From 0d73c83d7966f2fff154587cc2a58f1929727930 Mon Sep 17 00:00:00 2001
From: bjorn-stevens <64255981+bjorn-stevens@users.noreply.github.com>
Date: Tue, 16 Aug 2022 18:20:53 +0200
Subject: [PATCH] v0.5 renamed and cleaned up functions and vars

Replaced use of es_liq by es_liq_default, likewise for ice to make the
intent clear.

Replaced use of TK and PPa by T and P to ensure consistency.
---
 examples/examples.ipynb           | 195 +++++++++---------------------
 moist_thermodynamics/functions.py | 153 +++++++++++------------
 setup.py                          |   2 +-
 3 files changed, 134 insertions(+), 216 deletions(-)

diff --git a/examples/examples.ipynb b/examples/examples.ipynb
index d019579..77a3abc 100644
--- a/examples/examples.ipynb
+++ b/examples/examples.ipynb
@@ -48,7 +48,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 648x288 with 2 Axes>"
       ]
@@ -61,16 +61,16 @@
    ],
    "source": [
     "Tw = np.arange(273.15, 330.0)\n",
-    "es = svp.liq_tetens(Tw)\n",
+    "es = svp.liq_analytic(Tw)\n",
     "es_def = svp.liq_wagner_pruss(Tw)\n",
     "err1 = (es / es_def - 1.0) * 100.0\n",
     "\n",
     "Tc = np.arange(230.0, 273.15)\n",
-    "es = svp.liq_tetens(Tc)\n",
+    "es = svp.liq_analytic(Tc)\n",
     "es_def = svp.liq_murphy_koop(Tc)\n",
     "err2 = (es / es_def - 1.0) * 100.0\n",
     "\n",
-    "es = svp.ice_tetens(Tc)\n",
+    "es = svp.ice_analytic(Tc)\n",
     "es_def = svp.ice_wagner_etal(Tc)\n",
     "err3 = (es / es_def - 1.0) * 100.0\n",
     "\n",
@@ -550,15 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "129d7bbb-7015-484c-ac3a-97093314bc88",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "id": "5d88d98d-a59e-41fc-a5b6-124a8f97947b",
    "metadata": {},
    "outputs": [
@@ -566,7 +558,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Best fit parameters for liquid: a=17.4143, b=33.6308\n",
+      "Best fit parameters for liquid: a=17.4185, b=33.5714\n",
       "Best fit parameters for ice:    a=22.0422, b=5.0000\n"
      ]
     }
@@ -574,48 +566,41 @@
    "source": [
     "from scipy.optimize import curve_fit\n",
     "\n",
+    "def liq_error(T,a,b):\n",
+    "    return np.abs(svp.tetens(T,a,b)/svp.liq_wagner_pruss(T) -1.)\n",
     "\n",
-    "def liq_error(T, a, b):\n",
-    "    return np.abs(svp.tetens(T, a, b) / svp.liq_wagner_pruss(T) - 1.0)\n",
+    "def ice_error(T,a,b):\n",
+    "    return np.abs(svp.tetens(T,a,b)/svp.ice_wagner_etal(T) -1.)\n",
     "\n",
-    "\n",
-    "def ice_error(T, a, b):\n",
-    "    return np.abs(svp.tetens(T, a, b) / svp.ice_wagner_etal(T) - 1.0)\n",
-    "\n",
-    "\n",
-    "T = np.arange(270.0, 310.0, 0.1)\n",
+    "T = np.arange(270.,310.,0.1)\n",
     "\n",
     "rng = np.random.default_rng()\n",
     "y_noise = 0.001 * rng.normal(size=T.size)\n",
-    "ydata = y_noise\n",
-    "popt, pcov = curve_fit(\n",
-    "    liq_error, T, ydata, bounds=((16.0, 33.0), (19.0, 36.0)), method=\"dogbox\"\n",
-    ")\n",
+    "ydata =  y_noise\n",
+    "popt, pcov = curve_fit(liq_error, T, ydata, bounds = ((16.,33.), (19.,36.)), method='dogbox')\n",
     "a_liq = popt[0]\n",
     "b_liq = popt[1]\n",
-    "print(f\"Best fit parameters for liquid: a={a_liq:.4f}, b={b_liq:.4f}\")\n",
+    "print (f'Best fit parameters for liquid: a={a_liq:.4f}, b={b_liq:.4f}')\n",
     "\n",
-    "T = np.arange(230.0, 260.0, 0.01)\n",
+    "T = np.arange(230.,260.,0.01)\n",
     "rng = np.random.default_rng()\n",
     "y_noise = 0.001 * rng.normal(size=T.size)\n",
     "ydata = y_noise\n",
-    "popt, pcov = curve_fit(\n",
-    "    ice_error, T, ydata, bounds=((20.0, 5.0), (23.0, 8.0)), method=\"dogbox\"\n",
-    ")\n",
+    "popt, pcov = curve_fit(ice_error, T, ydata, bounds = ((20.,5.), (23.,8.)), method='dogbox')\n",
