clean up

docs/pyremo.rst

@@ -7,6 +7,7 @@ Subpackages
 .. toctree::
 
    pyremo.physics
+   pyremo.prsint
    pyremo.tables
 
 Submodules
@@ -92,14 +93,6 @@ pyremo.ftp module
    :undoc-members:
    :show-inheritance:
 
-pyremo.pyremo module
---------------------
-
-.. automodule:: pyremo.pyremo
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
 pyremo.remo\_ds module
 ----------------------
 

notebooks/prsint.ipynb

@@ -2,23 +2,23 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "elementary-argument",
+   "id": "transparent-adolescent",
    "metadata": {},
    "source": [
     "# Pressure interpolation"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "neutral-promise",
+   "id": "particular-nancy",
    "metadata": {},
    "source": [
     "The pressure interpolation is included in pyremo. Historically, REMO has its own interpolator although theoretically the algorithm is similar to what can be found, e.g., in [metpy](https://unidata.github.io/MetPy/latest/examples/sigma_to_pressure_interpolation.html) or [GeoCAT](https://geocat-comp.readthedocs.io/en/latest/user_api/generated/geocat.comp.interp_hybrid_to_pressure.html#geocat.comp.interp_hybrid_to_pressure). However, REMO's algorithm includes a vertical spline fitting and requires temperature, pressure and orography as input. pyremo provides an API based on xarray and also a command line tool that is similar to the former fortran executable. We show an example of how to use the pyremo API here since it gives more control about input and output formats."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "racial-folder",
+   "id": "approved-australian",
    "metadata": {},
    "source": [
     "Let's load an example dataset:"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "straight-rogers",
+   "id": "parental-range",
    "metadata": {},
    "outputs": [
     {
@@ -634,32 +634,21 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "premier-excuse",
+   "cell_type": "markdown",
+   "id": "environmental-activation",
    "metadata": {},
-   "outputs": [],
    "source": [
     "The dataset contains a number of 3D variables on model levels (hybrid sigma) that can be interpolated to pressure levels. We will use the temperature here."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "labeled-ridge",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pyremo.prsint import pressure_interpolation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "regulated-memory",
+   "execution_count": 30,
+   "id": "phantom-event",
    "metadata": {},
    "outputs": [],
    "source": [
+    "from pyremo.prsint import pressure_interpolation\n",
     "# define pressure levels in hPa\n",
     "plev = [100,200,500,850,950]\n",
     "t_plev = pressure_interpolation(ds.T, plev=[200, 400, 500, 800, 950], \n",
@@ -668,7 +657,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "closed-estonia",
+   "id": "exterior-footwear",
    "metadata": {},
    "source": [
     "The output dataset `t_plev` has the same structure and meta information as the input dataset. The algorithm utilizes xarray's [apply_ufunc](https://xarray.pydata.org/en/stable/examples/apply_ufunc_vectorize_1d.html) implementation which is particularly useful if the input dataset has a temporal dimension. The pressure interpolation will then also work with dask's vectorization for distributed computing."
@@ -677,7 +666,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "prepared-paradise",
+   "id": "involved-bermuda",
    "metadata": {},
    "outputs": [
     {