diff --git a/examples/examples.ipynb b/examples/examples.ipynb
index f1ad1e9fc3ff704a90a9df8e65a710ae1e97d27f..77a3abcc69bc1398cf4190a10de473d3337e3485 100644
--- a/examples/examples.ipynb
+++ b/examples/examples.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "b7c5c488-b68c-4504-85a0-bbe1575c7f65",
    "metadata": {},
    "outputs": [],
@@ -13,6 +13,7 @@
     "\n",
     "from moist_thermodynamics import functions as mt\n",
     "from moist_thermodynamics import constants\n",
+    "from moist_thermodynamics import saturation_vapor_pressures as svp\n",
     "\n",
     "i4T = np.vectorize(mt.invert_for_temperature)"
    ]
@@ -26,14 +27,17 @@
     "\n",
     "Usage of the moist thermodynamic functions is documented through a number of examples\n",
     "\n",
-    "1. constructing a moist adiabat.\n",
-    "2. sensitivity of moist adiabat to saturation vapor pressure \n",
-    "3. lcl computations\n",
-    "4. Integrating the first law to arrive at the moist adiabat\n",
+    "1. saturation vapor pressure\n",
+    "2. constructing a moist adiabat.\n",
+    "3. sensitivity of moist adiabat to saturation vapor pressure \n",
+    "4. lcl computations\n",
+    "5. Integrating the first law to arrive at the moist adiabat\n",
     "\n",
-    "## 1. Constructing a moist adiabat\n",
+    "## 1. saturation vapor pressure \n",
     "\n",
-    "This shows how simple it is to construct a moist adiabat.  For the example it is constructed by assuming a constant $\\theta_\\mathrm{l}$ but the same answer (with the caveats of the next example) would arise if we were to define it in terms of constant $\\theta_\\mathrm{e}$ or $\\theta_\\mathrm{s}$"
+    "We compare the error of the much simpler Teten's formulae to those of the reference formulae for the three cases of liquid, super-cooled liquid and ice.  The reference fits for these are respectively Wagner and Pruss, Koop and Murray, and Wagner et al.  For liquid the fits are quite good, to within better than 0.15%.  For ice and super-cooled water they simple formulae are less accurate, with errors of a few percent at 230 K and larger for colder temperatures.\n",
+    "\n",
+    "In the second plot we compare the extrapolation of the Wanger and Pruss formula, which was derived for temperatures between the triple and critical points, for saturation with respect to super cooled liquid using Murphy and Koop as the super-cooled reference.   Murphy and Koop is fit for temperatures (123K-332K) spanning conditions of earth's atmosphere. The comparison is for extreme temperatures.  Generally extrapolating Wagner and Pruss for super cooled water is a better fit from the Murray specification of the Teten's formual."
    ]
   },
   {
@@ -44,22 +48,74 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "array([611.65715494, 222.65143353])"
+       "<Figure size 648x288 with 2 Axes>"
       ]
      },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "mt.es_liq_hardy(np.asarray([273.16, 260.0]))"
+    "Tw = np.arange(273.15, 330.0)\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_analytic(Tc)\n",
+    "es_def = svp.liq_murphy_koop(Tc)\n",
+    "err2 = (es / es_def - 1.0) * 100.0\n",
+    "\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",
+    "sns.set_context(\"talk\")\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(9, 4), constrained_layout=True)\n",
+    "\n",
+    "ax[0].plot(Tw, err1, label=f\"liquid\", c=\"dodgerblue\")\n",
+    "ax[0].plot(Tc, err2, label=f\"super cooled\", ls=\"dotted\", c=\"dodgerblue\")\n",
+    "ax[0].plot(Tc, err3, label=f\"ice\", c=\"violet\")\n",
+    "\n",
+    "Tw = np.arange(273.16, 430)\n",
+    "es = svp.liq_wagner_pruss(Tw)\n",
+    "es_def = svp.liq_murphy_koop(Tw)\n",
+    "err1 = (es / es_def - 1.0) * 100.0\n",
+    "ax[1].plot(Tw, err1, label=f\"liquid\", c=\"dodgerblue\")\n",
+    "Tw = np.arange(150, 273.16)\n",
+    "es_def = svp.liq_wagner_pruss(Tw)\n",
+    "es = svp.liq_murphy_koop(Tw)\n",
+    "err1 = (es / es_def - 1.0) * 100.0\n",
+    "ax[1].plot(Tw, err1, label=f\"super-cooled\", c=\"dodgerblue\", ls=\"dotted\")\n",
+    "\n",
+    "ax[0].hlines(0, 230, 330.0, ls=\"dashed\", color=\"grey\")\n",
+    "ax[0].legend()\n",
+    "ax[1].legend()\n",
+    "ax[1].set_xlabel(\"$T$ / K\")\n",
+    "ax[0].set_xlabel(\"$T$ / K\")\n",
+    "ax[0].set_ylabel(\"Error / %\")\n",
+    "ax[1].set_ylabel(\"Error / %\")\n",
+    "\n",
+    "sns.despine(offset=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "292f88d8-2b95-4a3f-9f20-9cb09ceeada3",
+   "metadata": {},
+   "source": [
+    "## 2. Constructing a moist adiabat\n",
+    "\n",
+    "This shows how simple it is to construct a moist adiabat.  For the example it is constructed by assuming a constant $\\theta_\\mathrm{l}$ but the same answer (with the caveats of the next example) would arise if we were to define it in terms of constant $\\theta_\\mathrm{e}$ or $\\theta_\\mathrm{s}$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "0f765565-ed26-4cc7-a859-bebf9b020aea",
    "metadata": {},
    "outputs": [
@@ -84,7 +140,7 @@
     }
    ],
    "source": [
-    "es = mt.es_liq_analytic\n",
+    "es = svp.liq_analytic\n",
     "p2q = mt.partial_pressure_to_specific_humidity\n",
     "theta_l = mt.theta_l\n",
     "i4T = np.vectorize(mt.invert_for_temperature)\n",
@@ -124,20 +180,20 @@
    "id": "b2f6c280-e7b0-48ac-acc5-15053cabe4d0",
    "metadata": {},
    "source": [
-    "## 2. Sensitivity (small) of moist adiabat on saturation vapor pressure \n",
+    "## 3. Sensitivity (small) of moist adiabat on saturation vapor pressure \n",
     "\n",
     "The derivation of the moist potential temperatures assumes a Rankine fluid, i.e., constant specific heats. Specific heats vary with temperature however, especially $c_i$.  This variation is encoded in the best fits to the saturation vapor pressure, so that an adiabat defined in terms of a best fit saturation vapor pressure will differ depending on whether it assumes $\\theta_\\mathrm{e},$ $\\theta_\\mathrm{l},$ or $\\theta_\\mathrm{s}.$  This sensitivity vanishes (right plot, note $x$-axis scale) when we replace the more accurate saturation vapor pressures with less accurate expressions, albeit consistent with a Rankine fluid."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "321bddff-0bb6-4b3a-a3f0-1dae1c50c852",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 648x504 with 2 Axes>"
       ]
@@ -162,21 +218,21 @@
     "sns.set_context(\"talk\")\n",
     "fig, ax = plt.subplots(1, 2, figsize=(9, 7), constrained_layout=True, sharey=True)\n",
     "\n",
-    "es = mt.es_liq_analytic\n",
+    "es = svp.liq_analytic\n",
     "TKl = np.maximum(i4T(theta_l, theta_l(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "TKe = np.maximum(i4T(theta_e, theta_e(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "TKs = np.maximum(i4T(theta_s, theta_s(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "ax[1].plot(TKe - TKl, P / 100.0, label=f\"$\\\\theta_e-\\\\theta_l$\")\n",
     "ax[1].plot(TKs - TKl, P / 100.0, label=f\"$\\\\theta_s-\\\\theta_l$\")\n",
-    "ax[1].set_title(\"es_liq_analytic\")\n",
+    "ax[1].set_title(\"liq_analytic\")\n",
     "\n",
-    "es = mt.es_liq\n",
+    "es = svp.liq_wagner_pruss\n",
