LVE Data imported from Excel can be fitted with theories now

jorge-ramirez-upm · Dec 15, 2023 · 22fc20a · 22fc20a
1 parent 3bf8807
commit 22fc20a
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Showing 2 changed files with 29 additions and 14 deletions.
diff --git a/RepTate/gui/ImportExcelWindow.py b/RepTate/gui/ImportExcelWindow.py
@@ -353,7 +353,7 @@ def get_data(self):
                     z.append(np.nan)
                     flag_nan = True
 
-        # Sort and clean data? JR
+        # Sort and clean data
         x = np.array(x)
         y = np.array(y)
         ind = np.argsort(x)
@@ -380,7 +380,6 @@ def get_data(self):
                 znotnan = z[~np.isnan(z)]
                 zznan = np.interp(xznan, xznotnan, znotnan, left=0, right=0)
                 z[np.isnan(z)] = zznan
-        # End sort and clean data - JR
 
         res_dic = {
             "error": False,

diff --git a/RepTate/gui/QTheory.py b/RepTate/gui/QTheory.py
@@ -922,12 +922,20 @@ def func_fit(self, x, *param_in):
                         conditiony = (yexp[:, i] > self.ymin) * (yexp[:, i] < self.ymax)
                     else:
                         conditiony = np.ones_like(yexp[:, i], dtype=bool)
-                    conditionnaninf = (
-                        (~np.isnan(xexp)[:, 0])
-                        * (~np.isnan(yexp)[:, 0])
-                        * (~np.isinf(xexp)[:, 0])
-                        * (~np.isinf(yexp)[:, 0])
-                    )
+                    if view.n > 1:
+                        conditionnaninf = (
+                            (np.prod(~np.isnan(xexp), axis=1, dtype=bool))
+                            * (np.prod(~np.isnan(yexp), axis=1, dtype=bool))
+                            * (np.prod(~np.isinf(xexp), axis=1, dtype=bool))
+                            * (np.prod(~np.isinf(yexp), axis=1, dtype=bool))
+                        )
+                    else:
+                        conditionnaninf = (
+                            (~np.isnan(xexp)[:, 0])
+                            * (~np.isnan(yexp)[:, 0])
+                            * (~np.isinf(xexp)[:, 0])
+                            * (~np.isinf(yexp)[:, 0])
+                        )
                     ycond = np.extract(
                         conditionx * conditiony * conditionnaninf, yth[:, i]
                     )
@@ -989,12 +997,20 @@ def do_fit(self, line):
                         conditiony = (yexp[:, i] > self.ymin) * (yexp[:, i] < self.ymax)
                     else:
                         conditiony = np.ones_like(yexp[:, i], dtype=bool)
-                    conditionnaninf = (
-                        (~np.isnan(xexp)[:, 0])
-                        * (~np.isnan(yexp)[:, 0])
-                        * (~np.isinf(xexp)[:, 0])
-                        * (~np.isinf(yexp)[:, 0])
-                    )
+                    if view.n > 1:
+                        conditionnaninf = (
+                            (np.prod(~np.isnan(xexp), axis=1, dtype=bool))
+                            * (np.prod(~np.isnan(yexp), axis=1, dtype=bool))
+                            * (np.prod(~np.isinf(xexp), axis=1, dtype=bool))
+                            * (np.prod(~np.isinf(yexp), axis=1, dtype=bool))
+                        )
+                    else:
+                        conditionnaninf = (
+                            (~np.isnan(xexp)[:, 0])
+                            * (~np.isnan(yexp)[:, 0])
+                            * (~np.isinf(xexp)[:, 0])
+                            * (~np.isinf(yexp)[:, 0])
+                        )
                     xcond = np.extract(
                         conditionx * conditiony * conditionnaninf, xexp[:, i]
                     )