Fix some typos

Author: alexfikl

docs/index.rst

@@ -104,7 +104,7 @@ References
 
 .. [RN17] C. Rackauckas, Q. Nie: Adaptive methods for stochastic differential equations via natural embeddings and rejection sampling with memory, Discrete Cont. Dyn.-B 22, pp. 2731–2761 (2017), `10.3934/dcdsb.2017133 <http://dx.doi.org/10.3934/dcdsb.2017133>`_.
 
-.. [R10] A. Rößler, Runge–Kutta methods for the strong approximation of solutions of stochastic differential equations, SIAM J. Numer. Anal. 48, pp. 922–952 (2010) `10.1137/09076636X <http://dx.doi.org/10.1137/09076636X>`_.
+.. [R10] A. Rößler, Runge–Kutta methods for the strong approximation of solutions of stochastic differential equations, SIAM J. Numerical Anal. 48, pp. 922–952 (2010) `10.1137/09076636X <http://dx.doi.org/10.1137/09076636X>`_.
 
 .. _JiTCODE: http://github.com/neurophysik/jitcode
 

jitcsde/_jitcsde.py

@@ -62,7 +62,7 @@ class jitcsde(jitcxde):
 		
 		*	A SymEngine function object used in `f_sym` to represent the function call. If you want to use any JiTCSDE features that need the derivative, this must have a properly defined `f_diff` method with the derivative being another callback function (or constant).
 		*	The Python function to be called. This function will receive the state array (`y`) as the first argument. All further arguments are whatever you use as arguments of the SymEngine function in `f_sym`. These can be any expression that you might use in the definition of the derivative and contain, e.g., dynamical variables, time, control parameters, and helpers. The only restriction is that the arguments are floats (and not vectors or similar). The return value must also be a float (or something castable to float). It is your responsibility to ensure that this function adheres to these criteria, is deterministic and sufficiently smooth with respect its arguments; expect nasty errors otherwise.
-		*	The number of arguments, **excluding** the state array as mandatory first argument. This means if you have a variadic Python function, you cannot just call it with different numbers of arguments in `f_sym`, but you have to define separate callbacks for each of numer of arguments.
+		*	The number of arguments, **excluding** the state array as mandatory first argument. This means if you have a variadic Python function, you cannot just call it with different numbers of arguments in `f_sym`, but you have to define separate callbacks for each of number of arguments.
 		
 		See `this example <https://github.com/neurophysik/jitcdde/blob/master/examples/sunflower_callback.py>`_ (for JiTCDDE) for how to use this.
 	

pyproject.toml

@@ -100,3 +100,7 @@ combine-as-imports = true
 known-local-folder = [ "jitcsde" ]
 known-first-party = [ "jitcxde_common" ]
 lines-after-imports = 2
+
+[tool.typos.default.extend-words]
+# short for Stratonovich used in various names
+strat = "strat"

tests/kmc.py

@@ -48,7 +48,7 @@ def KMC(Xs, h, kmax=4, nbins=100):
 	Max = max(Xs) + (max(Xs)-min(Xs))/nbins*0.5
 	# bin borders
 	X_bins = np.linspace(Min,Max,nbins+1)
-	# intialise results with middles of bins:
+	# initialise results with middles of bins:
 	results = [(X_bins[1:]+X_bins[:-1])/2]
 
 	def Bin(value):

tests/test_jitcsde.py

@@ -47,7 +47,7 @@ def test_numpy_rng(self):
 		self.SDE.compile_C(numpy_rng=True,extra_compile_args=compile_args)
 		self.test_default()
 
-	def test_reproducability(self):
+	def test_reproducibility(self):
 		self.test_default()
 
 	def test_Python_core(self):

tests/test_jumps.py

@@ -46,7 +46,7 @@ def test_default(self):
 		self.SDE.set_seed(42)
 		self.SDE.set_initial_value(initial_value,0.0)
 
-	def test_reproducability(self):
+	def test_reproducibility(self):
 		self.test_default()
 
 	def test_Python_core(self):