General spelling corrections.

Author: ronnied

README.rst

@@ -54,7 +54,7 @@ or use the new function that also contains the stability chart and more:
     a.select_poles()
 
 The stability chart displayes calculated poles and the user can hand-pick the stable ones. Reconstruction is done on-the-fly. 
-In this case the reconstruction is not necessary since the user can access FRF matrix and modal constant matrix:
+In this case the reconstruction is not necessary since the user can access the FRF matrix and modal constant matrix:
 
 ::
 

pyEMA Showcase.ipynb

@@ -22,7 +22,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The experiment is shown in the figure below; the beam was excited at 6 locations with a impact hammer, while the response was measured at 7 locations using piezo accelerometers (camera data is not used in this showcase; for image EMA, see https://github.com/ladisk/ImageBasedModalAnalysisTutorial):\n",
+    "The experiment is shown in the figure below; the beam was excited at 6 locations with an impact hammer, while the response was measured at 7 locations using piezo accelerometers (camera data is not used in this showcase; for image EMA, see https://github.com/ladisk/ImageBasedModalAnalysisTutorial):\n",
     "<img width=500 src=\"./data/experiment_1.jpg\">"
    ]
   },
@@ -77,7 +77,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And from the FRF (`H1_main` is of dimensions: #inputs, #outputs, frequency) only the response accelerometer position at index 1 will be later used:"
+    "And from the FRF (`H1_main` is of dimensions: #inputs, #outputs, frequency) only the response accelerometer position at index 1 will be used later:"
    ]
   },
   {
@@ -197,7 +197,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Alternatively to selecting from stabilisation chart, the frequencies can be defines as a list, e.g. (comment out to use):"
+    "As an alternative to selecting from the stabilisation chart, the frequencies can be defined as a list, e.g. (comment out to use):"
    ]
   },
   {

pyEMA/pole_picking.py

@@ -76,7 +76,7 @@ def __init__(self, Model):
         self.get_stability()
         self.plot_stability()
 
-        # Integrate matplotib figure
+        # Integrate matplotlib figure
         canvas = FigureCanvasTkAgg(self.fig, self.root)
         canvas.get_tk_widget().pack(side='top', fill='both', expand=1)
         NavigationToolbar2Tk(canvas, self.root)
@@ -132,7 +132,7 @@ def plot_frf(self, initial=False):
     def get_stability(self, fn_temp=0.001, xi_temp=0.05):
         """Get the stability matrix.
         
-        :param fn_temp: Natural frequency stability crieterion.
+        :param fn_temp: Natural frequency stability criterion.
         :param xi_temp: Damping stability criterion.
         """
         Nmax = self.Model.pol_order_high
@@ -145,13 +145,13 @@ def plot_stability(self, update_ticks=False):
             self.ax1.clear()
             self.ax1.grid(True)
 
-            # stable eigenfrequencues, unstable damping ratios
+            # stable eigenfrequencies, unstable damping ratios
             a = np.argwhere((self.test_fn > 0) & ((self.test_xi == 0) | (self.xi_temp <= 0)))
             # stable eigenfrequencies, stable damping ratios
             b = np.argwhere((self.test_fn > 0) & ((self.test_xi > 0) & (self.xi_temp > 0)))
-            # unstable eigenfrequencues, unstable damping ratios
+            # unstable eigenfrequencies, unstable damping ratios
             c = np.argwhere((self.test_fn == 0) & ((self.test_xi == 0) | (self.xi_temp <= 0)))
-            # unstable eigenfrequencues, stable damping ratios
+            # unstable eigenfrequencies, stable damping ratios
             d = np.argwhere((self.test_fn == 0) & ((self.test_xi > 0) & (self.xi_temp > 0)))
 
             p1 = self.ax1.plot(self.fn_temp[a[:, 0], a[:, 1]], 1+a[:, 1], 'bx',
@@ -202,13 +202,13 @@ def plot_cluster(self, update_ticks=False):
             self.ax1.clear()
             self.ax1.grid(True)
 
