fixing 1d convolution markdown file (#879)

Author: pillarxyz

contents/convolutions/1d/1d.md

@@ -57,7 +57,7 @@ With this in mind, we can almost directly transcribe the discrete equation into
 {% sample lang="cs" %}
 [import:63-84, lang:"csharp"](code/csharp/1DConvolution.cs)
 {% sample lang="py" %}
-[import:18-27, lang:"python"](code/python/1d_convolution.py)
+[import:20-31, lang:"python"](code/python/1d_convolution.py)
 {% endmethod %}
 
 The easiest way to reason about this code is to read it as you might read a textbook.
@@ -192,7 +192,7 @@ Here it is again for clarity:
 {% sample lang="cs" %}
 [import:63-84, lang:"csharp"](code/csharp/1DConvolution.cs)
 {% sample lang="py" %}
-[import:18-27, lang:"python"](code/python/1d_convolution.py)
+[import:20-31, lang:"python"](code/python/1d_convolution.py)
 {% endmethod %}
 
 Here, the main difference between the bounded and unbounded versions is that the output array size is smaller in the bounded case.
@@ -204,7 +204,7 @@ For an unbounded convolution, the function would be called with a the output arr
 {% sample lang="cs" %}
 [import:96-97, lang:"csharp"](code/csharp/1DConvolution.cs)
 {% sample lang="py" %}
-[import:37-38, lang:"python"](code/python/1d_convolution.py)
+[import:41-42, lang:"python"](code/python/1d_convolution.py)
 {% endmethod %}
 
 On the other hand, the bounded call would set the output array size to simply be the length of the signal
@@ -215,7 +215,7 @@ On the other hand, the bounded call would set the output array size to simply be
 {% sample lang="cs" %}
 [import:98-99, lang:"csharp"](code/csharp/1DConvolution.cs)
 {% sample lang="py" %}
-[import:40-41, lang:"python"](code/python/1d_convolution.py)
+[import:44-45, lang:"python"](code/python/1d_convolution.py)
 {% endmethod %}
 
 Finally, as we mentioned before, it is possible to center bounded convolutions by changing the location where we calculate the each point along the filter.
@@ -227,7 +227,7 @@ This can be done by modifying the following line:
 {% sample lang="cs" %}
 [import:71-71, lang:"csharp"](code/csharp/1DConvolution.cs)
 {% sample lang="py" %}
-[import:22-22, lang:"python"](code/python/1d_convolution.py)
+[import:25-25, lang:"python"](code/python/1d_convolution.py)
 {% endmethod %}
 
 Here, `j` counts from `i-length(filter)` to `i`.
@@ -263,7 +263,7 @@ In code, this typically amounts to using some form of modulus operation, as show
 {% sample lang="cs" %}
 [import:38-61, lang:"csharp"](code/csharp/1DConvolution.cs)
 {% sample lang="py" %}
-[import:5-15, lang:"python"](code/python/1d_convolution.py)
+[import:5-17, lang:"python"](code/python/1d_convolution.py)
 {% endmethod %}
 
 This is essentially the same as before, except for the modulus operations, which allow us to work on a periodic domain.