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\n\n\nclim_recal.convert.HADsResamplerManager(self, *, input_paths=RAW_HADS_PATH, output_paths=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=HADS_SUB_PATH, start_index=0, stop_index=None, start_date=HADS_START_DATE, end_date=HADS_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=HADsResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)\nClass to manage processing HADs resampling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to CPM files to process. If Path, will be propegated with files matching\n\n\noutput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to to save processed CPM files to. If Path will be propagated to match input_paths.\n\n\nvariables\n\nWhich VariableOptions to include.\n\n\ncrop_regions\n\nRegionOptions (like Manchester, Scotland etc.) to crop results to.\n\n\ncrop_paths\n\nWhere to save region crop files.\n\n\nsub_path\nPath\nPath to include at the stem of input_paths.\n\n\nstart_index\n\nIndex to begin iterating input files for resampling or cropping.\n\n\nstop_index\n\nIndex to to run from start_index to when resampling or cropping. If None, iterate full list of paths.\n\n\nstart_date\ndate\nNot yet implemented, but in future from what date to generate start index from.\n\n\nend_date\ndate\nNot yet implemented, but in future from what date to generate stop index from.\n\n\nconfigs\nlist[HADsResampler]\nList of HADsResampler instances to iterate resampling or cropping.\n\n\nconfig_default_kwargs\ndict[str, Any]\nParameters passed to all running self.configs.\n\n\ncalc_class\ntype[HADsResampler]\nclass to construct all self.configs instances with.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\ncpm_for_coord_alignment\nT_Dataset | PathLike\nCPM Path or Dataset to match alignment with.\n\n\ncpm_for_coord_alignment_path_converted\nbool\nWhether a Path passed to cpm_for_coord_alignment should be processed.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_hads_output_path: Path = getfixture(\n...         'resample_test_hads_output_path')\n&gt;&gt;&gt; hads_resampler_manager: HADsResamplerManager = HADsResamplerManager(\n...     variables=VariableOptions.all(),\n...     output_paths=resample_test_hads_output_path,\n...     )\n&gt;&gt;&gt; hads_resampler_manager\n&lt;HADsResamplerManager(variables_count=3, input_paths_count=3)&gt;\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nset_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\nyield_configs\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.convert.HADsResamplerManager.set_cpm_for_coord_alignment()\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\nclim_recal.convert.HADsResamplerManager.yield_configs()\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.convert.IterCalcBase(self, *, input_path=Path(), output_path=RESAMPLE_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection')\nBase class to inherit for HADs and CPM.\n\n\n\n\n\nName\nDescription\n\n\n\n\nmax_count\nMaximum length of self.input_files ignoring start_index and start_index.\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nexecute\nRun all steps for processing\n\n\nset_input_files\nReplace self.input and process self.input_files.\n\n\n\n\n\nclim_recal.convert.IterCalcBase.execute(**kwargs)\nRun all steps for processing\n\n\n\nclim_recal.convert.IterCalcBase.set_input_files(new_input_path=None)\nReplace self.input and process self.input_files.\n\n\n\n\n\nclim_recal.convert.IterCalcManagerBase(self, *, input_paths=Path(), output_paths=Path(), variables=(VariableOptions.default()), sub_path=Path(), start_index=0, stop_index=None, start_date=None, end_date=None, configs=list(), config_default_kwargs=dict(), calc_class=None, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False)\nBase class to inherit for HADs and CPM resampler managers.\n\n\n\n\n\nName\nDescription\n\n\n\n\ninput_folder\nReturn self._input_path set by set_input_paths().\n\n\nmax_count\nMaximum length of self.input_files ignoring start_index and start_index.\n\n\noutput_folder\nReturn self._output_path set by set_output_paths().\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nVarirableInBaseImportPathError\nChecking import path validity for self.variables.\n\n\n\n\n\nclim_recal.convert.IterCalcManagerBase.VarirableInBaseImportPathError()\nChecking import path validity for self.variables.\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\ncheck_paths\nCheck and set input, resample and crop paths.\n\n\nexecute_configs\nRun all resampler configurations\n\n\nset_input_paths\nPropagate self.input_paths if needed.\n\n\nset_output_paths\nPropagate self.output_paths if needed.\n\n\nyield_configs\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.convert.IterCalcManagerBase.check_paths(run_set_data_paths=True)\nCheck and set input, resample and crop paths.\n\n\n\nclim_recal.convert.IterCalcManagerBase.execute_configs(multiprocess=False, cpus=None, return_resamplers=False, return_path=True, **kwargs)\nRun all resampler configurations\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nmultiprocess\nbool\nIf True run parameters in resample_configs with multiprocess_execute.\nFalse\n\n\ncpus\nint | None\nNumber of cpus to pass to multiprocess_execute.\nNone\n\n\nreturn_resamplers\nbool\nReturn instances of generated HADsResampler or CPMResampler, or return the results of each execute call.\nFalse\n\n\nreturn_path\nbool\nReturn Path to results object if True, else resampled Dataset.\nTrue\n\n\nkwargs\n\nParameters to path to sampler execute calls.\n{}\n\n\n\n\n\n\n\nclim_recal.convert.IterCalcManagerBase.set_input_paths()\nPropagate self.input_paths if needed.\n\n\n\nclim_recal.convert.IterCalcManagerBase.set_output_paths()\nPropagate self.output_paths if needed.\n\n\n\nclim_recal.convert.IterCalcManagerBase.yield_configs()\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nreproject_2_2km_file_name\nReturn tweaked path to indicate standard day projection.\n\n\nreproject_standard_calendar_file_name\nReturn tweaked path to indicate standard day projection.\n\n\n\n\n\nclim_recal.convert.reproject_2_2km_file_name(path)\nReturn tweaked path to indicate standard day projection.\n\n\n\nclim_recal.convert.reproject_standard_calendar_file_name(path)\nReturn tweaked path to indicate standard day projection.",
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+    "title": "1 clim_recal.convert",
     "section": "",
-    "text": "clim_recal.utils.xarray\n\n\n\n\n\nName\nDescription\n\n\n\n\napply_geo_func\nApply a Callable to netcdf_source file and export via to_netcdf.\n\n\ncftime360_to_date\nConvert a Datetime360Day into a date.\n\n\ncftime_range_gen\nConvert a banded time index a banded standard (Gregorian).\n\n\ncheck_xarray_path_and_var_name\nCheck and return a T_Dataset instances and included variable name.\n\n\nconvert_xr_calendar\nConvert cpm 360 day time series to a standard 365/366 day time series.\n\n\nconverted_output_path\nsource_path in export_folder via new_path_name_func.\n\n\ncpm_check_converted\nCheck if cpm_xr_time_series is likely already reprojected.\n\n\ncpm_reproject_with_standard_calendar\nConvert raw cpm_xr_time_series to an 365/366 days and 27700 coords.\n\n\ncpm_xarray_to_standard_calendar\nConvert a CPM nc file of 360 day calendars to standard calendar.\n\n\ncrop_xarray\nCrop xr_time_series with crop_path shapefile.\n\n\nensure_xr_dataset\nReturn xr_time_series as a xarray.Dataset instance.\n\n\nexecute_configs\nRun all resampler configurations\n\n\nfile_name_to_start_end_dates\nReturn dates of file name with date_format-date_format structure.\n\n\ngdal_warp_wrapper\nExecute the gdalwrap function within python.\n\n\ngenerate_360_to_standard\nReturn array_to_expand 360 days expanded to 365 or 366 days.\n\n\nget_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\nhads_resample_and_reproject\nResample HADs xarray time series to 2.2km.\n\n\ninterpolate_xr_ts_nans\nInterpolate nan values in a Dataset time series.\n\n\nplot_xarray\nPlot da with **kwargs to path.\n\n\nprogress_wrapper\nIterate over instance with or without a progress bar.\n\n\nregion_crop_file_name\nGenerate a file name for a regional crop.\n\n\nxr_reproject_crs\nReproject source_xr to target_xr coordinate structure.\n\n\n\n\n\nclim_recal.utils.xarray.apply_geo_func(source_path, func, export_folder, new_path_name_func=None, to_netcdf=True, to_raster=False, export_path_as_output_path_kwarg=False, return_results=False, **kwargs)\nApply a Callable to netcdf_source file and export via to_netcdf.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsource_path\nPathLike\nnetcdf file to apply func to.\nrequired\n\n\nfunc\nReprojectFuncType\nCallable to modify netcdf.\nrequired\n\n\nexport_folder\nPathLike\nWhere to save results.\nrequired\n\n\nnew_path_name_func\nCallable[[Path], Path] | None\nCallabe to generate new path to save to.\nNone\n\n\nto_netcdf\nbool\nWhether to call to_netcdf() method on results Dataset.\nTrue\n\n\nto_raster\nbool\nWhether to call rio.to_raster() on results Dataset.\nFalse\n\n\nexport_path_as_output_path_kwarg\nbool\nWhether to add output_path = export_path to kwargs passed to func. Meant for cases calling gdal_warp_wrapper.\nFalse\n\n\nreturn_results\nbool\nWhether to return results, which would be a Dataset or GDALDataset (the latter if gdal_warp_wrapper is used).\nFalse\n\n\n**kwargs\n\nOther parameters passed to func call.\n{}\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nEither a Path to generated file or converted xarray object.\n\n\n\n\n\n\n\n\nclim_recal.utils.xarray.cftime360_to_date(cf_360)\nConvert a Datetime360Day into a date.\n\n\n&gt;&gt;&gt; cftime360_to_date(Datetime360Day(1980, 1, 1))\ndatetime.date(1980, 1, 1)\n\n\n\n\nclim_recal.utils.xarray.cftime_range_gen(time_data_array, **kwargs)\nConvert a banded time index a banded standard (Gregorian).\n\n\n\nclim_recal.utils.xarray.check_xarray_path_and_var_name(xr_time_series, variable_name, ignore_warnings=True)\nCheck and return a T_Dataset instances and included variable name.