Merge pull request #21 from itsMig/master

Modify plot_single_session() to return data, to be handled by different plotting engine (intended for D4Xgui)

Author: mdaeron

D47crunch/__init__.py

@@ -2091,6 +2091,7 @@ def plot_sessions(self, dir = 'output', figsize = (8,8), filetype = 'pdf', dpi =
 			sp = self.plot_single_session(session, xylimits = 'constant')
 			ppl.savefig(f'{dir}/D{self._4x}_plot_{session}.{filetype}', **({'dpi': dpi} if filetype.lower() == 'png' else {}))
 			ppl.close(sp.fig)
+			
 
 
 	@make_verbal
@@ -2217,6 +2218,10 @@ def consolidate_sessions(self):
 		if self.standardization_method == 'pooled':
 			for session in self.sessions:
 
+				# different (better?) computation of D4x repeatability for each session:
+				sqresiduals = [(r[f'D{self._4x}'] - self.samples[r['Sample']][f'D{self._4x}'])**2 for r in self.sessions[session]['data']]
+				self.sessions[session][f'r_D{self._4x}'] = np.mean(sqresiduals)**.5
+
 				self.sessions[session]['a'] = self.standardization.params.valuesdict()[f'a_{pf(session)}']
 				i = self.standardization.var_names.index(f'a_{pf(session)}')
 				self.sessions[session]['SE_a'] = self.standardization.covar[i,i]**.5
@@ -2557,32 +2562,39 @@ def plot_single_session(self,
 		out = _SessionPlot()
 		anchors = [a for a in self.anchors if [r for r in self.sessions[session]['data'] if r['Sample'] == a]]
 		unknowns = [u for u in self.unknowns if [r for r in self.sessions[session]['data'] if r['Sample'] == u]]
+		anchors_d = [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors]
+		anchors_D = [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors]
+		unknowns_d = [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns]
+		unknowns_D = [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns]
+		anchor_avg = (np.array([ np.array([
+				np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
+				np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
+				]) for sample in anchors]).T,
+			np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T)
+		unknown_avg = (np.array([ np.array([
+				np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
+				np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
+				]) for sample in unknowns]).T,
+			np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T)
+		
 
 		if fig == 'new':
 			out.fig = ppl.figure(figsize = (6,6))
 			ppl.subplots_adjust(.1,.1,.9,.9)
 
 		out.anchor_analyses, = ppl.plot(
-			[r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
-			[r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors],
+			anchors_d,
+			anchors_D,
 			**kw_plot_anchors)
 		out.unknown_analyses, = ppl.plot(
-			[r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
-			[r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns],
+			unknowns_d,
+			unknowns_D,
 			**kw_plot_unknowns)
 		out.anchor_avg = ppl.plot(
-			np.array([ np.array([
-				np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
-				np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
-				]) for sample in anchors]).T,
-			np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T,
+			*anchor_avg,
 			**kw_plot_anchor_avg)
 		out.unknown_avg = ppl.plot(
-			np.array([ np.array([
-				np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1,
-				np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1
-				]) for sample in unknowns]).T,
-			np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T,
+			*unknown_avg,
 			**kw_plot_unknown_avg)
 		if xylimits == 'constant':
 			x = [r[f'd{self._4x}'] for r in self]
@@ -2623,6 +2635,20 @@ def plot_single_session(self,
 			cval = np.arange(np.ceil(SI.min() / .001) * .001, np.ceil(SI.max() / .001 + 1) * .001, cinterval)
 			out.contour = ppl.contour(XI, YI, SI, cval, **kw_contour_error)
 			out.clabel = ppl.clabel(out.contour)
+			contour = (XI, YI, SI, cval, cinterval)
+
+		if fig == None:
+			return {
+			'anchors':anchors,
+			'unknowns':unknowns,
+			'anchors_d':anchors_d,
+			'anchors_D':anchors_D,
+			'unknowns_d':unknowns_d,
+			'unknowns_D':unknowns_D,
+			'anchor_avg':anchor_avg,
+			'unknown_avg':unknown_avg,
+			'contour':contour,
+			}
 
 		ppl.xlabel(x_label)
 		ppl.ylabel(y_label)

changelog.md

@@ -1,5 +1,9 @@
 # Changelog
 
+### Bugfix
+* Simpler (better?) computation of D4x repeatability at the session level when using pooled regression method.
+
+
 ## v2.4.0
 *Released on 2023-10-04*
 

todo.md

@@ -6,3 +6,4 @@
 * improve `test_virtual_data()` to populate with non-default parameters
 * use a true CSV parser?
 * add (many) plot customization options to CLI?
+* implement individual weighing of replicates, e.g. according to the sqrt of the number of IRMS acquisitions