Stock Market Analysis.html

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/* Icons urls */

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  --jp-icon-add-above: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTQiIGhlaWdodD0iMTQiIHZpZXdCb3g9IjAgMCAxNCAxNCIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGcgY2xpcC1wYXRoPSJ1cmwoI2NsaXAwXzEzN18xOTQ5MikiPgo8cGF0aCBjbGFzcz0ianAtaWNvbjMiIGQ9Ik00Ljc1IDQuOTMwNjZINi42MjVWNi44MDU2NkM2LjYyNSA3LjAxMTkxIDYuNzkzNzUgNy4xODA2NiA3IDcuMTgwNjZDNy4yMDYyNSA3LjE4MDY2IDcuMzc1IDcuMDExOTEgNy4zNzUgNi44MDU2NlY0LjkzMDY2SDkuMjVDOS40NTYyNSA0LjkzMDY2IDkuNjI1IDQuNzYxOTEgOS42MjUgNC41NTU2NkM5LjYyNSA0LjM0OTQxIDkuNDU2MjUgNC4xODA2NiA5LjI1IDQuMTgwNjZINy4zNzVWMi4zMDU2NkM3LjM3NSAyLjA5OTQxIDcuMjA2MjUgMS45MzA2NiA3IDEuOTMwNjZDNi43OTM3NSAxLjkzMDY2IDYuNjI1IDIuMDk5NDEgNi42MjUgMi4zMDU2NlY0LjE4MDY2SDQuNzVDNC41NDM3NSA0LjE4MDY2IDQuMzc1IDQuMzQ5NDEgNC4zNzUgNC41NTU2NkM0LjM3NSA0Ljc2MTkxIDQuNTQzNzUgNC45MzA2NiA0Ljc1IDQuOTMwNjZaIiBmaWxsPSIjNjE2MTYxIiBzdHJva2U9IiM2MTYxNjEiIHN0cm9rZS13aWR0aD0iMC43Ii8+CjwvZz4KPHBhdGggY2xhc3M9ImpwLWljb24zIiBmaWxsLXJ1bGU9ImV2ZW5vZGQiIGNsaXAtcnVsZT0iZXZlbm9kZCIgZD0iTTExLjUgOS41VjExLjVMMi41IDExLjVWOS41TDExLjUgOS41Wk0xMiA4QzEyLjU1MjMgOCAxMyA4LjQ0NzcyIDEzIDlWMTJDMTMgMTIuNTUyMyAxMi41NTIzIDEzIDEyIDEzTDIgMTNDMS40NDc3MiAxMyAxIDEyLjU1MjMgMSAxMlY5QzEgOC40NDc3MiAxLjQ0NzcxIDggMiA4TDEyIDhaIiBmaWxsPSIjNjE2MTYxIi8+CjxkZWZzPgo8Y2xpcFBhdGggaWQ9ImNsaXAwXzEzN18xOTQ5MiI+CjxyZWN0IGNsYXNzPSJqcC1pY29uMyIgd2lkdGg9IjYiIGhlaWdodD0iNiIgZmlsbD0id2hpdGUiIHRyYW5zZm9ybT0ibWF0cml4KC0xIDAgMCAxIDEwIDEuNTU1NjYpIi8+CjwvY2xpcFBhdGg+CjwvZGVmcz4KPC9zdmc+Cg==);
  --jp-icon-add-below: url(data:image/svg+xml;base64,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);
  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
  --jp-icon-bell: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE2IDE2IiB2ZXJzaW9uPSIxLjEiPgogICA8cGF0aCBjbGFzcz0ianAtaWNvbjIganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMzMzMzMzIgogICAgICBkPSJtOCAwLjI5Yy0xLjQgMC0yLjcgMC43My0zLjYgMS44LTEuMiAxLjUtMS40IDMuNC0xLjUgNS4yLTAuMTggMi4yLTAuNDQgNC0yLjMgNS4zbDAuMjggMS4zaDVjMC4wMjYgMC42NiAwLjMyIDEuMSAwLjcxIDEuNSAwLjg0IDAuNjEgMiAwLjYxIDIuOCAwIDAuNTItMC40IDAuNi0xIDAuNzEtMS41aDVsMC4yOC0xLjNjLTEuOS0wLjk3LTIuMi0zLjMtMi4zLTUuMy0wLjEzLTEuOC0wLjI2LTMuNy0xLjUtNS4yLTAuODUtMS0yLjItMS44LTMuNi0xLjh6bTAgMS40YzAuODggMCAxLjkgMC41NSAyLjUgMS4zIDAuODggMS4xIDEuMSAyLjcgMS4yIDQuNCAwLjEzIDEuNyAwLjIzIDMuNiAxLjMgNS4yaC0xMGMxLjEtMS42IDEuMi0zLjQgMS4zLTUuMiAwLjEzLTEuNyAwLjMtMy4zIDEuMi00LjQgMC41OS0wLjcyIDEuNi0xLjMgMi41LTEuM3ptLTAuNzQgMTJoMS41Yy0wLjAwMTUgMC4yOCAwLjAxNSAwLjc5LTAuNzQgMC43OS0wLjczIDAuMDAxNi0wLjcyLTAuNTMtMC43NC0wLjc5eiIgLz4KPC9zdmc+Cg==);
  --jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
  --jp-icon-bug: url(data:image/svg+xml;base64,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);
  --jp-icon-build: url(data:image/svg+xml;base64,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);
  --jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
  --jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
  --jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
  --jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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  --jp-icon-r-kernel: url(data:image/svg+xml;base64,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);
  --jp-icon-react: url(data:image/svg+xml;base64,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);
  --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
  --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
  --jp-icon-regex: url(data:image/svg+xml;base64,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);
  --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
  --jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
  --jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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  --jp-icon-toc: url(data:image/svg+xml;base64,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);
  --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
  --jp-icon-trusted: url(data:image/svg+xml;base64,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);
  --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
  --jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
  --jp-icon-users: url(data:image/svg+xml;base64,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);
  --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
  --jp-icon-word: url(data:image/svg+xml;base64,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);
  --jp-icon-yaml: url(data:image/svg+xml;base64,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);
}

/* Icon CSS class declarations */

.jp-AddAboveIcon {
  background-image: var(--jp-icon-add-above);
}

.jp-AddBelowIcon {
  background-image: var(--jp-icon-add-below);
}

.jp-AddIcon {
  background-image: var(--jp-icon-add);
}

.jp-BellIcon {
  background-image: var(--jp-icon-bell);
}

.jp-BugDotIcon {
  background-image: var(--jp-icon-bug-dot);
}

.jp-BugIcon {
  background-image: var(--jp-icon-bug);
}

.jp-BuildIcon {
  background-image: var(--jp-icon-build);
}

.jp-CaretDownEmptyIcon {
  background-image: var(--jp-icon-caret-down-empty);
}

.jp-CaretDownEmptyThinIcon {
  background-image: var(--jp-icon-caret-down-empty-thin);
}

.jp-CaretDownIcon {
  background-image: var(--jp-icon-caret-down);
}

.jp-CaretLeftIcon {
  background-image: var(--jp-icon-caret-left);
}

.jp-CaretRightIcon {
  background-image: var(--jp-icon-caret-right);
}

.jp-CaretUpEmptyThinIcon {
  background-image: var(--jp-icon-caret-up-empty-thin);
}

.jp-CaretUpIcon {
  background-image: var(--jp-icon-caret-up);
}

.jp-CaseSensitiveIcon {
  background-image: var(--jp-icon-case-sensitive);
}

.jp-CheckIcon {
  background-image: var(--jp-icon-check);
}

.jp-CircleEmptyIcon {
  background-image: var(--jp-icon-circle-empty);
}

.jp-CircleIcon {
  background-image: var(--jp-icon-circle);
}

.jp-ClearIcon {
  background-image: var(--jp-icon-clear);
}

.jp-CloseIcon {
  background-image: var(--jp-icon-close);
}

.jp-CodeCheckIcon {
  background-image: var(--jp-icon-code-check);
}

.jp-CodeIcon {
  background-image: var(--jp-icon-code);
}

.jp-CollapseAllIcon {
  background-image: var(--jp-icon-collapse-all);
}

.jp-ConsoleIcon {
  background-image: var(--jp-icon-console);
}

.jp-CopyIcon {
  background-image: var(--jp-icon-copy);
}

.jp-CopyrightIcon {
  background-image: var(--jp-icon-copyright);
}

.jp-CutIcon {
  background-image: var(--jp-icon-cut);
}

.jp-DeleteIcon {
  background-image: var(--jp-icon-delete);
}

.jp-DownloadIcon {
  background-image: var(--jp-icon-download);
}

.jp-DuplicateIcon {
  background-image: var(--jp-icon-duplicate);
}

.jp-EditIcon {
  background-image: var(--jp-icon-edit);
}

.jp-EllipsesIcon {
  background-image: var(--jp-icon-ellipses);
}

.jp-ErrorIcon {
  background-image: var(--jp-icon-error);
}

.jp-ExpandAllIcon {
  background-image: var(--jp-icon-expand-all);
}

.jp-ExtensionIcon {
  background-image: var(--jp-icon-extension);
}

.jp-FastForwardIcon {
  background-image: var(--jp-icon-fast-forward);
}

.jp-FileIcon {
  background-image: var(--jp-icon-file);
}

.jp-FileUploadIcon {
  background-image: var(--jp-icon-file-upload);
}

.jp-FilterDotIcon {
  background-image: var(--jp-icon-filter-dot);
}

.jp-FilterIcon {
  background-image: var(--jp-icon-filter);
}

.jp-FilterListIcon {
  background-image: var(--jp-icon-filter-list);
}

.jp-FolderFavoriteIcon {
  background-image: var(--jp-icon-folder-favorite);
}

.jp-FolderIcon {
  background-image: var(--jp-icon-folder);
}

.jp-HomeIcon {
  background-image: var(--jp-icon-home);
}

.jp-Html5Icon {
  background-image: var(--jp-icon-html5);
}

.jp-ImageIcon {
  background-image: var(--jp-icon-image);
}

.jp-InfoIcon {
  background-image: var(--jp-icon-info);
}

.jp-InspectorIcon {
  background-image: var(--jp-icon-inspector);
}

.jp-JsonIcon {
  background-image: var(--jp-icon-json);
}

.jp-JuliaIcon {
  background-image: var(--jp-icon-julia);
}

.jp-JupyterFaviconIcon {
  background-image: var(--jp-icon-jupyter-favicon);
}

.jp-JupyterIcon {
  background-image: var(--jp-icon-jupyter);
}

.jp-JupyterlabWordmarkIcon {
  background-image: var(--jp-icon-jupyterlab-wordmark);
}