\n\n\n\nclim_recal.utils.xarray.convert_xr_calendar(xr_time_series, align_on=DEFAULT_CALENDAR_ALIGN, calendar=CFCalendarSTANDARD, use_cftime=False, missing_value=np.nan, interpolate_na=False, ensure_output_type_is_dataset=False, interpolate_method=DEFAULT_INTERPOLATION_METHOD, keep_crs=True, keep_attrs=True, limit=1, engine=NETCDF4_XARRAY_ENGINE, check_cftime_cols=None, cftime_range_gen_kwargs=None)\nConvert cpm 360 day time series to a standard 365/366 day time series.\n\n\nShort time examples (like 2 skipped out of 8 days) raises: ValueError(\"date_range_like was unable to generate a range as the source frequency was not inferable.\")\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nxr_time_series\nT_DataArray | T_Dataset | PathLike\nA DataArray or Dataset to convert to calendar time.\nrequired\n\n\nalign_on\nConvertCalendarAlignOptions\nWhether and how to align calendar types.\nDEFAULT_CALENDAR_ALIGN\n\n\ncalendar\nCFCalendar\nType of calendar to convert xr_time_series to.\nCFCalendarSTANDARD\n\n\nuse_cftime\nbool\nWhether to enforce cftime vs datetime64 time format.\nFalse\n\n\nmissing_value\nAny | None\nMissing value to populate missing date interpolations with.\nnp.nan\n\n\nkeep_crs\nbool\nReapply initial Coordinate Reference System (CRS) after time projection.\nTrue\n\n\ninterpolate_na\nbool\nWhether to apply temporal interpolation for missing values.\nFalse\n\n\ninterpolate_method\nInterpOptions\nWhich InterpOptions method to apply if interpolate_na is True.\nDEFAULT_INTERPOLATION_METHOD\n\n\nkeep_attrs\nbool\nWhether to keep all attributes on after interpolate_na\nTrue\n\n\nlimit\nint\nLimit of number of continuous missing day values allowed in interpolate_na.\n1\n\n\nengine\nXArrayEngineType\nWhich XArrayEngineType to use in parsing files and operations.\nNETCDF4_XARRAY_ENGINE\n\n\nextrapolate_fill_value\n\nIf True, then pass fill_value=extrapolate. See: * https://docs.xarray.dev/en/stable/generated/xarray.Dataset.interpolate_na.html * https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d\nrequired\n\n\ncheck_cftime_cols\ntuple[str] | None\nColumns to check cftime format on\nNone\n\n\ncftime_range_gen_kwargs\ndict[str, Any] | None\nAny kwargs to pass to cftime_range_gen\nNone\n\n\n**kwargs\n\nAny additional parameters to pass to interpolate_na.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nValueError\nLikely from xarray calling date_range_like.\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nT_DataArrayOrSet\nConverted xr_time_series to specified calendar with optional interpolation.\n\n\n\n\n\n\nCertain values may fail to interpolate in cases of 360 -&gt; 365/366 (Gregorian) calendar. Examples include projecting CPM data, which is able to fill in measurement values (e.g. tasmax) but the year and month_number variables have nan values",
+    "text": "Name\nDescription\n\n\n\n\nCPMResampler\nCPM specific changes to HADsResampler.\n\n\nCPMResamplerManager\nClass to manage processing CPM resampling.\n\n\nHADsResampler\nManage resampling HADs datafiles for modelling.\n\n\nHADsResamplerManager\nClass to manage processing HADs resampling.\n\n\nIterCalcBase\nBase class to inherit for HADs and CPM.\n\n\nIterCalcManagerBase\nBase class to inherit for HADs and CPM resampler managers.\n\n\n\n\n\nclim_recal.convert.CPMResampler(self, *, input_path=RAW_CPM_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', prior_time_series=None)\nCPM specific changes to HADsResampler.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_path\nPathLike | None\nPath to CPM files to process.\n\n\noutput\n\nPath to save processed CPM files.\n\n\ninput_files\n\nPath or Paths of NCF files to reproject.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\nresampling_func\n\nFunction to call on self.input_files.\n\n\ncrop\n\nPath or file to spatially crop input_files with.\n\n\nfinal_crs\n\nCoordinate Reference System (CRS) to return final format in.\n\n\ninput_file_extension\n\nFile extensions to glob input_files with.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_cpm_output_path: Path = getfixture(\n...         'resample_test_cpm_output_path')\n&gt;&gt;&gt; cpm_resampler: CPMResampler = CPMResampler(\n...     input_path=RAW_CPM_TASMAX_PATH,\n...     output_path=resample_test_cpm_output_path,\n... )\n&gt;&gt;&gt; cpm_resampler\n&lt;CPMResampler(count=..., max_count=...,...\n    ...input_path='.../tasmax/01/latest',...\n    ...output_path='.../test-run-results_..._.../cpm')&gt;\n&gt;&gt;&gt; pprint(cpm_resampler.input_files)\n(...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_19801201-19811130.nc'),\n ...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_19811201-19821130.nc'),\n ...\n ...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_20791201-20801130.nc'))\n\n\n\n\nclim_recal.convert.CPMResamplerManager(self, *, input_paths=RAW_CPM_PATH, output_paths=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=CPM_SUB_PATH, start_index=0, stop_index=None, start_date=CPM_START_DATE, end_date=CPM_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=CPMResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, runs=RunOptions.preferred())\nClass to manage processing CPM resampling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to CPM files to process. If Path, will be propegated with files matching\n\n\noutput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to to save processed CPM files to. If Path will be propagated to match input_paths.\n\n\nvariables\n\nWhich VariableOptions to include.\n\n\nruns\nSequence[RunOptions | str]\nWhich RunOptions to include.\n\n\ncrop_regions\n\nRegionOptions (like Manchester, Scotland etc.) to crop results to.\n\n\ncrop_paths\n\nWhere to save region crop files.\n\n\nsub_path\nPath\nPath to include at the stem of input_paths.\n\n\nstart_index\n\nIndex to begin iterating input files for resampling or cropping.\n\n\nstop_index\n\nIndex to to run from start_index to when resampling or cropping. If None, iterate full list of paths.\n\n\nstart_date\ndate\nNot yet implemented, but in future from what date to generate start index from.\n\n\nend_date\ndate\nNot yet implemented, but in future from what date to generate stop index from.\n\n\nconfigs\nlist[CPMResampler]\nList of HADsResampler instances to iterate resampling or cropping.\n\n\nconfig_default_kwargs\n\nParameters passed to all running self.configs.\n\n\ncalc_class\ntype[CPMResampler]\nclass to construct all self.configs instances with.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_cpm_output_path: Path = getfixture(\n...         'resample_test_cpm_output_path')\n&gt;&gt;&gt; cpm_resampler_manager: CPMResamplerManager = CPMResamplerManager(\n...     stop_index=9,\n...     output_paths=resample_test_cpm_output_path,\n...     )\n&gt;&gt;&gt; cpm_resampler_manager\n&lt;CPMResamplerManager(variables_count=1, runs_count=4,\n                     input_paths_count=4)&gt;\n&gt;&gt;&gt; configs: tuple[CPMResampler, ...] = tuple(\n...     cpm_resampler_manager.yield_configs())\n&gt;&gt;&gt; pprint(configs)\n(&lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/05/latest',\n               output_path='.../cpm/tasmax/05')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/06/latest',\n               output_path='.../cpm/tasmax/06')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/07/latest',\n               output_path='.../cpm/tasmax/07')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/08/latest',\n               output_path='.../cpm/tasmax/08')&gt;)\n\n\n\n\nclim_recal.convert.HADsResampler(self, *, input_path=RAW_HADS_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', input_file_x_column_name=HADS_XDIM, input_file_y_column_name=HADS_YDIM, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)\nManage resampling HADs datafiles for modelling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_path\nPathLike | None\nPath to HADs files to process.\n\n\noutput\n\nPath to save processed HADS files.\n\n\ninput_files\nIterable[PathLike] | None\nPath or Paths of NCF files to resample.\n\n\nresampling_func\n\nFunction to call on self.input_files.\n\n\ncrop\n\nPath or file to spatially crop input_files with.\n\n\nfinal_crs\nstr\nCoordinate Reference System (CRS) to return final format in.\n\n\ninput_file_x_column_name\nstr\nColumn name in input_files or input for x coordinates.\n\n\ninput_file_y_column_name\nstr\nColumn name in input_files or input for y coordinates.\n\n\ninput_file_extension\nNETCDF_OR_TIF\nFile extensions to glob input_files with.\n\n\nstart_index\n\nFirst index of file to iterate processing from.\n\n\nstop_index\n\nLast index of files to iterate processing from as a count from start_index. If None, this will simply iterate over all available files.\n\n\ncpm_for_coord_alignment\nT_Dataset | PathLike | None\nCPM Path or Dataset to match alignment with.\n\n\ncpm_for_coord_alignment_path_converted\nbool\nWhether a Path passed to cpm_for_coord_alignment should be processed.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_hads_output_path: Path = getfixture(\n...         'resample_test_hads_output_path')\n&gt;&gt;&gt; hads_resampler: HADsResampler = HADsResampler(\n...     output_path=resample_test_hads_output_path,\n... )\n&gt;&gt;&gt; hads_resampler\n&lt;HADsResampler(...count=504,...\n    ...input_path='.../tasmax/day',...\n    ...output_path='...run-results_..._.../hads')&gt;\n&gt;&gt;&gt; pprint(hads_resampler.input_files)\n(...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_19800101-19800131.nc'),\n ...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_19800201-19800229.nc'),\n ...,\n ...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_20211201-20211231.nc'))\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nset_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\n\n\nclim_recal.convert.HADsResampler.set_cpm_for_coord_alignment()\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\n\n\nclim_recal.convert.HADsResamplerManager(self, *, input_paths=RAW_HADS_PATH, output_paths=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=HADS_SUB_PATH, start_index=0, stop_index=None, start_date=HADS_START_DATE, end_date=HADS_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=HADsResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)\nClass to manage processing HADs resampling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to CPM files to process. If Path, will be propegated with files matching\n\n\noutput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to to save processed CPM files to. If Path will be propagated to match input_paths.