     "a_ice = popt[0]\n",
     "b_ice = popt[1]\n",
-    "print(f\"Best fit parameters for ice:    a={a_ice:.4f}, b={b_ice:.4f}\")"
+    "print (f'Best fit parameters for ice:    a={a_ice:.4f}, b={b_ice:.4f}')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 10,
    "id": "feac77dc-c04e-4fa2-b173-4dd143febcf7",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x216 with 3 Axes>"
       ]
@@ -628,117 +613,47 @@
    ],
    "source": [
     "sns.set_context(\"talk\")\n",
-    "fig, ax = plt.subplots(1, 3, figsize=(12, 3), constrained_layout=True)\n",
-    "\n",
-    "T = np.arange(200.0, 273.16)\n",
-    "es_c1 = (svp.ice_analytic(T) / svp.ice_wagner_etal(T) - 1.0) * 100.0\n",
-    "es_c2 = (svp.ice_analytic(T, ci=1861.0) / svp.ice_wagner_etal(T) - 1.0) * 100.0\n",
-    "es_c3 = (svp.tetens(T, 21.875, 7.66) / svp.ice_wagner_etal(T) - 1.0) * 100.0\n",
-    "es_c4 = (svp.tetens(T, a_ice, b_ice) / svp.ice_wagner_etal(T) - 1.0) * 100.0\n",
-    "\n",
-    "ax[0].plot(T, np.abs(es_c1), c=\"k\", label=\"Romps\")\n",
-    "ax[0].plot(T, np.abs(es_c2), c=\"k\", ls=\"dotted\", label=\"Romps best fit\")\n",
-    "ax[0].plot(T, np.abs(es_c3), c=\"violet\", label=\"Teten-Murray\")\n",
-    "ax[0].plot(T, np.abs(es_c4), c=\"violet\", ls=\"dotted\", label=\"Teten best fit\")\n",
-    "T = np.arange(235.0, 273.16)\n",
-    "es_sc1 = (svp.liq_analytic(T) / svp.liq_murphy_koop(T) - 1.0) * 100.0\n",
-    "es_sc2 = (svp.liq_analytic(T, cl=4119.0) / svp.liq_murphy_koop(T) - 1.0) * 100.0\n",
-    "es_sc3 = (svp.tetens(T, 17.269, 35.86) / svp.liq_murphy_koop(T) - 1.0) * 100.0\n",
-    "es_sc4 = (svp.tetens(T, a_liq, b_liq) / svp.liq_murphy_koop(T) - 1.0) * 100.0\n",
-    "\n",
-    "ax[1].plot(T, np.abs(es_sc1), c=\"k\")\n",
-    "ax[1].plot(T, np.abs(es_sc2), c=\"k\", ls=\"dotted\")\n",
-    "ax[1].plot(T, np.abs(es_sc3), c=\"violet\")\n",
-    "ax[1].plot(T, np.abs(es_sc4), c=\"violet\", ls=\"dotted\")\n",
-    "\n",
-    "T = np.arange(273.0, 330.0)\n",
-    "es_w1 = (svp.liq_analytic(T) / svp.liq_wagner_pruss(T) - 1.0) * 100.0\n",
-    "es_w2 = (svp.liq_analytic(T, cl=4119.0) / svp.liq_wagner_pruss(T) - 1.0) * 100.0\n",
-    "es_w3 = (svp.tetens(T, 17.269, 35.86) / svp.liq_wagner_pruss(T) - 1.0) * 100.0\n",
-    "es_w4 = (svp.tetens(T, a_liq, b_liq) / svp.liq_wagner_pruss(T) - 1.0) * 100.0\n",
-    "\n",
-    "ax[2].plot(T, np.abs(es_w1), c=\"k\", label=\"Romps\")\n",
-    "ax[2].plot(T, np.abs(es_w2), c=\"k\", ls=\"dotted\", label=\"Romps best fit\")\n",
-    "ax[2].plot(T, np.abs(es_w3), c=\"violet\", label=\"Teten-Murray\")\n",
-    "ax[2].plot(T, np.abs(es_w4), c=\"violet\", ls=\"dotted\", label=\"Teten best fit\")\n",
+    "fig, ax = plt.subplots(1,3,figsize=(12, 3), constrained_layout=True)\n",
+    "\n",
+    "T = np.arange(200.,273.16)\n",
+    "es_c1 = (svp.ice_analytic(T)/svp.ice_wagner_etal(T) - 1.)*100.\n",
+    "es_c2 = (svp.ice_analytic(T,cx=1861.)/svp.ice_wagner_etal(T) - 1.)*100.\n",
+    "es_c3 = (svp.tetens(T,21.875,7.66)/svp.ice_wagner_etal(T) - 1.)*100.\n",
+    "es_c4 = (svp.tetens(T,a_ice,b_ice)/svp.ice_wagner_etal(T) - 1.)*100.\n",
+    "\n",
+    "ax[0].plot(T,np.abs(es_c1),c='k',label='Romps')\n",
+    "ax[0].plot(T,np.abs(es_c2),c='k',ls='dotted',label='Romps best fit')\n",
+    "ax[0].plot(T,np.abs(es_c3),c='violet',label='Teten-Murray')\n",
+    "ax[0].plot(T,np.abs(es_c4),c='violet',ls='dotted',label='Teten best fit')\n",
+    "T = np.arange(235.,273.16)\n",
+    "es_sc1 = (svp.liq_analytic(T)/svp.liq_murphy_koop(T) - 1.)*100.\n",
+    "es_sc2 = (svp.liq_analytic(T,cx=4119.)/svp.liq_murphy_koop(T) - 1.)*100.\n",
+    "es_sc3 = (svp.tetens(T,17.269,35.86)/svp.liq_murphy_koop(T) - 1.)*100.\n",
+    "es_sc4 = (svp.tetens(T,a_liq,b_liq)/svp.liq_murphy_koop(T) - 1.)*100.\n",