.jp-KernelIcon {
  background-image: var(--jp-icon-kernel);
}

.jp-KeyboardIcon {
  background-image: var(--jp-icon-keyboard);
}

.jp-LaunchIcon {
  background-image: var(--jp-icon-launch);
}

.jp-LauncherIcon {
  background-image: var(--jp-icon-launcher);
}

.jp-LineFormIcon {
  background-image: var(--jp-icon-line-form);
}

.jp-LinkIcon {
  background-image: var(--jp-icon-link);
}

.jp-ListIcon {
  background-image: var(--jp-icon-list);
}

.jp-MarkdownIcon {
  background-image: var(--jp-icon-markdown);
}

.jp-MoveDownIcon {
  background-image: var(--jp-icon-move-down);
}

.jp-MoveUpIcon {
  background-image: var(--jp-icon-move-up);
}

.jp-NewFolderIcon {
  background-image: var(--jp-icon-new-folder);
}

.jp-NotTrustedIcon {
  background-image: var(--jp-icon-not-trusted);
}

.jp-NotebookIcon {
  background-image: var(--jp-icon-notebook);
}

.jp-NumberingIcon {
  background-image: var(--jp-icon-numbering);
}

.jp-OfflineBoltIcon {
  background-image: var(--jp-icon-offline-bolt);
}

.jp-PaletteIcon {
  background-image: var(--jp-icon-palette);
}

.jp-PasteIcon {
  background-image: var(--jp-icon-paste);
}

.jp-PdfIcon {
  background-image: var(--jp-icon-pdf);
}

.jp-PythonIcon {
  background-image: var(--jp-icon-python);
}

.jp-RKernelIcon {
  background-image: var(--jp-icon-r-kernel);
}

.jp-ReactIcon {
  background-image: var(--jp-icon-react);
}

.jp-RedoIcon {
  background-image: var(--jp-icon-redo);
}

.jp-RefreshIcon {
  background-image: var(--jp-icon-refresh);
}

.jp-RegexIcon {
  background-image: var(--jp-icon-regex);
}

.jp-RunIcon {
  background-image: var(--jp-icon-run);
}

.jp-RunningIcon {
  background-image: var(--jp-icon-running);
}

.jp-SaveIcon {
  background-image: var(--jp-icon-save);
}

.jp-SearchIcon {
  background-image: var(--jp-icon-search);
}

.jp-SettingsIcon {
  background-image: var(--jp-icon-settings);
}

.jp-ShareIcon {
  background-image: var(--jp-icon-share);
}

.jp-SpreadsheetIcon {
  background-image: var(--jp-icon-spreadsheet);
}

.jp-StopIcon {
  background-image: var(--jp-icon-stop);
}

.jp-TabIcon {
  background-image: var(--jp-icon-tab);
}

.jp-TableRowsIcon {
  background-image: var(--jp-icon-table-rows);
}

.jp-TagIcon {
  background-image: var(--jp-icon-tag);
}

.jp-TerminalIcon {
  background-image: var(--jp-icon-terminal);
}

.jp-TextEditorIcon {
  background-image: var(--jp-icon-text-editor);
}

.jp-TocIcon {
  background-image: var(--jp-icon-toc);
}

.jp-TreeViewIcon {
  background-image: var(--jp-icon-tree-view);
}

.jp-TrustedIcon {
  background-image: var(--jp-icon-trusted);
}

.jp-UndoIcon {
  background-image: var(--jp-icon-undo);
}

.jp-UserIcon {
  background-image: var(--jp-icon-user);
}

.jp-UsersIcon {
  background-image: var(--jp-icon-users);
}

.jp-VegaIcon {
  background-image: var(--jp-icon-vega);
}

.jp-WordIcon {
  background-image: var(--jp-icon-word);
}

.jp-YamlIcon {
  background-image: var(--jp-icon-yaml);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/**
 * (DEPRECATED) Support for consuming icons as CSS background images
 */

.jp-Icon,
.jp-MaterialIcon {
  background-position: center;
  background-repeat: no-repeat;
  background-size: 16px;
  min-width: 16px;
  min-height: 16px;
}

.jp-Icon-cover {
  background-position: center;
  background-repeat: no-repeat;
  background-size: cover;
}

/**
 * (DEPRECATED) Support for specific CSS icon sizes
 */

.jp-Icon-16 {
  background-size: 16px;
  min-width: 16px;
  min-height: 16px;
}

.jp-Icon-18 {
  background-size: 18px;
  min-width: 18px;
  min-height: 18px;
}

.jp-Icon-20 {
  background-size: 20px;
  min-width: 20px;
  min-height: 20px;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.lm-TabBar .lm-TabBar-addButton {
  align-items: center;
  display: flex;
  padding: 4px;
  padding-bottom: 5px;
  margin-right: 1px;
  background-color: var(--jp-layout-color2);
}

.lm-TabBar .lm-TabBar-addButton:hover {
  background-color: var(--jp-layout-color1);
}

.lm-DockPanel-tabBar .lm-TabBar-tab {
  width: var(--jp-private-horizontal-tab-width);
}

.lm-DockPanel-tabBar .lm-TabBar-content {
  flex: unset;
}

.lm-DockPanel-tabBar[data-orientation='horizontal'] {
  flex: 1 1 auto;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/**
 * Support for icons as inline SVG HTMLElements
 */

/* recolor the primary elements of an icon */
.jp-icon0[fill] {
  fill: var(--jp-inverse-layout-color0);
}

.jp-icon1[fill] {
  fill: var(--jp-inverse-layout-color1);
}

.jp-icon2[fill] {
  fill: var(--jp-inverse-layout-color2);
}

.jp-icon3[fill] {
  fill: var(--jp-inverse-layout-color3);
}

.jp-icon4[fill] {
  fill: var(--jp-inverse-layout-color4);
}

.jp-icon0[stroke] {
  stroke: var(--jp-inverse-layout-color0);
}

.jp-icon1[stroke] {
  stroke: var(--jp-inverse-layout-color1);
}

.jp-icon2[stroke] {
  stroke: var(--jp-inverse-layout-color2);
}

.jp-icon3[stroke] {
  stroke: var(--jp-inverse-layout-color3);
}

.jp-icon4[stroke] {
  stroke: var(--jp-inverse-layout-color4);
}

/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
  fill: var(--jp-layout-color0);
}

.jp-icon-accent1[fill] {
  fill: var(--jp-layout-color1);
}

.jp-icon-accent2[fill] {
  fill: var(--jp-layout-color2);
}

.jp-icon-accent3[fill] {
  fill: var(--jp-layout-color3);
}

.jp-icon-accent4[fill] {
  fill: var(--jp-layout-color4);
}

.jp-icon-accent0[stroke] {
  stroke: var(--jp-layout-color0);
}

.jp-icon-accent1[stroke] {
  stroke: var(--jp-layout-color1);
}

.jp-icon-accent2[stroke] {
  stroke: var(--jp-layout-color2);
}

.jp-icon-accent3[stroke] {
  stroke: var(--jp-layout-color3);
}

.jp-icon-accent4[stroke] {
  stroke: var(--jp-layout-color4);
}

/* set the color of an icon to transparent */
.jp-icon-none[fill] {
  fill: none;
}

.jp-icon-none[stroke] {
  stroke: none;
}

/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
  fill: var(--jp-brand-color0);
}

.jp-icon-brand1[fill] {
  fill: var(--jp-brand-color1);
}

.jp-icon-brand2[fill] {
  fill: var(--jp-brand-color2);
}

.jp-icon-brand3[fill] {
  fill: var(--jp-brand-color3);
}

.jp-icon-brand4[fill] {
  fill: var(--jp-brand-color4);
}

.jp-icon-brand0[stroke] {
  stroke: var(--jp-brand-color0);
}

.jp-icon-brand1[stroke] {
  stroke: var(--jp-brand-color1);
}

.jp-icon-brand2[stroke] {
  stroke: var(--jp-brand-color2);
}

.jp-icon-brand3[stroke] {
  stroke: var(--jp-brand-color3);
}

.jp-icon-brand4[stroke] {
  stroke: var(--jp-brand-color4);
}

/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
  fill: var(--jp-warn-color0);
}

.jp-icon-warn1[fill] {
  fill: var(--jp-warn-color1);
}

.jp-icon-warn2[fill] {
  fill: var(--jp-warn-color2);
}

.jp-icon-warn3[fill] {
  fill: var(--jp-warn-color3);
}

.jp-icon-warn0[stroke] {
  stroke: var(--jp-warn-color0);
}

.jp-icon-warn1[stroke] {
  stroke: var(--jp-warn-color1);
}

.jp-icon-warn2[stroke] {
  stroke: var(--jp-warn-color2);
}

.jp-icon-warn3[stroke] {
  stroke: var(--jp-warn-color3);
}

/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
  fill: var(--jp-icon-contrast-color0);
}

.jp-icon-contrast1[fill] {
  fill: var(--jp-icon-contrast-color1);
}

.jp-icon-contrast2[fill] {
  fill: var(--jp-icon-contrast-color2);
}

.jp-icon-contrast3[fill] {
  fill: var(--jp-icon-contrast-color3);
}

.jp-icon-contrast0[stroke] {
  stroke: var(--jp-icon-contrast-color0);
}

.jp-icon-contrast1[stroke] {
  stroke: var(--jp-icon-contrast-color1);
}

.jp-icon-contrast2[stroke] {
  stroke: var(--jp-icon-contrast-color2);
}

.jp-icon-contrast3[stroke] {
  stroke: var(--jp-icon-contrast-color3);
}

.jp-icon-dot[fill] {
  fill: var(--jp-warn-color0);
}

.jp-jupyter-icon-color[fill] {
  fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
}

.jp-notebook-icon-color[fill] {
  fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
}

.jp-json-icon-color[fill] {
  fill: var(--jp-json-icon-color, var(--jp-warn-color1));
}

.jp-console-icon-color[fill] {
  fill: var(--jp-console-icon-color, white);
}

.jp-console-icon-background-color[fill] {
  fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
}

.jp-terminal-icon-color[fill] {
  fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
}

.jp-terminal-icon-background-color[fill] {
  fill: var(
    --jp-terminal-icon-background-color,
    var(--jp-inverse-layout-color2)
  );
}

.jp-text-editor-icon-color[fill] {
  fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
}

.jp-inspector-icon-color[fill] {
  fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
}

/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
  fill: #fff;
}

.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
  fill: var(--jp-brand-color1);
}

/* stylelint-disable selector-max-class, selector-max-compound-selectors */

/**
* TODO: come up with non css-hack solution for showing the busy icon on top
*  of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
  > .lm-TabBar-tabCloseIcon
  > :not(:hover)
  > .jp-icon3[fill] {
  fill: none;
}

.lm-DockPanel-tabBar
  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
  > .lm-TabBar-tabCloseIcon
  > :not(:hover)
  > .jp-icon-busy[fill] {
  fill: var(--jp-inverse-layout-color3);
}

/* stylelint-enable selector-max-class, selector-max-compound-selectors */

/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
  fill: #fff;
}

#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
  fill: var(--jp-brand-color1);
}

/* special handling for splash icon CSS. While the theme CSS reloads during
   splash, the splash icon can loose theming. To prevent that, we set a
   default for its color variable */
:root {
  --jp-warn-color0: var(--md-orange-700);
}

/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
  margin-right: 4px;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/**
 * Support for alt colors for icons as inline SVG HTMLElements
 */