\n\n\nvariables\n\nWhich VariableOptions to include.\n\n\ncrop_regions\n\nRegionOptions (like Manchester, Scotland etc.) to crop results to.\n\n\ncrop_paths\n\nWhere to save region crop files.\n\n\nsub_path\nPath\nPath to include at the stem of input_paths.\n\n\nstart_index\n\nIndex to begin iterating input files for resampling or cropping.\n\n\nstop_index\n\nIndex to to run from start_index to when resampling or cropping. If None, iterate full list of paths.\n\n\nstart_date\ndate\nNot yet implemented, but in future from what date to generate start index from.\n\n\nend_date\ndate\nNot yet implemented, but in future from what date to generate stop index from.\n\n\nconfigs\nlist[HADsResampler]\nList of HADsResampler instances to iterate resampling or cropping.\n\n\nconfig_default_kwargs\ndict[str, Any]\nParameters passed to all running self.configs.\n\n\ncalc_class\ntype[HADsResampler]\nclass to construct all self.configs instances with.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\ncpm_for_coord_alignment\nT_Dataset | PathLike\nCPM Path or Dataset to match alignment with.\n\n\ncpm_for_coord_alignment_path_converted\nbool\nWhether a Path passed to cpm_for_coord_alignment should be processed.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_hads_output_path: Path = getfixture(\n...         'resample_test_hads_output_path')\n&gt;&gt;&gt; hads_resampler_manager: HADsResamplerManager = HADsResamplerManager(\n...     variables=VariableOptions.all(),\n...     output_paths=resample_test_hads_output_path,\n...     )\n&gt;&gt;&gt; hads_resampler_manager\n&lt;HADsResamplerManager(variables_count=3, input_paths_count=3)&gt;\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nset_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\nyield_configs\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.convert.HADsResamplerManager.set_cpm_for_coord_alignment()\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\nclim_recal.convert.HADsResamplerManager.yield_configs()\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.convert.IterCalcBase(self, *, input_path=Path(), output_path=RESAMPLE_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection')\nBase class to inherit for HADs and CPM.\n\n\n\n\n\nName\nDescription\n\n\n\n\nmax_count\nMaximum length of self.input_files ignoring start_index and start_index.\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nexecute\nRun all steps for processing\n\n\nset_input_files\nReplace self.input and process self.input_files.\n\n\n\n\n\nclim_recal.convert.IterCalcBase.execute(**kwargs)\nRun all steps for processing\n\n\n\nclim_recal.convert.IterCalcBase.set_input_files(new_input_path=None)\nReplace self.input and process self.input_files.\n\n\n\n\n\nclim_recal.convert.IterCalcManagerBase(self, *, input_paths=Path(), output_paths=Path(), variables=(VariableOptions.default()), sub_path=Path(), start_index=0, stop_index=None, start_date=None, end_date=None, configs=list(), config_default_kwargs=dict(), calc_class=None, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False)\nBase class to inherit for HADs and CPM resampler managers.\n\n\n\n\n\nName\nDescription\n\n\n\n\ninput_folder\nReturn self._input_path set by set_input_paths().\n\n\nmax_count\nMaximum length of self.input_files ignoring start_index and start_index.\n\n\noutput_folder\nReturn self._output_path set by set_output_paths().\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nVarirableInBaseImportPathError\nChecking import path validity for self.variables.\n\n\n\n\n\nclim_recal.convert.IterCalcManagerBase.VarirableInBaseImportPathError()\nChecking import path validity for self.variables.\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\ncheck_paths\nCheck and set input, resample and crop paths.\n\n\nexecute_configs\nRun all resampler configurations\n\n\nset_input_paths\nPropagate self.input_paths if needed.\n\n\nset_output_paths\nPropagate self.output_paths if needed.\n\n\nyield_configs\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.convert.IterCalcManagerBase.check_paths(run_set_data_paths=True)\nCheck and set input, resample and crop paths.\n\n\n\nclim_recal.convert.IterCalcManagerBase.execute_configs(multiprocess=False, cpus=None, return_resamplers=False, return_path=True, **kwargs)\nRun all resampler configurations\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nmultiprocess\nbool\nIf True run parameters in resample_configs with multiprocess_execute.\nFalse\n\n\ncpus\nint | None\nNumber of cpus to pass to multiprocess_execute.\nNone\n\n\nreturn_resamplers\nbool\nReturn instances of generated HADsResampler or CPMResampler, or return the results of each execute call.\nFalse\n\n\nreturn_path\nbool\nReturn Path to results object if True, else resampled Dataset.\nTrue\n\n\nkwargs\n\nParameters to path to sampler execute calls.\n{}\n\n\n\n\n\n\n\nclim_recal.convert.IterCalcManagerBase.set_input_paths()\nPropagate self.input_paths if needed.\n\n\n\nclim_recal.convert.IterCalcManagerBase.set_output_paths()\nPropagate self.output_paths if needed.\n\n\n\nclim_recal.convert.IterCalcManagerBase.yield_configs()\nGenerate a CPMResampler or HADsResampler for self.input_paths.",
     "crumbs": [
       "python",
       "Reference",
-      "Utilities",
-      "xarray"
+      "Data Resampling"
     ]
   },
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-    "text": "Name\nDescription\n\n\n\n\napply_geo_func\nApply a Callable to netcdf_source file and export via to_netcdf.\n\n\ncftime360_to_date\nConvert a Datetime360Day into a date.\n\n\ncftime_range_gen\nConvert a banded time index a banded standard (Gregorian).\n\n\ncheck_xarray_path_and_var_name\nCheck and return a T_Dataset instances and included variable name.\n\n\nconvert_xr_calendar\nConvert cpm 360 day time series to a standard 365/366 day time series.\n\n\nconverted_output_path\nsource_path in export_folder via new_path_name_func.\n\n\ncpm_check_converted\nCheck if cpm_xr_time_series is likely already reprojected.\n\n\ncpm_reproject_with_standard_calendar\nConvert raw cpm_xr_time_series to an 365/366 days and 27700 coords.\n\n\ncpm_xarray_to_standard_calendar\nConvert a CPM nc file of 360 day calendars to standard calendar.\n\n\ncrop_xarray\nCrop xr_time_series with crop_path shapefile.\n\n\nensure_xr_dataset\nReturn xr_time_series as a xarray.Dataset instance.\n\n\nexecute_configs\nRun all resampler configurations\n\n\nfile_name_to_start_end_dates\nReturn dates of file name with date_format-date_format structure.\n\n\ngdal_warp_wrapper\nExecute the gdalwrap function within python.\n\n\ngenerate_360_to_standard\nReturn array_to_expand 360 days expanded to 365 or 366 days.\n\n\nget_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\nhads_resample_and_reproject\nResample HADs xarray time series to 2.2km.\n\n\ninterpolate_xr_ts_nans\nInterpolate nan values in a Dataset time series.\n\n\nplot_xarray\nPlot da with **kwargs to path.\n\n\nprogress_wrapper\nIterate over instance with or without a progress bar.\n\n\nregion_crop_file_name\nGenerate a file name for a regional crop.\n\n\nxr_reproject_crs\nReproject source_xr to target_xr coordinate structure.\n\n\n\n\n\nclim_recal.utils.xarray.apply_geo_func(source_path, func, export_folder, new_path_name_func=None, to_netcdf=True, to_raster=False, export_path_as_output_path_kwarg=False, return_results=False, **kwargs)\nApply a Callable to netcdf_source file and export via to_netcdf.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsource_path\nPathLike\nnetcdf file to apply func to.\nrequired\n\n\nfunc\nReprojectFuncType\nCallable to modify netcdf.\nrequired\n\n\nexport_folder\nPathLike\nWhere to save results.\nrequired\n\n\nnew_path_name_func\nCallable[[Path], Path] | None\nCallabe to generate new path to save to.\nNone\n\n\nto_netcdf\nbool\nWhether to call to_netcdf() method on results Dataset.\nTrue\n\n\nto_raster\nbool\nWhether to call rio.to_raster() on results Dataset.\nFalse\n\n\nexport_path_as_output_path_kwarg\nbool\nWhether to add output_path = export_path to kwargs passed to func. Meant for cases calling gdal_warp_wrapper.\nFalse\n\n\nreturn_results\nbool\nWhether to return results, which would be a Dataset or GDALDataset (the latter if gdal_warp_wrapper is used).\nFalse\n\n\n**kwargs\n\nOther parameters passed to func call.\n{}\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nEither a Path to generated file or converted xarray object.\n\n\n\n\n\n\n\n\nclim_recal.utils.xarray.cftime360_to_date(cf_360)\nConvert a Datetime360Day into a date.\n\n\n&gt;&gt;&gt; cftime360_to_date(Datetime360Day(1980, 1, 1))\ndatetime.date(1980, 1, 1)\n\n\n\n\nclim_recal.utils.xarray.cftime_range_gen(time_data_array, **kwargs)\nConvert a banded time index a banded standard (Gregorian).\n\n\n\nclim_recal.utils.xarray.check_xarray_path_and_var_name(xr_time_series, variable_name, ignore_warnings=True)\nCheck and return a T_Dataset instances and included variable name.\n\n\n\nclim_recal.utils.xarray.convert_xr_calendar(xr_time_series, align_on=DEFAULT_CALENDAR_ALIGN, calendar=CFCalendarSTANDARD, use_cftime=False, missing_value=np.nan, interpolate_na=False, ensure_output_type_is_dataset=False, interpolate_method=DEFAULT_INTERPOLATION_METHOD, keep_crs=True, keep_attrs=True, limit=1, engine=NETCDF4_XARRAY_ENGINE, check_cftime_cols=None, cftime_range_gen_kwargs=None)\nConvert cpm 360 day time series to a standard 365/366 day time series.\n\n\nShort time examples (like 2 skipped out of 8 days) raises: ValueError(\"date_range_like was unable to generate a range as the source frequency was not inferable.\")\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nxr_time_series\nT_DataArray | T_Dataset | PathLike\nA DataArray or Dataset to convert to calendar time.\nrequired\n\n\nalign_on\nConvertCalendarAlignOptions\nWhether and how to align calendar types.\nDEFAULT_CALENDAR_ALIGN\n\n\ncalendar\nCFCalendar\nType of calendar to convert xr_time_series to.\nCFCalendarSTANDARD\n\n\nuse_cftime\nbool\nWhether to enforce cftime vs datetime64 time format.\nFalse\n\n\nmissing_value\nAny | None\nMissing value to populate missing date interpolations with.