refactor plots

Author: keflavich

docs/linelists/cdms/cdms.rst

@@ -221,11 +221,15 @@ other temperatures using curve fitting models:
    param, cov = curve_fit(f, temp[np.isfinite(part)], part[np.isfinite(part)])
    x = np.linspace(2.7,500)
    y = f(x,param[0])
-   plt.scatter(temp,part,c='r')
-   plt.plot(x,y,'k')
+   plt.clf()
+   plt.scatter(temp,part,c='r',label='CDMS Data')
+   plt.plot(x,y,'k',label='Fitted')
+   interp_Q = np.interp(x, temp, 10**part)
+   plt.plot(x, np.log10(interp_Q), label='Interpolated')
    plt.title('Partition Function vs Temperature')
    plt.xlabel('Temperature')
    plt.ylabel('Log10 of Partition Function')
+   plt.legend(loc='best')
 
 
 We can then compare linear interpolation to the fitted interpolation above:
@@ -241,22 +245,8 @@ We can then compare linear interpolation to the fitted interpolation above:
 .. plot::
    :context:
 
-   result = CDMS.get_species_table()
-   mol = result[result['TAG'] == 30501] #do not include signs of TAG for this
-   def f(T, a):
-       return np.log10(a*T**(1.5))
-   keys = [k for k in mol.keys() if 'lg' in k]
-   def tryfloat(x):
-       try:
-           return float(x)
-       except:
-           return np.nan
-   x = np.linspace(2.7,500)
-   y = f(x,param[0])
-   temp = np.array([float(k.split('(')[-1].split(')')[0]) for k in keys])
-   part = np.array([tryfloat(x) for x in mol[keys][0]])
-   param, cov = curve_fit(f, temp[np.isfinite(part)], part[np.isfinite(part)])
    interp_Q = np.interp(x, temp, 10**part)
+   plt.clf()
    plt.plot(x, (10**y-interp_Q)/10**y)
    plt.xlabel("Temperature")
    plt.ylabel("Fractional difference between linear and fitted")