/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
  fill: var(--jp-layout-color0);
}

.jp-icon-alt .jp-icon1[fill] {
  fill: var(--jp-layout-color1);
}

.jp-icon-alt .jp-icon2[fill] {
  fill: var(--jp-layout-color2);
}

.jp-icon-alt .jp-icon3[fill] {
  fill: var(--jp-layout-color3);
}

.jp-icon-alt .jp-icon4[fill] {
  fill: var(--jp-layout-color4);
}

.jp-icon-alt .jp-icon0[stroke] {
  stroke: var(--jp-layout-color0);
}

.jp-icon-alt .jp-icon1[stroke] {
  stroke: var(--jp-layout-color1);
}

.jp-icon-alt .jp-icon2[stroke] {
  stroke: var(--jp-layout-color2);
}

.jp-icon-alt .jp-icon3[stroke] {
  stroke: var(--jp-layout-color3);
}

.jp-icon-alt .jp-icon4[stroke] {
  stroke: var(--jp-layout-color4);
}

/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
  fill: var(--jp-inverse-layout-color0);
}

.jp-icon-alt .jp-icon-accent1[fill] {
  fill: var(--jp-inverse-layout-color1);
}

.jp-icon-alt .jp-icon-accent2[fill] {
  fill: var(--jp-inverse-layout-color2);
}

.jp-icon-alt .jp-icon-accent3[fill] {
  fill: var(--jp-inverse-layout-color3);
}

.jp-icon-alt .jp-icon-accent4[fill] {
  fill: var(--jp-inverse-layout-color4);
}

.jp-icon-alt .jp-icon-accent0[stroke] {
  stroke: var(--jp-inverse-layout-color0);
}

.jp-icon-alt .jp-icon-accent1[stroke] {
  stroke: var(--jp-inverse-layout-color1);
}

.jp-icon-alt .jp-icon-accent2[stroke] {
  stroke: var(--jp-inverse-layout-color2);
}

.jp-icon-alt .jp-icon-accent3[stroke] {
  stroke: var(--jp-inverse-layout-color3);
}

.jp-icon-alt .jp-icon-accent4[stroke] {
  stroke: var(--jp-inverse-layout-color4);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
  display: none !important;
}

/**
 * Support for hover colors for icons as inline SVG HTMLElements
 */

/**
 * regular colors
 */

/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
  fill: var(--jp-inverse-layout-color0);
}

.jp-icon-hover :hover .jp-icon1-hover[fill] {
  fill: var(--jp-inverse-layout-color1);
}

.jp-icon-hover :hover .jp-icon2-hover[fill] {
  fill: var(--jp-inverse-layout-color2);
}

.jp-icon-hover :hover .jp-icon3-hover[fill] {
  fill: var(--jp-inverse-layout-color3);
}

.jp-icon-hover :hover .jp-icon4-hover[fill] {
  fill: var(--jp-inverse-layout-color4);
}

.jp-icon-hover :hover .jp-icon0-hover[stroke] {
  stroke: var(--jp-inverse-layout-color0);
}

.jp-icon-hover :hover .jp-icon1-hover[stroke] {
  stroke: var(--jp-inverse-layout-color1);
}

.jp-icon-hover :hover .jp-icon2-hover[stroke] {
  stroke: var(--jp-inverse-layout-color2);
}

.jp-icon-hover :hover .jp-icon3-hover[stroke] {
  stroke: var(--jp-inverse-layout-color3);
}

.jp-icon-hover :hover .jp-icon4-hover[stroke] {
  stroke: var(--jp-inverse-layout-color4);
}

/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
  fill: var(--jp-layout-color0);
}

.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
  fill: var(--jp-layout-color1);
}

.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
  fill: var(--jp-layout-color2);
}

.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
  fill: var(--jp-layout-color3);
}

.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
  fill: var(--jp-layout-color4);
}

.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
  stroke: var(--jp-layout-color0);
}

.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
  stroke: var(--jp-layout-color1);
}

.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
  stroke: var(--jp-layout-color2);
}

.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
  stroke: var(--jp-layout-color3);
}

.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
  stroke: var(--jp-layout-color4);
}

/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
  fill: none;
}

.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
  stroke: none;
}

/**
 * inverse colors
 */

/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
  fill: var(--jp-layout-color0);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
  fill: var(--jp-layout-color1);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
  fill: var(--jp-layout-color2);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
  fill: var(--jp-layout-color3);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
  fill: var(--jp-layout-color4);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
  stroke: var(--jp-layout-color0);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
  stroke: var(--jp-layout-color1);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
  stroke: var(--jp-layout-color2);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
  stroke: var(--jp-layout-color3);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
  stroke: var(--jp-layout-color4);
}

/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
  fill: var(--jp-inverse-layout-color0);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
  fill: var(--jp-inverse-layout-color1);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
  fill: var(--jp-inverse-layout-color2);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
  fill: var(--jp-inverse-layout-color3);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
  fill: var(--jp-inverse-layout-color4);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
  stroke: var(--jp-inverse-layout-color0);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
  stroke: var(--jp-inverse-layout-color1);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
  stroke: var(--jp-inverse-layout-color2);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
  stroke: var(--jp-inverse-layout-color3);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
  stroke: var(--jp-inverse-layout-color4);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-IFrame {
  width: 100%;
  height: 100%;
}

.jp-IFrame > iframe {
  border: none;
}

/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
  position: relative;
}

body.lm-mod-override-cursor .jp-IFrame::before {
  content: '';
  position: absolute;
  top: 0;
  left: 0;
  right: 0;
  bottom: 0;
  background: transparent;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-HoverBox {
  position: fixed;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-FormGroup-content fieldset {
  border: none;
  padding: 0;
  min-width: 0;
  width: 100%;
}

/* stylelint-disable selector-max-type */

.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
  font-size: var(--jp-content-font-size2);
  border-color: var(--jp-input-border-color);
  border-style: solid;
  border-radius: var(--jp-border-radius);
  border-width: 1px;
  padding: 6px 8px;
  background: none;
  color: var(--jp-ui-font-color0);
  height: inherit;
}

.jp-FormGroup-content fieldset input[type='checkbox'] {
  position: relative;
  top: 2px;
  margin-left: 0;
}

.jp-FormGroup-content button.jp-mod-styled {
  cursor: pointer;
}

.jp-FormGroup-content .checkbox label {
  cursor: pointer;
  font-size: var(--jp-content-font-size1);
}

.jp-FormGroup-content .jp-root > fieldset > legend {
  display: none;
}

.jp-FormGroup-content .jp-root > fieldset > p {
  display: none;
}

/** copy of `input.jp-mod-styled:focus` style */
.jp-FormGroup-content fieldset input:focus,
.jp-FormGroup-content fieldset select:focus {
  -moz-outline-radius: unset;
  outline: var(--jp-border-width) solid var(--md-blue-500);
  outline-offset: -1px;
  box-shadow: inset 0 0 4px var(--md-blue-300);
}

.jp-FormGroup-content fieldset input:hover:not(:focus),
.jp-FormGroup-content fieldset select:hover:not(:focus) {
  background-color: var(--jp-border-color2);
}

/* stylelint-enable selector-max-type */

.jp-FormGroup-content .checkbox .field-description {
  /* Disable default description field for checkbox:
   because other widgets do not have description fields,
   we add descriptions to each widget on the field level.
  */
  display: none;
}

.jp-FormGroup-content #root__description {
  display: none;
}

.jp-FormGroup-content .jp-modifiedIndicator {
  width: 5px;
  background-color: var(--jp-brand-color2);
  margin-top: 0;
  margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
  flex-shrink: 0;
}

.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
  background-color: var(--jp-error-color0);
  margin-right: 0.5em;
}

/* RJSF ARRAY style */

.jp-arrayFieldWrapper legend {
  font-size: var(--jp-content-font-size2);
  color: var(--jp-ui-font-color0);
  flex-basis: 100%;
  padding: 4px 0;
  font-weight: var(--jp-content-heading-font-weight);
  border-bottom: 1px solid var(--jp-border-color2);
}

.jp-arrayFieldWrapper .field-description {
  padding: 4px 0;
  white-space: pre-wrap;
}

.jp-arrayFieldWrapper .array-item {
  width: 100%;
  border: 1px solid var(--jp-border-color2);
  border-radius: 4px;
  margin: 4px;
}

.jp-ArrayOperations {
  display: flex;
  margin-left: 8px;
}

.jp-ArrayOperationsButton {
  margin: 2px;
}

.jp-ArrayOperationsButton .jp-icon3[fill] {
  fill: var(--jp-ui-font-color0);
}

button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
  cursor: not-allowed;
  opacity: 0.5;
}

/* RJSF form validation error */

.jp-FormGroup-content .validationErrors {
  color: var(--jp-error-color0);
}

/* Hide panel level error as duplicated the field level error */
.jp-FormGroup-content .panel.errors {
  display: none;
}

/* RJSF normal content (settings-editor) */

.jp-FormGroup-contentNormal {
  display: flex;
  align-items: center;
  flex-wrap: wrap;
}

.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
  margin-left: 7px;
  color: var(--jp-ui-font-color0);
}

.jp-FormGroup-contentNormal .jp-FormGroup-description {
  flex-basis: 100%;
  padding: 4px 7px;
}

.jp-FormGroup-contentNormal .jp-FormGroup-default {
  flex-basis: 100%;
  padding: 4px 7px;
}

.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
  font-size: var(--jp-content-font-size1);
  font-weight: normal;
  min-width: 120px;
}

.jp-FormGroup-contentNormal fieldset:not(:first-child) {
  margin-left: 7px;
}

.jp-FormGroup-contentNormal .field-array-of-string .array-item {
  /* Display `jp-ArrayOperations` buttons side-by-side with content except
    for small screens where flex-wrap will place them one below the other.
  */
  display: flex;
  align-items: center;
  flex-wrap: wrap;
}

.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
  padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
  margin-top: 2px;
}

/* RJSF compact content (metadata-form) */

.jp-FormGroup-content.jp-FormGroup-contentCompact {
  width: 100%;
}

.jp-FormGroup-contentCompact .form-group {
  display: flex;
  padding: 0.5em 0.2em 0.5em 0;
}

.jp-FormGroup-contentCompact
  .jp-FormGroup-compactTitle
  .jp-FormGroup-description {
  font-size: var(--jp-ui-font-size1);
  color: var(--jp-ui-font-color2);
}

.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
  padding-bottom: 0.3em;
}

.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
  width: 100%;
  box-sizing: border-box;
}

.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
  padding-bottom: 7px;
}

.jp-FormGroup-contentCompact
  .jp-objectFieldWrapper
  .jp-objectFieldWrapper
  .form-group {
  padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
  margin-top: 2px;
}

.jp-FormGroup-contentCompact ul.error-detail {
  margin-block-start: 0.5em;
  margin-block-end: 0.5em;
  padding-inline-start: 1em;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.jp-SidePanel {
  display: flex;
  flex-direction: column;
  min-width: var(--jp-sidebar-min-width);
  overflow-y: auto;
  color: var(--jp-ui-font-color1);
  background: var(--jp-layout-color1);
  font-size: var(--jp-ui-font-size1);
}

