As you can see, develop version (version < 1) are treated a bit special in Julia, and different to semver. Read more here
Now we are ready to use the package from a different environment
diff --git a/material/1_mon/firststeps/firststeps_handout.html b/material/1_mon/firststeps/firststeps_handout.html index 8d0ed81..a897584 100644 --- a/material/1_mon/firststeps/firststeps_handout.html +++ b/material/1_mon/firststeps/firststeps_handout.html @@ -507,23 +507,23 @@typeof(myArray))
+initialize a vector (check with typeof(myArray))
[ brackets to index Arrays!
+MatLab: Notice the [ brackets to index Arrays!
. always means elementwise
+Python/R: . always means elementwise
end after each control sequence
+Python/R: end after each control sequence
; => positional, after => kwargs
+everything before the ; => positional, after => kwargs
b=123 optional positional argument
+List all methods with that function name - returns two functions, due to the b=123 optional positional argument
+(1,2,3)
+equivalent to +(1,2,3)
sqrt of a Vector
+Error - there is no method defined for the sqrt of a Vector
. applies the function to all elements of the container a - this works as “expected”
+the small . applies the function to all elements of the container a - this works as “expected”
undef array with the same size as a
+Function to initialize an undef array with the same size as a
The temp memory we need at each iteration is simply c[k]. And a nice sideeffect: By doing this, we get rid of any specialized “serialized” function, e.g. to do sum, or + or whatever. Those are typically the inbuilt C functions in Python/Matlab/R, that really speed up things. In Julia we do not need inbuilt functions for speed.
LinearAlgebra.QR(...) to access a function
+Requires to write LinearAlgebra.QR(...) to access a function
LinearAlgebra is a Base package, and always available
+LinearAlgebra is a Base package, and always available
Array, as Matrix is a convenience type-alias for Array with 2 dimensions. Same thing for Vector.
+equivalent to Array, as Matrix is a convenience type-alias for Array with 2 dimensions. Same thing for Vector.
2 of {Float64,2} is not mandatory
+the 2 of {Float64,2} is not mandatory
Much more on Wednesday in the lecture LinearAlgebra!
c
+returns c
StepRange between characters
+a StepRange between characters
Array{Chars}
+a Array{Chars}
String
+a String
* and not + - as plus implies from algebra that a+b == b+a which obviously is not true for string concatenation. But a*b !== b*a - at least for matrices.
+Indeed, * and not + - as plus implies from algebra that a+b == b+a which obviously is not true for string concatenation. But a*b !== b*a - at least for matrices.
true. Note the small r before the r"regular expression" - nifty!
+Returns true. Note the small r before the r"regular expression" - nifty!
::RegexMatch - access via m.match & m.offset (index) - or m.captures / m.offsets if you defined capture-groups
+Returns a ::RegexMatch - access via m.match & m.offset (index) - or m.captures / m.offsets if you defined capture-groups
Variables are in global scope in the REPL for debugging convenience
@@ -834,7 +834,7 @@REPL as well as in a script, because we modify the content of a, not a itself
+This works “correctly” in the REPL as well as in a script, because we modify the content of a, not a itself
SimulationResults type immediately available after running using MyStatsPackage. To use the other “internal” functions, one would use MyStatsPackage.rse_sum.
+This makes the SimulationResults type immediately available after running using MyStatsPackage. To use the other “internal” functions, one would use MyStatsPackage.rse_sum.
import MyStatsPackage
diff --git a/material/1_mon/firststeps/tasks.html b/material/1_mon/firststeps/tasks.html
index daf7c0d..e84cf6d 100644
--- a/material/1_mon/firststeps/tasks.html
+++ b/material/1_mon/firststeps/tasks.html
@@ -428,8 +428,8 @@ The exercise
Open a new script statistic_functions.jl in VSCode in a folder of your choice.
implement a function called rse_sum1. This function should return true if provided with the following test: res_sum(1:36) == 666. You should further make use of a for-loop.
implement a second function called rse_mean, which calculates the mean of the provided vector. Make sure to use the rse_sum function! Test it using res_mean(-15:17) == 1
implement a function called rse_sum1. This function should return true if provided with the following test: rse_sum(1:36) == 666. You should further make use of a for-loop.
implement a second function called rse_mean, which calculates the mean of the provided vector. Make sure to use the rse_sum function! Test it using rse_mean(-15:17) == 1
Next implement a standard deviation function rse_std: \(\sqrt{\frac{\sum((x-mean(x))^2)}{n-1}}\), this time you should use elementwise/broadcasting operators. Test it with rse_std(1:3) == 1
Finally, we will implement rse_tstat, returning the t-value with length(x)-1 DF, that the provided Array actually has a mean of 0. The formula is \(\frac{mean(x)}{std(x) / (sqrt(x))}\) Test it with rse_tstat(2:3) == 5. Add the keyword argument σ that allows the user to optionally provide a pre-calculated standard deviation.