     "TKl = np.maximum(i4T(theta_l, theta_l(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "TKe = np.maximum(i4T(theta_e, theta_e(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "TKs = np.maximum(i4T(theta_s, theta_s(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "ax[0].plot(TKe - TKl, P / 100.0, label=f\"$\\\\theta_e-\\\\theta_l$\")\n",
     "ax[0].plot(TKs - TKl, P / 100.0, label=f\"$\\\\theta_s-\\\\theta_l$\")\n",
-    "ax[0].set_title(\"es_liq\")\n",
+    "ax[0].set_title(\"liq\")\n",
     "\n",
     "plt.gca().invert_yaxis()\n",
     "\n",
@@ -194,14 +250,14 @@
    "id": "b2fd8753-736f-459c-a435-0c50ad8eeae9",
    "metadata": {},
    "source": [
-    "## 3. Calculations of lifting condensation level\n",
+    "## 4. Calculations of lifting condensation level\n",
     "\n",
     "We compare three different formulations of the lifting condensation level, one due to Romps (2017) is not included in the moist_thermodynamics library, but is included here for sake of comparision.  The analysis shows that the simple bolton approximations work very well, as well as those of Romps if one uses the wagner saturation vapor pressure data.  Had we performed this comparison with the analytic formula using the specific heats specified by Romps, the comparison would have been more favorable for the Romps formulation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "a53539ae-7920-41b9-aa41-fed0031ce16b",
    "metadata": {},
    "outputs": [],
@@ -344,13 +400,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "9b2830db-855d-467d-ac66-cc9154ab7caa",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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t9PR0Zs+eze23317u+9LT0/n6668ZPtz3CZCeW+6btymF56dvAOCSvi24dlAbdwOqgJuGtudgZh4T5mzlo0XJNKoTx59Hdjr+G8NIKHQBFN35H60ZsG6Z/SpERJ4D7gHmAKONMYEz2kspL4mPt6fN2rVrf7f9/vvvJzIykptuuul320eOHElCQgL169cnOTmZ8ePH+y1W5Y696Tnc8dEyPAZ6NI/nH+d2D5o76QfHnMDFfVoA8OLPm/h6xS6XIwosoZAAbHQeO5Z9wZki2BYoBLZW9IAi8gJwH7YewBhjTKYX4lQq4Jx33nnExcVx00038fHHH/PNN99w4YUXMnHiRCZNmkSnTr+/Y/rxxx9JS0sjKyuLc845h9GjR7sUufIHj8dw/+e/cSgrn4Sa0bx2Ve+gGlAnIjx1QQ8GOVMU7/98Jct3aGXLIqGQABQNwz6jnNcGA7WA+RWZASDWK9jm/h+Bs4wxWd4KVKlA06ZNG77//nsaNWrEuHHjuPrqq8nKymL27NlceumlR31fbGwsV199NcuXLyclJcWPESt/end+EnM32X/fZy44kRb1gq/LKzoyglev7E2bBjXJK/Bw03tL2XdYG3QhdBKAjcBwESkuYeYUAnrcefp6qe01ReQEEWlV+iDOgL83gFuBqcA5xphsXwevlNuGDx/OwoULycnJIT09nalTpzJ48OBjvic/P58PP/yQJk2a/G72gAodm/Zl8MzU9QBc3KcFo7v7t8qfNyXUjGHiuH7UiYviQEYut3+4jPxCz/HfGOKCfhCgMaZQRK4DfgamOEV79mBLAXcDpgAflnpLf2zSMBtb2KfI/wE3ANnACuDBcvq5ZpWtFKhUODnttNOIiIggIiKC7t27M2XKlKDpD1YVV+gx3PfZb+QWeGhRrwb/d3ZXt0OqtvaNavPvS3py4+QlLE46xPPTN/DQmV3cDstVQZ8AABhjfhWRAcBj2At/LWyf/wPACxVYBwDsWAGAGsBDx9hvVjVCVSpoJSUluR2C8pP35ifx2047bvpfF59EnbholyPyjpFdm3DzsPa8PnsLE+ZspU/ren5fvyCQhEQCAGCMWQmcX4H9ZlFObWRjzDhgnLfjUkqpYLInPZvnf7Bjqy/r15IB7UKri+e+MzqxbMchFm1L5f7PV3JiiwQSq1BDIxSEwhgApZRSXvLYN2vJzC2gYe0YHhxzgtvheF1UpF25sF7NaNKz87n3sxV4wrRSoCYASimlAJi5YT/T1uwF4JGxXUmoGeNyRL7RpG4cz1x4IgC/bD7IxHnbXI7IHZoAKKWUIr/Qw5Pf2WrnA9s14JyTmrkckW+N6pbI5f1bAvDs9PWs33v4OO8IPZoAKKWU4sOFO9i8PxMRe/cfDrM7HhnblbYNa5FfaHjg85UUhNnUQE0AlFIqzKVn5fPCTyUD/7o2C48lnmvGRPHMBT0AWLkznUm/hFdXgCYASimFnfuek19ITn6h26H43UszNpGWlU/t2Cj+PLKz2+H41YB2Dbj65NYA/OuHjSSlHHE5Iv/RBEAppYBHv1nDCY9M455PVrgdil/tTstm8vztANxyansa1Yl1OSL/e2B0Z5rFx5Fb4OEvX6wMm1kBmgAopRQQGWH7vAvC5Mu/yEszNpNX6KFRnVjGn9L2+G8IQXXionnS6QpYuC2VjxbvcDki/9AEQCmlgOhIJwEIo4Fg2w8e4bMlyQDcPrwDNWKCZ6U/bxveuTEX9GoOwNPfr2d/RugvGKQJgFJKYQvEQHi1APz3p00UeAzNE2pwmTMlLpw9MrYr9WpGk5lbULwQUijTBEAppYBopwsgXFaJ27w/g69W7ALgztM7EBsVvnf/RerViuGB0bb64ZfLdrF0e6rLEfmWJgBKKQDmzp3L2LFjSUxMJDY2liZNmjB27FgOHTrkdmh+UdwCUBgeLQCvztqCMdC6QU0u7N3C7XACxiV9W3Jii3gA/u/rNRSGcIuQJgBKKTZs2MDpp59OfHw8b7/9NjNmzGDixIl06dKFWrVquR2eX0Q5YwDyQ/gLv0hyahZfr9gNwC3D2hcnP8oOBn3snG4ArNl9mI8Whe6AwJBZDVApVXU///wz+fn5nHXWWYwZM6Z4+9ixY12Myr+inC6AQk/odwG8OXcrhR5DYt04zu/d3O1wAk6vVvW4pG8LPl2yk+d/2MBZPZpSr1borYugaZ9SigsvvJAOHTpw5ZVXkpiYyFVXXcWMGTPcDsuvoiLCowvgQEYunyy2I/9vGNJW+/6P4oHRJ1AnLoq0rHye/2GD2+H4hLYAKFVVhQVweJfbUZSo2xwiq3ZKv/TSSyQmJvLRRx+xZ88eXnvtNUaMGMELL7zAXXfd5eVAA1PRNMBQHwQ46Zdt5BZ4qFczmsv7t3I7nIDVsHYs94zoxD++XcvHi5O57pQ2dGhcx+2wvEoTAKWq6vAu+O+JbkdR4q6VUK91pd/25JNPMmnSJFatWkWDBg0AOOuss+jRowdPPPFEcQLQpk0b9u/fT0REBHXr1uXSSy/l+eefJzIyNO4gw2Ea4JHcAt5fYKv+jRvUllqxegk4lqsHtmby/CSSDmbxz2kbePOavm6H5FXaBaBUGNu/fz+PP/44d9xxR/HFHyAiIoJ+/fpx8OBBPKX6xGfMmEFmZiZz587l008/ZdKkSW6E7RNFYwBCuQvgi2U7ycgpIDYqgqsHVj5ZDDfRkRHF0wJ/XLuPRdtCa1qgpn9KVVXd5vauO1DUrfxgrmnTppGbm8uoUaP+8NqePXto3bo1ERF/vE9o3749p5xyCitWrKhKpAEp2mkBCNUuAI/H8PYvSQBc0Ls59UNwUJsvjOmeSM+WCaxITuOp79fx1a2DQmapZE0AlKqqyKgqNbkHkm3b7PKniYmJv9uenp7O7Nmzuf3228t938aNG5k7dy733Xefz2P0l8jiWQCh2QIwa+N+tjkr3V0XpjX/q0JE+OuZXbhkwnxWJKcxdfVezuzR1O2wvEK7AJQKY/HxtuDJ2rVrf7f9/vvvJzIykptuuul320eOHEnt2rXp3LkzAwYM4LbbbvNbrL4W6oMAJ81LAmBIx4Z0ahJag9l8rX/b+ozs2gSAZ6etD5m/EU0AlApj5513HnFxcdx00018/PHHfPPNN1x44YVMnDiRSZMm0alTp9/t/+OPP5KRkcE333zD8uXLycjIcCly7yueBhiCLQAb9mYwb3MKQNiu+FddfxndmQiBpINZIVMcSBMApcJYmzZt+P7772nUqBHjxo3j6quvJisri9mzZ3PppZeW+x4R4eyzz2bEiBE8+eSTfo7Yd6IiQ3cQ4AcL7cj/tg1rMaxTI5ejCU4dGtfh0n52waRXZm4mJ7/Q5YiqTxMApcLc8OHDWbhwITk5OaSnpzN16lQGDx583Pfdf//9vPXWW6SkpPghSt8rHgQYYpUAj+QW8OUyW6/iygGtiIgIjQFsbrj9tI5ERwr7Dufy4cLgbwXQBEApVSUnnHACw4YN47///a/boXhF0TRAY0JrIOA3v+0mM7eAmKgIXfSnmpon1OCyfrZ40quztpCdF9ytADoLQClVIUlJSX/Y9t133/k/EB8p6gIAKPB4iIwI/gJHxpjiwj9jQ7Sevb/dOrw9nyxJJiUzl/cXbOfGoe3cDqnKtAVAKaUoGQQIoTMOYOXOdNbsPgzAlSdr2V9vaBpfgyucEsqvz95CVl6ByxFVnSYASilFmRaAEEkAigb/nZBYh96t6rkcTei45dT2xEZFcPBIHpPnb3c7nCrTBEAppSgZBAihMRAwM7eA//22B4ArBrQKmep1gaBJ3TiuOtkWAZswewuZucHZCqAJgFJKUTIIEEKjBeD7lXvIzi8kJiqCc0+qfJlodWw3D2tPXHQEh7Lyi8dZBBtNAJRSijItACFQ6e2zpckAjOqWSHzNaJejCT2N6sRyRX/bCjBx3ragrAugCYBSFRAREUFhYfCd4IGgsLCw3AWFAk1kqRaAYJ8GmJRyhMVJhwC4uI9O/fOVG4e2JTpSOJCRy+dLd7odTqUF/lmpVACIi4sjNzeX1NTQWg7U11JTU8nNzSUuLs7tUI6r7DTAYFZ0MWoaH8cpHRq6HE3oahpfg/N72e6VCXO2UBBkLUdaB0CpCmjYsCG5ubns27ePtLQ0IiODf464rxUWFpKbk0OdrCQaJtZyO5zjio4o3QUQvC0AhR7DF8tsAnBh7xa/a9lQ3nfzsPZ8tnQnyanZfLtyD+f1Cp7xFtoCoFQFiAjNmzenYcOGREdrf+px5WcTvXcZDZe9QPPvrkQWTXA7ouMKlWmAv25JYU96DgAXafO/z7VrVJszu9vlgV+btQVPEHUfaQuAUhUkIjRqpAupHNO+tTD/FVj1KRTm2W2RMWACv2k0VKYBfrXc1v3v27oebRoGfstLKLjl1PZ8t2oPG/ZlMGP9fkY4SwcHOk0AlFLVYwxsnQm/vgxbfi7ZXqM+9LsB+t8ItRu7F18FhcI0wOy8Qqav3gsQVE3Rwa5783iGdWrE7I0HeHXWZk7v0jgo6i5oAqCUqpqCPFj9Bcx/GfatLtneoAMMvA1OvAxiaroXXyWV7isP1kGAP6/fx5G8QqIihDN7NHU7nLBy66ntmb3xAMt2pLF0+yH6tqnvdkjHpQmAUqpysg/B0ndg4QTI2FOyvfVgGHQ7dBwFQTDtrywRISpCKPCYoG0BmLJ8NwDDOjWivi7841f929bnpJYJ/Jacxltzt2kCoJQKIYe2w4LXYNlkyD9it0kkdDvfXvib9XI3Pi+IinQSgCBsAUjLymP2xv0AnKvN/34nItwwuC13fLSc6Wv3sv3gEVo3COwxGJoAKKWObdcy+PUlWDulZDBfTG3oMw4G/AkSQmeVueiICHLwBOU0wO9X7SW/0FAzJpKRXYJjEFqoGdM9keYJNdiVls3bvyTx6Dnd3A7pmDQBUEr9kccDm3+0F/6kuSXb6zSDk2+G3tdCjQTXwvOVoqmAwdgF8PUKO/p/VLdEasRonQo3REVGMG5QG578fh2fLknmnpGdiK8RuNOGNQFQSpUoyIWVn9qBfQfWl2xv0h0G3QHdLoCo0O1bjnKmAgZbF8D+jBwWJdkqleec1MzlaMLbpf1b8p+fNnIkr5CPF+3gT8Paux3SUWkCoJSC7DRY+jYseB0y95ZsbzfcXvjbnwZBMK2puoqmAgZbC8D0NfswBurERWnpX5fVjYvm0n6tmPTLNt75NYnxg9v+rsZEINEEQKlwlr4LFrwKS9+FvAy7TSKh+4X2wt/0RHfj87PiLoAgawGYusrOxhjZtQkxUYF5sQkn153Shnd+3cae9By+X7WHc3sG5qBMTQCUCkf71tr+/VWfgqfAboupbfv2T74FElq6G59LitYDCKZBgAczc1mw9SBAcUla5a6W9Wsyunsi36/ay8R52zQBUEq5zBjY/iv88l/YNL1ke63GdmBf3/FQo5578QWAkkGAwdMC8MPafXgM1I6NYnBHbf4PFNcPbsv3q/aycmc6K5LT6Nkywe2Q/kATAKVCnacQ1n9nL/y7lpRsb9DBNvOfeBlE+2a5XmMMK3em0yyhBo3qxPrkM7wpKqJoEGDwtAB87zT/n96lMXHROvo/UPRuVY9uzeqyZvdhJv+aRM9Le7od0h9oZ5FSoaog1/btv9IfPr265OLfoh9c+gHcttjO5ffBxf9ARi5vztnKqP/M4dxXfuHTJcle/wxfiHZaAIKlC+DQkTx+3WKb/8do839AERGuGdgagG9X7iElM9fliP5IWwCUCjU5h+2I/vmv/n5Ef6fRcMpd0GqgT0b05xd6mLF+P58t2cmsDft/dxe9IjnN65/nC0