+    "\n",
+    "ax[1].plot(T,np.abs(es_sc1),c='k')\n",
+    "ax[1].plot(T,np.abs(es_sc2),c='k',ls='dotted')\n",
+    "ax[1].plot(T,np.abs(es_sc3),c='violet')\n",
+    "ax[1].plot(T,np.abs(es_sc4),c='violet',ls='dotted')\n",
+    "\n",
+    "T = np.arange(273.,330.)\n",
+    "es_w1 = (svp.liq_analytic(T)/svp.liq_wagner_pruss(T) - 1.)*100.\n",
+    "es_w2 = (svp.liq_analytic(T,cx=4119.)/svp.liq_wagner_pruss(T) - 1.)*100.\n",
+    "es_w3 = (svp.tetens(T,17.269,35.86)/svp.liq_wagner_pruss(T) - 1.)*100.\n",
+    "es_w4 = (svp.tetens(T,a_liq,b_liq)/svp.liq_wagner_pruss(T) - 1.)*100.\n",
+    "\n",
+    "ax[2].plot(T,np.abs(es_w1),c='k',label='Romps')\n",
+    "ax[2].plot(T,np.abs(es_w2),c='k',ls='dotted',label='Romps best fit')\n",
+    "ax[2].plot(T,np.abs(es_w3),c='violet',label='Teten-Murray')\n",
+    "ax[2].plot(T,np.abs(es_w4),c='violet',ls='dotted',label='Teten best fit')\n",
     "\n",
     "ax[0].legend()\n",
     "for a in ax:\n",
-    "    a.set_ylabel(\"abs error / %\")\n",
-    "    a.set_xlabel(\"T / K\")\n",
+    "    a.set_ylabel('abs error / %')\n",
+    "    a.set_xlabel('T / K')\n",
     "\n",
-    "sns.despine(offset=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "fef2f42d-8685-4cd8-9a4c-e0014f721f8c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def analytic(T, lx, cx):\n",
-    "    \"\"\"returns saturation vapor pressure over a given phase\n",
-    "\n",
-    "    Uses the rankine (constant specific heat, negligible condensate volume) approximations to\n",
-    "    calculate the saturation vapor pressure over a phase with the specific heat cx, and phase\n",
-    "    change enthalpy (from vapor) lx, at temperature T.\n",
-    "\n",
-    "    Args:\n",
-    "        T: temperature in kelvin\n",
-    "        lx: phase change enthalpy between vapor and given phase (liquid, ice)\n",
-    "        cx: specific heat capacity of given phase (liquid, ice)\n",
-    "\n",
-    "    Returns:\n",
-    "        value of saturation vapor pressure over liquid water in Pa\n",
-    "\n",
-    "    Reference:\n",
-    "        Romps, D. M. Exact Expression for the Lifting Condensation Level. Journal of the Atmospheric\n",
-    "        Sciences 74, 3891–3900 (2017).\n",
-    "        Romps, D. M. Accurate expressions for the dew point and frost point derived from the Rankine-\n",
-    "        Kirchhoff approximations. Journal of the Atmospheric Sciences (2021) doi:10.1175/JAS-D-20-0301.1.\n",
-    "\n",
-    "    >>> analytic(np.asarray([273.16,305.]))\n",
-    "    array([ 611.655     , 4711.13161169])\n",
-    "    \"\"\"\n",
-    "    TvT = constants.temperature_water_vapor_triple_point\n",
-    "    PvT = constants.pressure_water_vapor_triple_point\n",
-    "    Rv = constants.water_vapor_gas_constant\n",
-    "\n",
-    "    c1 = (constants.cpv - cx) / Rv\n",
-    "    c2 = lx / (Rv * TvT) - c1\n",
-    "    es = PvT * np.exp(c2 * (1.0 - TvT / T)) * (T / TvT) ** c1\n",
-    "    return es"
+    "sns.despine (offset=10)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "93db947c-bb33-4ea1-8d8a-b8edf9f6aec1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4711.131611687174"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "analytic(305.0, constants.lvT, constants.cl)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "86a706f5-ec56-44ca-97ce-5190cd27843c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/moist_thermodynamics/functions.py b/moist_thermodynamics/functions.py
index fec3736..469b28c 100644
--- a/moist_thermodynamics/functions.py
+++ b/moist_thermodynamics/functions.py
@@ -14,11 +14,11 @@ from scipy import interpolate, optimize
 from . import constants
 from . import saturation_vapor_pressures
 