diff --git a/material/3_wed/linalg/slides.html b/material/3_wed/linalg/slides.html
index a361f8e..2b36586 100644
--- a/material/3_wed/linalg/slides.html
+++ b/material/3_wed/linalg/slides.html
@@ -385,7 +385,7 @@ Slides
Exercise
-The exercise is rendered as html here but can also be downloaded as html here but can also be downloaded Exercise
a.download = 'file.ipynb'; a.click();
window.URL.revokeObjectURL(downloadURL);
document.body.removeChild(a);
- });">
+ });">
diff --git a/material/3_wed/vis/handout.html b/material/3_wed/vis/handout.html
index cfa1946..cd6aee0 100644
--- a/material/3_wed/vis/handout.html
+++ b/material/3_wed/vis/handout.html
@@ -568,7 +568,7 @@ Bonus: Makie Int
- 1
-
-
@lift does the heavy lifting (hrhr) here. It adds a listener to obs_ix, whenever that value is changed, the value of the output of @lift is changed as well ## Task 2: Interactivity Click here for the next task
+@lift does the heavy lifting (hrhr) here. It adds a listener to obs_ix, whenever that value is changed, the value of the output of @lift is changed as well ## Task 2: Interactivity Click here for the next task
diff --git a/material/3_wed/vis/tasks.html b/material/3_wed/vis/tasks.html
index c8c6445..19387bf 100644
--- a/material/3_wed/vis/tasks.html
+++ b/material/3_wed/vis/tasks.html
@@ -707,7 +707,7 @@ Rotate the labels
- 1
-
-Note the
; before xlims, this enforces that a NamedTuple is created
+Note the ; before xlims, this enforces that a NamedTuple is created
diff --git a/material/4_thu/parallel/slides.html b/material/4_thu/parallel/slides.html
index cec59e2..7ade63f 100644
--- a/material/4_thu/parallel/slides.html
+++ b/material/4_thu/parallel/slides.html
@@ -383,7 +383,7 @@ Slides
Exercise
-The exercise is rendered as html here but can also be downloaded as html here but can also be downloaded Exercise
a.download = 'file.ipynb'; a.click();
window.URL.revokeObjectURL(downloadURL);
document.body.removeChild(a);
- });">
+ });">
diff --git a/search.json b/search.json
index 1b199c4..0c51b27 100644
--- a/search.json
+++ b/search.json
@@ -480,7 +480,7 @@
"href": "material/1_mon/firststeps/tasks.html",
"title": "Task 1",
"section": "",
- "text": "Typically, you work in a Julia script ending in scriptname.jl\nYou concurrently have a REPL open, to not reload all packages etc. everytime. Further you typically have Revise.jl running in the background to automatically update your custom Packages / Modules (more to that later).\nYou can mark some code and execute it using ctrl + enter - you can also generate code-blocks using #--- and run a whole code-block using alt+enter\n\n\n\n\nOpen a new script statistic_functions.jl in VSCode in a folder of your choice.\nimplement a function called rse_sum1. This function should return true if provided with the following test: res_sum(1:36) == 666. You should further make use of a for-loop.\nimplement a second function called rse_mean, which calculates the mean of the provided vector. Make sure to use the rse_sum function! Test it using res_mean(-15:17) == 1\nNext implement a standard deviation function rse_std: \\(\\sqrt{\\frac{\\sum((x-mean(x))^2)}{n-1}}\\), this time you should use elementwise/broadcasting operators. Test it with rse_std(1:3) == 1\nFinally, we will implement rse_tstat, returning the t-value with length(x)-1 DF, that the provided Array actually has a mean of 0. The formula is \\(\\frac{mean(x)}{std(x) / (sqrt(x))}\\) Test it with rse_tstat(2:3) == 5. Add the keyword argument σ that allows the user to optionally provide a pre-calculated standard deviation.\n\nWell done! You now have all functions defined with which we will continue our journey.\n\n\n\n\n\n\nBonus Task ProgressMeter\n\n\n\nfor very large sums we might want to add a progressmeter. To do so:\n]add ProgressMeter\nusing ProgressMeter\n...\n@showprogress for ...\n\n\nAnd that’s it! You should have a nice progress bar now"