XrARQGySDAH9fto9Bji/+c2llXqgw055zUnKe+X096dj6fLE7mtuEd3A7pdzQBUCpUZO63pXoXT4TcdLstIgp6XAyD7oQmXX3ysTsOZvH+wu18sXQnB4/kFW+vGxfFuT2bc3HfFvRoHu+Tz/a2ojoAwdICUDT6f/gJ2vwfiGrERHJpv5a8MWcrHyzYzp+Gtiv+GwsEmgAoFexSt8GvL8LyD6DQaWaMrmlH9A+8zScj+j0ew+xNB3hv/nZmbtiPca6XIjCkYyMu7tOCkV2bBN1FKTqIpgGmZ+czb3MKoKP/A9lVA1rz5tyt7E7P4ad1+xndPdHtkIppAqBUsNq7Gua9AGu+LKnRX6MeDLgZ+t8ENb2/Gll6Vj6fLU3m/QXbSTqYVby9aXwcV/RvxUV9W9A0vobXP9dfigcBBkELwM/r9pFfaIiLjtDm/wDWqkFNhnduzIz1+5k8P0kTAKVUNWyfby/8pafy1W1uR/T3vgZivL8CWXJqFhPnbeOTxclk5xcWbx/UvgHXDGzDiC6NA6pps6qCaRDgtNV2fMewTo2oFatf5YHs6oGtmbF+P79uOcjm/Rl0aFzH7ZAATQCUCg7GwOafYO6/YMf8ku0NO8Epd9t+fh/U6F+zO5035mzl25V7KHQG9dWKieTCPi24+uTWdGwSGF9k3lIyDTCwuwBy8guZu8k2/4/sGjh3lKp8wzo2onWDmmw/mMXk+dv5x7nd3Q4J0ARAqcDmKYR139gL/95VJdub9YYhf4bOZ0GEd++8jTHM33KQ12ZvKb7IACTWjeP6wW25rH9L6sQF7gpn1RFZPAYgsFsA5m89SHZ+ISIwXJv/A15EhHDVgNY8+f06vlq2i4fGdAmIFRtDJgEQkR7AY8BQoBawFXgHeMEYU1CJ4wwFHgb6AzHAOuA1Y8xEb8es1FEV5NkyvfNegIObS7a3HWYv/G2HeX0qnzGGOZtSeOHHjb+bttexcW1uGtqOc3s2D/k689ERwVEJ8Od1+wDo06oeDWoHfoElBRf2acFz0zeQkVvAtyt3c3Ff98tth0QCICKDgJ+ASOBTYDdwFvAsMEhELjDGHDelF5ELnfcfAT4GMoALgbdEpIcx5m7f/AZKOfKzYdlk+OVFOLyzZPsJY2Hwn6FFH69/pDGGXzYf5IWfNrJ0+6Hi7f3a1OPmYe0Z3rkxERGhvxIglFoOOIDHABhj+HndfgBO79LE5WhURdWvFcPo7ol889tuPlq0QxMAbxCRSGASUAM40xgz1dn+CDYpOA+4AvjgOMeJByYA+cBQY8wKZ/s/gEXAXSLymTHmF9/8Jiqs5WbY+fvzX4YjB+w2iYQeF9k+fh/N4f91i73jX5xUcuEf2K4Bd4/oyIB2DXzymYGseBBgAHcBrNl9mD3pOQCM6NLY5WhUZVzevxXf/LabZTvSWL/3MCck1nU1nqBPAIDhQGdgZtHFH8AYk+ckAbOAmzlOAgBcDDQA3i66+DvHSReRp7DdCTcDmgAo78lKhYUTYOHrkJNmt0XGQM8rbdW++m198rG/JafxzNT1zHdWkQPo37Y+94zoxMD24XfhL1IyDTBwuwCK7v5b1a9Jh8a1XY5GVcbJ7erTtmEttqUc4eNFyTx6TjdX4wmVBADgh3Jem4dtzh8oIrHGmGMVYz7WcYrmW51apQiVKitzv73bXzwR8jLttqgadkW+QbdD3WY++dgdB7N47ocN/O+33cXb+rauxz0jOzGofQPEByWCg0lUEEwD/Hm97f8/vUvjsP/3CjYiwuX9W/LU9+v5ctlO/jL6BFcHA4ZCAtDJedxU9gVjTKGIbAO6A+2wA/qqcpy9IpIJtBCRmsaYrLL7iEjaceKMB9KPs48KdYd32/79pe9AQbbdFlsX+t8IJ98KtXyznOuhI3m8NGMz7y1IKr64dWlalwfHnMDQjg0D+kLiz3MrKsDXAth3OIeVO+2vOkL7/4PShb3tYMDDOQV8v2oPF/Zp4VosoZAAFBUZP9oXwGHnMcELx6nt7PeHBECpY0rbYUf0L38fCp16+TXq24t+/xuhRoJPPjYnv5B3fk3ilZmbycixk2Gaxcdx7xmdOb9X87AZ3FdRxYMAA3QMwIz1tvm/TmwU/dp4v9Kj8r0GtWMZ1S2Rb1fu4aNFOzQB8BNvndHlHscYk3CsN1XgLkaFooNbYN6/4bePwePMRq3V2Fbt6zseYn3Xh/vzun089r+17Ei1+Wqd2ChuHd6B605pE1Q1+v15bhVNA8wP0DEARdP/hnVuFPJTMkPZFf1b8e3KPSzZfoiN+zLo5FJBrVBIAIru2I+23FjdMvtV9ziHj/K6UiVSNtniPSs/BeOUzq3TDAbfbcv1RvuuXn5SyhH+8e3a4rvFqAjh6oGtueO0jtSv5f1qgaEkkKcB5uQXFi/+o83/we3kdg1o06AmSQez+GRxMo+M9c0sn+OpcgIgIn2BAUA9oGwqaowxj1cnsErY6Dx2LPuCM0WwLVCILQx0vOP0dY6ztMxxErHN/zvL6/9Xqtj+9TD3eVj9RckCPfGtYMg9dmR/lO+KtmTlFfDKzM28OWcbec4d7OAODXn0nK4BU3s80EUF8DTAXzankJPvITJCdPGfIBcRIVzctyXPTd/AlOW7eHDMCUS7sJZGpRMAEakBfAmcAQi2SbyoI9GU2uavBGAm8FcnnmfKvDYYWxVw3nFmABQd5wrnOB+XeW2U8zirWpGq0LVvLcx5FtZMobiXqF4bGHIfnHQZRPqudK4xhmmr9/KPb9cWzw9vnlCDR8Z2YVS3xIAe4BdoogN4GuDsjbY+RO9WCSTU1JacYHdB7+b864cNHDySx4z1+xnVzf9rOlSlBeD/sBfJJ4GfsRfOa4H9wEPYgjzXeCvACpiJvXsfLiJjShUCiqEkCXm9aGcRqQm0ArKMMTtKHeczbAJxhYi8WKoQUDw2wQBbKEipEvvWwOx/wtqvS7bVbw9D77ML9Pjwwg+wJz2bR6as4SenbzgmKoKbh7XnlmHtA6LWeLCJLJ4FEHgtAEUJwLBOevcfCprG12BIx0bM3niAz5YkB00CcBHwmTHm/0SkqGLILmPMDBH5GVgMjMMmAz7nTPW7DpuMTBGRT4A92FLA3YApwIel3tIfmzTMptS8fqfgz83AJ8AcESldCrg18KIxZp7PfyEVHPauthf+dd+UbGvQEYY9AN0ugEjfDq8p9BjeX7Cd56ZvIDPXDi4c3rkRj53TnVYNavr0s0NZyXLAgdUCkJRyhO0Hbe/jsE5a/S9UXNy3BbM3HmDmhgPsz8ihcZ04v35+Vb6lWgL/dv67aGHwGABjTIGIfATcgp8SAOdzfxWRAdjFgM6iZDGgB7CLAVUonTfGfC4ip2EXA7oUiMbWDngC0MWAVPkX/oadYNhfoNv5EOH7u+4NezN48MuVLN+RZj++dgx/P7sbY09sqs391RSo0wDnbLJ3/w1qxdCtmbvlY5X3jOjShPga0aRn5zNl+S5uGtrer59flQQgo9T7MgAPULpsWTrg97YMY8xK4PwK7DeLkjEL5b0+G9s6oFSJfWth1tOuXvhzCwp5ecZmXpu1pfgCdUnfFvz1zC7aJ+wlUcWrAQZYAuA0/w/p2FBrN4SQuOhIzu3ZjMnzt/PZkp3cOKSdX5P4qiQAW3Cq5jnN72uw3QKTxEZ+AZDsvRCVctH+9TD7md8P7vPzhR9g7e7D/PnTFazfmwFAmwY1eeqCHgxq75vKgeGqaCR2IHUB5BV4+HWLXbNhmI7+DzmX9G3J5Pnb2bQ/kxXJafRqVc9vn12VBOAnYLyI3G2MKcQOjHtZRLZgvyHbUjJoTqngdGCjbepf/QXFF/4GHeHUB/164S8o9PD67C389+dN5BcaIgRuGtqeu0d0DKpiPsGiaBBgIHUBLNmeSlae7W0d0lETgFDTrVldTkisw/q9GXy2dGfAJwDPAO/hNKMbY14VkTjgKuyYgDeBZ70WoVL+lLoVZj8LKz8pmcdfvx0Me9AuzeunCz/A5v2Z3PvpCn5zar+3a1iL5y85id5+/IIIN4HYAlA0+r9787o0rO27OhLKHSK2JsDj367lfyt2839ju/otua90AmCMyQQ2lNn2b0oGBioVfNJ2wJznYPkHJZX76rWxTf09LvH5qP7SPB7DpF+28dz0DeQW2AvRdae04YFR7q4cFg5infK6eQWBkwDM2Wir/w3Vu/+QdV7PZjz9/Toycgv4ce0+zj7JN6uBllWpbzURaYRdVS/FGLPFNyEp5UeH99jKfUvfBU++3Rbf0k7nO+lyn8/jL2vf4Rz+/OkKftls+3ybJ9TguYtP1L5+PylKAHILPBhjXJ9Vse9wDuv22OrjOv8/dDWoHcupnRvz07p9TFm+K7ASABGJAF4FbsBp+heR+cD5xpgDvgtPKR85kmJX51v8FhTY6nnUaQpD7rW1+n1Ysvdofl63j/s++41DWTYRubRvS/42tgt14vybhISz2FJNr3mFHmKj3G1xKRr9Xzs2it6ttesnlJ3fqzk/rdvH7I0HOJiZSwM/dPdUtAXgduAmYDcwH1svfxB2AOAFvglNKR/IToP5L8OC1yAv026r1QgG/xn6XufTRXqOJie/kGemruedX5MAiK8RzT8vPJHR3f1fGSzcxZZaYS+3IAASgE22+X9Q+wau1IpX/nN6l8bUiY0iI7eAb1fu4dpBbXz+mRVNAK7BFsQ52RiTASAibwLjRCTBGJPmo/iU8o68I7DwdfjlRchJs9viEuCUu2DAnyCmlithbdqXwR0fLS+e3te/bX3+c2lPmiX4PxFREBddcpHNyS+kroutL4Uew1ynANBQbf4PeXHRkYzpkcinS3by1fJdAZUAdAb+UXTxd7wEXI+tCbDI24Ep5RUFubD0HZjzPByxy+MSUxtOvhUG3gY1ElwJyxjDJ4uTefR/a4pXeLv79I7cOrxD8VQ05X+l7/hz890dCLh6VzppTneQ9v+Hh/N7teDTJTtZkZzGtpQjtG3o2xuTiiYAtbDN/6XtLvWaUoHFU2in8s18GtKdNZ8iY6H/jTD4Hqjl3qC67LxCHv5qFV8u3wXYgX4vXt6TPq3ruxaTssp2Abjply22+b91g5q0rK/rO4SDAW3r0zQ+jj3pOUxZvot7Rnby6edVZhZA2coYRc/1dkUFDmNg/Xcw43E4sN5ui4iCXlfbkf11/TO69mi2HsjklveXsWGfbUwb3S2Rf150IvE1dKBfIPhdC0BB4TH29L1fnZkgOgMkfERECOf2bM7rs7cwZcUu7h7R0aczUSqTAJwpIqVHJdXEJgEXi0jPMvsaY8wL1Q1OqUrZNgd+egx2LSnZ1v0iGP5XaODfRTbKM3XVHu7/fCWZuQVERggPjTmB6we3dX2qmSoRGx0YLQA5+YUsTkoF4JQODY6ztwol5/eyCcD2g1ks25FGHx/O/qhMAnCF81PWn8rZZgBNAJR/7PkNfnoUtswo2dZxFJz+CCT2cC2sIvmFHp6Zup6J87YB0KRuLC9f0Zt+bbTJP9D8rgvAxTEAy3YcKk5ABrbTBCCcdE6sQ5emdVm35zBTlu8KiARguM8iUKqqUrfBzCdh1Wcl21oNhNP/Dq0HuhdXKfsP53DrB8tYsv0QYL/MX7y8F43qaEnXQCQixERFkFfgcbULoKj5v0vTun6ZD64CywW9mvPknsP8b+VuHhnblZgo30wBrVAC4CyRq1RgyDxgy/YumVRSva9xV3vh7zQKAqRJ/bfkNG56bwn7DucCcOup7fnzyE7Fa86rwBTrJAA5LrYAFA0APKW93v2Ho3N6NuOpqetIy8pn9sYDjOzaxCef478C50pVV24mzH8Ffn2xpIhP3RZw2sNw4qV+XajneL5ctpMHv1xFXoGH2rFRvHBpT5+dxMq7YqMiyaDAtRaAjJx8VjoLQJ3SQQcAhqMmdeM4pX1D5m1OYcryXYGVAIjIQGx1wI5AA/44E8AYY9wfdaVCQ2EBLH8PZj0Nmfvsthr1YMh90O8GiI5zN75SCgo9/HPaet6ca/v72zSoyVvX9qVD4zouR6YqqvR6AG5YuDWVQo8hKkLo31bHiYSr83o1Z97mFH5ct4/DOfk+KUpV6QRARK4B3gbygY3ADm8HpRRgp/RtmAo//R1SNtptUXFw8i1wyt2uFfE5mvSsfG7/aBlznfKtQzs14qXLehFfU6f4BZOiaoBuJQBFzf89WyZQK1YbacPV6O6JPPzVKnILPPywZh8X9Wnh9c+oyl/Xw9jlgEcYY8oWB1LKO3YuhR8fge2/OBsEel5pp/TFN3c1tPJs3p/BjZOXsi3lCAB/GtqOB0afoFX9glBRLYDcfHe6AIrn/2vzf1irHRvF6V0a8/2qvXy7cnfAJACtgfv14q984lCSncu/5suSbR1GwohHIbG7W1Ed0y+bU7j5/aVk5BQQExXBPy/swfm9vH+yKv+IdbEF4EBGbnGRKB0AqMae2IzvV+1l3qYUDh3Jo16tGK8evyoJwE5A56Uo78pOg7nPw8IJUJhntyWeCGc8Du1OdTOyY/p0cTJ//WoVBR5D4zqxvHlNX05qmeB2WKoa3BwD8KvT/F8jOpJerXT533A3vHNjasZEkpVXyLQ1e7m8fyuvHr8q85FeB64UkcAZcq2CV2G+vei/2At+fcle/Os2h/MnwE2zA/bi7/EYnp22nge+WEmBx3BCYh2m3HaKXvxDgJtdAEXN//3a1vfZ3G8VPGrERBbPAPh2pfcb3Y/bAiAiQ8tsWgJcCCwSkVeAbcAfzhRjzByvRKhCkzGw4Xv44RFI3WK3xdSGwXfDybdBTOAufpKTX8i9n/3Gdyv3AHaltpev6EUdF5eOVd7jagvAVp3/r35v7InN+HrFbuZvOciBjFyvFhGrSBfALP64EFDRyKa3jvKaAbSFQJVv7yqY/ldbux9AIqD3tXaAX+3G7sZ2HAczc7lx8hKW7UgD4MoBrXjsnG5a3CeExEU7LQB+rgOwKy2b5NRsAAZqAqAcQzs1pE5cFBk5BUxdvYdrBrbx2rErkgBc57VPU+Etcz/MeAKWTaY4b+wwAs54Ahp3cTW0itiWcoRrJy1iR2oWIvDwmV10MZ8QVNwC4OdKgIu22eb/2rFRdG1a16+frQJXbFQkZ3RN5ItlO/n2Nz8nAMaYd732aSo85efAwtdgzr8gz45wpmFnGPUkdBzpbmwVtHJnGte9vZiDR/KIi47gP5f2YnT3xOO/UQUdt2YBLNpmV//r07qetiip3zn7pKZ8sWwni7ensjc9h8R47xQ/0yoTyneMgfXfwvSHIW273VajHgx/GPqMg8jg6DOfs/EAN7+/lKy8QhJqRjNpXD966wjtkFU8CNDPXQALt9oEYEA7rf6nfu+UDg2pVzOaQ1n5fLdqD9cPbuuV42qaqXxj/zqYfC58cpW9+EdEwcm3wp3Lof+NQXPxn7J8F+PfWUxWXiHNE2rw+c2D9OIf4oq6APy5GND+wzlsdYpIDdDyv6qM6MiI4hbH//3mvdkA2gKgvCsr1dbsXzwRjHMH1WEkjH4aGnZ0N7ZKemvuVp74bh0AJyTW4d3x/WlSN3DWHVC+4UYLwKIke/cfFx1Bj+YJfvtcFTzOPrEZHy1KZkVyGsmpWbSsX/2ZUpoAKO/wFMLSt2HGk5Btv8xo0AFGPQ2dznA3tkryeAxPT11XvKBP/7b1efOavsTXCI5WC1U9bqwFUNT837tVPZ3/r8o1oF0DGtaOJSUzl+9W7eHmYdVfb0//0lT17VgAbwyD7+61F/+YOnZk/y3zg+7iX1Do4YEvVhZf/Ed3S2Ty+P568Q8jbswCKBoAOKCtTv9T5YuMEM7sYbsBvFUUSBMAVXUZe+HLP8GkUXZuP0Cvq+DOZTDoDojybt1qX8sr8HDnx8v5fOlOAK4Y0IpXruxdPC9chYdYP9cBSD2SV1z/X5f/Vccy9sRmAKzedbh44bHqqFACICLJIvKSiJyuJYAVhfnw68vwUl9Y+bHd1qwX3DADzn0l4Iv5lCc7r5Cb3lvC96v2AnDzsPY8eV53Xc0vDPm7EuBip/8/JjKCXq0S/PKZKjj1bV2PJnVtJcCpq/dU+3gVbQH4BjgP+BE4ICLvicj5IhK49VqVb2ydDa+dAj88bOf016gPZ79oL/4t+rgdXZVk5ORz7duLmLXhAAD3j+rMg2NO0AI/YapkEKB/EoCi/v+TWsZra5M6pogIYUz3pgBMdW5WqnW8iuxkjLnNGNMSOBmYAPQFvgBSRORrERknItp5Fcoy9sLn18PkcyBlgy3f2+9GuGMp9LkWIoKzN+nQkTyuemthcR/sY+d047bhHVyOSrmpZBqgf7oAFiXZCoDa/68qYowzHXDVrnSSU7OqdaxKfWsbYxYZYx4yxnQBugKPA4nARGCviMwUkTtFpHW1olKBo7AAFrxmm/tXf263tRxgV+o763moGbx9lvszcrjsjQX8tjOdCIHnLjqRawe1cTss5bKStQB83wJwOCeftbsPA9r/ryqmb5v6NKztnW6AKt+2GWPWG2OeNsYMAFoB92BXBXwe2Coiy0RkdLWiU+7asRDeOBWmPWib+2s2sH38102Dpie6HV217E3P4dIJC9iwL4PoSOHlK3pzcd+WboelAkBRKeBCj6Gg0LdJwNKkQ3iMHeHdp7UWmFLHFxkhjO5ulwj+vprdAF5ptzXG7DLGvGyMGQE0xi4glAR098bxlZ9lpcLXt8OkM2DfKkCgz3Vw+xI7yj9Im/uL7EnP5rI35rMt5QixURG8cU1fzuzR1O2wVICILTUP39etAAucBYC6N4+nVqyWZVEVc6YzDmBFchq707KrfByv/8UZY9KAyc6PCibGwG8f2wF+WfaLicQTYewL0KKvu7F5ya60bC5/YwE7UrOIi47grWv6MbhjQ7fDUgGkaBAg2ASglveWX/+DorEnJ2vzv6qE/m3rU79WDKlH8pi2ei/jq7g2QHDfyinvSdlsB/hNudle/GPqwJjn4KZZIXPx33koi8vemF988Z90rV781R/9vgXAdwMBs/IKWLUzHdD+f1U5UZERjOpmuwGqMw5A25zCXUEuzHsB5v4LCvPstq7nwehnoG7oNIsnp2Zx2RsL2JWWTc2YSCaN68fJ7XTUtfqjojEA4NtqgEu3H6LAYxCxA7uUqowzezRl8/5MzurRFGNMlaYtawIQzpLmwf/uhoOb7PP4VnDWv4KufO/x7DiYxeVv2ot/rZhI3r6uv95xqaOKK9UFkOPDFoCi5v8uiXW11LSqtCEdGzGkY6NqHUMTgHCUnQY//h8se9c+l0gYdDsM+wvE1HI1NG/bcTCLS9+Yz570HGrHRvHOdf30bksdU42YkgQgK893CUBRAaAB7fTvUVVRYX61llav8BgAEbleRKaJyK8i8oKINKvypyr3rP0GXulfcvFv3gf+NAdG/iPkLv47D9k7/6KL/7vj++vFXx1XbFQERRWgj+QW+OQzcvILWZGcBsAAbY1SVbF7Obw+2H6nV1GFWgBE5EZsBcAiJwOXicgpxpitVf505T+Hd8P398P6b+3z6Fpw+iPQ/yaICL3yo3vTc7jyrYXFff7vju+n86xVhYgItWOjOJxT4LMEYEVyGnlOjYF+mpSqyijMh7n/hjnPgqfAfq93PAOi4yp9qIp2AdwKJAOXADuB0cC/gX8B51f6U5X/eDyw7B348e+QayuO0WEEnPVvqBeaBRv3Z+RwxVsL2H7QGe0/rh99WuuXrKq4ogQgM9c3XQBF/f8dG9emQW0fzjNUoeXABvjqT/buH6BhZzj/9Spd/KHiCUB74B/GmIXO84kiUhf4p4jUMsZUf11C5X2p2+CbOyBprn1eswGM/if0uAhCdKGbVKe2/9YDR4iJsvP8dbS/qqyiojy+agFY6BQA0v5/VSGeQljwKsx4AgpyAIGBt8Fpf4PoGlU+bEUTgNrA7jLbpmFbADoDy6ocgfI+jwcWvwk/PQr5zmIRPS6xU/tqhe7FMC3LXvw37sskOlKYcFUfneevqqQoAcj0QQKQV+Bh6fZDAPTXBYDU8aRshq9vhWTn/juhFZz3GrQZXO1DV2cWwH7nsW61o1Dek7IZvrkddsy3z+s0tZX8Oo9xNy4fO5yTz7WTFrF2z2GiImxt/+EnNHY7LBWkavuwBWDVrnRynPoCOgBQHZWnEBa+Dj//w7nrB/qOtwO2Y+t45SMqkwCcJSKHgCXGmAOltms1wUDgKYT5r8DMJ0v+WHpdDWc8ATUSXA3N17LzCrn+ncXFq/r957KejOqW6HZYKojVirUDY32RABT1/7dpUJMmdavWd6tC3MEtMOVWSF5gn8e3hHNegvbDvfoxlUkALgcuAxCRZGA1YICuIrLcGHPIq5Gpiju4BabcUtJEFN8Szv4vdDjd3bj8IK/Awy0fLGVxkv3ze/7ikxh7os5QVdVT0gXg/UGAxf3/2vyvyvJ4YNEE+OkxKHAW+ekzDkY+DnHeb2yvaAIQD/R2fvo4j6MBAf4L/FdEdgMrnZ/fjDEfez1a9XtFff0//r3kj8XLTUSBrNBjuOfTFczaYBukHj+3Gxf0buFyVCoU+KoLoNBjWJJU1P+vzf+qlINb7CqsO361z+u2gHNe9OmNXIUSAGNMBjDb+QFARGoBPSlJCPoAZwBjsC0DmgD40qHt8PVtJSP86zaHc1+G9qe5G5efGGP425RVfLfSLoRx3xmduHpgG3eDUiGjeBZAnncTgLW7DxcPLNQZAAoof9B272ts921cvE8/usqDAJ2pf784PwCISBzQy/lRvmAMLJsM0/8KeZl2W8+rYPRTPv9jCRTGGJ6Zup6PFiUDcNPQdtw2vIPLUalQUttHswCKmv+bJ9SgRb2aXj22CkKp2+xd//Z59nnd5nD2i9BxhF8+3qtrARhjcoD5zo/ytoI8+ORK2PSDfV6rsW0iCvER/mW9OmsLE+bYApSX9WvJQ2NOqNJKWEodTa0Y3wwCXOgMANTR/2HO44HFbzl3/U4ZnV5XwSj/3shVtBTwi0AzY8xFzvME4B/AKUA+sAj4oFShIL8R+80/HrgF6ALkOfE8aYyZU4njnAKcCwwH2mCnN+4GfgaeMcZs9m7kVRAVA7XtGtB0vxDOfB5qhtcXyXsLtvPc9A0AnNWjKU+e30Mv/srrSgoBeW8QoMdjWJxkEwDt/w9jBzbaAm1FI/zrNLV3/S6swlrRFoCzgHdKPf8EGAlkAzFAf+A2EfkOuMoYc9ibQR7Hf4A7ge3A60Ad7GyFmSJyiTHmiwoe5wugEbb14gOgABgIXI9d9+AMY8yvXo698kY9BZ1GQZez3Y7E775buYf/+3o1AMM6NeKFS3sSGaEXf+V9vugC2Lg/g7SsfAAGaHXK8FOYD7/8F2b/Ewrz7LaeV8GoJ12bql3RBCARuxYAItIXGAZcCnxhjPGIyEnAdcBtwDwROdkYk+WLgEsTkcHYi/9GoL8xJt3Z/iqwAJggIj8VbT+O/wDvGWN2lfmMvwJPAm8A3b0YftXE1Q3Li/+vW1K455MVGAO9WyXw+lV9iInSEhTKN0qXAjbGeKWVqWj530Z1YmnTQPv/w8ru5fD1HbBvlX2e0Mre9Xt5Xn9lVfQbNL/Uf58FvGWM+cwY4wEwxvxmjLkbGIVthr/Xq1Ee3c3O45OlL/LGmBXAR0AD4KKKHMgY80zZi7/jn9iWjm4ionVlXbBmdzo3TV5KXqGHDo1rM2lcv9+t2a6UtxUlAAUeQ26BxyvHXFSq/1+7rcJEfjb8+H/w5mnOxV/g5Fvh1gWuX/yh4gnALqBomPXpwMzydjLGzADexikY5AdF/wd/KOe16c7jqdX8DIPtDoDfJ0LKD5JTsxj39mIycwtoGh/H5PH9SagZ43ZYKsQVdQGAd7oBjDGlCgBp/39YSJoHrw2yzf7GA41OgOt/hNFPQ0wtt6MDKt4F8B1wqzP47xRKLq7lWQZcXc24jsupQ9AMyDTG7C1nl03OY6dqftTF2HEFC47VlSAiacc5TjxQka4I5UjJzOXqiQs5kJFL3bgo3h3fn2YJVV/5SgUnN86tolLAYLsBGlZzyd6tKUdIybT9vtr/H+Jy0m1xtqVv2+cR0TDkXhjyZ4gKrKWfK5oAPI69k74V2IZtDr/ZGPN6Ofv2AXK8E94xFc2VONqJXzQQMaGqHyAibYGXgELgvqoeR1XekdwCxr+zmKSDWcRGRTBpXD86NQn96oYqMHi7BaCo/79ezWg6NKpd7eOpALVhKnz7Z8hwFs9t3gfOeRmadHU3rqOoTCXA/iLSHZsAxACLROQW4EtgDfaiPwo7Je+bigYgIjuB5pWI+TFjzKOV2N9UYt9iItIYmIqdGXCnMeaXY+1vjEk4zvHSqhJHOMor8HDz+0tZ6Szu8/IVvenbRptNw5Ub51atUgmAN6YCLnKa//u3rU+EzlwJPZkHYNqDsPpz+zyqBpz+CAy4GSICd7xSpQoBGWNWO/95xJk3/ybwd0ousgLsAR6oxGG/BCrz7b7SeSy68z9a1YS6ZfarMOfiPwPoDNxtjHmpssdQVWOM4cEvVzJ3UwoAT53fg5Fdm7gclQo30ZERxERFkFfgqXYxINv/XzT/X5v/Q4rHA8vfswP9ctLstrZD7Qj/+m1dDa0iqlMKeD9wroh0AgYDDYGdwDfGmMxKHOfOKn7+EWcBomYikljOOICOzuPGyhxXRJpii/+cANxmjHm1KvGpqnnhp018ucxOxvjzyE5c1r+VyxGpcFU7NorUgrxqdwEkp2azJ932iuoAwBCyfz18ezfscArfxsXb+v29roYgmeVR7VLAxpiNVPIi60UzgSuxixBNLvPaKOdxVkUPJiItsHf+HYCbjTFveCFGVUGfLk7mxZ/t2M1L+7bkjtO0vr9yT63YSFKPVL8c8IKttvk/vkY0XZt6f0lX5Wf5OTD3eZj3H/A4E8N6XGIL+tRu7GpoleXVtQBc8Do2AXhYRL4uVQioJ3A5cBD4vPQbnDv8eGBP6VH9ItIKm1C0Aa43xrztj19AWbM3HuChr2yRjKGdGvHE+d11rrRyVe3YaCC72i0ARQmA9v+HgK2z4Nt7INWuRUK9tjD230G7CmtQJwDGmHnOOgV3Ar+JyBeUlAKOBv5UztS9p4FrsZUL3ym1fTb24r8UaC0ij5bzkf8xxqR58VdQ2EI/t76/lEKPoWvTurx6ZW+iI7XKn3JXQo1oAA5l5VX5GMaY4gTgZJ3+F7yOpMD0h2Gls8p9RBScchcMvR+ig3dqclAnAI67gVXYKYq3YIv1LACeqMxiQNiLP9hpjH2Oss87QFoVYlRHsSstm+veXsyRvEKaxcfx9nX9fjcFSym31K9tC06lHql6/a/k1Gx2O/3/J7fT/v+gYwys+AB++BtkH7LbWg6As/8Ljbu4G5sXBP03rTHGAG85PxXZfxwwrpzt2jbnZ+nZ+Vz39iL2Z+RSJy6Kd8b3p0ndOLfDUgqA+jWLEoDcKh+jdP9/l0Tt/w8qBzba5v7t8+zzuHgY8Rj0vhYiQqOFMugTABWc8gs93PrBUjbuyyQ6UphwVR8t9KMCSv1aRQlA1bsAtP8/COVnw7wX7E/Rqn3dL7IrsdYJrSnJmgAovzPG8LevVvPLZvvl+M8LT2RQB11nSQWWBrWrlwBo/38Q2jgdpj4Ah5Ls84TWdpBfhxGuhuUrmgAov3t99lY+WZIMwF2nd+SC3i1cjkipP6pXs3oJgPb/B5G0HTD1QdjwnX0eEQ2D7rCD/GJCd+lmTQCUX32/ag//nLYegPN6NuPuER2P8w6l3NHA6QJIy86n0GOIrGQTvvb/B4GCPJj/Esx+Dgqy7ba2w+DM56FRddeRC3yaACi/WZGcxj2frACgX5t6/POiE3WuvwpY9ZwEwBhIy8qjQSVXBNT+/wC3dRZ8dx8cdBaOrZ0Io5+CbhcETSW/6tIEQPnFzkNZ3PDuEnILPLRuUJMJV/clNipwF8lQqqgFAGw3QGUSAO3/D2CH98D0v8KaL+1zibSL9pz6IMSFV0uNJgDK5w7n5HP9O0tIycwlvkY0k8b1Kx5hrVSgqlcmAagM7f8PQIX5sHACzHoa8pzlaloNtM39id3djc0lmgAonyoo9HD7h8vZsC+DqAjh9av60F7XQ1dBIDoygjpxUWTkFFQ6AdD+/wCzfT58dy/sX2Of12wIZzwOJ10eNs395dEEQPnUE9+tY87GAwA8fUEPBrbX5lAVPBrUiiEjp4CDVUwAtP/fZZkH7FK9v33obBDoOx5OfwRq1HM1tECgCYDymfcXbOedX5MAuHlYey7u29LdgJSqpPq1Ykg6mMWhSiQAxhjmbk4BYKD2/7ujMB8WvQmznoFcZzmYZr3hrH9B897uxhZANAFQPjFvUwp//8Y2t53RtQkPjOrsckRKVV7RWJXKtACs35vBgQxbPnhop0Y+iUsdw9ZZMPUvcMBONyYuAU7/P+gzDiJ04HFpmgAor9t6IJNbP7Cr+3VpWpcXLu2pzaAqKBUlAJVZEbCoy6t5Qg3aN6rlk7hUOQ5thx8ehnX/czYI9L0OTnsEaupAzPJoAqC8Kj0rn+vfXcLhnAIa1o7lrWv7UktX91NBqqEz9W/f4ZwKv2fOJpsADOnYUOtc+EN+NvzyX1u7v8D5d2o1EMb8E5qe5G5sAU6/mZXX5Bd6uPXDpWxLOUJMVARvXNOH5gnBu1a2Ui3q2TKwyanZFdo/K6+AxdvssrHa/O9jxsC6b2D63yB9h91WpymMfBx6XBTWo/srShMA5RXGGB79Zk3xAj/PXXQivVvpKFsV3FrWtwnsnvRs8gs9REceexnYhVtTySv0ECFwSntd4Mpn9q+z/fzbZtvnkTEw8DYYch/E6jTjitIEQHnFewu288FCm4XfcVoHzu3Z3OWIlKq+lk4LgMfAnrQcWjU49sIws53+/54tE4ivGe3z+MJOdpod2b/oDTCFdlun0Xap3gbtXQ0tGGkCoKrtl80pPPa/tQCM6Z7IPSNCfxENFR6aJdRAxLY2Jx/KOm4CMNfp/9fmfy/zeGDF+/DTY5Blp1hSvz2MfgY6neFubEFMEwBVLUkpR7j1g2UUegxdm9blX5ecpCP+VciIiYqgad04dqfnkJyadcx9d6Vls+XAEQCGdNQEwGuSF8PU+2H3cvs8prZdpvfkWyCqcgs0qd/TBEBVWUZOPjdMXkJ6dj4Na8fw5rV9qRmjf1IqtLSoX9MmAIeOnQD8vG4fYMv/ntQi3h+hhba0ZPjpUVj9ecm2Ey+FEY9B3aauhRVK9NtaVUmhx3DXxyvYvD+T6Ehb419H/KtQ1LJeTRZtSz3uTIAf1tgE4PQujYk6zmBBdQy5mXZa368vlkzra3oSjHkWWp3sbmwhRhMAVSXPTl/PjPX7AXjyvB70baOFNlRoKpoJcKwWgPSs/OL6/2d0TfRLXCHH44GVH8PP/4CMPXZb7URbxe+kyyFCkypv0wRAVdpXy3cyYfZWAMaf0pZL+mmNfxW6WhbXAjh6AjBjwz4KPIa46AiG6QDAyts+H6Y/VNLPHxUHg+6AU+7WaX0+pAmAqpTlOw7xly9WAXak81/PPMHliJTyrQ6N7QUoJTOPfYdzaFI37g/7/O83e8c6tGMjasRovfkKO5QEP/4d1k4p2db9QhjxKCS0cimo8KEJgKqwfYdz+NN7S8kr8NCuYS1euryX9nWqkNelaV1ioyLILfCwfMchRnf//QC0g5m5xfP/tf5FBeVmwNx/w/xXoNAunETzPjDqaWg1wN3Ywoh+e6sKyckv5KbJS9ifkUuduCjevLYv8TW00IkKfTFREfRobkf1L9+R9ofXv1u1h0KPoU5sFKd3aezn6IKMpxCWTYYXe8O8f9uLf51mcP4bcP1PevH3M20BUMdljOGhL1fx2850RODFy3vRvpH2y6nw0atVAku2H/pDAmCM4dMlyQCM6ZFIXLQ2/x/Vtrm2n3+v7UIkqgYMvtv29cfoqolu0ARAHdebc7fy1fJdADw4+gSGd9a7HBVe7LoW21i5K+13awIs2pbK6l2HAbi0n/ZZlyt1K/zwCKz/tmTbiZfZ0f3x2mXiJk0A1DHN2rCfZ6auB+D8Xs25aWg7lyNSyv96OQtb5eR7WJyUyiBnoZ+J87YBtvZ/n9a6+NXv5KTDnOdh4etQmGe3tehvy/e26ONubArQBEAdw5YDmdzx0XI8Bk5qEc/TF/TQ9c1VWEqMj6NnywRWJKfxzi9JDGrfkEXbUvlhrS3+c/3gti5HGEAKC2D5ZJjxZEnd/viWMPIx6HaBLtMbQDQBUOVKz87nxslLyMgpoHGdWCZc3Vf7N1VYu2FIW27/cDk/rtvH3E0H+Ps3awCbHJ/ZQ0vTArBlJkx/GPbb/zdE14Ih98DA2yFaK4UGGk0A1B/YMr/L2XrgCDGREbx+dR8S4/8491mpcDK6WyLNE2qwKy2bqycuAiAqQnjmwhOJDPcFsFI2ww9/g41TnQ0CPa+E0x+BOloZMVDpNED1B89OW8+sDXZe81MX9HAGQCkV3qIiI3jx8p7Fa17E14jmP5f1pEvTui5H5qLsQzDtr/DqgJKLf6tBcNNMOO8VvfgHODHGuB1DWBCRNABjTEIFdnftH+Wr5Tu555PfANuv+cjYrm6FosJLlW+h/X1uZecVMnfTAfq0rkeD2mG6HG1hASx9G2Y+BdmpdltCazjjcehyjvbzB5aj/mNoAuAnwZAA/JacxsUT5pNX4GFIx4a8Pa6fVvpT/hI0CUDY2/QT/PAwHLCzg4ipA0PvhQG3QLR2FQago55bOgZAAbD/cA43vbeEvAIPbRvW4uXLe+vFXylV4sAGO8Bv84/OBoHe18Bpf4PaWhskGGkCoGyZ3/eWsu9wLrVjo3jzmj7E19Qyv0opICsVZj0Di98CU2i3tRkCo5+GxB7uxqaqRROAMGeM4eGvVrMiOc0p89uTDo3ruB2WUspthfn2oj/rGchJs9vqt4ORj8MJZ2k/fwjQBCDMTZy3jS+W7QTggVEncNoJTVyOSCnlKmNg43Q7re/gJrstNh6GPQD9b4KoGHfjU16jCUAYm7PxAE99vw6Ac3s24+ZhWuZXqbC2by1M/ytsnWmfSwT0uQ6G/xVqNXQ3NuV1mgCEqc37M7ntw2V4DJzYIp5/XniilvlVKlwdSbFT+pa+DcZjt7UbDqOegiY6FThUaQIQhtKy8rjh3cWlyvz20TK/SoWjgjxYNAFmPwe56XZbgw72wt/xDO3nD3GaAISZ/EIPt324jKSDWcRGRfDGNX1pGq81upUKK8bA+u/gx0fscr0AcfFw6kPQ7waI1FlA4UATgDDz+Ldr+WXzQQCevehEerZMcDcgpZR/7V0F0x6CpLn2uUTai/6pD0LN+u7GpvxKE4Aw8t6C7Uyevx2A24d34NyezV2OSCnlN0dS4Od/wLLJFBdE7DASRj0JjTq7GppyhyYAYeLXzSk86ixfOqpbE/48spPLESml/KKwAJZMhJlPQo7Tz9+ws9PPP8Ld2JSrNAEIA9tSjnDLB8so9Bi6NK3Lvy/pSUS4L1+qVDjYNgem/gX2r7XP4+Jh+MPQd7z28ytNAEJdelY+17+7mPTsfBrWjuHNa/pQK1b/2ZUKaWnJtpDP2inOBoE+18Jp/we1GrgZmQogeiUIYXkFHm5+fylbDxwhJjKC16/qQ4t6Nd0OSynlK/k58OuLMPffUJBtt7UcAGOehWY9XQ1NBR5NAEKUMYa/TVnF/K0lI/77ttERvkqFpKJpfdP/Cml2oC+1E2HkP+DES3Q+vyqXJgAh6rXZW/h0ia3xf/eIjpzXS0f8KxWSDmyEaX+BLTPs84hoGHgrDL0fYnVhL3V0mgCEoO9W7uHZaRsAOL9Xc+46vaPLESmlvC4vC+Y8C7++BJ4Cu63DCBj9DDTUc14dnyYAIWb5jkP8+dMVAPRvU59nLuyhNf6VCjUbpsH390P6Dvu8Xht74e80Wpv7VYVpAhBCklOzuHHyEnILPLRpUJMJV/chNkpr/CsVMtKSYdqDsP5b+zwyBobcC6fcDdFxroamgo8mACEiPTuf8e8sJiUzj/ga0Uwa1496tXTdbqVCQmE+LHgNZj0D+Ufstnanwpn/goYdXA1NBS9NAEJATn4hN05ewqb9mURHChOu7kO7RrXdDksp5Q07FsK398B+W8mT2k1sFb/uF2pzv6oWTQCCXKHHcPfHK1i0LRWA5y46iZPbaaEPpYJedppdrW/ZZGeDQP8b4bS/2Yp+SlVThNsBVJdY14vIEhE5IiKHRGS6iAyt5nFjRWS1iBgRSfJSuF5ljOHv36xm2pq9ADx8Zhed7qdUKFj3LbwyoOTi37Qn3DgDznxOL/7Ka0KhBeA/wJ3AduB1oA5wGTBTRC4xxnxRxeM+AbT2SoQ+8tKMzby/wI4CvnFIW24c2s7liJRS1ZK5347uLyrhG10LTv8/e+cfoQN6lXeJMcbtGKpMRAYDc4GNQH9jTLqzvSewAMgE2hdtr+RxZwN3AK8A240xbaoZaxqAMSahArsf9x/lo0U7eOjLVYCd6/+vi0/SBX5UMKvyH6+3zy1XGAMrP7Ej/LMP2W3thsPZ/4V6AX0fogLfUc+tYO8CuNl5fLL0Rd4YswL4CGgAXFSZA4pILeBdbALwmnfC9K4f1uzl4a/sxX9op0Y8e9GJevFXKlilJcMHF8NXf7IX/7gEOO81uPorvfgrnwr2BGC48/hDOa9Ndx5PreQx/wU0Bq43Adg8sjgplTs+Wo7HwEkt4nntyt5ERwb7P6NSYcgYWDIJXj0ZNv9ot3U5B25bBD2v0BH+yueCdgyAc6feDMg0xuwtZ5dNzmOnShxzFPAn4FZjzLZKxpN2nF3igUp1RZSVW1DIHR8uJ7fAQ9uGtZg0rp8u7atCnj/OLb87vAe+uR02/2Sf12oMZz0PXc91Ny4VVoL51rFoKOzRTvzDzmNCRQ4mIvWAicAM7GDCgBMbFclrV/Wmc5M6TB7fnwa1Y90OSSlVWau/cO76nYt/j0vgtoV68Vd+5/rto4jsBCozd+0xY8yjldi/os34L2OTiio1/R9vAFIF7mIqpFereky9a4j2+auw4a9zy+eyUuH7+2wCAFCjPoz9N3Q73924VNhyPQEAvgQqs1D9Suex6M7/aJNi65bZ76hE5BzgCmzTf1IlYnGFXvyVCjKbfoKvb4NMp7ey02g4+0Wo08TduFRYcz0BMMbcWcX3HRGR3UAzEUksZxxA0XqYGytwuN7O46si8mo5r7cWEeN8rl59lVIVU5ALPz0GC16xz2Nqw+inodfVOshPuc71BKCaZgJXAmcAk8u8Nsp5nFWB4yzD9v+X53psPYFPqhCfUipcpWyGL8bDnt/s85YnwwUT7NK9SgWAsCsEJCJNsd0GeypSIMi58w+4QkBKhZjQKgS04iP47l67cp9EwND7YegDEBns91wqCB313Arqv0ZjzDwReRFbCvg3EfmCklLA0cCfyrnIPw1cC1wHvOPHcJVSoS43w174VzoNhnWawYVvQpvB7salVDmCOgFw3A2sAm4FbgHysXf/Txhj5rgYl1IqnOxaBp+Ph0NOCZHOZ8G5L0PNyoxxVsp/groLIJgEZDOlUoEjeLsAPB6Y/zL8/Bh4CiAyFkY9Cf1u0IF+KhCEZheAUkq5KnM/fHUzbPnZPm/YCS6aBIk93I1LqQrQBEAppapi048w5RY4csA+730NjH4GYmq5G5dSFaQJgFJKFfF4IOI4FdLzc+DH/4NFE+zz2Lpw9n+g+4U+D08pbwrmtQCUUsp7khfDG0Nt1b6j2bsK3hxecvFvNRBunqcXfxWUdBCgn7g+UEmpwOb+IMB3z4Fts22hnlsXQHSNktdyM2H2MzD/VTCFIJFw6kMw5M8QEVnV0JXyh6OeW5oA+IkmAEodk/sJwIGN8Nog8OTDgJvhjCch9zCs+hx++Q8c3mX3a9ARznsNWvarashK+ZMmAG7TBECpY3I/AQD4+XGY+7z976g4KMwD4yl5PuQ+OOVOiNKluFXQ0GmASil1XEPvhyP7Yfn7UJBjt0XVgBMvsc39WsdfhRBtAfATbQFQ6pgCowWgSOpW2L/eTulr2f/34wGUCi7aBeA2TQCUOqbASgCUCh1HPbd0GqBSSikVhjQBUEoppcKQJgBKKaVUGNIEQCmllApDmgAopZRSYUgTAKWUUioMaQKglFJKhSFNAJRSSqkwpAmAUkopFYZ0LQD/Sa/EvlWuiqZUGNJzS6kq0FLASimlVBjSLgCllFIqDGkCoJRSSoUhTQCUUkqpMKQJgFJKKRWGNAFQSimlwpAmAEoppVQY0gRAKaWUCkNaCCjAiMh2IN7tOJTys3RjTGtffoCeWypMHfXc0hYA5W/xhPeXsP7+gfX7B1o8yj/03x2tBKj8TETSAIwxCe5G4g79/QPr9w+0eJR/6L+7pS0ASimlVBjSBEAppZQKQ5oAKKWUUmFIEwCllFIqDGkCoJRSSoUhTQCUUkqpMKQJgFJKKRWGtA6AUkopFYa0BUAppZQKQ5oAKKWUUmFIEwCllFIqDGkCoJRSSoUhTQCU34nIVSJinJ9xbsfjLyIyRkS+FZEDIpIrIski8o2InOx2bL4k1vkiMkNEdotItohsFpH3RKSHSzH1EJEvRSTFiWeNiNwvIrpEehATkQYicoOIfOX8jWWLSLqIzBOR60XkD9c85+/zehFZIiJHROSQiEwXkaFu/A7+pLMAlF+JSHNgNRAF1AauM8a842pQfiAiLwB3A7uA74EUoDFwMvCaMeYV96LzLRH5N3APcACYAqQCXYGzgELgTGPMT36MZxDwExAJfArsdmLp5sR3gdEvxqAkIjcDrwF7gRnADqAJcAF2+d8vgYtK//uKyH+BO4HtwBdAHeAyoBZwiTHmC3/+Dv6kCYDyKxGZDnTEnmj3EQYJgIjcAryK/Z2vMsbklHk92hiT70pwPiYiidgL7B7gRGPMwVKvXQF8AMwyxgz3UzyRwBqgMzbxmOpsj8EmBUOw/0Yf+CMe5V0ichr2xuI7Y0xhqe2JwCKgJXCxMeZzZ/tgYC6wEehvjEl3tvcEFgCZQPui7aFGuwCU3zgXwpHA9cARl8PxCxGJA/6BveMfV/biDxCqF39Ha0CABaUv/o5vnceGfoxnOPbiP7Po4g9gjMkDHnGe3uzHeJQXGWNmGGO+KX3xd7bvBV53np5a6qWif+snS1/kjTErgI+ABsBFPgvYZZoAKL8QkfbAc8CrxpiZbsfjRyOxF7jPgXynL/xBEbnducsIdZuAPOBkEWlQ5rWznUd//j0UtTT8UM5r87CJ6UARifVfSMpP8pzH0gn3sf4epjuPp/oqILfpgBflc87Am3eB/cBfXA7H3/o6jxnAKmz3RzER+Qq4xhiT6e/A/MEYkyoifwH+DawVkSnAIaALcCa2W+RhP4bUyXncVPYFY0yhiGwDugPtgHV+jEv5kDO481rn6TRnWy2gGZDptBCUVfQ30qmc10KCtgAof7gPGASMN8aERdN/KY2dxz9j7y4HYQcZ9cP2SZ6PHbQUsowx/wEuBmKBm7BJ4DnYvvi3jTEZfgwn3nk8Wp/uYecxwfehKD96BpvYTTPGFN3Zh/3fgiYAyqdEpDu2D/wVY8wsl8NxQ6TzWACca4yZb4zJNMYswV4EM4ErRaSFaxH6mNMC8CnwJtAGO0hrEJALfCsid7oX3VHp6OgQ4fx93QtsAK6uwiFC9m9BEwDla5Oxo8AfdDsQlxxyHpcbY3aUfsEYsw9YiB0k18ffgfmDiJyKvfuaYoy53xiz3RhzxBgzH5sAZQNPiUhtP4VUdLcXf5TX65bZTwUxEbkN+C+2O+dUY0xKqZfD/m9BEwDla72AtkBmqeI/Bvi78/rbzrZHXYvQtzY4j2lHeb0oQajh+1BcMdZ5nFX2BScBWoedb32Cn+LZ6Dx2LPuCM0WwLbY2wVY/xaN8RETuBl7G1h05tWw/v9MduRuo7UwTLKvob2RjOa+FBB0EqHxt4lG298YmB3Owg22W+S0i/5rhPHYRESmnwEw35zHJfyH5VdFo+qNN9WvkPOb6IRawMw7+CpyBbZkobTA2GZlnjPFXPMoHnG6nZ4AVwMgyd/6lzQSuxP49TC7z2ijncZYPQgwMxhj90R+//wCPYvvWxrkdix9+12+d3/X2Mtuvc7ZvBiLdjtNHv/ulzu+4B2ha5rUbnNf2+uv3x47J2OB87phS22OwyagBrnT7/5v+VOvf+BHn33EJUP84+w529t0AxJfa3hPIwdbviPdFnIHwoy0ASvnercCvwEsicjawEtvkPRbIwiZBhcd4fzD7HNsKchqwzpn2eAD7BTsS8GATI7/8/sZO9bsO+BmYIiKfYJOT0qWAP/RHLMr7RORa7KDjQmyFvztFpOxuK4wxUwCMMfNE5EVsKeDfRKR0KeBo4E8mRKsAgnYBKOVzxpgdItIXO+5hLLb4yCHgY+BxY8xaN+PzJeeCOwa4A/ulehEQh72z+hJ43tgBgf6M6VcRGQA8hr3w18L2+T8AvGCcW0AVlNo6j5HYtTfK8y420StyN7ZGx63ALdhCQQuAJ4wxc3wRZKDQtQCUUkqpMKSzAJRSSqkwpAmAUkopFYY0AVBKKaXCkCYASimlVBjSBEAppZQKQ5oAKKWUUmFIEwCllFIqDGkCoJRSSoUhTQCUCmAicpuILBKRHBGZ5XY8SoUKPbe0FLBSgW4PdlWzfsBAl2NRKpSE/bmlLQBKHYWIjBMRIyKnuhWDMeZLY8yXwD63YlDK2/TcCgyaAKiQISKviMguKWf5L6VU1em5FZq0C0CFBOeL6Vzg60BfzU1EYrFLjR5NdggvD6yCjJ5boUtbAFSo6Ac05/fLfAaqiUDGMX6GuBeaUn+g51aI0gRA+ZWInCQiX4tIuogcFpEpItJURDJF5KNqHPp8IB2YWYEY2ojIF87npzvxtBWRpIqMBhaRh53+y5dEJKLU9god1xhzlTFGjvFz3BiUKkvPLT23Kku7AJTfiMjpwLfAduAJIBsYB0wFagErqnH484HvjDH5x4mhATAXaAK8DqzD3hXMdGI41nsjgJeBW4CHjDHPeOO4x/nMKOx5GgVEiEgc4DHG5FX1mCr06LlVeXpuAcYY/dEfn/8AjYAU4FegRqnt8UAqYIBRVTx2F+f9F1Vg32edfa88yvZZpbaNc7adCtQAvgLygGuqc9xK/m6POu8v/VOlY+lPaP7ouaXnVlV/tAtA+ctfgAbAncaY7KKNxph0YJnzdEUVj30ekAtMq8C+Z2Pn/5ZtEn3+GO+pD/wIjADONsZM9tJxj8sY86j5YzPmqdU5pgo5em5VgZ5bOgZA+c9lwBxjzJKjvL7XGLMPwOnXO68Sxz4f+NEYk1mBfdsCm40xntIbjTH7gbSjvOcdYBAwxhgz3YvHVcob9NxSVaIJgPI5EUnEjiL+wxeU0/fXgyreoYhIC6Avvh2h/AngAf5PRGr48HOUqhQ9t1R1aAKg/KFooE55c4jPBRrjfEmJyGdAK+AjZ/TyO8c59nnOcb+pYCxJQIfSI4ydz20MJBzlPR8AVwHDgW9FpKaXjqtUdem5papMEwDlD8lAITCs9EYRaQ285DxdAWCMuRjYAVxujKltjBl3nGOfD8wzxhyoYCz/A5oCl5fZft+x3mSM+dh5zxBgqojU9sZxlaomPbdUlek0QOVzxpg8EZkMXCciXwPfAS2BG7F1uJtThWZKEakHDAUeqMTb/glcAbwtIv2B9cBg4BTsSOqjVjozxnwuIvnAp8B0ERljjDlc3eMqVVV6bum5VR3aAqD85U7gDWAA8C/n8XxgN5AFbKrCMc/GJrFTKvoGY0wK9svjW2A89sulNrYJUrDzp4/1/q+BC4A+wA8ikuCN4ypVDXpuqSoRZz6kUq4QkWRgpzFmYKltW4E/G2OmHOe9XwFtjTE9vRBHA+zdxARjzM3VPZ6vj6vU8ei5pY5HWwCUa5wMvwV/bKLcB3SswCHmA3+twueWN9r4L87jj5U9nq+Pq1Rl6bmlKkJbAJRrRGQIMAe42RgzodT2sdgBTPWAL4wx13v5c2dhS6YuASKB04Gx2EpqQ00VVwvz1XGVqiw9t1RF6CBA5aYezuOK0huNMd9i+/t85X/ANdhpTjWAndi+08eq+UXiq+MqVVl6bqnj0hYApZRSKgzpGACllFIqDGkCoJRSSoUhTQCUUkqpMKQJgFJKKRWGNAFQSimlwpAmAEoppVQY0gRAKaWUCkOaACillFJhSBMApZRSKgz9P8RIUAFtTLppAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 504x360 with 2 Axes>"
       ]
@@ -370,7 +426,7 @@
     "Plcl_R = np.zeros(len(qt))\n",
     "\n",
     "for i, x in enumerate(qt):\n",
-    "    RH = mt.mixing_ratio_to_partial_pressure(x / (1.0 - x), PPa) / mt.es_liq(TK)\n",
+    "    RH = mt.mixing_ratio_to_partial_pressure(x / (1.0 - x), PPa) / svp.liq_analytic(TK)\n",
     "    Plcl_R[i] = lcl(PPa, TK, RH)\n",
     "    Plcl_X[i] = mt.plcl(TK, PPa, x)\n",
     "    Plcl_B[i] = mt.plcl_bolton(TK, PPa, x)\n",
@@ -394,7 +450,9 @@
     "Plcl_B = np.zeros(len(qt))\n",
     "Plcl_R = np.zeros(len(qt))\n",
     "for i, x in enumerate(qt):\n",
-    "    RH = mt.mixing_ratio_to_partial_pressure(x / (1.0 - x), PPa) / mt.es_liq(TK)\n",
+    "    RH = mt.mixing_ratio_to_partial_pressure(x / (1.0 - x), PPa) / svp.liq_wagner_pruss(\n",
+    "        TK\n",
+    "    )\n",
     "    Plcl_R[i] = lcl(PPa, TK, RH)\n",
     "    Plcl_X[i] = mt.plcl(TK, PPa, x)\n",
     "    Plcl_B[i] = mt.plcl_bolton(TK, PPa, x)\n",
@@ -417,14 +475,14 @@
    "id": "cb6f2331-c0f2-471b-bffe-b21097f6ff49",
    "metadata": {},
    "source": [
-    "## 4. Integrating the first law to arrive at the moist adiabat\n",
+    "## 5. Integrating the first law to arrive at the moist adiabat\n",
     "\n",
     "This example shows how to construct a moist adiabat allowing for equilibrium freezing.  To do so it makes use of two calls of the moist_adiabat function.  The first calculates the moist adiabat assuming condensation only produces ice, the other only liquid, with the latter being valid for temperatures above T0, the former for temperatures below T0, and an isothermal T0 layer residing in between.  The result is plotted in terms of the dry potential temperature to better highlight the enhanced stability."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "id": "4a05ea68-9c61-449b-9945-d8f87fbb057a",
    "metadata": {
     "tags": []
@@ -492,11 +550,110 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1417891f-4fe5-4069-99e1-ca43c26af90b",
+   "execution_count": 8,
+   "id": "5d88d98d-a59e-41fc-a5b6-124a8f97947b",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best fit parameters for liquid: a=17.4185, b=33.5714\n",
+      "Best fit parameters for ice:    a=22.0422, b=5.0000\n"
+     ]
+    }
+   ],
+   "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 ice_error(T,a,b):\n",
+    "    return np.abs(svp.tetens(T,a,b)/svp.ice_wagner_etal(T) -1.)\n",
+    "\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(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",
+    "\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(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}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "feac77dc-c04e-4fa2-b173-4dd143febcf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x216 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_context(\"talk\")\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",
+    "\n",
+    "sns.despine (offset=10)"
+   ]
   }
  ],
  "metadata": {
diff --git a/examples/saturation-water-vapor.ipynb b/examples/saturation-water-vapor.ipynb
index 2e695b93a647aebc89d729cef22edd851979ba4d..2a0fd38c8de85b5a84fc2e55bad2bb2f726182bb 100644
--- a/examples/saturation-water-vapor.ipynb
+++ b/examples/saturation-water-vapor.ipynb
@@ -354,9 +354,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x625.763 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "sia_dir = './data/'\n",
@@ -440,9 +453,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'es_iapws' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m$e_\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mmathrm\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124ms,x}}/e_\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mmathrm\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124ms,ref}} - 1$\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      6\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_yscale(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlog\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m es_ref \u001b[38;5;241m=\u001b[39m \u001b[43mes_iapws\u001b[49m\n\u001b[1;32m      9\u001b[0m es_w \u001b[38;5;241m=\u001b[39m es(TK,formula\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwagner-pruss\u001b[39m\u001b[38;5;124m\"\u001b[39m,state\u001b[38;5;241m=\u001b[39mstate)\n\u001b[1;32m     10\u001b[0m es_r \u001b[38;5;241m=\u001b[39m es(TK,formula\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mromps\u001b[39m\u001b[38;5;124m'\u001b[39m,state\u001b[38;5;241m=\u001b[39mstate)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'es_iapws' is not defined"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "state = 'liq'\n",
     "fig = plt.figure(figsize=(10,5))\n",
diff --git a/moist_thermodynamics/functions.py b/moist_thermodynamics/functions.py
index 9db6cabfa09f17b402d064e04c895d16a18fcbe2..469b28c2a471d1e1ab7fb3b8f3bdeb378eaf4e70 100644
--- a/moist_thermodynamics/functions.py
+++ b/moist_thermodynamics/functions.py
@@ -8,101 +8,17 @@ copygright, bjorn stevens Max Planck Institute for Meteorology, Hamburg
 License: BSD-3C
 """
 #
-from . import constants
 import numpy as np
 from scipy import interpolate, optimize
 