.jp-SidePanel-header {
  flex: 0 0 auto;
  display: flex;
  border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
  font-size: var(--jp-ui-font-size0);
  font-weight: 600;
  letter-spacing: 1px;
  margin: 0;
  padding: 2px;
  text-transform: uppercase;
}

.jp-SidePanel-toolbar {
  flex: 0 0 auto;
}

.jp-SidePanel-content {
  flex: 1 1 auto;
}

.jp-SidePanel-toolbar,
.jp-AccordionPanel-toolbar {
  height: var(--jp-private-toolbar-height);
}

.jp-SidePanel-toolbar.jp-Toolbar-micro {
  display: none;
}

.lm-AccordionPanel .jp-AccordionPanel-title {
  box-sizing: border-box;
  line-height: 25px;
  margin: 0;
  display: flex;
  align-items: center;
  background: var(--jp-layout-color1);
  color: var(--jp-ui-font-color1);
  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
  box-shadow: var(--jp-toolbar-box-shadow);
  font-size: var(--jp-ui-font-size0);
}

.jp-AccordionPanel-title {
  cursor: pointer;
  user-select: none;
  -moz-user-select: none;
  -webkit-user-select: none;
  text-transform: uppercase;
}

.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
  /* Title is rotated for horizontal accordion panel using CSS */
  display: block;
  transform-origin: top left;
  transform: rotate(-90deg) translate(-100%);
}

.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
  user-select: none;
  text-overflow: ellipsis;
  white-space: nowrap;
  overflow: hidden;
}

.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
  transform: rotate(-90deg);
  margin: auto 0;
  height: 16px;
}

.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
  transform: rotate(0deg);
}

.lm-AccordionPanel .jp-AccordionPanel-toolbar {
  background: none;
  box-shadow: none;
  border: none;
  margin-left: auto;
}

.lm-AccordionPanel .lm-SplitPanel-handle:hover {
  background: var(--jp-layout-color3);
}

.jp-text-truncated {
  overflow: hidden;
  text-overflow: ellipsis;
  white-space: nowrap;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-Spinner {
  position: absolute;
  display: flex;
  justify-content: center;
  align-items: center;
  z-index: 10;
  left: 0;
  top: 0;
  width: 100%;
  height: 100%;
  background: var(--jp-layout-color0);
  outline: none;
}

.jp-SpinnerContent {
  font-size: 10px;
  margin: 50px auto;
  text-indent: -9999em;
  width: 3em;
  height: 3em;
  border-radius: 50%;
  background: var(--jp-brand-color3);
  background: linear-gradient(
    to right,
    #f37626 10%,
    rgba(255, 255, 255, 0) 42%
  );
  position: relative;
  animation: load3 1s infinite linear, fadeIn 1s;
}

.jp-SpinnerContent::before {
  width: 50%;
  height: 50%;
  background: #f37626;
  border-radius: 100% 0 0;
  position: absolute;
  top: 0;
  left: 0;
  content: '';
}

.jp-SpinnerContent::after {
  background: var(--jp-layout-color0);
  width: 75%;
  height: 75%;
  border-radius: 50%;
  content: '';
  margin: auto;
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  right: 0;
}

@keyframes fadeIn {
  0% {
    opacity: 0;
  }

  100% {
    opacity: 1;
  }
}

@keyframes load3 {
  0% {
    transform: rotate(0deg);
  }

  100% {
    transform: rotate(360deg);
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

button.jp-mod-styled {
  font-size: var(--jp-ui-font-size1);
  color: var(--jp-ui-font-color0);
  border: none;
  box-sizing: border-box;
  text-align: center;
  line-height: 32px;
  height: 32px;
  padding: 0 12px;
  letter-spacing: 0.8px;
  outline: none;
  appearance: none;
  -webkit-appearance: none;
  -moz-appearance: none;
}

input.jp-mod-styled {
  background: var(--jp-input-background);
  height: 28px;
  box-sizing: border-box;
  border: var(--jp-border-width) solid var(--jp-border-color1);
  padding-left: 7px;
  padding-right: 7px;
  font-size: var(--jp-ui-font-size2);
  color: var(--jp-ui-font-color0);
  outline: none;
  appearance: none;
  -webkit-appearance: none;
  -moz-appearance: none;
}

input[type='checkbox'].jp-mod-styled {
  appearance: checkbox;
  -webkit-appearance: checkbox;
  -moz-appearance: checkbox;
  height: auto;
}

input.jp-mod-styled:focus {
  border: var(--jp-border-width) solid var(--md-blue-500);
  box-shadow: inset 0 0 4px var(--md-blue-300);
}

.jp-select-wrapper {
  display: flex;
  position: relative;
  flex-direction: column;
  padding: 1px;
  background-color: var(--jp-layout-color1);
  box-sizing: border-box;
  margin-bottom: 12px;
}

.jp-select-wrapper:not(.multiple) {
  height: 28px;
}

.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
  box-shadow: var(--jp-input-box-shadow);
  background-color: var(--jp-input-active-background);
}

select.jp-mod-styled:hover {
  cursor: pointer;
  color: var(--jp-ui-font-color0);
  background-color: var(--jp-input-hover-background);
  box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
}

select.jp-mod-styled {
  flex: 1 1 auto;
  width: 100%;
  font-size: var(--jp-ui-font-size2);
  background: var(--jp-input-background);
  color: var(--jp-ui-font-color0);
  padding: 0 25px 0 8px;
  border: var(--jp-border-width) solid var(--jp-input-border-color);
  border-radius: 0;
  outline: none;
  appearance: none;
  -webkit-appearance: none;
  -moz-appearance: none;
}

select.jp-mod-styled:not([multiple]) {
  height: 32px;
}

select.jp-mod-styled[multiple] {
  max-height: 200px;
  overflow-y: auto;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-switch {
  display: flex;
  align-items: center;
  padding-left: 4px;
  padding-right: 4px;
  font-size: var(--jp-ui-font-size1);
  background-color: transparent;
  color: var(--jp-ui-font-color1);
  border: none;
  height: 20px;
}

.jp-switch:hover {
  background-color: var(--jp-layout-color2);
}

.jp-switch-label {
  margin-right: 5px;
  font-family: var(--jp-ui-font-family);
}

.jp-switch-track {
  cursor: pointer;
  background-color: var(--jp-switch-color, var(--jp-border-color1));
  -webkit-transition: 0.4s;
  transition: 0.4s;
  border-radius: 34px;
  height: 16px;
  width: 35px;
  position: relative;
}

.jp-switch-track::before {
  content: '';
  position: absolute;
  height: 10px;
  width: 10px;
  margin: 3px;
  left: 0;
  background-color: var(--jp-ui-inverse-font-color1);
  -webkit-transition: 0.4s;
  transition: 0.4s;
  border-radius: 50%;
}

.jp-switch[aria-checked='true'] .jp-switch-track {
  background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
}

.jp-switch[aria-checked='true'] .jp-switch-track::before {
  /* track width (35) - margins (3 + 3) - thumb width (10) */
  left: 19px;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

:root {
  --jp-private-toolbar-height: calc(
    28px + var(--jp-border-width)
  ); /* leave 28px for content */
}

.jp-Toolbar {
  color: var(--jp-ui-font-color1);
  flex: 0 0 auto;
  display: flex;
  flex-direction: row;
  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
  box-shadow: var(--jp-toolbar-box-shadow);
  background: var(--jp-toolbar-background);
  min-height: var(--jp-toolbar-micro-height);
  padding: 2px;
  z-index: 8;
  overflow-x: hidden;
}

/* Toolbar items */

.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
  flex-grow: 1;
  flex-shrink: 1;
}

.jp-Toolbar-item.jp-Toolbar-kernelStatus {
  display: inline-block;
  width: 32px;
  background-repeat: no-repeat;
  background-position: center;
  background-size: 16px;
}

.jp-Toolbar > .jp-Toolbar-item {
  flex: 0 0 auto;
  display: flex;
  padding-left: 1px;
  padding-right: 1px;
  font-size: var(--jp-ui-font-size1);
  line-height: var(--jp-private-toolbar-height);
  height: 100%;
}

/* Toolbar buttons */

/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
  color: transparent;
  border: none;
  box-sizing: border-box;
  outline: none;
  appearance: none;
  -webkit-appearance: none;
  -moz-appearance: none;
  padding: 0;
  margin: 0;
}

button.jp-ToolbarButtonComponent {
  background: var(--jp-layout-color1);
  border: none;
  box-sizing: border-box;
  outline: none;
  appearance: none;
  -webkit-appearance: none;
  -moz-appearance: none;
  padding: 0 6px;
  margin: 0;
  height: 24px;
  border-radius: var(--jp-border-radius);
  display: flex;
  align-items: center;
  text-align: center;
  font-size: 14px;
  min-width: unset;
  min-height: unset;
}

button.jp-ToolbarButtonComponent:disabled {
  opacity: 0.4;
}

button.jp-ToolbarButtonComponent > span {
  padding: 0;
  flex: 0 0 auto;
}

button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
  font-size: var(--jp-ui-font-size1);
  line-height: 100%;
  padding-left: 2px;
  color: var(--jp-ui-font-color1);
  font-family: var(--jp-ui-font-family);
}

#jp-main-dock-panel[data-mode='single-document']
  .jp-MainAreaWidget
  > .jp-Toolbar.jp-Toolbar-micro {
  padding: 0;
  min-height: 0;
}

#jp-main-dock-panel[data-mode='single-document']
  .jp-MainAreaWidget
  > .jp-Toolbar {
  border: none;
  box-shadow: none;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.jp-WindowedPanel-outer {
  position: relative;
  overflow-y: auto;
}

.jp-WindowedPanel-inner {
  position: relative;
}

.jp-WindowedPanel-window {
  position: absolute;
  left: 0;
  right: 0;
  overflow: visible;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/* Sibling imports */

body {
  color: var(--jp-ui-font-color1);
  font-size: var(--jp-ui-font-size1);
}

/* Disable native link decoration styles everywhere outside of dialog boxes */
a {
  text-decoration: unset;
  color: unset;
}

a:hover {
  text-decoration: unset;
  color: unset;
}

/* Accessibility for links inside dialog box text */
.jp-Dialog-content a {
  text-decoration: revert;
  color: var(--jp-content-link-color);
}

.jp-Dialog-content a:hover {
  text-decoration: revert;
}

/* Styles for ui-components */
.jp-Button {
  color: var(--jp-ui-font-color2);
  border-radius: var(--jp-border-radius);
  padding: 0 12px;
  font-size: var(--jp-ui-font-size1);

  /* Copy from blueprint 3 */
  display: inline-flex;
  flex-direction: row;
  border: none;
  cursor: pointer;
  align-items: center;
  justify-content: center;
  text-align: left;
  vertical-align: middle;
  min-height: 30px;
  min-width: 30px;
}

