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minjk-bl
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Add and implement Statistics package
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Lines changed: 4037 additions & 576 deletions

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‎visualpython/css/m_stats/descStats.css‎

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/*
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* Project Name : Visual Python
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* Description : GUI-based Python code generator
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* File Name : descStats.css
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* Author : Black Logic
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* Note : stylesheet for descStats.html
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* License : GNU GPLv3 with Visual Python special exception
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* Date : 2023. 05. 31
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* Change Date :
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*/
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.vp-percentile-box {
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width: 300px;
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height: 100px;
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border: 0.25px solid var(--vp-border-gray-color);
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padding: 0 5px;
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}
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.vp-percentile-item {
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height: 30px;
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width: 100%;
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border-bottom: 0.25px solid var(--vp-border-gray-color);
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padding: 0 10px;
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line-height: 30px;
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display: flex;
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column-gap: 5px;
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align-items: center;
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}
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.vp-percentile-value {
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width: 95%;
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}
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.vp-percentile-box:not(.disabled) .vp-percentile-remove {
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cursor: pointer;
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}
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.vp-percentile-box.disabled {
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background-color: var(--vp-light-gray-color);
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cursor: not-allowed;
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}

‎visualpython/css/m_stats/regression.css‎

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/*
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* Project Name : Visual Python
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* Description : GUI-based Python code generator
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* File Name : regression.css
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* Author : Black Logic
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* Note : stylesheet for regression.html
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* License : GNU GPLv3 with Visual Python special exception
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* Date : 2023. 05. 31
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* Change Date :
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*/
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.vp-categorical-box {
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display: grid;
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grid-template-columns: repeat(2, 1fr);
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border: 0.25px solid var(--vp-border-gray-color);
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padding: 5px;
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min-height: 30px;
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}

‎visualpython/css/menuFrame.css‎

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background-color: var(--vp-background-color);
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border-bottom: 1px solid var(--vp-border-gray-color);
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}
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.vp-package-manager {
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/* display: inline-flex; */
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display: none;
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float: right;
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position: relative;
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font-size: 18px;
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font-weight: bold;
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color: var(--vp-highlight-color);
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margin: 8px 6px 8px 3px;
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cursor: pointer;
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width: 18px;
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height: 18px;
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}
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.vp-version-updater {
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display: none;
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float: right;
@@ -82,14 +95,12 @@ input.vp-menu-search-box {
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.vp-menu-search-icon {
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width: 20px;
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height: 20px;
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position: relative;
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right: 30px;
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top: 6px;
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/* LAB: img to background-image */
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position: absolute;
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right: 45px;
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top: 15px;
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background-image: url(../img/search.svg);
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background-repeat: no-repeat;
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background-size: contain;
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height: 100%;
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}
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/* LAB: img to background-image */
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#vp_wrapper.lab .vp-menu-search-icon {
@@ -428,37 +439,40 @@ input.vp-menu-search-box {
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}
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/* statistics */
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.vp-menuitem.apps .stats_probDist {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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background: top / contain no-repeat url(../img/apps/apps_probDist.svg);
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}
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.vp-menuitem.apps .stats_descStats {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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background: top / contain no-repeat url(../img/apps/apps_descStats.svg);
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}
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.vp-menuitem.apps .stats_normTest {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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background: top / contain no-repeat url(../img/apps/apps_probDist.svg);
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}
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.vp-menuitem.apps .stats_equalVarTest {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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background: top / contain no-repeat url(../img/apps/apps_equalVarTest.svg);
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}
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.vp-menuitem.apps .stats_corrAnalysis {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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background: top / contain no-repeat url(../img/apps/apps_corrAnalysis.svg);
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}
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.vp-menuitem.apps .stats_reliabAnalysis {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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background: top / contain no-repeat url(../img/apps/apps_corrAnalysis.svg);
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}
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.vp-menuitem.apps .stats_chi2test {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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background: top / contain no-repeat url(../img/apps/apps_chi2test.svg);
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}
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.vp-menuitem.apps .stats_studentstTest {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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background: top / contain no-repeat url(../img/apps/apps_studentstTest.svg);
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}
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.vp-menuitem.apps .stats_anova {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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background: top / contain no-repeat url(../img/apps/apps_anova.svg);
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}
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.vp-menuitem.apps .stats_factorAnalysis {
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background: top / contain no-repeat url(../img/apps/apps_factorAnalysis.svg);
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}
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.vp-menuitem.apps .stats_regression {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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background: top / contain no-repeat url(../img/apps/apps_regression.svg);
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}
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.vp-menuitem.apps .stats_factorAnalysis {
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background: top / contain no-repeat url(../img/apps/apps_white.svg);
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.vp-menuitem.apps .stats_logisticRegression {
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background: top / contain no-repeat url(../img/apps/apps_logisticRegression.svg);
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}
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/* machine learning */
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.vp-menuitem.apps .ml_dataSet {