+from . import constants
+from . import saturation_vapor_pressures
 
-def planck(T, nu):
-    """Planck source function (J/m2 per steradian per Hz)
-
-    Args:
-        T: temperature in kelvin
-        nu: frequency in Hz
-
-    Returns:
-        Returns the radiance in the differential frequency interval per unit steradian. Usually we
-        multiply by $\pi$ to convert to irradiances
-
-    >>> planck(300,1000*constants.c)
-    8.086837160291128e-15
-    """
-    c = constants.speed_of_light
-    h = constants.planck_constant
-    kB = constants.boltzmann_constant
-    return (2 * h * nu**3 / c**2) / (np.exp(h * nu / (kB * T)) - 1)
-
-
-def es_liq(T):
-    """Returns saturation vapor pressure (Pa) over planer liquid water
-
-    Encodes the empirical fits of Wagner and Pruss (2002), Eq 2.5a (page 399). Their formulation
-    is compared to other fits in the example scripts used in this package, and deemed to be the
-    best reference.
-
-    Args:
-        T: temperature in kelvin
-
-    Reference:
-        W. Wagner and A. Pruß , "The IAPWS Formulation 1995 for the Thermodynamic Properties
-    of Ordinary Water Substance for General and Scientific Use", Journal of Physical and Chemical
-    Reference Data 31, 387-535 (2002) https://doi.org/10.1063/1.1461829
-
-    >>> es_liq(np.asarray([273.16,305.]))
-    array([ 611.65706974, 4719.32683147])
-    """
-    TvC = constants.temperature_water_vapor_critical_point
-    PvC = constants.pressure_water_vapor_critical_point
-
-    vt = 1.0 - T / TvC
-    es = PvC * np.exp(
-        TvC
-        / T
-        * (
-            -7.85951783 * vt
-            + 1.84408259 * vt**1.5
-            - 11.7866497 * vt**3
-            + 22.6807411 * vt**3.5
-            - 15.9618719 * vt**4
-            + 1.80122502 * vt**7.5
-        )
-    )
-    return es
-
-
-def es_ice(T):
-    """Returns sublimation vapor pressure (Pa) over simple (Ih) ice
-
-    Encodes the emperical fits of Wagner et al., (2011) which also define the IAPWS standard for
-    sublimation vapor pressure over ice-Ih
-
-    Args:
-        T: temperature in kelvin
-
-    Reference:
-        Wagner, W., Riethmann, T., Feistel, R. & Harvey, A. H. New Equations for the Sublimation
-        Pressure and Melting Pressure of H 2 O Ice Ih. Journal of Physical and Chemical Reference
-        Data 40, 043103 (2011).
-
-
-    >>> es_ice(np.asarray([273.16,260.]))
-    array([611.655     , 195.80103377])
-    """
-    TvT = constants.temperature_water_vapor_triple_point
-    PvT = constants.pressure_water_vapor_triple_point
-
-    a1 = -0.212144006e2
-    a2 = 0.273203819e2
-    a3 = -0.610598130e1
-    b1 = 0.333333333e-2
-    b2 = 0.120666667e1
-    b3 = 0.170333333e1
-    theta = T / TvT
-    es = PvT * np.exp((a1 * theta**b1 + a2 * theta**b2 + a3 * theta**b3) / theta)
-    return es
+es_liq_default = saturation_vapor_pressures.liq_wagner_pruss
+es_ice_default = saturation_vapor_pressures.ice_wagner_etal
 
 
-def es_mxd(T):
+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
@@ -120,152 +36,27 @@ def es_mxd(T):
     return np.minimum(es_liq(T), es_ice(T))
 
 
-def es_liq_murphykoop(T):
-    """Returns saturation vapor pressure (Pa) over liquid water
-
-    Encodes the empirical fit (Eq. 10) of Murphy and Koop (2011) which improves on the Wagner and
-    Pruß fits for supercooled conditions.
-
-    Args:
-        T: temperature in kelvin
-
-    Reference:
-        Murphy, D. M. & Koop, T. Review of the vapour pressures of ice and supercooled water for
-        atmospheric applications. Q. J. R. Meteorol. Soc. 131, 1539–1565 (2005).
-
-    >>> es_liq_murphykoop(np.asarray([273.16,140.]))
-    array([6.11657044e+02, 9.39696372e-07])
-    """
-
-    X = np.tanh(0.0415 * (T - 218.8)) * (
-        53.878 - 1331.22 / T - 9.44523 * np.log(T) + 0.014025 * T
-    )
-    return np.exp(54.842763 - 6763.22 / T - 4.210 * np.log(T) + 0.000367 * T + X)
-
-
-def es_liq_hardy(T):
-    """Returns satruation vapor pressure (Pa) over liquid water
-
-    Encodes the empirical fit (Eq. 10) of Hardy (1998) which is often used in the postprocessing
-    of radiosondes
-
-    Args:
-        T: temperature in kelvin
-
-    Reference:
-        Hardy, B., 1998, ITS-90 Formulations for Vapor Pressure, Frostpoint Temperature, Dewpoint
-        Temperature, and Enhancement Factors in the Range –100 to +100 °C, The Proceedings of the
-        Third International Symposium on Humidity & Moisture, London, England
-
-    >>> es_liq_hardy(np.asarray([273.16,260.]))
-    array([611.65715494, 222.65143353])
-    """
-    X = (
-        -2.8365744e3 / (T * T)
-        - 6.028076559e3 / T
-        + 19.54263612
-        - 2.737830188e-2 * T
-        + 1.6261698e-5 * T**2
-        + 7.0229056e-10 * T**3
-        - 1.8680009e-13 * T**4
-        + 2.7150305 * np.log(T)
-    )
-    return np.exp(X)
-
-
-def es_liq_analytic(T, delta_cl=constants.delta_cl):
-    """Analytic approximation for saturation vapor pressure over iquid
-
-    Uses the rankine (constant specific heat, negligible condensate volume) approximations to
-    calculate the saturation vapor pressure over liquid.  The procedure is described in Eq(4) of
-    Romps (2017) and best approximates the actual value for specific heats that differ slightly
-    from the best estimates of these quantities which are provided as default quantities.
-    Romps recommends cl = 4119 J/kg/K, and cpv = 1861 J/kg/K.
-
-    Args:
-        T: temperature in kelvin
-        delta_cl: differnce between isobaric specific heat capacity of vapor and that of liquid.
-
-    Returns:
-        value of saturation vapor pressure over liquid water in Pa
-
-    Reference:
-        Romps, D. M. Exact Expression for the Lifting Condensation Level. Journal of the Atmospheric
-        Sciences 74, 3891–3900 (2017).
-        Romps, D. M. Accurate expressions for the dew point and frost point derived from the Rankine-
-        Kirchhoff approximations. Journal of the Atmospheric Sciences (2021) doi:10.1175/JAS-D-20-0301.1.
-
-    >>> es_liq_analytic(np.asarray([273.16,305.]))
-    array([ 611.655     , 4711.13161169])
-    """
-    TvT = constants.temperature_water_vapor_triple_point
-    PvT = constants.pressure_water_vapor_triple_point
-    lvT = constants.vaporization_enthalpy_triple_point
-    Rv = constants.water_vapor_gas_constant
-
-    c1 = delta_cl / Rv
-    c2 = lvT / (Rv * TvT) - c1
-    es = PvT * np.exp(c2 * (1.0 - TvT / T)) * (T / TvT) ** c1
-    return es
-
-
-def es_ice_analytic(T, delta_ci=constants.delta_ci):
-    """Analytic approximation for saturation vapor pressure over ice
-
-    Uses the rankine (constant specific heat, negligible condensate volume) approximations to
-    calculate the saturation vapor pressure over ice.  The procedure is described in Eq(4) of
-    Romps (2017) and best approximates the actual value for specific heats that differ slightly
-    from the best estimates of these quantities which are provided as default quantities.
-    Romps recommends ci = 1861 J/kg/K, and cpv = 1879 J/kg/K.
-
-    Args:
-        T: temperature in kelvin
-        delta_cl: differnce between isobaric specific heat capacity of vapor and that of liquid.
-
-    Returns:
-        value of saturation vapor pressure over liquid water in Pa
-
-    Reference:
-        Romps, D. M. Exact Expression for the Lifting Condensation Level. Journal of the Atmospheric
-        Sciences 74, 3891–3900 (2017).
-        Romps, D. M. Accurate expressions for the dew point and frost point derived from the Rankine-
-        Kirchhoff approximations. Journal of the Atmospheric Sciences (2021) doi:10.1175/JAS-D-20-0301.1.
-
-
-    >>> es_ice_analytic(np.asarray([273.16,260.]))
-    array([611.655     , 195.99959431])
-    """
-    TvT = constants.temperature_water_vapor_triple_point
-    PvT = constants.pressure_water_vapor_triple_point
-    lsT = constants.sublimation_enthalpy_triple_point
-    Rv = constants.water_vapor_gas_constant
-
-    c1 = delta_ci / Rv
-    c2 = lsT / (Rv * TvT) - c1
-    es = PvT * np.exp(c2 * (1.0 - TvT / T)) * (T / TvT) ** c1
-    return es
-
-
-def es_mxd_analytic(T, delta_cl=constants.delta_cl, delta_ci=constants.delta_ci):
-    """Returns the minimum of the analytic sublimation and saturation vapor pressure
-
-    Calculates both the sublimation vapor pressure over ice Ih using es_ice_analytic and
-    that over planar water using es_liq_analytic, and returns the minimum of the two
-    quantities.
+def planck(T, nu):
+    """Planck source function (J/m2 per steradian per Hz)
 
     Args:
         T: temperature in kelvin
+        nu: frequency in Hz
 
     Returns:
-        value of es_ice_analytic(T) for T < 273.15 and es_liq_analytic(T) otherwise
+        Returns the radiance in the differential frequency interval per unit steradian. Usually we
+        multiply by $\pi$ to convert to irradiances
 
-    >>> es_ice_analytic(np.asarray([273.16,260.]))
-    array([611.655     , 195.99959431])
+    >>> planck(300,1000*constants.c)
+    8.086837160291128e-15
     """
-    return np.minimum(es_liq_analytic(T, delta_cl), es_ice_analytic(T, delta_ci))
+    c = constants.speed_of_light
+    h = constants.planck_constant
+    kB = constants.boltzmann_constant
+    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
@@ -281,10 +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
@@ -301,13 +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
@@ -338,7 +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)
@@ -358,31 +149,60 @@ def saturation_partition(P, ps, qt):
     return np.minimum(qt, qs)
 
 
-def static_energy(TK, qv=0.0, ql=0.0, qi=0.0):
-    """Returns the static energy, defaulting to that for a dry atmosphere
+def static_energy(T, Z, qv=0, ql=0, qi=0, hv0=constants.cpv * constants.T0):
+    """Returns the static energy
 
-    The moist static energy is calculated so that it includes the effects of composition
-    on the specific heat if specific humidities are included, but defaults to the dry static energy
+    The static energy is calculated so that it includes the effects of composition on the
+    specific heat if specific humidities are included.  Different common forms of the static
+    energy arise from different choices of the reference state and condensate loading:
+        - hv0 = cpv*T0      -> frozen, liquid moist static energy
+        - hv0 = ls0 + ci*T0 -> frozen moist static energy
+        - hv0 = cpv*T0      -> liquid water static energy if qi= 0 (default if qv /= 0)
+        - hv0 = lv0 + cl*T0 -> moist static energy if qi= 0.
+        - qv=ql=q0=0        -> dry static energy (default)
+
+    Because the composition weights the reference enthalpies, different choices do not differ by
+    a constant, but rather by a constant weighted by the specific masses of the different water
+    phases.
 