\nnp.nan\n\n\nkeep_crs\nbool\nReapply initial Coordinate Reference System (CRS) after time projection.\nTrue\n\n\ninterpolate_na\nbool\nWhether to apply temporal interpolation for missing values.\nFalse\n\n\ninterpolate_method\nInterpOptions\nWhich InterpOptions method to apply if interpolate_na is True.\nDEFAULT_INTERPOLATION_METHOD\n\n\nkeep_attrs\nbool\nWhether to keep all attributes on after interpolate_na\nTrue\n\n\nlimit\nint\nLimit of number of continuous missing day values allowed in interpolate_na.\n1\n\n\nengine\nXArrayEngineType\nWhich XArrayEngineType to use in parsing files and operations.\nNETCDF4_XARRAY_ENGINE\n\n\nextrapolate_fill_value\n\nIf True, then pass fill_value=extrapolate. See: * https://docs.xarray.dev/en/stable/generated/xarray.Dataset.interpolate_na.html * https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d\nrequired\n\n\ncheck_cftime_cols\ntuple[str] | None\nColumns to check cftime format on\nNone\n\n\ncftime_range_gen_kwargs\ndict[str, Any] | None\nAny kwargs to pass to cftime_range_gen\nNone\n\n\n**kwargs\n\nAny additional parameters to pass to interpolate_na.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nValueError\nLikely from xarray calling date_range_like.\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nT_DataArrayOrSet\nConverted xr_time_series to specified calendar with optional interpolation.\n\n\n\n\n\n\nCertain values may fail to interpolate in cases of 360 -&gt; 365/366 (Gregorian) calendar. Examples include projecting CPM data, which is able to fill in measurement values (e.g. tasmax) but the year and month_number variables have nan values",
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-    "text": "clim_recal.ceda_ftp_download\n\n\n\n\n\nName\nDescription\n\n\n\n\ndownload_ftp\nFunction to connect to the CEDA archive and download data.\n\n\n\n\n\nclim_recal.ceda_ftp_download.download_ftp(input, output, username, password, order)\nFunction to connect to the CEDA archive and download data.\nYou need to have a user account and provide your username and FTP password.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ninput\nstr\nPath where the CEDA data to download is located (e.g /badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/1km/tasmin/day/v20220310 or top level folder like /badc/ukcp18/data/land-cpm/uk/2.2km/rcp85 if you want to download all files in all sub-directories).\nrequired\n\n\noutput\nstr\nPath to save the downloaded data - sub-directories will be created automatically under the output directory.\nrequired\n\n\nusername\nstr\nCEDA registered username\nrequired\n\n\npassword\nstr\nCEDA FPT password (obtained as explained in https://help.ceda.ac.uk/article/280-ftp)\nrequired\n\n\norder\nint\nOrder in which to run download 0: default order of file from FTP server 1: reverse order 2: shuffle. This functionality allows to run several downloads in parallel without rewriting files that are being downloaded.\nrequired",
+    "text": "clim_recal.utils.xarray\n\n\n\n\n\nName\nDescription\n\n\n\n\napply_geo_func\nApply a Callable to netcdf_source file and export via to_netcdf.\n\n\ncftime360_to_date\nConvert a Datetime360Day into a date.\n\n\ncftime_range_gen\nConvert a banded time index a banded standard (Gregorian).\n\n\ncheck_xarray_path_and_var_name\nCheck and return a T_Dataset instances and included variable name.\n\n\nconvert_xr_calendar\nConvert cpm 360 day time series to a standard 365/366 day time series.\n\n\nconverted_output_path\nsource_path in export_folder via new_path_name_func.\n\n\ncpm_check_converted\nCheck if cpm_xr_time_series is likely already reprojected.\n\n\ncpm_reproject_with_standard_calendar\nConvert raw cpm_xr_time_series to an 365/366 days and 27700 coords.\n\n\ncpm_xarray_to_standard_calendar\nConvert a CPM nc file of 360 day calendars to standard calendar.\n\n\ncrop_xarray\nCrop xr_time_series with crop_path shapefile.\n\n\nensure_xr_dataset\nReturn xr_time_series as a xarray.Dataset instance.\n\n\nexecute_configs\nRun all resampler configurations\n\n\nfile_name_to_start_end_dates\nReturn dates of file name with date_format-date_format structure.\n\n\ngdal_warp_wrapper\nExecute the gdalwrap function within python.\n\n\ngenerate_360_to_standard\nReturn array_to_expand 360 days expanded to 365 or 366 days.\n\n\nget_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\nhads_resample_and_reproject\nResample HADs xarray time series to 2.2km.\n\n\ninterpolate_xr_ts_nans\nInterpolate nan values in a Dataset time series.\n\n\nplot_xarray\nPlot da with **kwargs to path.\n\n\nprogress_wrapper\nIterate over instance with or without a progress bar.\n\n\nregion_crop_file_name\nGenerate a file name for a regional crop.\n\n\nxr_reproject_crs\nReproject source_xr to target_xr coordinate structure.\n\n\n\n\n\nclim_recal.utils.xarray.apply_geo_func(source_path, func, export_folder, new_path_name_func=None, to_netcdf=True, to_raster=False, export_path_as_output_path_kwarg=False, return_results=False, **kwargs)\nApply a Callable to netcdf_source file and export via to_netcdf.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsource_path\nPathLike\nnetcdf file to apply func to.\nrequired\n\n\nfunc\nReprojectFuncType\nCallable to modify netcdf.\nrequired\n\n\nexport_folder\nPathLike\nWhere to save results.\nrequired\n\n\nnew_path_name_func\nCallable[[Path], Path] | None\nCallabe to generate new path to save to.\nNone\n\n\nto_netcdf\nbool\nWhether to call to_netcdf() method on results Dataset.\nTrue\n\n\nto_raster\nbool\nWhether to call rio.to_raster() on results Dataset.\nFalse\n\n\nexport_path_as_output_path_kwarg\nbool\nWhether to add output_path = export_path to kwargs passed to func. Meant for cases calling gdal_warp_wrapper.\nFalse\n\n\nreturn_results\nbool\nWhether to return results, which would be a Dataset or GDALDataset (the latter if gdal_warp_wrapper is used).\nFalse\n\n\n**kwargs\n\nOther parameters passed to func call.\n{}\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nEither a Path to generated file or converted xarray object.\n\n\n\n\n\n\n\n\nclim_recal.utils.xarray.cftime360_to_date(cf_360)\nConvert a Datetime360Day into a date.\n\n\n&gt;&gt;&gt; cftime360_to_date(Datetime360Day(1980, 1, 1))\ndatetime.date(1980, 1, 1)\n\n\n\n\nclim_recal.utils.xarray.cftime_range_gen(time_data_array, **kwargs)\nConvert a banded time index a banded standard (Gregorian).\n\n\n\nclim_recal.utils.xarray.check_xarray_path_and_var_name(xr_time_series, variable_name, ignore_warnings=True)\nCheck and return a T_Dataset instances and included variable name.\n\n\n\nclim_recal.utils.xarray.convert_xr_calendar(xr_time_series, align_on=DEFAULT_CALENDAR_ALIGN, calendar=CFCalendarSTANDARD, use_cftime=False, missing_value=np.nan, interpolate_na=False, ensure_output_type_is_dataset=False, interpolate_method=DEFAULT_INTERPOLATION_METHOD, keep_crs=True, keep_attrs=True, limit=1, engine=NETCDF4_XARRAY_ENGINE, check_cftime_cols=None, cftime_range_gen_kwargs=None)\nConvert cpm 360 day time series to a standard 365/366 day time series.\n\n\nShort time examples (like 2 skipped out of 8 days) raises: ValueError(\"date_range_like was unable to generate a range as the source frequency was not inferable.\")\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nxr_time_series\nT_DataArray | T_Dataset | PathLike\nA DataArray or Dataset to convert to calendar time.\nrequired\n\n\nalign_on\nConvertCalendarAlignOptions\nWhether and how to align calendar types.\nDEFAULT_CALENDAR_ALIGN\n\n\ncalendar\nCFCalendar\nType of calendar to convert xr_time_series to.\nCFCalendarSTANDARD\n\n\nuse_cftime\nbool\nWhether to enforce cftime vs datetime64 time format.\nFalse\n\n\nmissing_value\nAny | None\nMissing value to populate missing date interpolations with.\nnp.nan\n\n\nkeep_crs\nbool\nReapply initial Coordinate Reference System (CRS) after time projection.\nTrue\n\n\ninterpolate_na\nbool\nWhether to apply temporal interpolation for missing values.\nFalse\n\n\ninterpolate_method\nInterpOptions\nWhich InterpOptions method to apply if interpolate_na is True.\nDEFAULT_INTERPOLATION_METHOD\n\n\nkeep_attrs\nbool\nWhether to keep all attributes on after interpolate_na\nTrue\n\n\nlimit\nint\nLimit of number of continuous missing day values allowed in interpolate_na.\n1\n\n\nengine\nXArrayEngineType\nWhich XArrayEngineType to use in parsing files and operations.\nNETCDF4_XARRAY_ENGINE\n\n\nextrapolate_fill_value\n\nIf True, then pass fill_value=extrapolate. 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-    "text": "Name\nDescription\n\n\n\n\ndownload_ftp\nFunction to connect to the CEDA archive and download data.\n\n\n\n\n\nclim_recal.ceda_ftp_download.download_ftp(input, output, username, password, order)\nFunction to connect to the CEDA archive and download data.\nYou need to have a user account and provide your username and FTP password.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ninput\nstr\nPath where the CEDA data to download is located (e.g /badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.1.0.0/1km/tasmin/day/v20220310 or top level folder like /badc/ukcp18/data/land-cpm/uk/2.2km/rcp85 if you want to download all files in all sub-directories).\nrequired\n\n\noutput\nstr\nPath to save the downloaded data - sub-directories will be created automatically under the output directory.\nrequired\n\n\nusername\nstr\nCEDA registered username\nrequired\n\n\npassword\nstr\nCEDA FPT password (obtained as explained in https://help.ceda.ac.uk/article/280-ftp)\nrequired\n\n\norder\nint\nOrder in which to run download 0: default order of file from FTP server 1: reverse order 2: shuffle. This functionality allows to run several downloads in parallel without rewriting files that are being downloaded.\nrequired",
