def plot_standard_curve(result, encoding='jpg'):
if not result.isCalibrated:
raise Exception('Can not plot standard curve if experiment is not calibrated.')
fig = Figure()
ax1 = fig.add_subplot(111)
fig.set_size_inches(_plot_size[0],_plot_size[1], forward=True)
fig.set_facecolor(plot_bgcolor)
ax1.set_title('fitted curve')
ax1.grid(True)
background_mean, background_std = result.background_intensity
# avoid division by zero with perfect testing images
if background_mean == 0:
print('warning: background is zero')
background_mean = np.float64(1e-8)
calibX, calibY = result.calibration_data
calibY_snr = calibY / background_mean
calibY_snr_err = result.intensity_stddev[result.calibration_data_indices] / background_mean
curve = fittedCurve(calibX, calibY_snr)
# plot fitted curve
x = np.linspace(0,np.max(calibX),10)
y = map(curve,x)
ax1.plot(x, y, color='black', linewidth=1, label='fitted curve')
# plot calibration points
#ax1.plot(calibX, calibY_snr, marker='x', color='black', linewidth=0) #, yerr=calibY_snr_err)
ax1.errorbar(calibX, calibY_snr, yerr=calibY_snr_err, marker='x', color='black', linewidth=0)
ax1.set_xlabel('concentration [%s]'%_concentration_units)
ax1.set_ylabel('signal / background')
if background_std > 0:
# don't plot lod, and loq if std_dev of background is not known.
# limit of detection
lod = result.limit_of_detection / background_mean
ax1.axhline(y=lod, linewidth=1, color='g', label='LoD', linestyle='--')
# limit of quantification
loq = result.limit_of_quantification / background_mean
ax1.axhline(y=loq, linewidth=1, color='b', label='LoQ', linestyle='-.')
ax1.legend(loc=0)
#plt.margins(0.2)
#fig.subplots_adjust(hspace=0.2, wspace=0.2, bottom=0.15)
#fig.tight_layout()
return fig
def _intensity_to_concentration(self):
c, i = self.calibration_data
curve = curvefit.fittedCurve(i, c)
return curve
def _concentration_to_intensity(self):
c, i = self.calibration_data
curve = curvefit.fittedCurve(c, i)
return curve
def _intensity_to_concentration(self): c,i = self.calibration_data curve = curvefit.fittedCurve(i,c) return curve
def _concentration_to_intensity(self): c,i = self.calibration_data curve = curvefit.fittedCurve(c,i) return curve