-es_liq = saturation_vapor_pressures.liq_wagner_pruss
-es_ice = saturation_vapor_pressures.ice_wagner_etal
+es_liq_default = saturation_vapor_pressures.liq_wagner_pruss
+es_ice_default = saturation_vapor_pressures.ice_wagner_etal
 
 
-def es_mxd(T, es_liq=es_liq, es_ice=es_ice):
+def es_mxd(T, es_liq=es_liq_default, es_ice=es_ice_default):
     """Returns the minimum of the sublimation and saturation vapor pressure
 
     Calculates both the sublimation vapor pressure over ice Ih using es_ice and that over planar
@@ -56,7 +56,7 @@ def planck(T, nu):
     return (2 * h * nu**3 / c**2) / (np.exp(h * nu / (kB * T)) - 1)
 
 
-def vaporization_enthalpy(TK, delta_cl=constants.delta_cl):
+def vaporization_enthalpy(T, delta_cl=constants.delta_cl):
     """Returns the vaporization enthlapy of water (J/kg)
 
     The vaporization enthalpy is calculated from a linear depdence on temperature about a
@@ -72,10 +72,10 @@ def vaporization_enthalpy(TK, delta_cl=constants.delta_cl):
     """
     T0 = constants.standard_temperature
     lv0 = constants.vaporization_enthalpy_stp
-    return lv0 + delta_cl * (TK - T0)
+    return lv0 + delta_cl * (T - T0)
 