+    "text": "Name\nDescription\n\n\n\n\napply_geo_func\nApply a Callable to netcdf_source file and export via to_netcdf.\n\n\ncftime360_to_date\nConvert a Datetime360Day into a date.\n\n\ncftime_range_gen\nConvert a banded time index a banded standard (Gregorian).\n\n\ncheck_xarray_path_and_var_name\nCheck and return a T_Dataset instances and included variable name.\n\n\nconvert_xr_calendar\nConvert cpm 360 day time series to a standard 365/366 day time series.\n\n\nconverted_output_path\nsource_path in export_folder via new_path_name_func.\n\n\ncpm_check_converted\nCheck if cpm_xr_time_series is likely already reprojected.\n\n\ncpm_reproject_with_standard_calendar\nConvert raw cpm_xr_time_series to an 365/366 days and 27700 coords.\n\n\ncpm_xarray_to_standard_calendar\nConvert a CPM nc file of 360 day calendars to standard calendar.\n\n\ncrop_xarray\nCrop xr_time_series with crop_path shapefile.\n\n\nensure_xr_dataset\nReturn xr_time_series as a xarray.Dataset instance.\n\n\nexecute_configs\nRun all resampler configurations\n\n\nfile_name_to_start_end_dates\nReturn dates of file name with date_format-date_format structure.\n\n\ngdal_warp_wrapper\nExecute the gdalwrap function within python.\n\n\ngenerate_360_to_standard\nReturn array_to_expand 360 days expanded to 365 or 366 days.\n\n\nget_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\nhads_resample_and_reproject\nResample HADs xarray time series to 2.2km.\n\n\ninterpolate_xr_ts_nans\nInterpolate nan values in a Dataset time series.\n\n\nplot_xarray\nPlot da with **kwargs to path.\n\n\nprogress_wrapper\nIterate over instance with or without a progress bar.\n\n\nregion_crop_file_name\nGenerate a file name for a regional crop.\n\n\nxr_reproject_crs\nReproject source_xr to target_xr coordinate structure.\n\n\n\n\n\nclim_recal.utils.xarray.apply_geo_func(source_path, func, export_folder, new_path_name_func=None, to_netcdf=True, to_raster=False, export_path_as_output_path_kwarg=False, return_results=False, **kwargs)\nApply a Callable to netcdf_source file and export via to_netcdf.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsource_path\nPathLike\nnetcdf file to apply func to.\nrequired\n\n\nfunc\nReprojectFuncType\nCallable to modify netcdf.\nrequired\n\n\nexport_folder\nPathLike\nWhere to save results.\nrequired\n\n\nnew_path_name_func\nCallable[[Path], Path] | None\nCallabe to generate new path to save to.\nNone\n\n\nto_netcdf\nbool\nWhether to call to_netcdf() method on results Dataset.\nTrue\n\n\nto_raster\nbool\nWhether to call rio.to_raster() on results Dataset.\nFalse\n\n\nexport_path_as_output_path_kwarg\nbool\nWhether to add output_path = export_path to kwargs passed to func. Meant for cases calling gdal_warp_wrapper.\nFalse\n\n\nreturn_results\nbool\nWhether to return results, which would be a Dataset or GDALDataset (the latter if gdal_warp_wrapper is used).\nFalse\n\n\n**kwargs\n\nOther parameters passed to func call.\n{}\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nEither a Path to generated file or converted xarray object.\n\n\n\n\n\n\n\n\nclim_recal.utils.xarray.cftime360_to_date(cf_360)\nConvert a Datetime360Day into a date.\n\n\n&gt;&gt;&gt; cftime360_to_date(Datetime360Day(1980, 1, 1))\ndatetime.date(1980, 1, 1)\n\n\n\n\nclim_recal.utils.xarray.cftime_range_gen(time_data_array, **kwargs)\nConvert a banded time index a banded standard (Gregorian).\n\n\n\nclim_recal.utils.xarray.check_xarray_path_and_var_name(xr_time_series, variable_name, ignore_warnings=True)\nCheck and return a T_Dataset instances and included variable name.\n\n\n\nclim_recal.utils.xarray.convert_xr_calendar(xr_time_series, align_on=DEFAULT_CALENDAR_ALIGN, calendar=CFCalendarSTANDARD, use_cftime=False, missing_value=np.nan, interpolate_na=False, ensure_output_type_is_dataset=False, interpolate_method=DEFAULT_INTERPOLATION_METHOD, keep_crs=True, keep_attrs=True, limit=1, engine=NETCDF4_XARRAY_ENGINE, check_cftime_cols=None, cftime_range_gen_kwargs=None)\nConvert cpm 360 day time series to a standard 365/366 day time series.\n\n\nShort time examples (like 2 skipped out of 8 days) raises: ValueError(\"date_range_like was unable to generate a range as the source frequency was not inferable.\")\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nxr_time_series\nT_DataArray | T_Dataset | PathLike\nA DataArray or Dataset to convert to calendar time.\nrequired\n\n\nalign_on\nConvertCalendarAlignOptions\nWhether and how to align calendar types.\nDEFAULT_CALENDAR_ALIGN\n\n\ncalendar\nCFCalendar\nType of calendar to convert xr_time_series to.\nCFCalendarSTANDARD\n\n\nuse_cftime\nbool\nWhether to enforce cftime vs datetime64 time format.\nFalse\n\n\nmissing_value\nAny | None\nMissing value to populate missing date interpolations with.\nnp.nan\n\n\nkeep_crs\nbool\nReapply initial Coordinate Reference System (CRS) after time projection.\nTrue\n\n\ninterpolate_na\nbool\nWhether to apply temporal interpolation for missing values.\nFalse\n\n\ninterpolate_method\nInterpOptions\nWhich InterpOptions method to apply if interpolate_na is True.\nDEFAULT_INTERPOLATION_METHOD\n\n\nkeep_attrs\nbool\nWhether to keep all attributes on after interpolate_na\nTrue\n\n\nlimit\nint\nLimit of number of continuous missing day values allowed in interpolate_na.\n1\n\n\nengine\nXArrayEngineType\nWhich XArrayEngineType to use in parsing files and operations.\nNETCDF4_XARRAY_ENGINE\n\n\nextrapolate_fill_value\n\nIf True, then pass fill_value=extrapolate. See: * https://docs.xarray.dev/en/stable/generated/xarray.Dataset.interpolate_na.html * https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d\nrequired\n\n\ncheck_cftime_cols\ntuple[str] | None\nColumns to check cftime format on\nNone\n\n\ncftime_range_gen_kwargs\ndict[str, Any] | None\nAny kwargs to pass to cftime_range_gen\nNone\n\n\n**kwargs\n\nAny additional parameters to pass to interpolate_na.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nValueError\nLikely from xarray calling date_range_like.\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nT_DataArrayOrSet\nConverted xr_time_series to specified calendar with optional interpolation.\n\n\n\n\n\n\nCertain values may fail to interpolate in cases of 360 -&gt; 365/366 (Gregorian) calendar. Examples include projecting CPM data, which is able to fill in measurement values (e.g. tasmax) but the year and month_number variables have nan values",
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       "python",
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-    "objectID": "docs/reference/clim_recal.resample.html",
-    "href": "docs/reference/clim_recal.resample.html",
-    "title": "1 clim_recal.resample",
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+    "title": "1 clim_recal.pipeline",
     "section": "",
-    "text": "clim_recal.resample\nResample UKHADS data and UKCP18 data.\n\nUKCP18 is resampled temporally from a 360 day calendar to a standard (365/366 day) calendar and projected to British National Grid (BNG) EPSG:27700 from its original rotated polar grid.\nUKHADS is resampled spatially from 1km to 2.2km in BNG aligned with the projected UKCP18\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nCPMResampler\nCPM specific changes to HADsResampler.\n\n\nCPMResamplerManager\nClass to manage processing CPM resampling.\n\n\nHADsResampler\nManage resampling HADs datafiles for modelling.\n\n\nHADsResamplerManager\nClass to manage processing HADs resampling.\n\n\nResamplerBase\nBase class to inherit for HADs and CPM.\n\n\nResamplerManagerBase\nBase class to inherit for HADs and CPM resampler managers.\n\n\n\n\n\nclim_recal.resample.CPMResampler(self, *, input_path=RAW_CPM_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', prior_time_series=None)\nCPM specific changes to HADsResampler.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_path\nPathLike | None\nPath to CPM files to process.\n\n\noutput\n\nPath to save processed CPM files.\n\n\ninput_files\n\nPath or Paths of NCF files to reproject.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\nresampling_func\n\nFunction to call on self.input_files.\n\n\ncrop\n\nPath or file to spatially crop input_files with.\n\n\nfinal_crs\n\nCoordinate Reference System (CRS) to return final format in.\n\n\ninput_file_extension\n\nFile extensions to glob input_files with.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_cpm_output_path: Path = getfixture(\n...         'resample_test_cpm_output_path')\n&gt;&gt;&gt; cpm_resampler: CPMResampler = CPMResampler(\n...     input_path=RAW_CPM_TASMAX_PATH,\n...     output_path=resample_test_cpm_output_path,\n... )\n&gt;&gt;&gt; cpm_resampler\n&lt;CPMResampler(count=..., max_count=...,...\n    ...input_path='.../tasmax/01/latest',...\n    ...output_path='.../test-run-results_..._.../cpm')&gt;\n&gt;&gt;&gt; pprint(cpm_resampler.input_files)\n(...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_19801201-19811130.nc'),\n ...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_19811201-19821130.nc'),\n ...\n ...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_20791201-20801130.nc'))\n\n\n\n\nclim_recal.resample.CPMResamplerManager(self, *, input_paths=RAW_CPM_PATH, output_paths=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=CPM_SUB_PATH, start_index=0, stop_index=None, start_date=CPM_START_DATE, end_date=CPM_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=CPMResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, runs=RunOptions.preferred())\nClass to manage processing CPM resampling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to CPM files to process. If Path, will be propegated with files matching\n\n\noutput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to to save processed CPM files to. If Path will be propagated to match input_paths.\n\n\nvariables\n\nWhich VariableOptions to include.\n\n\nruns\nSequence[RunOptions | str]\nWhich RunOptions to include.\n\n\ncrop_regions\n\nRegionOptions (like Manchester, Scotland etc.) to crop results to.\n\n\ncrop_paths\n\nWhere to save region crop files.\n\n\nsub_path\nPath\nPath to include at the stem of input_paths.\n\n\nstart_index\n\nIndex to begin iterating input files for resampling or cropping.\n\n\nstop_index\n\nIndex to to run from start_index to when resampling or cropping. If None, iterate full list of paths.\n\n\nstart_date\ndate\nNot yet implemented, but in future from what date to generate start index from.\n\n\nend_date\ndate\nNot yet implemented, but in future from what date to generate stop index from.\n\n\nconfigs\nlist[CPMResampler]\nList of HADsResampler instances to iterate resampling or cropping.\n\n\nconfig_default_kwargs\n\nParameters passed to all running self.configs.\n\n\ncalc_class\ntype[CPMResampler]\nclass to construct all self.configs instances with.