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minjk-bl
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Change icon & menu item, block colors
1 parent 37c1b60 commit 263ec67

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5 files changed

+133
-122
lines changed

5 files changed

+133
-122
lines changed

‎css/boardFrame.css

Lines changed: 10 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -239,39 +239,39 @@
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background-color: rgb(253, 177, 133);
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}
241241
.vp-block.visualization .vp-block-header {
242-
background-color: rgb(249, 227, 214);
242+
background-color: #E8ECD0;
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}
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.vp-block.visualization.vp-focus .vp-block-header,
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.vp-block.visualization.vp-focus-child .vp-block-header {
246-
background-color: rgb(253, 177, 133);
246+
background-color: #C6CE94;
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}
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.vp-block.machine_learning .vp-block-header {
249-
background-color: #E8ECD0;
249+
background-color: #E5EEF8;
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}
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.vp-block.machine_learning.vp-focus .vp-block-header,
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.vp-block.machine_learning.vp-focus-child .vp-block-header {
253-
background-color: #C6CE94;
253+
background-color: #BFD5F0;
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}
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.vp-block.logic-define .vp-block-header {
256-
background-color: rgb(213, 231, 222);
256+
/* background-color: rgb(213, 231, 222); */
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}
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.vp-block.logic-define.vp-focus .vp-block-header,
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.vp-block.logic-define.vp-focus-child .vp-block-header {
260-
background-color: rgb(138, 214, 176);
260+
/* background-color: rgb(138, 214, 176); */
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}
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.vp-block.logic-control .vp-block-header {
263-
background-color: rgb(253, 239, 221);
263+
/* background-color: rgb(253, 239, 221); */
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}
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.vp-block.logic-control.vp-focus .vp-block-header,
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.vp-block.logic-control.vp-focus-child .vp-block-header {
267-
background-color: rgb(255, 207, 115);
267+
/* background-color: rgb(255, 207, 115); */
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}
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.vp-block.library .vp-block-header {
270-
background-color: rgb(249, 227, 214);
270+
/* background-color: rgb(249, 227, 214); */
271271
}
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.vp-block.library.vp-focus .vp-block-header,
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.vp-block.library.vp-focus-child .vp-block-header {
274-
background-color: rgb(253, 177, 133);
274+
/* background-color: rgb(253, 177, 133); */
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}
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/* block button group */
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.vp-block-button-group {

‎css/menuFrame.css

Lines changed: 9 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -230,13 +230,16 @@
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background: #E56139;
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}
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.vp-menuitem.apps.vp-color-apps5 {
233-
background: #BEB727;
233+
background: #97AA4E;
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}
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.vp-menuitem.apps.vp-color-apps6 {
236-
background: #91A541;
236+
background: #88B4E9;
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}
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.vp-menuitem.apps.vp-color-apps7 {
239-
background: #718E41;
239+
background: #6C9BD1;
240+
}
241+
.vp-menuitem.apps.vp-color-apps8 {
242+
background: #578BC7;
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}
241244
.vp-menuitem.apps.vp-color-preparing {
242245
background: var(--gray-color);
@@ -252,14 +255,14 @@
252255
}
253256
/* MenuItem - Logic */
254257
.vp-menuitem.logic-define {
255-
background-color: rgb(213, 231, 222);
258+
/* background-color: rgb(213, 231, 222); */
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}
257260
.vp-menuitem.logic-control {
258-
background-color: rgb(253, 239, 221);
261+
/* background-color: rgb(253, 239, 221); */
259262
}
260263
/* MenuItem - Library */
261264
.vp-menuitem.library {
262-
background-color: rgb(249, 227, 214);
265+
/* background-color: rgb(249, 227, 214); */
263266
}
264267