.jp-Button:disabled {
  cursor: not-allowed;
}

.jp-Button:empty {
  padding: 0 !important;
}

.jp-Button.jp-mod-small {
  min-height: 24px;
  min-width: 24px;
  font-size: 12px;
  padding: 0 7px;
}

/* Use our own theme for hover styles */
.jp-Button.jp-mod-minimal:hover {
  background-color: var(--jp-layout-color2);
}

.jp-Button.jp-mod-minimal {
  background: none;
}

.jp-InputGroup {
  display: block;
  position: relative;
}

.jp-InputGroup input {
  box-sizing: border-box;
  border: none;
  border-radius: 0;
  background-color: transparent;
  color: var(--jp-ui-font-color0);
  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
  padding-bottom: 0;
  padding-top: 0;
  padding-left: 10px;
  padding-right: 28px;
  position: relative;
  width: 100%;
  -webkit-appearance: none;
  -moz-appearance: none;
  appearance: none;
  font-size: 14px;
  font-weight: 400;
  height: 30px;
  line-height: 30px;
  outline: none;
  vertical-align: middle;
}

.jp-InputGroup input:focus {
  box-shadow: inset 0 0 0 var(--jp-border-width)
      var(--jp-input-active-box-shadow-color),
    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}

.jp-InputGroup input:disabled {
  cursor: not-allowed;
  resize: block;
  background-color: var(--jp-layout-color2);
  color: var(--jp-ui-font-color2);
}

.jp-InputGroup input:disabled ~ span {
  cursor: not-allowed;
  color: var(--jp-ui-font-color2);
}

.jp-InputGroup input::placeholder,
input::placeholder {
  color: var(--jp-ui-font-color2);
}

.jp-InputGroupAction {
  position: absolute;
  bottom: 1px;
  right: 0;
  padding: 6px;
}

.jp-HTMLSelect.jp-DefaultStyle select {
  background-color: initial;
  border: none;
  border-radius: 0;
  box-shadow: none;
  color: var(--jp-ui-font-color0);
  display: block;
  font-size: var(--jp-ui-font-size1);
  font-family: var(--jp-ui-font-family);
  height: 24px;
  line-height: 14px;
  padding: 0 25px 0 10px;
  text-align: left;
  -moz-appearance: none;
  -webkit-appearance: none;
}

.jp-HTMLSelect.jp-DefaultStyle select:disabled {
  background-color: var(--jp-layout-color2);
  color: var(--jp-ui-font-color2);
  cursor: not-allowed;
  resize: block;
}

.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
  cursor: not-allowed;
}

/* Use our own theme for hover and option styles */
/* stylelint-disable-next-line selector-max-type */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
  background-color: var(--jp-layout-color2);
  color: var(--jp-ui-font-color0);
}

select {
  box-sizing: border-box;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/

.jp-StatusBar-Widget {
  display: flex;
  align-items: center;
  background: var(--jp-layout-color2);
  min-height: var(--jp-statusbar-height);
  justify-content: space-between;
  padding: 0 10px;
}

.jp-StatusBar-Left {
  display: flex;
  align-items: center;
  flex-direction: row;
}

.jp-StatusBar-Middle {
  display: flex;
  align-items: center;
}

.jp-StatusBar-Right {
  display: flex;
  align-items: center;
  flex-direction: row-reverse;
}

.jp-StatusBar-Item {
  max-height: var(--jp-statusbar-height);
  margin: 0 2px;
  height: var(--jp-statusbar-height);
  white-space: nowrap;
  text-overflow: ellipsis;
  color: var(--jp-ui-font-color1);
  padding: 0 6px;
}

.jp-mod-highlighted:hover {
  background-color: var(--jp-layout-color3);
}

.jp-mod-clicked {
  background-color: var(--jp-brand-color1);
}

.jp-mod-clicked:hover {
  background-color: var(--jp-brand-color0);
}

.jp-mod-clicked .jp-StatusBar-TextItem {
  color: var(--jp-ui-inverse-font-color1);
}

.jp-StatusBar-HoverItem {
  box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
}

.jp-StatusBar-TextItem {
  font-size: var(--jp-ui-font-size1);
  font-family: var(--jp-ui-font-family);
  line-height: 24px;
  color: var(--jp-ui-font-color1);
}

.jp-StatusBar-GroupItem {
  display: flex;
  align-items: center;
  flex-direction: row;
}

.jp-Statusbar-ProgressCircle svg {
  display: block;
  margin: 0 auto;
  width: 16px;
  height: 24px;
  align-self: normal;
}

.jp-Statusbar-ProgressCircle path {
  fill: var(--jp-inverse-layout-color3);
}

.jp-Statusbar-ProgressBar-progress-bar {
  height: 10px;
  width: 100px;
  border: solid 0.25px var(--jp-brand-color2);
  border-radius: 3px;
  overflow: hidden;
  align-self: center;
}

.jp-Statusbar-ProgressBar-progress-bar > div {
  background-color: var(--jp-brand-color2);
  background-image: linear-gradient(
    -45deg,
    rgba(255, 255, 255, 0.2) 25%,
    transparent 25%,
    transparent 50%,
    rgba(255, 255, 255, 0.2) 50%,
    rgba(255, 255, 255, 0.2) 75%,
    transparent 75%,
    transparent
  );
  background-size: 40px 40px;
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 14px;
  color: #fff;
  text-align: center;
  animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
}

.jp-Statusbar-ProgressBar-progress-bar p {
  color: var(--jp-ui-font-color1);
  font-family: var(--jp-ui-font-family);
  font-size: var(--jp-ui-font-size1);
  line-height: 10px;
  width: 100px;
}

@keyframes jp-Statusbar-ExecutionTime-progress-bar {
  0% {
    background-position: 0 0;
  }

  100% {
    background-position: 40px 40px;
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/

:root {
  --jp-private-commandpalette-search-height: 28px;
}

/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/

.lm-CommandPalette {
  padding-bottom: 0;
  color: var(--jp-ui-font-color1);
  background: var(--jp-layout-color1);

  /* This is needed so that all font sizing of children done in ems is
   * relative to this base size */
  font-size: var(--jp-ui-font-size1);
}

/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/

.jp-ModalCommandPalette {
  position: absolute;
  z-index: 10000;
  top: 38px;
  left: 30%;
  margin: 0;
  padding: 4px;
  width: 40%;
  box-shadow: var(--jp-elevation-z4);
  border-radius: 4px;
  background: var(--jp-layout-color0);
}

.jp-ModalCommandPalette .lm-CommandPalette {
  max-height: 40vh;
}

.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
  display: none;
}

.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
  display: none;
}

.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
  margin-left: 4px;
  margin-right: 4px;
}

.jp-ModalCommandPalette
  .lm-CommandPalette
  .lm-CommandPalette-item.lm-mod-disabled {
  display: none;
}

/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/

.lm-CommandPalette-search {
  padding: 4px;
  background-color: var(--jp-layout-color1);
  z-index: 2;
}

.lm-CommandPalette-wrapper {
  overflow: overlay;
  padding: 0 9px;
  background-color: var(--jp-input-active-background);
  height: 30px;
  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}

.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
  box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}

.jp-SearchIconGroup {
  color: white;
  background-color: var(--jp-brand-color1);
  position: absolute;
  top: 4px;
  right: 4px;
  padding: 5px 5px 1px;
}

.jp-SearchIconGroup svg {
  height: 20px;
  width: 20px;
}

.jp-SearchIconGroup .jp-icon3[fill] {
  fill: var(--jp-layout-color0);
}

.lm-CommandPalette-input {
  background: transparent;
  width: calc(100% - 18px);
  float: left;
  border: none;
  outline: none;
  font-size: var(--jp-ui-font-size1);
  color: var(--jp-ui-font-color0);
  line-height: var(--jp-private-commandpalette-search-height);
}

.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
  color: var(--jp-ui-font-color2);
  font-size: var(--jp-ui-font-size1);
}

/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/

.lm-CommandPalette-header:first-child {
  margin-top: 0;
}

.lm-CommandPalette-header {
  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
  color: var(--jp-ui-font-color1);
  cursor: pointer;
  display: flex;
  font-size: var(--jp-ui-font-size0);
  font-weight: 600;
  letter-spacing: 1px;
  margin-top: 8px;
  padding: 8px 0 8px 12px;
  text-transform: uppercase;
}

.lm-CommandPalette-header.lm-mod-active {
  background: var(--jp-layout-color2);
}

.lm-CommandPalette-header > mark {
  background-color: transparent;
  font-weight: bold;
  color: var(--jp-ui-font-color1);
}

.lm-CommandPalette-item {
  padding: 4px 12px 4px 4px;
  color: var(--jp-ui-font-color1);
  font-size: var(--jp-ui-font-size1);
  font-weight: 400;
  display: flex;
}

.lm-CommandPalette-item.lm-mod-disabled {
  color: var(--jp-ui-font-color2);
}

.lm-CommandPalette-item.lm-mod-active {
  color: var(--jp-ui-inverse-font-color1);
  background: var(--jp-brand-color1);
}

.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
  color: var(--jp-ui-inverse-font-color0);
}

.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
  fill: var(--jp-layout-color0);
}

.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
  color: var(--jp-ui-inverse-font-color1);
  background: var(--jp-brand-color1);
}

.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
  background: var(--jp-layout-color2);
}

.lm-CommandPalette-itemContent {
  overflow: hidden;
}

.lm-CommandPalette-itemLabel > mark {
  color: var(--jp-ui-font-color0);
  background-color: transparent;
  font-weight: bold;
}

.lm-CommandPalette-item.lm-mod-disabled mark {
  color: var(--jp-ui-font-color2);
}

.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
  margin: 0 4px 0 0;
  position: relative;
  width: 16px;
  top: 2px;
  flex: 0 0 auto;
}

.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
  opacity: 0.6;
}

.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
  flex: 0 0 auto;
}

.lm-CommandPalette-itemCaption {
  display: none;
}

.lm-CommandPalette-content {
  background-color: var(--jp-layout-color1);
}

.lm-CommandPalette-content:empty::after {
  content: 'No results';
  margin: auto;
  margin-top: 20px;
  width: 100px;
  display: block;
  font-size: var(--jp-ui-font-size2);
  font-family: var(--jp-ui-font-family);
  font-weight: lighter;
}

.lm-CommandPalette-emptyMessage {
  text-align: center;
  margin-top: 24px;
  line-height: 1.32;
  padding: 0 8px;
  color: var(--jp-content-font-color3);
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-Dialog {
  position: absolute;
  z-index: 10000;
  display: flex;
  flex-direction: column;
  align-items: center;
  justify-content: center;
  top: 0;
  left: 0;
  margin: 0;
  padding: 0;
  width: 100%;
  height: 100%;
  background: var(--jp-dialog-background);
}

.jp-Dialog-content {
  display: flex;
  flex-direction: column;
  margin-left: auto;
  margin-right: auto;
  background: var(--jp-layout-color1);
  padding: 24px 24px 12px;
  min-width: 300px;
  min-height: 150px;
  max-width: 1000px;
  max-height: 500px;
  box-sizing: border-box;
  box-shadow: var(--jp-elevation-z20);
  word-wrap: break-word;
  border-radius: var(--jp-border-radius);