 
-def sublimation_enthalpy(TK, delta_ci=constants.delta_ci):
+def sublimation_enthalpy(T, delta_ci=constants.delta_ci):
     """Returns the sublimation enthlapy of water (J/kg)
 
     The sublimation enthalpy is calculated from a linear depdence on temperature about a
@@ -92,13 +92,13 @@ def sublimation_enthalpy(TK, delta_ci=constants.delta_ci):
     """
     T0 = constants.standard_temperature
     ls0 = constants.sublimation_enthalpy_stp
-    return ls0 + delta_ci * (TK - T0)
+    return ls0 + delta_ci * (T - T0)
 
 
 def partial_pressure_to_mixing_ratio(pp, p):
     """Returns the mass mixing ratio given the partial pressure and pressure
 
-    >>> partial_pressure_to_mixing_ratio(es_liq(300.),60000.)
+    >>> partial_pressure_to_mixing_ratio(es_liq_default(300.),60000.)
     0.0389569254590098
     """
     eps1 = constants.rd_over_rv
@@ -129,7 +129,7 @@ def partial_pressure_to_specific_humidity(pp, p):
     situations where condensate is present one should instead calculate
     $q = r*(1-qt)$ which would require an additional argument
 
-    >>> partial_pressure_to_specific_humidity(es_liq(300.),60000.)
+    >>> partial_pressure_to_specific_humidity(es_liq_default(300.),60000.)
     0.037496189210922945
     """
     r = partial_pressure_to_mixing_ratio(pp, p)
@@ -202,7 +202,7 @@ def static_energy(T, Z, qv=0, ql=0, qi=0, hv0=constants.cpv * constants.T0):
     return h
 
 
-def theta(TK, PPa, qv=0.0, ql=0.0, qi=0.0):
+def theta(T, P, qv=0.0, ql=0.0, qi=0.0):
     """Returns the potential temperature for an unsaturated moist fluid
 
     This expressed the potential temperature in away that makes it possible to account
@@ -210,8 +210,8 @@ def theta(TK, PPa, qv=0.0, ql=0.0, qi=0.0):
     adiabatic factor R/cp.  The default is the usualy dry potential temperature.
 
     Args:
-        TK: temperature in kelvin
-        PPa: pressure in pascal
+        T: temperature in kelvin
+        P: pressure in pascal
         qv: specific vapor mass
         ql: specific liquid mass
         qi: specific ice mass
@@ -227,18 +227,18 @@ def theta(TK, PPa, qv=0.0, ql=0.0, qi=0.0):
 
     qd = 1.0 - qv - ql - qi
     kappa = (qd * Rd + qv * Rv) / (qd * cpd + qv * cpv + ql * cl + qi * ci)
-    return TK * (P0 / PPa) ** kappa
+    return T * (P0 / P) ** kappa
 
 
-def theta_e_bolton(TK, PPa, qt, es=es_liq):
+def theta_e_bolton(T, P, qt, es=es_liq_default):
     """Returns the pseudo equivalent potential temperature.
 
     Following Eq. 43 in Bolton (1980) the (pseudo) equivalent potential temperature
     is calculated and returned by this function
 
     Args:
-        TK: temperature in kelvin
-        PPa: pressure in pascal
+        T: temperature in kelvin
+        P: pressure in pascal
         qt: specific total water mass
         es: form of the saturation vapor pressure to use
 
@@ -251,19 +251,19 @@ def theta_e_bolton(TK, PPa, qt, es=es_liq):
     r2p = mixing_ratio_to_partial_pressure
 
     rv = np.minimum(
-        qt / (1.0 - qt), p2r(es(TK), PPa)
+        qt / (1.0 - qt), p2r(es(T), P)
     )  # mixing ratio of vapor (not gas Rv)
-    pv = r2p(rv, PPa)
+    pv = r2p(rv, P)
 
-    TL = 55.0 + 2840.0 / (3.5 * np.log(TK) - np.log(pv / 100.0) - 4.805)
+    TL = 55.0 + 2840.0 / (3.5 * np.log(T) - np.log(pv / 100.0) - 4.805)
     return (
-        TK
-        * (P0 / PPa) ** (0.2854 * (1.0 - 0.28 * rv))
+        T
+        * (P0 / P) ** (0.2854 * (1.0 - 0.28 * rv))
         * np.exp((3376.0 / TL - 2.54) * rv * (1 + 0.81 * rv))
     )
 