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_cpm_output_path: Path = getfixture(\n...         'resample_test_cpm_output_path')\n&gt;&gt;&gt; cpm_resampler_manager: CPMResamplerManager = CPMResamplerManager(\n...     stop_index=9,\n...     output_paths=resample_test_cpm_output_path,\n...     )\n&gt;&gt;&gt; cpm_resampler_manager\n&lt;CPMResamplerManager(variables_count=1, runs_count=4,\n                     input_paths_count=4)&gt;\n&gt;&gt;&gt; configs: tuple[CPMResampler, ...] = tuple(\n...     cpm_resampler_manager.yield_configs())\n&gt;&gt;&gt; pprint(configs)\n(&lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/05/latest',\n               output_path='.../cpm/tasmax/05')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/06/latest',\n               output_path='.../cpm/tasmax/06')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/07/latest',\n               output_path='.../cpm/tasmax/07')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/08/latest',\n               output_path='.../cpm/tasmax/08')&gt;)\n\n\n\n\nclim_recal.resample.HADsResampler(self, *, input_path=RAW_HADS_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', input_file_x_column_name=HADS_XDIM, input_file_y_column_name=HADS_YDIM, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)\nManage resampling HADs datafiles for modelling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_path\nPathLike | None\nPath to HADs files to process.\n\n\noutput\n\nPath to save processed HADS files.\n\n\ninput_files\nIterable[PathLike] | None\nPath or Paths of NCF files to resample.\n\n\nresampling_func\n\nFunction to call on self.input_files.\n\n\ncrop\n\nPath or file to spatially crop input_files with.\n\n\nfinal_crs\nstr\nCoordinate Reference System (CRS) to return final format in.\n\n\ninput_file_x_column_name\nstr\nColumn name in input_files or input for x coordinates.\n\n\ninput_file_y_column_name\nstr\nColumn name in input_files or input for y coordinates.\n\n\ninput_file_extension\nNETCDF_OR_TIF\nFile extensions to glob input_files with.\n\n\nstart_index\n\nFirst index of file to iterate processing from.\n\n\nstop_index\n\nLast index of files to iterate processing from as a count from start_index. If None, this will simply iterate over all available files.\n\n\ncpm_for_coord_alignment\nT_Dataset | PathLike | None\nCPM Path or Dataset to match alignment with.\n\n\ncpm_for_coord_alignment_path_converted\nbool\nWhether a Path passed to cpm_for_coord_alignment should be processed.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_hads_output_path: Path = getfixture(\n...         'resample_test_hads_output_path')\n&gt;&gt;&gt; hads_resampler: HADsResampler = HADsResampler(\n...     output_path=resample_test_hads_output_path,\n... )\n&gt;&gt;&gt; hads_resampler\n&lt;HADsResampler(...count=504,...\n    ...input_path='.../tasmax/day',...\n    ...output_path='...run-results_..._.../hads')&gt;\n&gt;&gt;&gt; pprint(hads_resampler.input_files)\n(...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_19800101-19800131.nc'),\n ...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_19800201-19800229.nc'),\n ...,\n ...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_20211201-20211231.nc'))\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nset_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\n\n\nclim_recal.resample.HADsResampler.set_cpm_for_coord_alignment()\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\n\n\nclim_recal.resample.HADsResamplerManager(self, *, input_paths=RAW_HADS_PATH, output_paths=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=HADS_SUB_PATH, start_index=0, stop_index=None, start_date=HADS_START_DATE, end_date=HADS_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=HADsResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)\nClass to manage processing HADs resampling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to CPM files to process. If Path, will be propegated with files matching\n\n\noutput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to to save processed CPM files to. If Path will be propagated to match input_paths.\n\n\nvariables\n\nWhich VariableOptions to include.\n\n\ncrop_regions\n\nRegionOptions (like Manchester, Scotland etc.) to crop results to.\n\n\ncrop_paths\n\nWhere to save region crop files.\n\n\nsub_path\nPath\nPath to include at the stem of input_paths.\n\n\nstart_index\n\nIndex to begin iterating input files for resampling or cropping.\n\n\nstop_index\n\nIndex to to run from start_index to when resampling or cropping. If None, iterate full list of paths.\n\n\nstart_date\ndate\nNot yet implemented, but in future from what date to generate start index from.\n\n\nend_date\ndate\nNot yet implemented, but in future from what date to generate stop index from.\n\n\nconfigs\nlist[HADsResampler]\nList of HADsResampler instances to iterate resampling or cropping.\n\n\nconfig_default_kwargs\ndict[str, Any]\nParameters passed to all running self.configs.\n\n\ncalc_class\ntype[HADsResampler]\nclass to construct all self.configs instances with.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\ncpm_for_coord_alignment\nT_Dataset | PathLike\nCPM Path or Dataset to match alignment with.\n\n\ncpm_for_coord_alignment_path_converted\nbool\nWhether a Path passed to cpm_for_coord_alignment should be processed.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_hads_output_path: Path = getfixture(\n...         'resample_test_hads_output_path')\n&gt;&gt;&gt; hads_resampler_manager: HADsResamplerManager = HADsResamplerManager(\n...     variables=VariableOptions.all(),\n...     output_paths=resample_test_hads_output_path,\n...     )\n&gt;&gt;&gt; hads_resampler_manager\n&lt;HADsResamplerManager(variables_count=3, input_paths_count=3)&gt;\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nset_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\nyield_configs\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.resample.HADsResamplerManager.set_cpm_for_coord_alignment()\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\nclim_recal.resample.HADsResamplerManager.yield_configs()\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.resample.ResamplerBase(self, *, input_path=Path(), output_path=RESAMPLE_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection')\nBase class to inherit for HADs and CPM.\n\n\n\n\n\nName\nDescription\n\n\n\n\nmax_count\nMaximum length of self.input_files ignoring start_index and start_index.\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nexecute\nRun all steps for processing\n\n\nset_input_files\nReplace self.input and process self.input_files.\n\n\n\n\n\nclim_recal.resample.ResamplerBase.execute(**kwargs)\nRun all steps for processing\n\n\n\nclim_recal.resample.ResamplerBase.set_input_files(new_input_path=None)\nReplace self.input and process self.input_files.\n\n\n\n\n\nclim_recal.resample.ResamplerManagerBase(self, *, input_paths=Path(), output_paths=Path(), variables=(VariableOptions.default()), sub_path=Path(), start_index=0, stop_index=None, start_date=None, end_date=None, configs=list(), config_default_kwargs=dict(), calc_class=None, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False)\nBase class to inherit for HADs and CPM resampler managers.\n\n\n\n\n\nName\nDescription\n\n\n\n\ninput_folder\nReturn self._input_path set by set_input_paths().\n\n\nmax_count\nMaximum length of self.input_files ignoring start_index and start_index.\n\n\noutput_folder\nReturn self._output_path set by set_output_paths().\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nVarirableInBaseImportPathError\nChecking import path validity for self.variables.\n\n\n\n\n\nclim_recal.resample.ResamplerManagerBase.VarirableInBaseImportPathError()\nChecking import path validity for self.variables.\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\ncheck_paths\nCheck and set input, resample and crop paths.\n\n\nexecute_configs\nRun all resampler configurations\n\n\nset_input_paths\nPropagate self.input_paths if needed.\n\n\nset_output_paths\nPropagate self.output_paths if needed.\n\n\nyield_configs\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.resample.ResamplerManagerBase.check_paths(run_set_data_paths=True)\nCheck and set input, resample and crop paths.\n\n\n\nclim_recal.resample.ResamplerManagerBase.execute_configs(multiprocess=False, cpus=None, return_resamplers=False, return_path=True, **kwargs)\nRun all resampler configurations\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nmultiprocess\nbool\nIf True run parameters in resample_configs with multiprocess_execute.\nFalse\n\n\ncpus\nint | None\nNumber of cpus to pass to multiprocess_execute.\nNone\n\n\nreturn_resamplers\nbool\nReturn instances of generated HADsResampler or CPMResampler, or return the results of each execute call.\nFalse\n\n\nreturn_path\nbool\nReturn Path to results object if True, else resampled Dataset.\nTrue\n\n\nkwargs\n\nParameters to path to sampler execute calls.\n{}\n\n\n\n\n\n\n\nclim_recal.resample.ResamplerManagerBase.set_input_paths()\nPropagate self.input_paths if needed.\n\n\n\nclim_recal.resample.ResamplerManagerBase.set_output_paths()\nPropagate self.output_paths if needed.\n\n\n\nclim_recal.resample.ResamplerManagerBase.yield_configs()\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nreproject_2_2km_file_name\nReturn tweaked path to indicate standard day projection.\n\n\nreproject_standard_calendar_file_name\nReturn tweaked path to indicate standard day projection.\n\n\n\n\n\nclim_recal.resample.reproject_2_2km_file_name(path)\nReturn tweaked path to indicate standard day projection.\n\n\n\nclim_recal.resample.reproject_standard_calendar_file_name(path)\nReturn tweaked path to indicate standard day projection.",
+    "text": "clim_recal.pipeline\nWrappers to automate the entire pipeline.\nFollowing Andy’s very helpful excel file, this manages a reproduction of all steps of the debiasing pipeline.",
     "crumbs": [
       "python",
       "Reference",
-      "Data Resampling"
+      "Pipeline"
     ]
   },
   {
-    "objectID": "docs/reference/clim_recal.resample.html#classes",
-    "href": "docs/reference/clim_recal.resample.html#classes",