+ "text": "Typically, you work in a Julia script ending in scriptname.jl\nYou concurrently have a REPL open, to not reload all packages etc. everytime. Further you typically have Revise.jl running in the background to automatically update your custom Packages / Modules (more to that later).\nYou can mark some code and execute it using ctrl + enter - you can also generate code-blocks using #--- and run a whole code-block using alt+enter\n\n\n\n\nOpen a new script statistic_functions.jl in VSCode in a folder of your choice.\nimplement a function called rse_sum1. This function should return true if provided with the following test: rse_sum(1:36) == 666. You should further make use of a for-loop.\nimplement a second function called rse_mean, which calculates the mean of the provided vector. Make sure to use the rse_sum function! Test it using rse_mean(-15:17) == 1\nNext implement a standard deviation function rse_std: \\(\\sqrt{\\frac{\\sum((x-mean(x))^2)}{n-1}}\\), this time you should use elementwise/broadcasting operators. Test it with rse_std(1:3) == 1\nFinally, we will implement rse_tstat, returning the t-value with length(x)-1 DF, that the provided Array actually has a mean of 0. The formula is \\(\\frac{mean(x)}{std(x) / (sqrt(x))}\\) Test it with rse_tstat(2:3) == 5. Add the keyword argument σ that allows the user to optionally provide a pre-calculated standard deviation.\n\nWell done! You now have all functions defined with which we will continue our journey.\n\n\n\n\n\n\nBonus Task ProgressMeter\n\n\n\nfor very large sums we might want to add a progressmeter. To do so:\n]add ProgressMeter\nusing ProgressMeter\n...\n@showprogress for ...\n\n\nAnd that’s it! You should have a nice progress bar now"
},
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"objectID": "material/1_mon/firststeps/tasks.html#wait---how-do-i-even-run-things-in-juliavscode",
@@ -494,7 +494,7 @@
"href": "material/1_mon/firststeps/tasks.html#the-exercise",
"title": "Task 1",
"section": "",
- "text": "Open a new script statistic_functions.jl in VSCode in a folder of your choice.\nimplement a function called rse_sum1. This function should return true if provided with the following test: res_sum(1:36) == 666. You should further make use of a for-loop.\nimplement a second function called rse_mean, which calculates the mean of the provided vector. Make sure to use the rse_sum function! Test it using res_mean(-15:17) == 1\nNext implement a standard deviation function rse_std: \\(\\sqrt{\\frac{\\sum((x-mean(x))^2)}{n-1}}\\), this time you should use elementwise/broadcasting operators. Test it with rse_std(1:3) == 1\nFinally, we will implement rse_tstat, returning the t-value with length(x)-1 DF, that the provided Array actually has a mean of 0. The formula is \\(\\frac{mean(x)}{std(x) / (sqrt(x))}\\) Test it with rse_tstat(2:3) == 5. Add the keyword argument σ that allows the user to optionally provide a pre-calculated standard deviation.\n\nWell done! You now have all functions defined with which we will continue our journey.\n\n\n\n\n\n\nBonus Task ProgressMeter\n\n\n\nfor very large sums we might want to add a progressmeter. To do so:\n]add ProgressMeter\nusing ProgressMeter\n...\n@showprogress for ...\n\n\nAnd that’s it! You should have a nice progress bar now"
+ "text": "Open a new script statistic_functions.jl in VSCode in a folder of your choice.\nimplement a function called rse_sum1. This function should return true if provided with the following test: rse_sum(1:36) == 666. You should further make use of a for-loop.\nimplement a second function called rse_mean, which calculates the mean of the provided vector. Make sure to use the rse_sum function! Test it using rse_mean(-15:17) == 1\nNext implement a standard deviation function rse_std: \\(\\sqrt{\\frac{\\sum((x-mean(x))^2)}{n-1}}\\), this time you should use elementwise/broadcasting operators. Test it with rse_std(1:3) == 1\nFinally, we will implement rse_tstat, returning the t-value with length(x)-1 DF, that the provided Array actually has a mean of 0. The formula is \\(\\frac{mean(x)}{std(x) / (sqrt(x))}\\) Test it with rse_tstat(2:3) == 5. Add the keyword argument σ that allows the user to optionally provide a pre-calculated standard deviation.\n\nWell done! You now have all functions defined with which we will continue our journey.\n\n\n\n\n\n\nBonus Task ProgressMeter\n\n\n\nfor very large sums we might want to add a progressmeter. To do so:\n]add ProgressMeter\nusing ProgressMeter\n...\n@showprogress for ...\n\n\nAnd that’s it! You should have a nice progress bar now"
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index a51f558..cac7a47 100644
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