 
-def theta_e(TK, PPa, qt, es=es_liq):
+def theta_e(T, P, qt, es=es_liq_default):
     """Returns the equivalent potential temperature
 
     Follows Eq. 11 in Marquet and Stevens (2022). The closed form solutionis derived for a
@@ -272,8 +272,8 @@ def theta_e(TK, PPa, qt, es=es_liq):
     accurate, but more consistent, formulations are on the order of millikelvin
 
     Args:
-        TK: temperature in kelvin
-        PPa: pressure in pascal
+        T: temperature in kelvin
+        P: pressure in pascal
         qt: total water specific humidity (unitless)
         es: form of the saturation vapor pressure
 
@@ -289,20 +289,20 @@ def theta_e(TK, PPa, qt, es=es_liq):
     cl = constants.liquid_water_specific_heat
     lv = vaporization_enthalpy
 
-    ps = es(TK)
-    qv = saturation_partition(PPa, ps, qt)
+    ps = es(T)
+    qv = saturation_partition(P, ps, qt)
 
     Re = (1.0 - qt) * Rd
     R = Re + qv * Rv
-    pv = qv * (Rv / R) * PPa
+    pv = qv * (Rv / R) * P
     RH = pv / ps
     cpe = cpd + qt * (cl - cpd)
     omega_e = RH ** (-qv * Rv / cpe) * (R / Re) ** (Re / cpe)
-    theta_e = TK * (P0 / PPa) ** (Re / cpe) * omega_e * np.exp(qv * lv(TK) / (cpe * TK))
+    theta_e = T * (P0 / P) ** (Re / cpe) * omega_e * np.exp(qv * lv(T) / (cpe * T))
     return theta_e
 
 
-def theta_l(TK, PPa, qt, es=es_liq):
+def theta_l(T, P, qt, es=es_liq_default):
     """Returns the liquid-water potential temperature
 
     Follows Eq. 16 in Marquet and Stevens (2022). The closed form solutionis derived for a
@@ -311,8 +311,8 @@ def theta_l(TK, PPa, qt, es=es_liq):
     accurate, but more consistent, formulations are on the order of millikelvin
 
     Args:
-        TK: temperature in kelvin
-        PPa: pressure in pascal
+        T: temperature in kelvin
+        P: pressure in pascal
         qt: total water specific humidity (unitless)
         es: form of the saturation vapor pressure
 
@@ -328,8 +328,8 @@ def theta_l(TK, PPa, qt, es=es_liq):
     cpv = constants.isobaric_water_vapor_specific_heat
     lv = vaporization_enthalpy
 
-    ps = es(TK)
-    qv = saturation_partition(PPa, ps, qt)
+    ps = es(T)
+    qv = saturation_partition(P, ps, qt)
     ql = qt - qv
 
     R = Rd * (1 - qt) + qv * Rv
@@ -337,13 +337,11 @@ def theta_l(TK, PPa, qt, es=es_liq):
     cpl = cpd + qt * (cpv - cpd)
 
     omega_l = (R / Rl) ** (Rl / cpl) * (qt / (qv + 1.0e-15)) ** (qt * Rv / cpl)
-    theta_l = (
-        (TK * (P0 / PPa) ** (Rl / cpl)) * omega_l * np.exp(-ql * lv(TK) / (cpl * TK))
-    )
+    theta_l = (T * (P0 / P) ** (Rl / cpl)) * omega_l * np.exp(-ql * lv(T) / (cpl * T))
     return theta_l
 
 
-def theta_s(TK, PPa, qt, es=es_liq):
+def theta_s(T, P, qt, es=es_liq_default):
     """Returns the entropy potential temperature
 
     Follows Eq. 18 in Marquet and Stevens (2022). The closed form solutionis derived for a
@@ -352,8 +350,8 @@ def theta_s(TK, PPa, qt, es=es_liq):
     accurate, but more consistent, formulations are on the order of millikelvin
 
     Args:
-        TK: temperature in kelvin
-        PPa: pressure in pascal
+        T: temperature in kelvin
+        P: pressure in pascal
         qt: total water specific humidity (unitless)
         es: form of the saturation vapor pressure
 