‎visualpython/css/root.css‎

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/* image icons */
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/* LAB: img to background-image */
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.vp-icon-setting {
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background: center / contain no-repeat url(../img/setting.svg);
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width: 100%;
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height: 100%;
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}
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.vp-icon-refresh {
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background: top / contain no-repeat url(../img/refresh.svg);
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height: 100%;
@@ -483,7 +488,13 @@ button.vp-button {
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hr.vp-extra-menu-line {
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margin: 5px 0px 7px 0px;
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}
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/* width style*/
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/* height style */
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#vp_wrapper .h150,
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.vp-popup-frame .h150,
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.vp-inner-popup-body .h150 {
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height: 150px !important;
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}
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/* width style */
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#vp_wrapper .wp100,
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.vp-popup-frame .wp100,
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.vp-inner-popup-body .wp100 {
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display: flex;
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gap: 5px;
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}
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.vp-flex-gap10 {
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display: flex;
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gap: 10px;
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}
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/* Grid style */
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.vp-grid-box {
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display: grid;
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align-items: baseline;
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align-content: space-evenly;
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}
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.vp-grid-col-120 {
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display: grid;
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grid-template-columns: 120px auto;
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grid-row-gap: 5px;
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align-items: baseline;
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align-content: space-evenly;
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}
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.vp-grid-col-130 {
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display: grid;
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grid-template-columns: 130px auto;
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display: grid;
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grid-template-columns: 160px auto;
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grid-row-gap: 5px;
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align-items: baseline;
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/* align-items: baseline; */
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align-items: start;
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align-content: space-evenly;
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}
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/* Table style */

‎visualpython/data/libraries.json‎

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"icon": "apps/apps.svg"
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}
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},
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{
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"id" : "stats_factorAnalysis",
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"type" : "function",
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"level": 1,
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"name" : "Factor Analysis",
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"tag" : "FACTOR ANALYSIS,STATS,STATISTICS",
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"path" : "visualpython - statistics - factor analysis",
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"desc" : "Factor analysis",
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"file" : "m_stats/FactorAnalysis",
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"apps" : {
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"color": 17,
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"icon": "apps/apps.svg"
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}
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},
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{
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"id" : "stats_regression",
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"type" : "function",
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}
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},
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{
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"id" : "stats_factorAnalysis",
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"id" : "stats_logisticRegression",
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"type" : "function",
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"level": 1,
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"name" : "Factor Analysis",
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"tag" : "FACTOR ANALYSIS,STATS,STATISTICS",
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"path" : "visualpython - statistics - factor analysis",
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"desc" : "Factor analysis",
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"file" : "m_stats/FactorAnalysis",
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"name" : "Logistic Regression",
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"tag" : "LOGISTIC REGRESSION,STATS,STATISTICS",
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"path" : "visualpython - statistics - logistic regression",
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"desc" : "Logistic regression",
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"file" : "m_stats/LogisticRegression",
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"apps" : {
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"color": 17,
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"icon": "apps/apps.svg"