-    "title": "1 clim_recal.resample",
-    "section": "",
-    "text": "Name\nDescription\n\n\n\n\nCPMResampler\nCPM specific changes to HADsResampler.\n\n\nCPMResamplerManager\nClass to manage processing CPM resampling.\n\n\nHADsResampler\nManage resampling HADs datafiles for modelling.\n\n\nHADsResamplerManager\nClass to manage processing HADs resampling.\n\n\nResamplerBase\nBase class to inherit for HADs and CPM.\n\n\nResamplerManagerBase\nBase class to inherit for HADs and CPM resampler managers.\n\n\n\n\n\nclim_recal.resample.CPMResampler(self, *, input_path=RAW_CPM_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', prior_time_series=None)\nCPM specific changes to HADsResampler.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_path\nPathLike | None\nPath to CPM files to process.\n\n\noutput\n\nPath to save processed CPM files.\n\n\ninput_files\n\nPath or Paths of NCF files to reproject.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\nresampling_func\n\nFunction to call on self.input_files.\n\n\ncrop\n\nPath or file to spatially crop input_files with.\n\n\nfinal_crs\n\nCoordinate Reference System (CRS) to return final format in.\n\n\ninput_file_extension\n\nFile extensions to glob input_files with.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_cpm_output_path: Path = getfixture(\n...         'resample_test_cpm_output_path')\n&gt;&gt;&gt; cpm_resampler: CPMResampler = CPMResampler(\n...     input_path=RAW_CPM_TASMAX_PATH,\n...     output_path=resample_test_cpm_output_path,\n... )\n&gt;&gt;&gt; cpm_resampler\n&lt;CPMResampler(count=..., max_count=...,...\n    ...input_path='.../tasmax/01/latest',...\n    ...output_path='.../test-run-results_..._.../cpm')&gt;\n&gt;&gt;&gt; pprint(cpm_resampler.input_files)\n(...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_19801201-19811130.nc'),\n ...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_19811201-19821130.nc'),\n ...\n ...Path('.../tasmax/01/latest/tasmax_...-cpm_uk_2.2km_01_day_20791201-20801130.nc'))\n\n\n\n\nclim_recal.resample.CPMResamplerManager(self, *, input_paths=RAW_CPM_PATH, output_paths=RESAMPLE_OUTPUT_PATH / CPM_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=CPM_SUB_PATH, start_index=0, stop_index=None, start_date=CPM_START_DATE, end_date=CPM_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=CPMResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, runs=RunOptions.preferred())\nClass to manage processing CPM resampling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to CPM files to process. If Path, will be propegated with files matching\n\n\noutput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to to save processed CPM files to. If Path will be propagated to match input_paths.\n\n\nvariables\n\nWhich VariableOptions to include.\n\n\nruns\nSequence[RunOptions | str]\nWhich RunOptions to include.\n\n\ncrop_regions\n\nRegionOptions (like Manchester, Scotland etc.) to crop results to.\n\n\ncrop_paths\n\nWhere to save region crop files.\n\n\nsub_path\nPath\nPath to include at the stem of input_paths.\n\n\nstart_index\n\nIndex to begin iterating input files for resampling or cropping.\n\n\nstop_index\n\nIndex to to run from start_index to when resampling or cropping. If None, iterate full list of paths.\n\n\nstart_date\ndate\nNot yet implemented, but in future from what date to generate start index from.\n\n\nend_date\ndate\nNot yet implemented, but in future from what date to generate stop index from.\n\n\nconfigs\nlist[CPMResampler]\nList of HADsResampler instances to iterate resampling or cropping.\n\n\nconfig_default_kwargs\n\nParameters passed to all running self.configs.\n\n\ncalc_class\ntype[CPMResampler]\nclass to construct all self.configs instances with.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_cpm_output_path: Path = getfixture(\n...         'resample_test_cpm_output_path')\n&gt;&gt;&gt; cpm_resampler_manager: CPMResamplerManager = CPMResamplerManager(\n...     stop_index=9,\n...     output_paths=resample_test_cpm_output_path,\n...     )\n&gt;&gt;&gt; cpm_resampler_manager\n&lt;CPMResamplerManager(variables_count=1, runs_count=4,\n                     input_paths_count=4)&gt;\n&gt;&gt;&gt; configs: tuple[CPMResampler, ...] = tuple(\n...     cpm_resampler_manager.yield_configs())\n&gt;&gt;&gt; pprint(configs)\n(&lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/05/latest',\n               output_path='.../cpm/tasmax/05')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/06/latest',\n               output_path='.../cpm/tasmax/06')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/07/latest',\n               output_path='.../cpm/tasmax/07')&gt;,\n &lt;CPMResampler(count=9, max_count=100,\n               input_path='.../tasmax/08/latest',\n               output_path='.../cpm/tasmax/08')&gt;)\n\n\n\n\nclim_recal.resample.HADsResampler(self, *, input_path=RAW_HADS_TASMAX_PATH, output_path=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection', input_file_x_column_name=HADS_XDIM, input_file_y_column_name=HADS_YDIM, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)\nManage resampling HADs datafiles for modelling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_path\nPathLike | None\nPath to HADs files to process.\n\n\noutput\n\nPath to save processed HADS files.\n\n\ninput_files\nIterable[PathLike] | None\nPath or Paths of NCF files to resample.\n\n\nresampling_func\n\nFunction to call on self.input_files.\n\n\ncrop\n\nPath or file to spatially crop input_files with.\n\n\nfinal_crs\nstr\nCoordinate Reference System (CRS) to return final format in.\n\n\ninput_file_x_column_name\nstr\nColumn name in input_files or input for x coordinates.\n\n\ninput_file_y_column_name\nstr\nColumn name in input_files or input for y coordinates.\n\n\ninput_file_extension\nNETCDF_OR_TIF\nFile extensions to glob input_files with.\n\n\nstart_index\n\nFirst index of file to iterate processing from.\n\n\nstop_index\n\nLast index of files to iterate processing from as a count from start_index. If None, this will simply iterate over all available files.\n\n\ncpm_for_coord_alignment\nT_Dataset | PathLike | None\nCPM Path or Dataset to match alignment with.\n\n\ncpm_for_coord_alignment_path_converted\nbool\nWhether a Path passed to cpm_for_coord_alignment should be processed.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_hads_output_path: Path = getfixture(\n...         'resample_test_hads_output_path')\n&gt;&gt;&gt; hads_resampler: HADsResampler = HADsResampler(\n...     output_path=resample_test_hads_output_path,\n... )\n&gt;&gt;&gt; hads_resampler\n&lt;HADsResampler(...count=504,...\n    ...input_path='.../tasmax/day',...\n    ...output_path='...run-results_..._.../hads')&gt;\n&gt;&gt;&gt; pprint(hads_resampler.input_files)\n(...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_19800101-19800131.nc'),\n ...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_19800201-19800229.nc'),\n ...,\n ...Path('.../tasmax/day/tasmax_hadukgrid_uk_1km_day_20211201-20211231.nc'))\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nset_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\n\n\nclim_recal.resample.HADsResampler.set_cpm_for_coord_alignment()\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\n\n\nclim_recal.resample.HADsResamplerManager(self, *, input_paths=RAW_HADS_PATH, output_paths=RESAMPLE_OUTPUT_PATH / HADS_OUTPUT_PATH, variables=(VariableOptions.default()), sub_path=HADS_SUB_PATH, start_index=0, stop_index=None, start_date=HADS_START_DATE, end_date=HADS_END_DATE, configs=list(), config_default_kwargs=dict(), calc_class=HADsResampler, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False, cpm_for_coord_alignment=RAW_CPM_TASMAX_PATH, cpm_for_coord_alignment_path_converted=False)\nClass to manage processing HADs resampling.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ninput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to CPM files to process. If Path, will be propegated with files matching\n\n\noutput_paths\nPathLike | Sequence[PathLike]\nPath or Paths to to save processed CPM files to. If Path will be propagated to match input_paths.\n\n\nvariables\n\nWhich VariableOptions to include.\n\n\ncrop_regions\n\nRegionOptions (like Manchester, Scotland etc.) to crop results to.\n\n\ncrop_paths\n\nWhere to save region crop files.\n\n\nsub_path\nPath\nPath to include at the stem of input_paths.\n\n\nstart_index\n\nIndex to begin iterating input files for resampling or cropping.\n\n\nstop_index\n\nIndex to to run from start_index to when resampling or cropping. If None, iterate full list of paths.\n\n\nstart_date\ndate\nNot yet implemented, but in future from what date to generate start index from.\n\n\nend_date\ndate\nNot yet implemented, but in future from what date to generate stop index from.\n\n\nconfigs\nlist[HADsResampler]\nList of HADsResampler instances to iterate resampling or cropping.\n\n\nconfig_default_kwargs\ndict[str, Any]\nParameters passed to all running self.configs.\n\n\ncalc_class\ntype[HADsResampler]\nclass to construct all self.configs instances with.\n\n\ncpus\n\nNumber of cpu cores to use during multiprocessing.\n\n\ncpm_for_coord_alignment\nT_Dataset | PathLike\nCPM Path or Dataset to match alignment with.\n\n\ncpm_for_coord_alignment_path_converted\nbool\nWhether a Path passed to cpm_for_coord_alignment should be processed.\n\n\n\n\n\n\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; resample_test_hads_output_path: Path = getfixture(\n...         'resample_test_hads_output_path')\n&gt;&gt;&gt; hads_resampler_manager: HADsResamplerManager = HADsResamplerManager(\n...     variables=VariableOptions.all(),\n...     output_paths=resample_test_hads_output_path,\n...     )\n&gt;&gt;&gt; hads_resampler_manager\n&lt;HADsResamplerManager(variables_count=3, input_paths_count=3)&gt;\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nset_cpm_for_coord_alignment\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\nyield_configs\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.resample.HADsResamplerManager.set_cpm_for_coord_alignment()\nCheck if cpm_for_coord_alignment is a Dataset, process if a Path.