@@ -386,18 +384,18 @@ def theta_s(TK, PPa, qt, es=es_liq):
     gamma = kappa / eps1
     r0 = e0 / (P0 - e0) / eta
 
-    ps = es(TK)
-    qv = saturation_partition(PPa, ps, qt)
+    ps = es(T)
+    qv = saturation_partition(P, ps, qt)
     ql = qt - qv
 
     R = Rd + qv * (Rv - Rd)
-    pv = qv * (Rv / R) * PPa
+    pv = qv * (Rv / R) * P
     RH = pv / ps
     rv = qv / (1 - qv)
 
     x1 = (
-        (TK / T0) ** (lmbd * qt)
-        * (P0 / PPa) ** (kappa * delta * qt)
+        (T / T0) ** (lmbd * qt)
+        * (P0 / P) ** (kappa * delta * qt)
         * (rv / r0) ** (-gamma * qt)
         * RH ** (gamma * ql)
     )
@@ -405,8 +403,8 @@ def theta_s(TK, PPa, qt, es=es_liq):
         -kappa * delta * qt
     )
     theta_s = (
-        (TK * (P0 / PPa) ** (kappa))
-        * np.exp(-ql * lv(TK) / (cpd * TK))
+        (T * (P0 / P) ** (kappa))
+        * np.exp(-ql * lv(T) / (cpd * T))
         * np.exp(qt * Lmbd)
         * x1
         * x2
@@ -414,15 +412,15 @@ def theta_s(TK, PPa, qt, es=es_liq):
     return theta_s
 
 
-def theta_es(TK, PPa, es=es_liq):
+def theta_es(T, P, es=es_liq_default):
     """Returns the saturated equivalent potential temperature
 
     Adapted from Eq. 11 in Marquet and Stevens (2022) with the assumption that the gas quanta is
     everywhere just saturated.
 
     Args:
-        TK: temperature in kelvin
-        PPa: pressure in pascal
+        T: temperature in kelvin
+        P: pressure in pascal
         qt: total water specific humidity (unitless)
         es: form of the saturation vapor pressure
 
@@ -438,20 +436,18 @@ def theta_es(TK, PPa, es=es_liq):
     p2q = partial_pressure_to_specific_humidity
     lv = vaporization_enthalpy
 
-    ps = es(TK)
-    qs = p2q(ps, PPa)
+    ps = es(T)
+    qs = p2q(ps, P)
 
     Re = (1.0 - qs) * Rd
     R = Re + qs * Rv
     cpe = cpd + qs * (cl - cpd)
     omega_e = (R / Re) ** (Re / cpe)
-    theta_es = (
-        TK * (P0 / PPa) ** (Re / cpe) * omega_e * np.exp(qs * lv(TK) / (cpe * TK))
-    )
+    theta_es = T * (P0 / P) ** (Re / cpe) * omega_e * np.exp(qs * lv(T) / (cpe * T))
     return theta_es
 
 
-def theta_rho(TK, PPa, qt, es=es_liq):
+def theta_rho(T, P, qt, es=es_liq_default):
     """Returns the density liquid-water potential temperature
 
     calculates $\theta_\mathrm{l} R/R_\mathrm{d}$ where $R$ is the gas constant of a
@@ -459,21 +455,21 @@ def theta_rho(TK, PPa, qt, es=es_liq):
     temperature baswed on the two component fluid thermodynamic constants.
 
     Args:
-        TK: temperature in kelvin
-        PPa: pressure in pascal
+        T: temperature in kelvin
+        P: pressure in pascal
         qt: total water specific humidity (unitless)
         es: form of the saturation vapor pressure
     """
     Rd = constants.dry_air_gas_constant
     Rv = constants.water_vapor_gas_constant
 
-    ps = es(TK)
-    qv = saturation_partition(PPa, ps, qt)
-    theta_rho = theta_l(TK, PPa, qt, es) * (1.0 - qt + qv * Rv / Rd)
+    ps = es(T)
+    qv = saturation_partition(P, ps, qt)
+    theta_rho = theta_l(T, P, qt, es) * (1.0 - qt + qv * Rv / Rd)
     return theta_rho
 
 
-def invert_for_temperature(f, f_val, P, qt, es=es_liq):
+def invert_for_temperature(f, f_val, P, qt, es=es_liq_default):
     """Returns temperature for an atmosphere whose state is given by f, P and qt
 