-            # stable eigenfrequencues, unstable damping ratios
+            # stable eigenfrequencies, unstable damping ratios
             a = np.argwhere((self.test_fn > 0) & ((self.test_xi == 0) | (self.xi_temp <= 0)))
             # stable eigenfrequencies, stable damping ratios
             b = np.argwhere((self.test_fn > 0) & ((self.test_xi > 0) & (self.xi_temp > 0)))
-            # unstable eigenfrequencues, unstable damping ratios
+            # unstable eigenfrequencies, unstable damping ratios
             c = np.argwhere((self.test_fn == 0) & ((self.test_xi == 0) | (self.xi_temp <= 0)))
-            # unstable eigenfrequencues, stable damping ratios
+            # unstable eigenfrequencies, stable damping ratios
             d = np.argwhere((self.test_fn == 0) & ((self.test_xi > 0) & (self.xi_temp > 0)))
 
             p1 = self.ax1.plot(self.fn_temp[a[:, 0], a[:, 1]], self.xi_temp[a[:, 0], a[:, 1]], 'bx',
@@ -270,7 +270,7 @@ def get_closest_poles_cluster(self):
                 min_.append([y_ind, sel, np.abs(self.Model.pole_freq[y_ind][sel]-self.x_data_pole), np.abs(self.Model.pole_xi[y_ind][sel]-self.y_data_pole[0])])
 
         min_a = np.asarray(min_)
-        # select the poles that have the frequency within 2% of observed range
+        # select the poles that have a frequency within 2% of the observed range
         mask = min_a[:, 2] < (self.Model.upper - self.Model.lower) * 0.02
         min_ind_x = np.argmin(min_a[mask][:, 3])
 
@@ -284,7 +284,7 @@ def get_closest_poles_cluster(self):
 
 
     def on_click(self, event):
-        # on button 1 press (left mouse button) + shift is held
+        # on button 1 press (left mouse button) + SHIFT is held
         if event.button == 1 and self.shift_is_held:
             self.y_data_pole = [event.ydata]
             self.x_data_pole = event.xdata
@@ -324,13 +324,13 @@ def on_click(self, event):
 
 
     def on_key_press(self, event):
-        """Function triggered on key press (shift)."""
+        """Function triggered on key press (SHIFT)."""
         if event.key == 'shift':
             self.shift_is_held = True
 
 
     def on_key_release(self, event):
-        """Function triggered on key release (shift)."""
+        """Function triggered on key release (SHIFT)."""
         if event.key == 'shift':
             self.shift_is_held = False
 

pyEMA/pyEMA.py

@@ -49,12 +49,12 @@ def __init__(self,
         except:
             raise Exception('lower must be float or integer')
         if self.lower < 0:
-            raise Exception('lower must be positive or equal to zero')
+            raise Exception('lower must be more than or equal to zero')
 
         try:
             self.upper = float(upper)
         except:
-            raise Exception('upper must be flaot or integer')
+            raise Exception('upper must be float or integer')
         if self.upper < self.lower:
             raise Exception('upper must be greater than lower')
 
@@ -64,7 +64,7 @@ def __init__(self,
             try:
                 self.frf = np.asarray(frf)
             except:
-                raise Exception('cannot contert frf to numpy ndarray')
+                raise Exception('cannot convert frf to numpy ndarray')
             if self.frf.ndim == 1:
                 self.frf = np.array([self.frf])
 
@@ -88,7 +88,7 @@ def __init__(self,
         except:
             raise Exception('cannot convert pol_order_high to integer')
         if self.pol_order_high <= 0:
-            raise Exception('pol_order_high must be positive')
+            raise Exception('pol_order_high must be more than zero')
 
         if not pyfrf:
             self.omega = 2 * np.pi * self.freq
@@ -101,7 +101,7 @@ def __init__(self,
 
     def add_frf(self, pyfrf_object):
         """
-        Add a FRF at a next location.
+        Add an FRF at a next location.
 
         This method can be used in relation to pyFRF from Open Modal (https://github.com/openmodal)
 
@@ -128,7 +128,7 @@ def get_poles(self, method='lscf', show_progress=True):
         Source: https://github.com/openmodal/OpenModal/blob/master/OpenModal/analysis/lscf.py
 
         The LSCF method is an frequency-domain Linear Least Squares
-        estimator optimized  for modal parameter estimation. The choice of
+        estimator optimized for modal parameter estimation. The choice of
         the most important algorithm characteristics is based on the
         results in [1] (Section 5.3.3.) and can be summarized as:
 
@@ -184,7 +184,7 @@ def get_poles(self, method='lscf', show_progress=True):
         """
         if method != 'lscf':
             raise Exception(
-                f'no method "{method}". Currently only "lscf" method is implemented.')
+                f'no method "{method}". Currently only the "lscf" method is implemented.')
 
         if show_progress:
             def tqdm_range(x): return tqdm(x, ncols=100)