  /* This is needed so that all font sizing of children done in ems is
   * relative to this base size */
  font-size: var(--jp-ui-font-size1);
  color: var(--jp-ui-font-color1);
  resize: both;
}

.jp-Dialog-content.jp-Dialog-content-small {
  max-width: 500px;
}

.jp-Dialog-button {
  overflow: visible;
}

button.jp-Dialog-button:focus {
  outline: 1px solid var(--jp-brand-color1);
  outline-offset: 4px;
  -moz-outline-radius: 0;
}

button.jp-Dialog-button:focus::-moz-focus-inner {
  border: 0;
}

button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
  outline-offset: 4px;
  -moz-outline-radius: 0;
}

button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
  outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
}

button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
  outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
}

button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
  outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
}

button.jp-Dialog-close-button {
  padding: 0;
  height: 100%;
  min-width: unset;
  min-height: unset;
}

.jp-Dialog-header {
  display: flex;
  justify-content: space-between;
  flex: 0 0 auto;
  padding-bottom: 12px;
  font-size: var(--jp-ui-font-size3);
  font-weight: 400;
  color: var(--jp-ui-font-color1);
}

.jp-Dialog-body {
  display: flex;
  flex-direction: column;
  flex: 1 1 auto;
  font-size: var(--jp-ui-font-size1);
  background: var(--jp-layout-color1);
  color: var(--jp-ui-font-color1);
  overflow: auto;
}

.jp-Dialog-footer {
  display: flex;
  flex-direction: row;
  justify-content: flex-end;
  align-items: center;
  flex: 0 0 auto;
  margin-left: -12px;
  margin-right: -12px;
  padding: 12px;
}

.jp-Dialog-checkbox {
  padding-right: 5px;
}

.jp-Dialog-checkbox > input:focus-visible {
  outline: 1px solid var(--jp-input-active-border-color);
  outline-offset: 1px;
}

.jp-Dialog-spacer {
  flex: 1 1 auto;
}

.jp-Dialog-title {
  overflow: hidden;
  white-space: nowrap;
  text-overflow: ellipsis;
}

.jp-Dialog-body > .jp-select-wrapper {
  width: 100%;
}

.jp-Dialog-body > button {
  padding: 0 16px;
}

.jp-Dialog-body > label {
  line-height: 1.4;
  color: var(--jp-ui-font-color0);
}

.jp-Dialog-button.jp-mod-styled:not(:last-child) {
  margin-right: 12px;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.jp-Input-Boolean-Dialog {
  flex-direction: row-reverse;
  align-items: end;
  width: 100%;
}

.jp-Input-Boolean-Dialog > label {
  flex: 1 1 auto;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-MainAreaWidget > :focus {
  outline: none;
}

.jp-MainAreaWidget .jp-MainAreaWidget-error {
  padding: 6px;
}

.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
  width: auto;
  padding: 10px;
  background: var(--jp-error-color3);
  border: var(--jp-border-width) solid var(--jp-error-color1);
  border-radius: var(--jp-border-radius);
  color: var(--jp-ui-font-color1);
  font-size: var(--jp-ui-font-size1);
  white-space: pre-wrap;
  word-wrap: break-word;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/**
 * google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 */
:root {
  --md-red-50: #ffebee;
  --md-red-100: #ffcdd2;
  --md-red-200: #ef9a9a;
  --md-red-300: #e57373;
  --md-red-400: #ef5350;
  --md-red-500: #f44336;
  --md-red-600: #e53935;
  --md-red-700: #d32f2f;
  --md-red-800: #c62828;
  --md-red-900: #b71c1c;
  --md-red-A100: #ff8a80;
  --md-red-A200: #ff5252;
  --md-red-A400: #ff1744;
  --md-red-A700: #d50000;
  --md-pink-50: #fce4ec;
  --md-pink-100: #f8bbd0;
  --md-pink-200: #f48fb1;
  --md-pink-300: #f06292;
  --md-pink-400: #ec407a;
  --md-pink-500: #e91e63;
  --md-pink-600: #d81b60;
  --md-pink-700: #c2185b;
  --md-pink-800: #ad1457;
  --md-pink-900: #880e4f;
  --md-pink-A100: #ff80ab;
  --md-pink-A200: #ff4081;
  --md-pink-A400: #f50057;
  --md-pink-A700: #c51162;
  --md-purple-50: #f3e5f5;
  --md-purple-100: #e1bee7;
  --md-purple-200: #ce93d8;
  --md-purple-300: #ba68c8;
  --md-purple-400: #ab47bc;
  --md-purple-500: #9c27b0;
  --md-purple-600: #8e24aa;
  --md-purple-700: #7b1fa2;
  --md-purple-800: #6a1b9a;
  --md-purple-900: #4a148c;
  --md-purple-A100: #ea80fc;
  --md-purple-A200: #e040fb;
  --md-purple-A400: #d500f9;
  --md-purple-A700: #a0f;
  --md-deep-purple-50: #ede7f6;
  --md-deep-purple-100: #d1c4e9;
  --md-deep-purple-200: #b39ddb;
  --md-deep-purple-300: #9575cd;
  --md-deep-purple-400: #7e57c2;
  --md-deep-purple-500: #673ab7;
  --md-deep-purple-600: #5e35b1;
  --md-deep-purple-700: #512da8;
  --md-deep-purple-800: #4527a0;
  --md-deep-purple-900: #311b92;
  --md-deep-purple-A100: #b388ff;
  --md-deep-purple-A200: #7c4dff;
  --md-deep-purple-A400: #651fff;
  --md-deep-purple-A700: #6200ea;
  --md-indigo-50: #e8eaf6;
  --md-indigo-100: #c5cae9;
  --md-indigo-200: #9fa8da;
  --md-indigo-300: #7986cb;
  --md-indigo-400: #5c6bc0;
  --md-indigo-500: #3f51b5;
  --md-indigo-600: #3949ab;
  --md-indigo-700: #303f9f;
  --md-indigo-800: #283593;
  --md-indigo-900: #1a237e;
  --md-indigo-A100: #8c9eff;
  --md-indigo-A200: #536dfe;
  --md-indigo-A400: #3d5afe;
  --md-indigo-A700: #304ffe;
  --md-blue-50: #e3f2fd;
  --md-blue-100: #bbdefb;
  --md-blue-200: #90caf9;
  --md-blue-300: #64b5f6;
  --md-blue-400: #42a5f5;
  --md-blue-500: #2196f3;
  --md-blue-600: #1e88e5;
  --md-blue-700: #1976d2;
  --md-blue-800: #1565c0;
  --md-blue-900: #0d47a1;
  --md-blue-A100: #82b1ff;
  --md-blue-A200: #448aff;
  --md-blue-A400: #2979ff;
  --md-blue-A700: #2962ff;
  --md-light-blue-50: #e1f5fe;
  --md-light-blue-100: #b3e5fc;
  --md-light-blue-200: #81d4fa;
  --md-light-blue-300: #4fc3f7;
  --md-light-blue-400: #29b6f6;
  --md-light-blue-500: #03a9f4;
  --md-light-blue-600: #039be5;
  --md-light-blue-700: #0288d1;
  --md-light-blue-800: #0277bd;
  --md-light-blue-900: #01579b;
  --md-light-blue-A100: #80d8ff;
  --md-light-blue-A200: #40c4ff;
  --md-light-blue-A400: #00b0ff;
  --md-light-blue-A700: #0091ea;
  --md-cyan-50: #e0f7fa;
  --md-cyan-100: #b2ebf2;
  --md-cyan-200: #80deea;
  --md-cyan-300: #4dd0e1;
  --md-cyan-400: #26c6da;
  --md-cyan-500: #00bcd4;
  --md-cyan-600: #00acc1;
  --md-cyan-700: #0097a7;
  --md-cyan-800: #00838f;
  --md-cyan-900: #006064;
  --md-cyan-A100: #84ffff;
  --md-cyan-A200: #18ffff;
  --md-cyan-A400: #00e5ff;
  --md-cyan-A700: #00b8d4;
  --md-teal-50: #e0f2f1;
  --md-teal-100: #b2dfdb;
  --md-teal-200: #80cbc4;
  --md-teal-300: #4db6ac;
  --md-teal-400: #26a69a;
  --md-teal-500: #009688;
  --md-teal-600: #00897b;
  --md-teal-700: #00796b;
  --md-teal-800: #00695c;
  --md-teal-900: #004d40;
  --md-teal-A100: #a7ffeb;
  --md-teal-A200: #64ffda;
  --md-teal-A400: #1de9b6;
  --md-teal-A700: #00bfa5;
  --md-green-50: #e8f5e9;
  --md-green-100: #c8e6c9;
  --md-green-200: #a5d6a7;
  --md-green-300: #81c784;
  --md-green-400: #66bb6a;
  --md-green-500: #4caf50;
  --md-green-600: #43a047;
  --md-green-700: #388e3c;
  --md-green-800: #2e7d32;
  --md-green-900: #1b5e20;
  --md-green-A100: #b9f6ca;
  --md-green-A200: #69f0ae;
  --md-green-A400: #00e676;
  --md-green-A700: #00c853;
  --md-light-green-50: #f1f8e9;
  --md-light-green-100: #dcedc8;
  --md-light-green-200: #c5e1a5;
  --md-light-green-300: #aed581;
  --md-light-green-400: #9ccc65;
  --md-light-green-500: #8bc34a;
  --md-light-green-600: #7cb342;
  --md-light-green-700: #689f38;
  --md-light-green-800: #558b2f;
  --md-light-green-900: #33691e;
  --md-light-green-A100: #ccff90;
  --md-light-green-A200: #b2ff59;
  --md-light-green-A400: #76ff03;
  --md-light-green-A700: #64dd17;
  --md-lime-50: #f9fbe7;
  --md-lime-100: #f0f4c3;
  --md-lime-200: #e6ee9c;
  --md-lime-300: #dce775;
  --md-lime-400: #d4e157;
  --md-lime-500: #cddc39;
  --md-lime-600: #c0ca33;
  --md-lime-700: #afb42b;
  --md-lime-800: #9e9d24;
  --md-lime-900: #827717;
  --md-lime-A100: #f4ff81;
  --md-lime-A200: #eeff41;
  --md-lime-A400: #c6ff00;
  --md-lime-A700: #aeea00;
  --md-yellow-50: #fffde7;
  --md-yellow-100: #fff9c4;
  --md-yellow-200: #fff59d;
  --md-yellow-300: #fff176;
  --md-yellow-400: #ffee58;
  --md-yellow-500: #ffeb3b;
  --md-yellow-600: #fdd835;
  --md-yellow-700: #fbc02d;
  --md-yellow-800: #f9a825;
  --md-yellow-900: #f57f17;
  --md-yellow-A100: #ffff8d;
  --md-yellow-A200: #ff0;
  --md-yellow-A400: #ffea00;
  --md-yellow-A700: #ffd600;
  --md-amber-50: #fff8e1;
  --md-amber-100: #ffecb3;
  --md-amber-200: #ffe082;
  --md-amber-300: #ffd54f;
  --md-amber-400: #ffca28;
  --md-amber-500: #ffc107;
  --md-amber-600: #ffb300;
  --md-amber-700: #ffa000;
  --md-amber-800: #ff8f00;
  --md-amber-900: #ff6f00;
  --md-amber-A100: #ffe57f;
  --md-amber-A200: #ffd740;
  --md-amber-A400: #ffc400;
  --md-amber-A700: #ffab00;
  --md-orange-50: #fff3e0;
  --md-orange-100: #ffe0b2;
  --md-orange-200: #ffcc80;
  --md-orange-300: #ffb74d;
  --md-orange-400: #ffa726;
  --md-orange-500: #ff9800;
  --md-orange-600: #fb8c00;
  --md-orange-700: #f57c00;
  --md-orange-800: #ef6c00;
  --md-orange-900: #e65100;
  --md-orange-A100: #ffd180;
  --md-orange-A200: #ffab40;
  --md-orange-A400: #ff9100;
  --md-orange-A700: #ff6d00;
  --md-deep-orange-50: #fbe9e7;
  --md-deep-orange-100: #ffccbc;
  --md-deep-orange-200: #ffab91;
  --md-deep-orange-300: #ff8a65;
  --md-deep-orange-400: #ff7043;
  --md-deep-orange-500: #ff5722;
  --md-deep-orange-600: #f4511e;
  --md-deep-orange-700: #e64a19;
  --md-deep-orange-800: #d84315;
  --md-deep-orange-900: #bf360c;
  --md-deep-orange-A100: #ff9e80;
  --md-deep-orange-A200: #ff6e40;
  --md-deep-orange-A400: #ff3d00;
  --md-deep-orange-A700: #dd2c00;
  --md-brown-50: #efebe9;
  --md-brown-100: #d7ccc8;
  --md-brown-200: #bcaaa4;
  --md-brown-300: #a1887f;
  --md-brown-400: #8d6e63;
  --md-brown-500: #795548;
  --md-brown-600: #6d4c41;
  --md-brown-700: #5d4037;
  --md-brown-800: #4e342e;
  --md-brown-900: #3e2723;
  --md-grey-50: #fafafa;
  --md-grey-100: #f5f5f5;
  --md-grey-200: #eee;
  --md-grey-300: #e0e0e0;
  --md-grey-400: #bdbdbd;
  --md-grey-500: #9e9e9e;
  --md-grey-600: #757575;
  --md-grey-700: #616161;
  --md-grey-800: #424242;
  --md-grey-900: #212121;
  --md-blue-grey-50: #eceff1;
  --md-blue-grey-100: #cfd8dc;
  --md-blue-grey-200: #b0bec5;
  --md-blue-grey-300: #90a4ae;
  --md-blue-grey-400: #78909c;
  --md-blue-grey-500: #607d8b;
  --md-blue-grey-600: #546e7a;
  --md-blue-grey-700: #455a64;
  --md-blue-grey-800: #37474f;
  --md-blue-grey-900: #263238;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/