‎visualpython/data/m_stats/statsLibrary.js‎

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code: '_rv = stats.bernoulli(${p})',
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description: 'A Bernoulli discrete random variable.',
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options: [
30-
{ name: 'p', component: ['input_number'], default: 0.6, usePair: true },
30+
{ name: 'p', component: ['input_number'], value: 0.6, required: true, usePair: true },
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]
3232
},
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'binomial': {
3434
name: 'Binomial',
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import: 'from scipy import stats',
36-
code: '_rv = stats.binom(${N}${p})',
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code: '_rv = stats.binom(${n}${p})',
3737
description: 'A binomial discrete random variable.',
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options: [
39-
{ name: 'N', component: ['input_number'], default: 10, usePair: true },
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{ name: 'p', component: ['input_number'], default: 0.6, usePair: true },
39+
{ name: 'n', component: ['input_number'], value: 10, required: true, usePair: true },
40+
{ name: 'p', component: ['input_number'], value: 0.6, required: true, usePair: true },
4141
]
4242
},
4343
'multinomial': {
4444
name: 'Multinomial',
4545
import: 'from scipy import stats',
46-
code: '_rv = stats.multinomial(${N}${mu})',
46+
code: '_rv = stats.multinomial(${n}${p})',
4747
description: 'A multinomial random variable.',
4848
options: [
49-
{ name: 'N', component: ['input_number'], default: 10, usePair: true },
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{ name: 'p', component: ['data_select'], usePair: true },
49+
{ name: 'n', component: ['input_number'], value: 10, required: true, usePair: true },
50+
{ name: 'p', component: ['data_select'], value: '[0.4, 0.6]', required: true, usePair: true },
5151
]
5252
},
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/** Continumous prob. dist. */
@@ -65,8 +65,8 @@ define([
6565
code: '_rv = stats.norm(${loc}${scale})',
6666
description: 'A normal continuous random variable.',
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options: [
68-
{ name: 'loc', component: ['input_number'], default: 0, usePair: true },
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{ name: 'scale', component: ['input_number'], default: 1, usePair: true },
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{ name: 'loc', component: ['input_number'], value: 0, usePair: true },
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{ name: 'scale', component: ['input_number'], value: 1, usePair: true },
7070
]
7171
},
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'beta': {
@@ -75,8 +75,8 @@ define([
7575
code: '_rv = stats.beta(${a}${b})',
7676
description: 'A beta continuous random variable.',
7777
options: [
78-
{ name: 'a', component: ['input_number'], usePair: true },
79-
{ name: 'b', component: ['input_number'], usePair: true },
78+
{ name: 'a', component: ['input_number'], required: true, usePair: true },
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{ name: 'b', component: ['input_number'], required: true, usePair: true },
8080
]
8181
},
8282
'gamma': {
@@ -85,7 +85,7 @@ define([
8585
code: '_rv = stats.gamma(${a})',
8686
description: 'A gamma continuous random variable.',
8787
options: [
88-
{ name: 'a', component: ['input_number'], usePair: true },
88+
{ name: 'a', component: ['input_number'], required: true, usePair: true },
8989
]
9090
},
9191
'studentst': {
@@ -94,7 +94,7 @@ define([
9494
code: '_rv = stats.t(${df})',
9595
description: "A Student's t continuous random variable.",
9696
options: [
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{ name: 'df', component: ['input_number'], usePair: true },
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{ name: 'df', component: ['input_number'], required: true, usePair: true },
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]
9999
},
100100
'chi2': {
@@ -103,7 +103,7 @@ define([
103103
code: '_rv = stats.chi2(${df})',
104104
description: 'A chi-squared continuous random variable.',
105105
options: [
106-
{ name: 'df', component: ['input_number'], usePair: true },
106+
{ name: 'df', component: ['input_number'], required: true, usePair: true },
107107
]
108108
},
109109
'f': {
@@ -112,8 +112,8 @@ define([
112112
code: '_rv = stats.f(${dfn}${dfd})',
113113
description: 'An F continuous random variable.',
114114
options: [
115-
{ name: 'dfn', component: ['input_number'], usePair: true },
116-
{ name: 'dfd', component: ['input_number'], usePair: true },
115+
{ name: 'dfn', component: ['input_number'], required: true, usePair: true },
116+
{ name: 'dfd', component: ['input_number'], required: true, usePair: true },
117117
]
118118
},
119119
'dirichlet': {
@@ -122,7 +122,7 @@ define([
122122
code: '_rv = stats.dirichlet(${alpha}${seed})',
123123
description: 'A Dirichlet random variable.',
124124
options: [
125-
{ name: 'alpha', component: ['input_number'], usePair: true },
125+
{ name: 'alpha', component: ['input_number'], required: true, usePair: true },
126126
{ name: 'seed', component: ['input_number'], usePair: true },
127127
]
128128
},
@@ -132,8 +132,8 @@ define([
132132
code: '_rv = stats.multivariate_normal(${mean}${cov}${allow_singular})',
133133
description: 'A multivariate normal random variable.',
134134
options: [
135-
{ name: 'mean', component: ['data_select'], default: '[0]', usePair: true },
136-
{ name: 'cov', component: ['data_select'], default: '[1]', usePair: true },
135+
{ name: 'mean', component: ['data_select'], value: '[0]', usePair: true },
136+
{ name: 'cov', component: ['data_select'], value: '[1]', usePair: true },
137137
{ name: 'allow_singular', component: ['bool_select'], default: 'False', usePair: true },
138138
]
139139
},

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