import polarizability
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
sns.set_palette("bright")
sns.set_style("whitegrid")
sns.set(font_scale=1.2)
expt = pd.read_csv("./tables/data_with_metadata.csv")
expt["temperature"] = expt["Temperature, K"]
pred = pd.read_csv("./tables/predictions.csv")
pred["polcorr"] = pd.Series(
dict((cas,
polarizability.dielectric_correction_from_formula(
formula, density * u.grams / u.milliliter))
for cas, (formula,
density) in pred[["formula", "density"]].iterrows()))
pred["corrected_dielectric"] = pred["polcorr"] + pred["dielectric"]
expt = expt.set_index(
["cas", "temperature"]
) # Can't do this because of duplicates # Should be fixed now, probably due to the CAS / name duplication issue found by Julie.
pred = pred.set_index(["cas", "temperature"])
pred["name"] = expt["components"]
pred["expt_density"] = expt["Mass density, kg/m3"]
pred["expt_dielectric"] = expt["Relative permittivity at zero frequency"]
pred["expt_density_std"] = expt["Mass density, kg/m3_uncertainty_bestguess"]
pred["expt_dielectric_std"] = expt[
"Relative permittivity at zero frequency_uncertainty_bestguess"]
import polarizability
import simtk.unit as u
import numpy as np
alpha = (0.32 + 0.17 * 2 + 0.57) * u.angstrom**3
alpha1 = polarizability.polarizability_from_formula("H2O")
alpha, alpha1, alpha - alpha1
delta = 4 * pi * 55.4 * u.molar * alpha
delta = delta * u.AVOGADRO_CONSTANT_NA
r = polarizability.dielectric_correction_from_formula(
"H2O", 1000. * (u.gram / u.milliliter))
r, delta, r - delta
def runner(expt_csv, pred_csv, dens_pdf, diff_pdf, diel_pdf, nocorr_pdf):
FIGURE_SIZE = (6.5, 6.5)
DPI = 1600
expt = pd.read_csv(expt_csv)
expt["temperature"] = expt["Temperature, K"]
pred = pd.read_csv(pred_csv)
pred["polcorr"] = pd.Series(
dict((cas,
polarizability.dielectric_correction_from_formula(
formula, density * u.grams / u.milliliter))
for cas, (formula,
density) in pred[["formula", "density"]].iterrows()))
pred["corrected_dielectric"] = pred["polcorr"] + pred["dielectric"]
expt = expt.set_index(
["cas", "temperature"]
) # Can't do this because of duplicates # Should be fixed now, probably due to the CAS / name duplication issue found by Julie.
pred = pred.set_index(["cas", "temperature"])
pred["expt_density"] = expt["Mass density, kg/m3"]
pred["expt_dielectric"] = expt["Relative permittivity at zero frequency"]
pred["expt_density_std"] = expt[
"Mass density, kg/m3_uncertainty_bestguess"]
pred["expt_dielectric_std"] = expt[
"Relative permittivity at zero frequency_uncertainty_bestguess"]
plt.figure(figsize=FIGURE_SIZE, dpi=DPI)
for (formula, grp) in pred.groupby("formula"):
x, y = grp["density"], grp["expt_density"]
xerr = grp["density_sigma"]
yerr = grp["expt_density_std"].replace(np.nan, 0.0)
x = x / 1000. # Convert kg / m3 to g / mL
y = y / 1000. # Convert kg / m3 to g / mL
xerr = xerr / 1000. # Convert kg / m3 to g / mL
yerr = yerr / 1000. # Convert kg / m3 to g / mL
plt.errorbar(x, y, xerr=xerr, yerr=yerr, fmt='.', label=formula)
plt.plot([.600, 1.400], [.600, 1.400], 'k', linewidth=1)
plt.xlim((.600, 1.400))
plt.ylim((.600, 1.400))
plt.xlabel("Predicted (%s)" % FF_NAME)
plt.ylabel("Experiment (ThermoML)")
plt.gca().set_aspect('equal', adjustable='box')
plt.draw()
