Fixed py2>py3 conversion bug in randomcontinents.py, and added a compilation flag to force use of fft991mod.f90 instead of fftmod.f90 (only expert users should play with this).

2021-07-10 11:38:57 -04:00
1 parent ca83676601
commit 2ac909fcc1
@@ -27,7 +27,7 @@ copyright = '2020, Adiv Paradise'
 author = 'Adiv Paradise'

 # The full version, including alpha/beta/rc tags
-release = '2.2.5'
+release = '2.2.6'


 # -- General configuration ---------------------------------------------------
@@ -137,7 +137,7 @@ class Model(object):

    """
    def __init__(self,resolution="T21",layers=10,ncpus=4,precision=8,debug=False,inityear=0,
-                recompile=False,optimization=None,mars=False,workdir="most",source=None,
+                recompile=False,optimization=None,mars=False,workdir="most",source=None,force991=False,
                modelname="MOST_EXP"):
        
        global sourcedir
@@ -259,6 +259,8 @@ class Model(object):
                extraflags+= "-O %s"%optimization
            if mars:
                extraflags+= "-m "
+            if force991:
+                extraflags+= "-f "
            os.system("cwd=$(pwd) && "+
                    "cd %s && ./compile.sh -n %d -p %d -r T%d -v %d "%(sourcedir,self.ncpus,
                                                                        precision,self.nsp,
@@ -43,6 +43,8 @@ helptext=$(cat <<-END
      -n:   Number of processing cores (MPI threads) to use. Default: 4

      -t:   Number of years to use in most_plasim_run script. Default: 10
+      
+      -f:   Force FFT991mod.f90

      -O:   Specify additional compiler optimization flags, like -mavx (leave out prepended
            hyphen).
@@ -169,6 +171,9 @@ while getopts "p:r:v:n:O:t:dhm" opt; do
                    echo "INVALID RESOLUTION PASSED! Reverting to T21."
                    ;;
            esac ;;
+        f)    
+            fftopt="fft991mod"
+            ;;
        v)
            levels=$OPTARG
            ;;
@@ -52,7 +52,11 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "kwv,kdata = readstarspec(\"proximacen_hr.dat\")"
+    "def bbtemp(teff,wvref):\n",
+    "    sbk = 5.67e-8\n",
+    "    const = 0.0143877735383\n",
+    "    bbf = 1.0/wvref**5 * (1.0/(np.exp(const/(teff*wvref*1e-6))-1))\n",
+    "    return bbf"
   ]
  },
  {
@@ -60,6 +64,15 @@
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
+   "source": [
+    "kwv,kdata = readstarspec(\"proximacen_hr.dat\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
   "source": [
    "wv1[:] = kwv[0:1024]*1.0e-6\n",
    "bb1[:] = kdata[0:1024]\n",
@@ -76,7 +89,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -85,7 +98,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -95,7 +108,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -104,7 +117,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -113,7 +126,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
@@ -140,14 +153,23 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "blackbody = bbtemp(3000,wavelengths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1008x216 with 2 Axes>"
      ]
     },
     "metadata": {},
@@ -155,10 +177,22 @@
    }
   ],
   "source": [
-    "plt.plot(wavelengths,bb3)\n",
-    "plt.plot(wavelengths,pbb3)\n",
-    "plt.xscale('log')\n",
-    "plt.yscale('log')"
+    "fig,axes = plt.subplots(1,2,figsize=(14,3),sharey=True,gridspec_kw={'wspace':0.05})\n",
+    "axes[1].plot(wavelengths,wavelengths*bb3)\n",
+    "axes[0].plot(wavelengths,wavelengths*blackbody)\n",
+    "#plt.plot(wavelengths,pbb3)\n",
+    "axes[0].set_xscale('log')\n",
+    "axes[0].set_yscale('log')\n",
+    "axes[1].set_xscale('log')\n",
+    "axes[0].set_title(\"3000 K Blackbody\")\n",
+    "axes[1].set_title(\"3000 K PHOENIX BT-Settl\")\n",
+    "axes[0].set_xlabel(\"Wavelength [$\\mu$m]\")\n",
+    "axes[1].set_xlabel(\"Wavelength [$\\mu$m]\")\n",
+    "axes[0].set_ylabel(\"$\\\\lambda{F}_\\\\lambda$\")\n",
+    "axes[0].set_xlim(wavelengths.min(),wavelengths.max())\n",
+    "axes[1].set_xlim(wavelengths.min(),wavelengths.max())\n",
+    "plt.savefig(\"3000k_spectra.png\",bbox_inches='tight')\n",
+    "plt.savefig(\"3000k_spectra.pdf\",bbox_inches='tight')"
   ]
  },
  {
@@ -35,7 +35,7 @@ def writeSRA(name,kcode,field,NLAT,NLON):
    """
    label=name+'_surf_%04d.sra'%kcode
    header=[kcode,0,20170927,0,NLON,NLAT,0,0]
-    fmap = field.reshape((NLAT*NLON/8,8))
+    fmap = field.reshape((int(NLAT*NLON//8),8))
    sheader = ''
    for h in header:
        sheader+=" %11d"%h
@@ -3,7 +3,7 @@ import os
 
 setup(
    name='exoplasim',
-    version='2.2.5',
+    version='2.2.6',
    packages=['exoplasim',],
    zip_safe=False,
    install_requires=["numpy","netCDF4","matplotlib","scipy"],