         Infers the temperature from a state description (f,P,qt), where
@@ -498,7 +494,7 @@ def invert_for_temperature(f, f_val, P, qt, es=es_liq):
     return optimize.newton(zero, 280.0, args=(f_val,))
 
 
-def invert_for_pressure(f, f_val, T, qt, es=es_liq):
+def invert_for_pressure(f, f_val, T, qt, es=es_liq_default):
     """Returns pressure for an atmosphere whose state is given by f, T and qt
 
         Infers the pressure from a state description (f,T,qt), where
@@ -523,7 +519,7 @@ def invert_for_pressure(f, f_val, T, qt, es=es_liq):
     return optimize.newton(zero, 80000.0, args=(f_val,))
 
 
-def plcl(TK, PPa, qt, es=es_liq):
+def plcl(T, P, qt, es=es_liq_default):
     """Returns the pressure at the lifting condensation level
 
     Calculates the lifting condensation level pressure using an interative solution under the
@@ -531,8 +527,8 @@ def plcl(TK, PPa, qt, es=es_liq):
     which depends on the expression for the saturation vapor pressure
 
     Args:
-        TK: temperature in kelvin
-        PPa: pressure in pascal
+        T: temperature in kelvin
+        P: pressure in pascal
         qt: specific total water mass
 
         >>> plcl(300.,102000.,17e-3)
@@ -541,23 +537,23 @@ def plcl(TK, PPa, qt, es=es_liq):
 
     def zero(P, Tl):
         p2r = partial_pressure_to_mixing_ratio
-        TK = invert_for_temperature(theta_l, Tl, P, qt, es=es)
-        qs = p2r(es(TK), P) * (1.0 - qt)
+        T = invert_for_temperature(theta_l, Tl, P, qt, es=es)
+        qs = p2r(es(T), P) * (1.0 - qt)
         return np.abs(qs / qt - 1.0)
 
-    Tl = theta_l(TK, PPa, qt, es=es)
+    Tl = theta_l(T, P, qt, es=es)
     return optimize.fsolve(zero, 80000.0, args=(Tl,))
 
 
-def plcl_bolton(TK, PPa, qt):
+def plcl_bolton(T, P, qt):
     """Returns the pressure at the lifting condensation level
 
     Following Bolton (1980) the lifting condensation level pressure is derived from the state
     of an air parcel.  Usually accurate to within about 10 Pa, or about 1 m
 
     Args:
-        TK: temperature in kelvin
-        PPa: pressure in pascal
+        T: temperature in kelvin
+        P: pressure in pascal
         qt: specific total water mass
 
     Reference:
@@ -575,9 +571,9 @@ def plcl_bolton(TK, PPa, qt):
 
     cp = cpd + qt * (cpv - cpd)
     R = Rd + qt * (Rv - Rd)
-    pv = r2p(qt / (1.0 - qt), PPa)
-    Tl = 55 + 2840.0 / (3.5 * np.log(TK) - np.log(pv / 100.0) - 4.805)
-    return PPa * (Tl / TK) ** (cp / R)
+    pv = r2p(qt / (1.0 - qt), P)
+    Tl = 55 + 2840.0 / (3.5 * np.log(T) - np.log(pv / 100.0) - 4.805)
+    return P * (Tl / T) ** (cp / R)
 
 
 def zlcl(Plcl, T, P, qt, z):
@@ -613,7 +609,14 @@ from scipy.integrate import ode
 
 
 def moist_adiabat(
-    Tbeg, Pbeg, Pend, dP, qt, cc=constants.cl, l=vaporization_enthalpy, es=es_liq
+    Tbeg,
+    Pbeg,
+    Pend,
+    dP,
+    qt,
+    cc=constants.cl,
+    l=vaporization_enthalpy,
+    es=es_liq_default,
 ):
     """Returns the temperature and pressure by integrating along a moist adiabat
 
diff --git a/setup.py b/setup.py
index a558231..a8d2f2a 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 
 setup(
     name="moist_thermodynamics",
-    version="0.4",
+    version="0.5",
     description="Constants and functions for the treatment of moist atmospheric thermodynamics",
     packages=find_packages(),
     install_requires=[
-- 
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