x, y = pred["density"], pred["expt_density"]
plt.title(r"Density [g cm$^{-3}$]")
plt.savefig(dens_pdf, bbox_inches="tight")
plt.figure(figsize=FIGURE_SIZE, dpi=DPI)
for (formula, grp) in pred.groupby("formula"):
x, y = grp["density"], grp["expt_density"]
xerr = grp["density_sigma"]
yerr = grp["expt_density_std"].replace(np.nan, 0.0)
x = x / 1000. # Convert kg / m3 to g / mL
y = y / 1000. # Convert kg / m3 to g / mL
xerr = xerr / 1000. # Convert kg / m3 to g / mL
yerr = yerr / 1000. # Convert kg / m3 to g / mL
plt.errorbar(x - y, y, xerr=xerr, yerr=yerr, fmt='.', label=formula)
plt.xlim((-0.1, 0.1))
plt.ylim((.600, 1.400))
plt.xlabel("Predicted - Experiment")
plt.ylabel("Experiment (ThermoML)")
plt.gca().set_aspect('auto', adjustable='box')
plt.draw()
x, y = pred["density"], pred["expt_density"]
plt.title(r"Density [g cm$^{-3}$]")
plt.savefig(diff_pdf, bbox_inches="tight")
yerr = pred["expt_dielectric_std"].replace(np.nan, 0.0)
xerr = pred["dielectric_sigma"].replace(np.nan, 0.0)
plt.figure(figsize=FIGURE_SIZE, dpi=DPI)
plt.xlabel("Predicted (%s)" % FF_NAME)
plt.ylabel("Experiment (ThermoML)")
plt.title("Inverse Static Dielectric Constant")
plt.plot([0.0, 1], [0.0, 1], 'k') # Guide
x, y = pred["dielectric"], pred["expt_dielectric"]
plt.errorbar(x**-1,
y**-1,
xerr=xerr * x**-2,
yerr=yerr * y**-2,
fmt='.',
label=FF_NAME) # Transform xerr and yerr for 1 / epsilon plot
plt.xlim((0.0, 1))
plt.ylim((0.0, 1))
plt.legend(loc=0)
plt.gca().set_aspect('equal', adjustable='box')
plt.draw()
plt.savefig(nocorr_pdf, bbox_inches="tight")
x, y = pred["corrected_dielectric"], pred["expt_dielectric"]
plt.errorbar(
x**-1,
y**-1,
xerr=xerr * x**-2,
yerr=yerr * y**-2,
fmt='.',
label="Corrected") # Transform xerr and yerr for 1 / epsilon plot
plt.xlim((0.0, 1.02))
plt.ylim((0.0, 1.02))
plt.legend(loc=0)
plt.gca().set_aspect('equal', adjustable='box')
plt.draw()
plt.savefig(diel_pdf, bbox_inches="tight")
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import simtk.unit as u
import polarizability
sns.set_palette("bright")
sns.set_style("whitegrid")
metadata = pd.read_table("./virtualchemistry.tab").set_index("cas")
data = pd.read_csv("./vchem.csv").set_index("cas")
data["density"]["646-06-0"] = 1060. # http://en.wikipedia.org/wiki/Dioxolane
data["formula"] = metadata.formula
data["polcorr"] = pd.Series(dict((cas, polarizability.dielectric_correction_from_formula(formula, density * u.grams / u.milliliter)) for cas, (formula, density) in data[["formula", "density"]].iterrows()))
data["gaff_corrected"] = data.gaff + data.polcorr
data["opls_corrected"] = data.opls + data.polcorr
figure()
x, y = data["expt"], data["gaff"]
ols_model = sm.OLS(y, x)
ols_results = ols_model.fit()
r2 = ols_results.rsquared
plot(x, y, 'o', label="GAFF (R^2 = %.3f)" % r2)
x, y = data["expt"], data["gaff_corrected"]
ols_model = sm.OLS(y, x)
ols_results = ols_model.fit()
import polarizability
import simtk.unit as u
import numpy as np
alpha = (0.32 + 0.17 * 2 + 0.57) * u.angstrom ** 3
alpha1 = polarizability.polarizability_from_formula("H2O")
alpha, alpha1, alpha - alpha1
delta = 4 * pi * 55.4 * u.molar * alpha
delta = delta * u.AVOGADRO_CONSTANT_NA
r = polarizability.dielectric_correction_from_formula("H2O", 1000. * (u.gram / u.milliliter))
r, delta, r - delta