265268
/* Task Bar */

‎data/libraries.json

Lines changed: 91 additions & 37 deletions
Original file line numberDiff line numberDiff line change
@@ -3047,20 +3047,6 @@
30473047
"icon": "apps/apps_markdown.svg"
30483048
}
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},
3050-
{
3051-
"id" : "visualize_chart",
3052-
"type" : "function",
3053-
"level": 1,
3054-
"name" : "Chart",
3055-
"tag" : "MATPLOTLIB,CHART,VISUALIZATION,VISUALIZE",
3056-
"path" : "visualpython - visualization - matplotlib",
3057-
"desc" : "Matplotlib chart creation",
3058-
"file" : "m_apps/Chart",
3059-
"apps" : {
3060-
"color": 3,
3061-
"icon": "apps/apps_chart.svg"
3062-
}
3063-
},
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{
30653051
"id" : "apps_pdf",
30663052
"type" : "function",
@@ -3071,7 +3057,7 @@
30713057
"desc" : "PDF",
30723058
"file" : "m_apps/PDF",
30733059
"apps" : {
3074-
"color": 4,
3060+
"color": 3,
30753061
"icon": "apps/apps_pymupdf.svg"
30763062
}
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},
@@ -3091,6 +3077,74 @@
30913077
}
30923078
]
30933079
},
3080+
{
3081+
"id" : "pkg_visualize",
3082+
"type" : "package",
3083+
"level": 0,
3084+
"name" : "Visualization",
3085+
"path" : "visualpython - visualization",
3086+
"desc" : "Visualization modules",
3087+
"open" : true,
3088+
"grid" : true,
3089+
"item" : [
3090+
{
3091+
"id" : "visualize_chartStyle",
3092+
"type" : "function",
3093+
"level": 1,
3094+
"name" : "Chart Style",
3095+
"tag" : "CHART STYLE SETTING,IMPORT CHART,VISUALIZATION,VISUALIZE",
3096+
"path" : "visualpython - visualization - chartsstyle",
3097+
"desc" : "Chart style setting",
3098+
"file" : "m_visualize/ChartSetting",
3099+
"apps" : {
3100+
"color": 5,
3101+
"icon": "apps/apps_style.svg"
3102+
}
3103+
},
3104+
{
3105+
"id" : "pd_plot",
3106+
"type" : "function",
3107+
"level": 1,
3108+
"name" : "Pandas",
3109+
"tag" : "PANDAS PLOT,PANDAS",
3110+
"path" : "visualpython - library - pandas - plot",
3111+
"desc" : "Pandas plot creation",
3112+
"file" : "m_library/m_pandas/plot",
3113+
"apps" : {
3114+
"color": 5,
3115+
"icon": "apps/apps_visualize.svg"
3116+
}
3117+
},
3118+
{
3119+
"id" : "visualize_chart",
3120+
"type" : "function",
3121+
"level": 1,
3122+
"name" : "Matplotlib",
3123+
"tag" : "MATPLOTLIB,CHART,VISUALIZATION,VISUALIZE",
3124+
"path" : "visualpython - visualization - matplotlib",
3125+
"desc" : "Matplotlib chart creation",
3126+
"file" : "m_apps/Chart",
3127+
"apps" : {
3128+
"color": 5,
3129+
"icon": "apps/apps_visualize.svg"
3130+
}
3131+
},
3132+
{
3133+
"id" : "visualize_seaborn",
3134+
"type" : "function",
3135+
"level": 1,
3136+
"name" : "Seaborn",
3137+
"tag" : "SEABORN,CHART,VISUALIZATION,VISUALIZE",
3138+
"path" : "visualpython - visualization - seaborn",
3139+
"desc" : "Seaborn chart creation",
3140+
"file" : "m_visualize/Seaborn",
3141+
"apps" : {
3142+
"color": 5,
3143+
"icon": "apps/apps_visualize.svg"
3144+
}
3145+
}
3146+
]
3147+
},
30943148
{
30953149
"id" : "pkg_ml",
30963150
"type" : "package",
@@ -3111,7 +3165,7 @@
31113165
"desc" : "Data sets for machine learning",
31123166
"file" : "m_ml/DataSets",
31133167
"apps" : {
3114-
"color": 5,
3168+
"color": 6,
31153169
"icon": "apps/apps_dataset.svg"
31163170
}
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},
@@ -3125,7 +3179,7 @@
31253179
"desc" : "Data preparation for machine learning",
31263180
"file" : "m_ml/DataPrep",
31273181
"apps" : {
3128-
"color": 5,
3182+
"color": 6,
31293183
"icon": "apps/apps_dataprep.svg"
31303184
}
31313185
},
@@ -3139,10 +3193,24 @@
31393193
"desc" : "Data split for machine learning",
31403194
"file" : "m_ml/dataSplit",
31413195
"apps" : {
3142-
"color": 5,
3196+
"color": 6,
31433197
"icon": "apps/apps_datasplit.svg"
31443198
}
31453199
},
3200+
{
3201+
"id" : "ml_evaluation",
3202+
"type" : "function",
3203+
"level": 1,
3204+
"name" : "Evaluation",
3205+
"tag" : "PERFORMANCE EVALUATION,MACHINE LEARNING,ML",
3206+
"path" : "visualpython - machine_learning - evaluation",
3207+
"desc" : "Performance evaluation for machine learning",
3208+
"file" : "m_ml/evaluation",
3209+
"apps" : {
3210+
"color": 6,
3211+
"icon": "apps/apps_evaluate.svg"
3212+
}
3213+
},
31463214
{
31473215
"id" : "ml_regression",
31483216
"type" : "function",
@@ -3153,7 +3221,7 @@
31533221
"desc" : "Regression model for machine learning",
31543222
"file" : "m_ml/Regression",
31553223
"apps" : {
3156-
"color": 5,
3224+
"color": 7,
31573225
"icon": "apps/apps_regression.svg"
31583226
}
31593227
},
@@ -3167,7 +3235,7 @@
31673235
"desc" : "Classification model for machine learning",
31683236
"file" : "m_ml/Classification",
31693237
"apps" : {
3170-
"color": 6,
3238+
"color": 7,
31713239
"icon": "apps/apps_classification.svg"
31723240
}
31733241
},
@@ -3181,7 +3249,7 @@
31813249
"desc" : "Clustering model for machine learning",
31823250
"file" : "m_ml/Clustering",
31833251
"apps" : {
3184-
"color": 6,
3252+
"color": 7,
31853253
"icon": "apps/apps_clustering.svg"
31863254
}
31873255
},
@@ -3195,7 +3263,7 @@
31953263
"desc" : "Dimension reduction model for machine learning",
31963264
"file" : "m_ml/DimensionReduction",
31973265
"apps" : {
3198-
"color": 6,
3266+
"color": 7,
31993267
"icon": "apps/apps_dimension.svg"
32003268
}
32013269
},
@@ -3209,23 +3277,9 @@
32093277
"desc" : "AutoML model for machine learning",
32103278
"file" : "m_ml/AutoML",
32113279
"apps" : {
3212-
"color": 6,
3280+
"color": 8,
32133281
"icon": "apps/apps_automl.svg"
32143282
}
3215-
},
3216-
{
3217-
"id" : "ml_evaluation",
3218-
"type" : "function",
3219-
"level": 1,
3220-
"name" : "Evaluation",
3221-
"tag" : "PERFORMANCE EVALUATION,MACHINE LEARNING,ML",
3222-
"path" : "visualpython - machine_learning - evaluation",
3223-
"desc" : "Performance evaluation for machine learning",
3224-
"file" : "m_ml/evaluation",
3225-
"apps" : {
3226-
"color": 7,
3227-
"icon": "apps/apps_evaluate.svg"
3228-
}
32293283
}
32303284
]
32313285
}

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