:root {
  /* This is the padding value to fill the gaps between lines containing spans with background color. */
  --jp-private-code-span-padding: calc(
    (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
  );
}

.jp-RenderedText {
  text-align: left;
  padding-left: var(--jp-code-padding);
  line-height: var(--jp-code-line-height);
  font-family: var(--jp-code-font-family);
}

.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
  color: var(--jp-content-font-color1);
  font-size: var(--jp-code-font-size);
  border: none;
  margin: 0;
  padding: 0;
}

.jp-RenderedText pre a:link {
  text-decoration: none;
  color: var(--jp-content-link-color);
}

.jp-RenderedText pre a:hover {
  text-decoration: underline;
  color: var(--jp-content-link-color);
}

.jp-RenderedText pre a:visited {
  text-decoration: none;
  color: var(--jp-content-link-color);
}

/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
  color: #3e424d;
}

.jp-RenderedText pre .ansi-red-fg {
  color: #e75c58;
}

.jp-RenderedText pre .ansi-green-fg {
  color: #00a250;
}

.jp-RenderedText pre .ansi-yellow-fg {
  color: #ddb62b;
}

.jp-RenderedText pre .ansi-blue-fg {
  color: #208ffb;
}

.jp-RenderedText pre .ansi-magenta-fg {
  color: #d160c4;
}

.jp-RenderedText pre .ansi-cyan-fg {
  color: #60c6c8;
}

.jp-RenderedText pre .ansi-white-fg {
  color: #c5c1b4;
}

.jp-RenderedText pre .ansi-black-bg {
  background-color: #3e424d;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-red-bg {
  background-color: #e75c58;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-green-bg {
  background-color: #00a250;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-yellow-bg {
  background-color: #ddb62b;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-blue-bg {
  background-color: #208ffb;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-magenta-bg {
  background-color: #d160c4;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-cyan-bg {
  background-color: #60c6c8;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-white-bg {
  background-color: #c5c1b4;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-black-intense-fg {
  color: #282c36;
}

.jp-RenderedText pre .ansi-red-intense-fg {
  color: #b22b31;
}

.jp-RenderedText pre .ansi-green-intense-fg {
  color: #007427;
}

.jp-RenderedText pre .ansi-yellow-intense-fg {
  color: #b27d12;
}

.jp-RenderedText pre .ansi-blue-intense-fg {
  color: #0065ca;
}

.jp-RenderedText pre .ansi-magenta-intense-fg {
  color: #a03196;
}

.jp-RenderedText pre .ansi-cyan-intense-fg {
  color: #258f8f;
}

.jp-RenderedText pre .ansi-white-intense-fg {
  color: #a1a6b2;
}

.jp-RenderedText pre .ansi-black-intense-bg {
  background-color: #282c36;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-red-intense-bg {
  background-color: #b22b31;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-green-intense-bg {
  background-color: #007427;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-yellow-intense-bg {
  background-color: #b27d12;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-blue-intense-bg {
  background-color: #0065ca;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-magenta-intense-bg {
  background-color: #a03196;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-cyan-intense-bg {
  background-color: #258f8f;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-white-intense-bg {
  background-color: #a1a6b2;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-default-inverse-fg {
  color: var(--jp-ui-inverse-font-color0);
}

.jp-RenderedText pre .ansi-default-inverse-bg {
  background-color: var(--jp-inverse-layout-color0);
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-bold {
  font-weight: bold;
}

.jp-RenderedText pre .ansi-underline {
  text-decoration: underline;
}

.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
  background: var(--jp-rendermime-error-background);
  padding-top: var(--jp-code-padding);
}

/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/

.jp-RenderedLatex {
  color: var(--jp-content-font-color1);
  font-size: var(--jp-content-font-size1);
  line-height: var(--jp-content-line-height);
}

/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
  padding: var(--jp-code-padding);
  text-align: left;
}

/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/

.jp-RenderedHTMLCommon {
  color: var(--jp-content-font-color1);
  font-family: var(--jp-content-font-family);
  font-size: var(--jp-content-font-size1);
  line-height: var(--jp-content-line-height);

  /* Give a bit more R padding on Markdown text to keep line lengths reasonable */
  padding-right: 20px;
}

.jp-RenderedHTMLCommon em {
  font-style: italic;
}

.jp-RenderedHTMLCommon strong {
  font-weight: bold;
}

.jp-RenderedHTMLCommon u {
  text-decoration: underline;
}

.jp-RenderedHTMLCommon a:link {
  text-decoration: none;
  color: var(--jp-content-link-color);
}

.jp-RenderedHTMLCommon a:hover {
  text-decoration: underline;
  color: var(--jp-content-link-color);
}

.jp-RenderedHTMLCommon a:visited {
  text-decoration: none;
  color: var(--jp-content-link-color);
}

/* Headings */

.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
  line-height: var(--jp-content-heading-line-height);
  font-weight: var(--jp-content-heading-font-weight);
  font-style: normal;
  margin: var(--jp-content-heading-margin-top) 0
    var(--jp-content-heading-margin-bottom) 0;
}

.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
  margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}

.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
  margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}

.jp-RenderedHTMLCommon h1 {
  font-size: var(--jp-content-font-size5);
}

.jp-RenderedHTMLCommon h2 {
  font-size: var(--jp-content-font-size4);
}

.jp-RenderedHTMLCommon h3 {
  font-size: var(--jp-content-font-size3);
}

.jp-RenderedHTMLCommon h4 {
  font-size: var(--jp-content-font-size2);
}

.jp-RenderedHTMLCommon h5 {
  font-size: var(--jp-content-font-size1);
}

.jp-RenderedHTMLCommon h6 {
  font-size: var(--jp-content-font-size0);
}

/* Lists */

/* stylelint-disable selector-max-type, selector-max-compound-selectors */

.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
  padding-left: 2em;
}

.jp-RenderedHTMLCommon ul {
  list-style: disc;
}

.jp-RenderedHTMLCommon ul ul {
  list-style: square;
}

.jp-RenderedHTMLCommon ul ul ul {
  list-style: circle;
}

.jp-RenderedHTMLCommon ol {
  list-style: decimal;
}

.jp-RenderedHTMLCommon ol ol {
  list-style: upper-alpha;
}

.jp-RenderedHTMLCommon ol ol ol {
  list-style: lower-alpha;
}

.jp-RenderedHTMLCommon ol ol ol ol {
  list-style: lower-roman;
}

.jp-RenderedHTMLCommon ol ol ol ol ol {
  list-style: decimal;
}

.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
  margin-bottom: 1em;
}

.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
  margin-bottom: 0;
}

/* stylelint-enable selector-max-type, selector-max-compound-selectors */

.jp-RenderedHTMLCommon hr {
  color: var(--jp-border-color2);
  background-color: var(--jp-border-color1);
  margin-top: 1em;
  margin-bottom: 1em;
}

.jp-RenderedHTMLCommon > pre {
  margin: 1.5em 2em;
}

.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
  border: 0;
  background-color: var(--jp-layout-color0);
  color: var(--jp-content-font-color1);
  font-family: var(--jp-code-font-family);
  font-size: inherit;
  line-height: var(--jp-code-line-height);
  padding: 0;
  white-space: pre-wrap;
}

.jp-RenderedHTMLCommon :not(pre) > code {
  background-color: var(--jp-layout-color2);
  padding: 1px 5px;
}

/* Tables */

.jp-RenderedHTMLCommon table {
  border-collapse: collapse;
  border-spacing: 0;
  border: none;
  color: var(--jp-ui-font-color1);
  font-size: var(--jp-ui-font-size1);
  table-layout: fixed;
  margin-left: auto;
  margin-bottom: 1em;
  margin-right: auto;
}

.jp-RenderedHTMLCommon thead {
  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
  vertical-align: bottom;
}

.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
  vertical-align: middle;
  padding: 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}

.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
  max-width: none;
}

:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
  text-align: right;
}

.jp-RenderedHTMLCommon th {
  font-weight: bold;
}

.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
  background: var(--jp-layout-color0);
}

.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
  background: var(--jp-rendermime-table-row-background);
}

