Create wiki.yml (#229)

* Create wiki.yml

Allow the creation of wiki files automatically for documentation purposes

* list docs for compiling

* runs python step

* Changed to using relative imports

* fix wiki error

* setup protobuf inside the action

* adding init.py

* try installing via an action

* the action does not work for us

* adding logging

* added new step

* maybe dont copy everything

* local copying

* Update wiki.yml

* Update wiki.yml

* trying something

* checking for protoc

* sequential execution

* sequential execution (2)

* success condition + full path names

* small syntax fix?

* smaller fix

* fix?

* fix? (2)

* Print dirs for debug.

* Duplicate works!
Sequential execution for Dependencies.

* Duplicate didn't solve the problem. Checking logs w/o it.

* Install wheel before protobuf setup.

* Split-up for debugging.

* Bunch of prints

* Catch exception?

* Full fix: added shared library refresh to install-protoc.sh, which supposedly fixes the .libs directory problem.

NOTE: Currently not skipping df_methods/summary.md to see if anything uploads.

* Using the output folder; reformatting files for proper markdown.

* Final touches (hopefully).

Co-authored-by: Matthew Mage <[email protected]>
Co-authored-by: DivvyCr <[email protected]>

.github/workflows/wiki.yml

@@ -0,0 +1,44 @@
+name: Update and upload DataFrame information.
+
+on:
+  push:
+    branches: [ master ]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v1
+      with:
+        python-version: '3.7' # Version range or exact version of a Python version to use, using SemVer's version range syntax
+        architecture: 'x64' # optional x64 or x86. Defaults to x64 if not specified
+
+    - name: Install/Update pip and wheel.
+      run:
+        python -m pip install --upgrade pip &&
+        pip install wheel
+
+    - name: Set-up and install protobuf.
+      run: chmod 777 ./_travis/install-protoc.sh && ./_travis/install-protoc.sh 2.6.1
+
+    - name: Install dependencies.
+      run: pip install -r requirements.txt
+
+    - name: Initialise.
+      run: python init.py
+
+    - name: Create document
+      run: python -m carball.tests.docs.data_frame_docs
+
+    - name: wiki-page-creator-action
+      uses: Decathlon/[email protected]
+      env:
+        GH_PAT: ${{ secrets.GH_WIKI_EDIT_TOKEN }}
+        ACTION_MAIL: [email protected]
+        ACTION_NAME: saltie
+        OWNER: SaltieRL
+        REPO_NAME: carball
+        MD_FOLDER: carball/tests/docs/
+        SKIP_MD: df_methods.md, df_summary.md

_travis/install-protoc.sh

@@ -13,8 +13,11 @@ unzip protobuf-${protoc_version}.zip
 cd protobuf-${protoc_version}
 echo "CONFIGURING PROTOBUF"
 ./configure
-echo "COMPILING PROTOBUF"
+echo "COMPILING and INSTALLING PROTOBUF and SHARED LIBRARIES (.libs)"
 make
+make check
+sudo make install
+sudo ldconfig
 echo "GETTING PROTOBUF VERSION"
 ./src/protoc --version
 echo "CLEANING UP"
@@ -25,3 +28,5 @@ pwd
 ls -lh
 echo "COPYING PROTOBUF TO NEW DIRECTORY"
 cp ./protobuf-${protoc_version}/src/protoc carball/generated/binaries
+cd carball/generated/binaries
+ls -lh

carball/tests/docs/data_frame_docs.py

@@ -1,7 +1,7 @@
 import os
 
-import carball
-from carball.tests.utils import get_replay_path
+from ... import analyze_replay_file
+from ..utils import get_replay_path
 
 
 # All relevant files begin with df_ .
@@ -10,16 +10,12 @@ def create_df_docs():
     replay_path = get_replay_path("SHORT_SAMPLE.replay")
 
     working_dir = os.path.dirname(__file__)
-    output_dir = os.path.join(working_dir, 'output')
 
     df_summary_path = os.path.join(working_dir, 'df_summary.md')
     df_methods_path = os.path.join(working_dir, 'df_methods.md')
-    df_docs_path = os.path.join(output_dir, "DATA_FRAME_INFO.md")
+    df_docs_path = os.path.join(working_dir, "data_frame.md")
 
-    if not os.path.exists(output_dir):
-        os.mkdir(output_dir)
-
-    analysis = carball.analyze_replay_file(replay_path, clean=False)
+    analysis = analyze_replay_file(replay_path, clean=False)
 
     data_frame = analysis.get_data_frame()
 
@@ -38,7 +34,7 @@ def create_df_docs():
 
     df_docs.write("\n")
 
-    df_docs.write("##Example (list of columns)\n")
+    df_docs.write("## Example (list of columns)\n")
     df_docs.write("Each subheading is the primary column heading, and each row is the secondary column heading."
                   "The 'Player' subheading will be unique for each player in the replay (i.e. for a 3v3 game, there "
                   "will be 6 distinct players)\n")
@@ -47,7 +43,7 @@ def create_df_docs():
     for c in data_frame.columns:
         if current_heading != c[0]:
             current_heading = c[0]
-            df_docs.write("\n####" + c[0])
+            df_docs.write("\n#### " + c[0])
 
         df_docs.write("\n\t" + c[1])
 

carball/tests/docs/df_methods.md

@@ -1,8 +1,8 @@
-##Important Methods
+## Important Methods
 To effectively use the DataFrame object, below are listed some of the more valuable methods. Please make sure that you
 search the pandas docs for clarifications, *before* asking questions.
 
-####data_frame.x.y
+#### data_frame.x.y
 For example, `data_frame.game.time`.
 
 This method simply narrows the scope to the selected column(s). The *x* variable refers to the first value of the tuple,
@@ -14,7 +14,7 @@ The first (left-most) column still refers to the consecutive in-game frames.
 **Notes:**
  * To see all available columns, either refer to the given example, or do data_frame.info(verbose=True)
 
-####data_frame.loc()
+#### data_frame.loc()
 
 For example, `data_frame.loc[1, 'Player']` `data_frame.loc[1, ('Player', 'ping')]`
 
@@ -25,6 +25,6 @@ to access (to access a single, specific column, use the desired tuple in bracket
  * The index is an integer, not a string.
  * The column keys are always strings.
 
-####data_frame.at()
+#### data_frame.at()
 
 Exactly the same as .loc(), but should preferably be used to access single values, as opposed to many rows/columns.

carball/tests/docs/df_summary.md

@@ -1,4 +1,4 @@
-##Summary
+## Summary
 The DataFrame is an instance of two-dimensional, size-mutable, potentially heterogeneous tabular data. It comes as part
 of the pandas package (often imported as *pd*).
 

init.py

@@ -6,12 +6,15 @@ def initalize_rattletrap():
         print('Issue adding rattletrap')
 
 def initialize_project():
+    print("Importing necessary methods...")
     from utils.create_proto import create_proto_files
     from utils.import_fixer import convert_to_relative_imports
-
+
+    print("Creating proto files...")
     create_proto_files()
     convert_to_relative_imports()
     initalize_rattletrap()
 
 if __name__ == "__main__":
+    print("Initialising project...")
     initialize_project()

utils/create_proto.py

@@ -86,7 +86,7 @@ def create_proto_files():
         print('creating proto file', file, end='\t')
         result = call([get_proto(), '--python_out=' + proto_dir, '--proto_path=' + current_dir, file])
         if result != 0:
-            raise result
+            raise ValueError(result)
         print(result)