\n\n\n\nclim_recal.resample.HADsResamplerManager.yield_configs()\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.resample.ResamplerBase(self, *, input_path=Path(), output_path=RESAMPLE_OUTPUT_PATH, variable_name=VariableOptions.default(), input_files=None, cpus=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, input_file_extension=NETCDF_EXTENSION_STR, export_file_extension=NETCDF_EXTENSION_STR, start_index=0, stop_index=None, _result_paths=dict(), _iter_calc_method_name='range_to_reprojection')\nBase class to inherit for HADs and CPM.\n\n\n\n\n\nName\nDescription\n\n\n\n\nmax_count\nMaximum length of self.input_files ignoring start_index and start_index.\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nexecute\nRun all steps for processing\n\n\nset_input_files\nReplace self.input and process self.input_files.\n\n\n\n\n\nclim_recal.resample.ResamplerBase.execute(**kwargs)\nRun all steps for processing\n\n\n\nclim_recal.resample.ResamplerBase.set_input_files(new_input_path=None)\nReplace self.input and process self.input_files.\n\n\n\n\n\nclim_recal.resample.ResamplerManagerBase(self, *, input_paths=Path(), output_paths=Path(), variables=(VariableOptions.default()), sub_path=Path(), start_index=0, stop_index=None, start_date=None, end_date=None, configs=list(), config_default_kwargs=dict(), calc_class=None, cpus=None, _input_path_dict=dict(), _output_path_dict=dict(), _strict_fail_if_var_in_input_path=True, _allow_check_fail=False)\nBase class to inherit for HADs and CPM resampler managers.\n\n\n\n\n\nName\nDescription\n\n\n\n\ninput_folder\nReturn self._input_path set by set_input_paths().\n\n\nmax_count\nMaximum length of self.input_files ignoring start_index and start_index.\n\n\noutput_folder\nReturn self._output_path set by set_output_paths().\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nVarirableInBaseImportPathError\nChecking import path validity for self.variables.\n\n\n\n\n\nclim_recal.resample.ResamplerManagerBase.VarirableInBaseImportPathError()\nChecking import path validity for self.variables.\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\ncheck_paths\nCheck and set input, resample and crop paths.\n\n\nexecute_configs\nRun all resampler configurations\n\n\nset_input_paths\nPropagate self.input_paths if needed.\n\n\nset_output_paths\nPropagate self.output_paths if needed.\n\n\nyield_configs\nGenerate a CPMResampler or HADsResampler for self.input_paths.\n\n\n\n\n\nclim_recal.resample.ResamplerManagerBase.check_paths(run_set_data_paths=True)\nCheck and set input, resample and crop paths.\n\n\n\nclim_recal.resample.ResamplerManagerBase.execute_configs(multiprocess=False, cpus=None, return_resamplers=False, return_path=True, **kwargs)\nRun all resampler configurations\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nmultiprocess\nbool\nIf True run parameters in resample_configs with multiprocess_execute.\nFalse\n\n\ncpus\nint | None\nNumber of cpus to pass to multiprocess_execute.\nNone\n\n\nreturn_resamplers\nbool\nReturn instances of generated HADsResampler or CPMResampler, or return the results of each execute call.\nFalse\n\n\nreturn_path\nbool\nReturn Path to results object if True, else resampled Dataset.\nTrue\n\n\nkwargs\n\nParameters to path to sampler execute calls.\n{}\n\n\n\n\n\n\n\nclim_recal.resample.ResamplerManagerBase.set_input_paths()\nPropagate self.input_paths if needed.\n\n\n\nclim_recal.resample.ResamplerManagerBase.set_output_paths()\nPropagate self.output_paths if needed.\n\n\n\nclim_recal.resample.ResamplerManagerBase.yield_configs()\nGenerate a CPMResampler or HADsResampler for self.input_paths.",
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+    "text": "4.1 Todo\n\nUpdate the example here\nRemove out of date elements\nHardcode the process below\nRefactor to facilitate testing\nEnsure HADs still works\nAdd function for UKCP\nCheck convert_xr_calendar doctest examples\nFix order of UKCP changes\n\nTo run this step in the pipeline the following should work for the default combindations of variables: tasmax, tasmin, and rainfall and the default set of runs: 05, 06, 07 and 08, assuming the necessary data is mounted.\nIf installed via pipx/pip etc. on your local path (or within Docker) clim-recal should be a command line function\n$ clim-recal --all-variables --default-runs --output-path /where/results/should/be/written\nclim-recal pipeline configurations:\n&lt;ClimRecalConfig(variables_count=3, runs_count=4, regions_count=1, methods_count=1,\n                 cpm_folders_count=12, hads_folders_count=3, resample_start_index=0,\n                 resample_stop_index=None, cpus=2)&gt;\n&lt;CPMResamplerManager(variables_count=3, runs_count=4, input_paths_count=12)&gt;\n&lt;HADsResamplerManager(variables_count=3, input_paths_count=3)&gt;\nOtherwise, you can install locally and either run via pdm from the python folder\n$ cd clim-recal/python\n$ pdm run clim-recal --all-variables --default-runs --output-path /where/results/should/be/written\n# Skipping output for brevity\nOr within an ipython or jupyter instance (truncated below for brevity)\n&gt;&gt;&gt; from clim_recal.pipeline import main\n&gt;&gt;&gt; main(all_variables=True, default_runs=True)  # doctest: +SKIP\nclim-recal pipeline configurations:\n&lt;ClimRecalConfig(variables_count=3, runs_count=4, ...&gt;\nRegardless of your route, once you’re confident with the configuration, add the --execute parameter to run. For example, assuming a local install:\n$ clim-recal --all-variables --default-runs --output-path /where/results/should/be/written --execute\nclim-recal pipeline configurations:\n&lt;ClimRecalConfig(variables_count=3, runs_count=4, regions_count=1, methods_count=1,\n                 cpm_folders_count=12, hads_folders_count=3, resample_start_index=0,\n                 resample_stop_index=None, cpus=2)&gt;\n&lt;CPMResamplerManager(variables_count=3, runs_count=4, input_paths_count=12)&gt;\n&lt;HADsResamplerManager(variables_count=3, input_paths_count=3)&gt;\nRunning CPM Standard Calendar projection...\n&lt;CPMResampler(count=100, max_count=100,\n              input_path='/mnt/vmfileshare/ClimateData/Raw/UKCP2.2/tasmax/05/latest',\n              output_path='/mnt/vmfileshare/ClimateData/CPM-365/test-run-3-may/resample/\n              cpm/tasmax/05/latest')&gt;\n 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100/100  [ 0:38:27 &lt; 0:00:00 , 0 it/s ]\n  87% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 87/100  [ 0:17:42 &lt; 0:03:07 , 0 it/s ]",
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-    "text": "Name\nDescription\n\n\n\n\nreproject_2_2km_file_name\nReturn tweaked path to indicate standard day projection.\n\n\nreproject_standard_calendar_file_name\nReturn tweaked path to indicate standard day projection.\n\n\n\n\n\nclim_recal.resample.reproject_2_2km_file_name(path)\nReturn tweaked path to indicate standard day projection.\n\n\n\nclim_recal.resample.reproject_standard_calendar_file_name(path)\nReturn tweaked path to indicate standard day projection.",
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+    "text": "8.1 Functions\n\n\n\nName\nDescription\n\n\n\n\nmain\nRun all elements of the pipeline.\n\n\n\n\n8.1.1 main\nclim_recal.pipeline.main(execute=False, hads_input_path=RAW_HADS_PATH, cpm_input_path=RAW_CPM_PATH, output_path=DEFAULT_OUTPUT_PATH, variables=(VariableOptions.default()), regions=(RegionOptions.default()), runs=(RunOptions.default()), methods=(MethodOptions.default()), all_variables=False, all_regions=False, default_runs=False, all_runs=False, all_methods=False, hads_projection=False, cpm_projection=False, crop_hads=True, crop_cpm=True, cpus=None, multiprocess=False, resample_start_index=0, resample_stop_index=None, crop_start_index=0, crop_stop_index=None, total=None, print_range_length=5, **kwargs)\nRun all elements of the pipeline.\n\n8.1.1.1 Parameters\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nvariables\nSequence[VariableOptions | str]\nVariables to include in the model, eg. tasmax, tasmin.\n(VariableOptions.default())\n\n\nruns\nSequence[RunOptions | str]\nWhich model runs to include, eg. “01”, “08”, “11”.\n(RunOptions.default())\n\n\nregions\nSequence[RegionOptions | str] | None\nWhich regions to crop data to. Future plans facilitate skipping to run for entire UK.\n(RegionOptions.default())\n\n\nmethods\nSequence[MethodOptions | str]\nWhich debiasing methods to apply.\n(MethodOptions.default())\n\n\noutput_path\nPathLike\nPath to save intermediate and final results to.\nDEFAULT_OUTPUT_PATH\n\n\ncpus\nint | None\nNumber of cpus to use when multiprocessing.\nNone\n\n\nmultiprocess\nbool\nWhether to use multiprocessing.\nFalse\n\n\nresample_start_index\nint\nIndex to start all iterations from.\n0\n\n\nresample_stop_index\nint | None\nNumber of files from resample_start_index to resample.\nNone\n\n\ncrop_start_index\nint\nIndex to start all crop iterations from.\n0\n\n\ncrop_stop_index\nint | None\nNumber of files iterating from crop_start_index to crop.\nNone\n\n\ntotal\nint | None\nTotal number of records to iterate over. 0 and None indicate all values from resample_start_index.\nNone\n\n\n**kwargs\n\nAdditional parameters to pass to a ClimRecalConfig.\n{}\n\n\n\n\n\n8.1.1.2 Notes\nThe default parameters here are meant to reflect the entire workflow process to ease reproducibility.\n\n\n8.1.1.3 Examples\nNote the _allow_check_fail parameters support running the examples without data mounted from a server.\n&gt;&gt;&gt; if not is_data_mounted:\n...     pytest.skip(mount_doctest_skip_message)\n&gt;&gt;&gt; main(variables=(\"rainfall\", \"tasmin\"),\n...      output_path=test_runs_output_path,\n... )\n'set_cpm_for_coord_alignment' for 'HADs' not speficied.\nDefaulting to 'self.cpm_input_path': '...'\nclim-recal pipeline configurations:\n&lt;ClimRecalConfig(variables_count=2, runs_count=1,\n                 regions_count=1, methods_count=1,\n                 cpm_folders_count=2, hads_folders_count=2,\n                 resample_start_index=0, resample_stop_index=None,\n                 crop_star_index=0, cpus=...)&gt;\n&lt;CPMResamplerManager(variables_count=2, runs_count=1,\n                     input_paths_count=2)&gt;\n&lt;HADsResamplerManager(variables_count=2, input_paths_count=2)&gt;\nNo steps run. Add '--execute' to run steps.",
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