     Args:
-        TK: temperature in kelvin
-        PPa: pressure in pascal
+        T: temperature in kelvin
+        Z: altitude (above mean sea-level) in meters
         qv: specific vapor mass
         ql: specific liquid mass
         qi: specific ice mass
+        hv0: reference vapor enthalpy
+
+        >>> static_energy(300.,600.,15.e-3,hv0=constants.lv0 + constants.cl * constants.T0)
+        358162.78621841426
+
     """
     cpd = constants.isobaric_dry_air_specific_heat
     cpv = constants.isobaric_water_vapor_specific_heat
     cl = constants.liquid_water_specific_heat
     ci = constants.frozen_water_specific_heat
+    lv0 = constants.lv0
+    ls0 = constants.ls0
+    T0 = constants.T0
     g = constants.gravity_earth
 
-    qt = qv + ql + qi
-    cp = cpd + qt * (cl - cpd)
-    return TK * cp + qv * lv + gz
+    qd = 1.0 - qv - ql - qi
+    cp = qd * cpd + qv * cpv + ql * cl + qi * ci
+
+    h = (
+        qd * cpd * T
+        + qv * cpv * T
+        + ql * cl * T
+        + qi * ci * T
+        + qv * (hv0 - cpv * T0)
+        + ql * (hv0 - lv0 - cl * T0)
+        + qi * (hv0 - ls0 - ci * T0)
+        + g * Z
+    )
+    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
@@ -390,8 +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
@@ -407,18 +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
 
@@ -431,19 +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
@@ -452,8 +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
 
@@ -469,20 +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
@@ -491,8 +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
 
@@ -508,8 +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
@@ -517,13 +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
@@ -532,8 +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
 
@@ -566,18 +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)
     )
@@ -585,8 +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
@@ -594,15 +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
 
@@ -618,20 +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
@@ -639,21 +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
@@ -678,7 +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
@@ -703,7 +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
@@ -711,8 +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)
@@ -721,23 +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:
@@ -755,9 +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):
@@ -793,7 +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/moist_thermodynamics/saturation_vapor_pressures.py b/moist_thermodynamics/saturation_vapor_pressures.py
new file mode 100644
index 0000000000000000000000000000000000000000..c89710a64dde48501ec9e508d5d4ed680425f9cc
--- /dev/null
+++ b/moist_thermodynamics/saturation_vapor_pressures.py
@@ -0,0 +1,301 @@
+# -*- coding: utf-8 -*-
+"""
+Provides a collection of fits for saturation and sublimation vapor pressure
+
+Author: Bjorn Stevens (bjorn.stevens@mpimet.mpg.de)
+copygright, bjorn stevens Max Planck Institute for Meteorology, Hamburg
+
+License: BSD-3C
+"""
+#
+from . import constants
+import numpy as np
+
+
+def liq_wagner_pruss(T):
+    """Returns saturation vapor pressure (Pa) over planer liquid water
+
+    Encodes the empirical fits of Wagner and Pruss (2002), Eq 2.5a (page 399). Their formulation
+    is compared to other fits in the example scripts used in this package, and deemed to be the
+    best reference.
+
+    The fit has been verified for TvT <= T < = TvC.  For super cooled water (T<TvT) it deviates
+    from the results of Murphy and Koop where were developed for super-cooled water.  It is about
+    10% larger at 200 K, 25 % larter at 150 K, and then decreases again so it is 12% smaller at
+    the limit (123K) of the Murphy and Koop fit.  For accurate fits for super-cooled water the
+    function of Murphy and Koop should be used.
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        W. Wagner and A. Pruß , "The IAPWS Formulation 1995 for the Thermodynamic Properties
+    of Ordinary Water Substance for General and Scientific Use", Journal of Physical and Chemical
+    Reference Data 31, 387-535 (2002) https://doi.org/10.1063/1.1461829
+
+    >>> liq_wagner_pruss(np.asarray([273.16,305.]))
+    array([ 611.65706974, 4719.32683147])
+    """
+    TvC = constants.temperature_water_vapor_critical_point
+    PvC = constants.pressure_water_vapor_critical_point
+
+    vt = 1.0 - T / TvC
+    es = PvC * np.exp(
+        TvC
+        / T
+        * (
+            -7.85951783 * vt
+            + 1.84408259 * vt**1.5
+            - 11.7866497 * vt**3
+            + 22.6807411 * vt**3.5
+            - 15.9618719 * vt**4
+            + 1.80122502 * vt**7.5
+        )
+    )
+    return es
+
+
+def ice_wagner_etal(T):
+    """Returns sublimation vapor pressure (Pa) over simple (Ih) ice
+
+    Encodes the emperical fits of Wagner et al., (2011) which also define the IAPWS standard for
+    sublimation vapor pressure over ice-Ih
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        Wagner, W., Riethmann, T., Feistel, R. & Harvey, A. H. New Equations for the Sublimation
+        Pressure and Melting Pressure of H 2 O Ice Ih. Journal of Physical and Chemical Reference
+        Data 40, 043103 (2011).
+
+
+    >>> ice_wagner_etal(np.asarray([273.16,260.]))
+    array([611.655     , 195.80103377])
+    """
+    TvT = constants.temperature_water_vapor_triple_point
+    PvT = constants.pressure_water_vapor_triple_point
+
+    a1 = -0.212144006e2
+    a2 = 0.273203819e2
+    a3 = -0.610598130e1
+    b1 = 0.333333333e-2
+    b2 = 0.120666667e1
+    b3 = 0.170333333e1
+    theta = T / TvT
+    es = PvT * np.exp((a1 * theta**b1 + a2 * theta**b2 + a3 * theta**b3) / theta)
+    return es
+
+
+def liq_murphy_koop(T):
+    """Returns saturation vapor pressure (Pa) over liquid water
+
+    Encodes the empirical fit (Eq. 10) of Murphy and Koop (2011) which improves on the Wagner and
+    Pruß fits for supercooled conditions.
+
+    The fit has been verified for 123K <= T < = 332 K
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        Murphy, D. M. & Koop, T. Review of the vapour pressures of ice and supercooled water for
+        atmospheric applications. Q. J. R. Meteorol. Soc. 131, 1539–1565 (2005).
+
+    >>> liq_murphy_koop(np.asarray([273.16,140.]))
+    array([6.11657044e+02, 9.39696372e-07])
+    """
+
+    X = np.tanh(0.0415 * (T - 218.8)) * (
+        53.878 - 1331.22 / T - 9.44523 * np.log(T) + 0.014025 * T
+    )
+    return np.exp(54.842763 - 6763.22 / T - 4.210 * np.log(T) + 0.000367 * T + X)
+
+
+def liq_hardy(T):
+    """Returns satruation vapor pressure (Pa) over liquid water
+
+    Encodes the empirical fit (Eq. 10) of Hardy (1998) which is often used in the postprocessing
+    of radiosondes
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        Hardy, B., 1998, ITS-90 Formulations for Vapor Pressure, Frostpoint Temperature, Dewpoint
+        Temperature, and Enhancement Factors in the Range –100 to +100 °C, The Proceedings of the
+        Third International Symposium on Humidity & Moisture, London, England
+
+    >>> liq_hardy(np.asarray([273.16,260.]))
+    array([611.65715494, 222.65143353])
+    """
+    X = (
+        -2.8365744e3 / (T * T)
+        - 6.028076559e3 / T
+        + 19.54263612
+        - 2.737830188e-2 * T
+        + 1.6261698e-5 * T**2
+        + 7.0229056e-10 * T**3
+        - 1.8680009e-13 * T**4
+        + 2.7150305 * np.log(T)
+    )
+    return np.exp(X)
+
+
+def analytic(T, lx, cx):
+    """returns saturation vapor pressure over a given phase
+
+    Uses the rankine (constant specific heat, negligible condensate volume) approximations to
+    calculate the saturation vapor pressure over a phase with the specific heat cx, and phase
+    change enthalpy (from vapor) lx, at temperature T.
+
+    Args:
+        T: temperature in kelvin
+        lx: phase change enthalpy between vapor and given phase (liquid, ice)
+        cx: specific heat capacity of given phase (liquid, ice)
+
+    Returns:
+        value of saturation vapor pressure over liquid water in Pa
+
+    Reference:
+        Romps, D. M. Exact Expression for the Lifting Condensation Level. Journal of the Atmospheric
+        Sciences 74, 3891–3900 (2017).
+        Romps, D. M. Accurate expressions for the dew point and frost point derived from the Rankine-
+        Kirchhoff approximations. Journal of the Atmospheric Sciences (2021) doi:10.1175/JAS-D-20-0301.1.
+
+    >>> analytic(305.,constants.lvT,constants.cl)
+    4711.131611687174
+    """
+    TvT = constants.temperature_water_vapor_triple_point
+    PvT = constants.pressure_water_vapor_triple_point
+    Rv = constants.water_vapor_gas_constant
+
+    c1 = (constants.cpv - cx) / Rv
+    c2 = lx / (Rv * TvT) - c1
+    es = PvT * np.exp(c2 * (1.0 - TvT / T)) * (T / TvT) ** c1
+    return es
+
+
+def liq_analytic(T, lx=constants.lvT, cx=constants.cl):
+    """Analytic approximation for saturation vapor pressure over iquid
+
+    Uses the rankine (constant specific heat, negligible condensate volume) approximations to
+    calculate the saturation vapor pressure over liquid.  The procedure is described in Eq(4) of
+    Romps (2017) and best approximates the actual value for specific heats that differ slightly
+    from the best estimates of these quantities which are provided as default quantities.
+    Romps recommends cl = 4119 J/kg/K, and cpv = 1861 J/kg/K.
+
+    Args:
+        T: temperature in kelvin
+        lx: enthalpy of vaporization, at triple point, default constants.lvT
+        cl: specific heat capacity of liquid at triple point
+
+    Returns:
+        value of saturation vapor pressure over liquid water in Pa
+
+    Reference:
+        Romps, D. M. Exact Expression for the Lifting Condensation Level. Journal of the Atmospheric
+        Sciences 74, 3891–3900 (2017).
+        Romps, D. M. Accurate expressions for the dew point and frost point derived from the Rankine-
+        Kirchhoff approximations. Journal of the Atmospheric Sciences (2021) doi:10.1175/JAS-D-20-0301.1.
+
+    >>> liq_analytic(np.asarray([273.16,305.]))
+    array([ 611.655     , 4711.13161169])
+    """
+    return analytic(T, lx, cx)
+
+
+def ice_analytic(T, lx=constants.lsT, cx=constants.ci):
+    """Analytic approximation for saturation vapor pressure over ice
+
+    Uses the rankine (constant specific heat, negligible condensate volume) approximations to
+    calculate the saturation vapor pressure over ice.  The procedure is described in Eq(4) of
+    Romps (2017) and best approximates the actual value for specific heats that differ slightly
+    from the best estimates of these quantities which are provided as default quantities.
+    Romps recommends ci = 1861 J/kg/K, and cpv = 1879 J/kg/K.
+
+    Args:
+        T: temperature in kelvin
+        lx: enthalpy of sublimation, at triple point, default constants.lsT
+        ci: specific heat capacity of ice Ih, at triple point
+
+    Returns:
+        value of saturation vapor pressure over ice in Pa
+
+    Reference:
+        Romps, D. M. Exact Expression for the Lifting Condensation Level. Journal of the Atmospheric
+        Sciences 74, 3891–3900 (2017).
+        Romps, D. M. Accurate expressions for the dew point and frost point derived from the Rankine-
+        Kirchhoff approximations. Journal of the Atmospheric Sciences (2021) doi:10.1175/JAS-D-20-0301.1.
+
+
+    >>> ice_analytic(np.asarray([273.16,260.]))
+    array([611.655     , 195.99959431])
+    """
+    return analytic(T, lx, cx)
+
+
+def tetens(T, a, b):
+    """Returns saturation vapor pressure over liquid using the Magnus-Teten's formula
+
+    This equation is written in a general form, with the constants a and b determining the fit.  As
+    such it can be specified for either ice or water, or adapted as originally impelemented in ICON,
+    in which case PvT and TvT need to be substituted by Pv0 and T0.
+
+    Args:
+        T: temperature in kelvin
+
+    >>> tetens(285.,17.269,35.86)
+    1389.7114123472836
+    """
+
+    es = constants.PvT * np.exp(a * (T - constants.TvT) / (T - b))
+    return es
+
+
+def liq_tetens(T):
+    """Returns saturation vapor pressure over liquid using the Magnus-Teten's formula
+
+    This equation is what is used in the ICON code, hence its inclusion in this library.  The original
+    ICON implementation followed Murray's choice of constants (T0=273.15, Pv0=610.78, a=17.269, b=35.86).
+    This implementation is referenced to the triple point values of temperature and vapor and with
+    revised constants (a,b) chosen to better agree with the fits of Wagner and Pruss
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        Murray, F. W. On the Computation of Saturation Vapor Pressure. Journal of Applied Meteorology
+        and Climatology 6, 203–204 (1967).
+
+    >>> liq_tetens(np.asarray([273.16,305.]))
+    array([ 611.655     , 4719.73680592])
+    """
+    a = 17.41463775
+    b = 33.6393413
+
+    return tetens(T, a, b)
+
+
+def ice_tetens(T):
+    """Returns saturation vapor pressure over liquid using the Magnus-Teten's formula
+
+    This equation is what is used in the ICON code, hence its inclusion in this library.  The original
+    ICON implementation followed Murray's choice of constants (T0=273.15, Pv0=610.78, a=21.875, b=7.66).
+    This implementation is referenced to the triple point values of temperature and vapor and with
+    revised constants (a,b) chosen to better agree with the fits of Wagner and Pruss
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        Murray, F. W. On the Computation of Saturation Vapor Pressure. Journal of Applied Meteorology
+        and Climatology 6, 203–204 (1967).
+
+    >>> ice_tetens(np.asarray([273.16,260.]))
+    array([611.655     , 196.10072658])
+    """
+    a = 22.0419977
+    b = 5.0
+
+    return tetens(T, a, b)
diff --git a/setup.py b/setup.py
index 10519fc24d275f273426f5fa6a822a787a13727d..a8d2f2abc8ff7409a86c6a5587d0f7123771defe 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 
 setup(
     name="moist_thermodynamics",
-    version="0.3",
+    version="0.5",
     description="Constants and functions for the treatment of moist atmospheric thermodynamics",
     packages=find_packages(),
     install_requires=[