.jp-RenderedHTMLCommon tbody tr:hover {
  background: var(--jp-rendermime-table-row-hover-background);
}

.jp-RenderedHTMLCommon p {
  text-align: left;
  margin: 0;
  margin-bottom: 1em;
}

.jp-RenderedHTMLCommon img {
  -moz-force-broken-image-icon: 1;
}

/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
  display: block;
  margin-left: 0;
  margin-right: 0;
  margin-bottom: 1em;
}

/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
  background-color: var(--jp-inverse-layout-color1);
}

[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
  background-color: var(--jp-inverse-layout-color1);
}

.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
  max-width: 100%;
  height: auto;
}

.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
  max-width: none;
}

.jp-RenderedHTMLCommon .alert {
  padding: var(--jp-notebook-padding);
  border: var(--jp-border-width) solid transparent;
  border-radius: var(--jp-border-radius);
  margin-bottom: 1em;
}

.jp-RenderedHTMLCommon .alert-info {
  color: var(--jp-info-color0);
  background-color: var(--jp-info-color3);
  border-color: var(--jp-info-color2);
}

.jp-RenderedHTMLCommon .alert-info hr {
  border-color: var(--jp-info-color3);
}

.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
  margin-bottom: 0;
}

.jp-RenderedHTMLCommon .alert-warning {
  color: var(--jp-warn-color0);
  background-color: var(--jp-warn-color3);
  border-color: var(--jp-warn-color2);
}

.jp-RenderedHTMLCommon .alert-warning hr {
  border-color: var(--jp-warn-color3);
}

.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
  margin-bottom: 0;
}

.jp-RenderedHTMLCommon .alert-success {
  color: var(--jp-success-color0);
  background-color: var(--jp-success-color3);
  border-color: var(--jp-success-color2);
}

.jp-RenderedHTMLCommon .alert-success hr {
  border-color: var(--jp-success-color3);
}

.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
  margin-bottom: 0;
}

.jp-RenderedHTMLCommon .alert-danger {
  color: var(--jp-error-color0);
  background-color: var(--jp-error-color3);
  border-color: var(--jp-error-color2);
}

.jp-RenderedHTMLCommon .alert-danger hr {
  border-color: var(--jp-error-color3);
}

.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
  margin-bottom: 0;
}

.jp-RenderedHTMLCommon blockquote {
  margin: 1em 2em;
  padding: 0 1em;
  border-left: 5px solid var(--jp-border-color2);
}

a.jp-InternalAnchorLink {
  visibility: hidden;
  margin-left: 8px;
  color: var(--md-blue-800);
}

h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
  visibility: visible;
}

.jp-RenderedHTMLCommon kbd {
  background-color: var(--jp-rendermime-table-row-background);
  border: 1px solid var(--jp-border-color0);
  border-bottom-color: var(--jp-border-color2);
  border-radius: 3px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
  display: inline-block;
  font-size: var(--jp-ui-font-size0);
  line-height: 1em;
  padding: 0.2em 0.5em;
}

/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
 * At the bottom of cells this is a bit too much as there is also spacing
 * between cells. Going all the way to 0 gets too tight between markdown and
 * code cells.
 */
.jp-RenderedHTMLCommon > *:last-child {
  margin-bottom: 0.5em;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-cursor-backdrop {
  position: fixed;
  width: 200px;
  height: 200px;
  margin-top: -100px;
  margin-left: -100px;
  will-change: transform;
  z-index: 100;
}

.lm-mod-drag-image {
  will-change: transform;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.jp-lineFormSearch {
  padding: 4px 12px;
  background-color: var(--jp-layout-color2);
  box-shadow: var(--jp-toolbar-box-shadow);
  z-index: 2;
  font-size: var(--jp-ui-font-size1);
}

.jp-lineFormCaption {
  font-size: var(--jp-ui-font-size0);
  line-height: var(--jp-ui-font-size1);
  margin-top: 4px;
  color: var(--jp-ui-font-color0);
}

.jp-baseLineForm {
  border: none;
  border-radius: 0;
  position: absolute;
  background-size: 16px;
  background-repeat: no-repeat;
  background-position: center;
  outline: none;
}

.jp-lineFormButtonContainer {
  top: 4px;
  right: 8px;
  height: 24px;
  padding: 0 12px;
  width: 12px;
}

.jp-lineFormButtonIcon {
  top: 0;
  right: 0;
  background-color: var(--jp-brand-color1);
  height: 100%;
  width: 100%;
  box-sizing: border-box;
  padding: 4px 6px;
}

.jp-lineFormButton {
  top: 0;
  right: 0;
  background-color: transparent;
  height: 100%;
  width: 100%;
  box-sizing: border-box;
}

.jp-lineFormWrapper {
  overflow: hidden;
  padding: 0 8px;
  border: 1px solid var(--jp-border-color0);
  background-color: var(--jp-input-active-background);
  height: 22px;
}

.jp-lineFormWrapperFocusWithin {
  border: var(--jp-border-width) solid var(--md-blue-500);
  box-shadow: inset 0 0 4px var(--md-blue-300);
}

.jp-lineFormInput {
  background: transparent;
  width: 200px;
  height: 100%;
  border: none;
  outline: none;
  color: var(--jp-ui-font-color0);
  line-height: 28px;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-JSONEditor {
  display: flex;
  flex-direction: column;
  width: 100%;
}

.jp-JSONEditor-host {
  flex: 1 1 auto;
  border: var(--jp-border-width) solid var(--jp-input-border-color);
  border-radius: 0;
  background: var(--jp-layout-color0);
  min-height: 50px;
  padding: 1px;
}

.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
  border-color: red;
  outline-color: red;
}

.jp-JSONEditor-header {
  display: flex;
  flex: 1 0 auto;
  padding: 0 0 0 12px;
}

.jp-JSONEditor-header label {
  flex: 0 0 auto;
}

.jp-JSONEditor-commitButton {
  height: 16px;
  width: 16px;
  background-size: 18px;
  background-repeat: no-repeat;
  background-position: center;
}

.jp-JSONEditor-host.jp-mod-focused {
  background-color: var(--jp-input-active-background);
  border: 1px solid var(--jp-input-active-border-color);
  box-shadow: var(--jp-input-box-shadow);
}

.jp-Editor.jp-mod-dropTarget {
  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
  box-shadow: var(--jp-input-box-shadow);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-DocumentSearch-input {
  border: none;
  outline: none;
  color: var(--jp-ui-font-color0);
  font-size: var(--jp-ui-font-size1);
  background-color: var(--jp-layout-color0);
  font-family: var(--jp-ui-font-family);
  padding: 2px 1px;
  resize: none;
}

.jp-DocumentSearch-overlay {
  position: absolute;
  background-color: var(--jp-toolbar-background);
  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
  border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
  top: 0;
  right: 0;
  z-index: 7;
  min-width: 405px;
  padding: 2px;
  font-size: var(--jp-ui-font-size1);

  --jp-private-document-search-button-height: 20px;
}

.jp-DocumentSearch-overlay button {
  background-color: var(--jp-toolbar-background);
  outline: 0;
}

.jp-DocumentSearch-overlay button:hover {
  background-color: var(--jp-layout-color2);
}

.jp-DocumentSearch-overlay button:active {
  background-color: var(--jp-layout-color3);
}

.jp-DocumentSearch-overlay-row {
  display: flex;
  align-items: center;
  margin-bottom: 2px;
}

.jp-DocumentSearch-button-content {
  display: inline-block;
  cursor: pointer;
  box-sizing: border-box;
  width: 100%;
  height: 100%;
}

.jp-DocumentSearch-button-content svg {
  width: 100%;
  height: 100%;
}

.jp-DocumentSearch-input-wrapper {
  border: var(--jp-border-width) solid var(--jp-border-color0);
  display: flex;
  background-color: var(--jp-layout-color0);
  margin: 2px;
}

.jp-DocumentSearch-input-wrapper:focus-within {
  border-color: var(--jp-cell-editor-active-border-color);
}

.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper {
  all: initial;
  overflow: hidden;
  display: inline-block;
  border: none;
  box-sizing: border-box;
}

.jp-DocumentSearch-toggle-wrapper {
  width: 14px;
  height: 14px;
}

.jp-DocumentSearch-button-wrapper {
  width: var(--jp-private-document-search-button-height);
  height: var(--jp-private-document-search-button-height);
}

.jp-DocumentSearch-toggle-wrapper:focus,
.jp-DocumentSearch-button-wrapper:focus {
  outline: var(--jp-border-width) solid
    var(--jp-cell-editor-active-border-color);
  outline-offset: -1px;
}

.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper,
.jp-DocumentSearch-button-content:focus {
  outline: none;
}

.jp-DocumentSearch-toggle-placeholder {
  width: 5px;
}

.jp-DocumentSearch-input-button::before {
  display: block;
  padding-top: 100%;
}

.jp-DocumentSearch-input-button-off {
  opacity: var(--jp-search-toggle-off-opacity);
}

.jp-DocumentSearch-input-button-off:hover {
  opacity: var(--jp-search-toggle-hover-opacity);
}

.jp-DocumentSearch-input-button-on {
  opacity: var(--jp-search-toggle-on-opacity);
}

.jp-DocumentSearch-index-counter {
  padding-left: 10px;
  padding-right: 10px;
  user-select: none;
  min-width: 35px;
  display: inline-block;
}

.jp-DocumentSearch-up-down-wrapper {
  display: inline-block;
  padding-right: 2px;
  margin-left: auto;
  white-space: nowrap;
}

.jp-DocumentSearch-spacer {
  margin-left: auto;
}

.jp-DocumentSearch-up-down-wrapper button {
  outline: 0;
  border: none;
  width: var(--jp-private-document-search-button-height);
  height: var(--jp-private-document-search-button-height);
  vertical-align: middle;
  margin: 1px 5px 2px;
}

.jp-DocumentSearch-up-down-button:hover {
  background-color: var(--jp-layout-color2);
}

.jp-DocumentSearch-up-down-button:active {
  background-color: var(--jp-layout-color3);
}

.jp-DocumentSearch-filter-button {
  border-radius: var(--jp-border-radius);
}

.jp-DocumentSearch-filter-button:hover {
  background-color: var(--jp-layout-color2);
}

.jp-DocumentSearch-filter-button-enabled {
  background-color: var(--jp-layout-color2);
}

.jp-DocumentSearch-filter-button-enabled:hover {
  background-color: var(--jp-layout-color3);
}

.jp-DocumentSearch-search-options {
  padding: 0 8px;
  margin-left: 3px;
  width: 100%;
  display: grid;
  justify-content: start;
  grid-template-columns: 1fr 1fr;
  align-items: center;
  justify-items: stretch;
}

.jp-DocumentSearch-search-filter-disabled {
  color: var(--jp-ui-font-color2);
}

.jp-DocumentSearch-search-filter {
  display: flex;
  align-items: center;
  user-select: none;
}

.jp-DocumentSearch-regex-error {
  color: var(--jp-error-color0);
}

.jp-DocumentSearch-replace-button-wrapper {
  overflow: hidden;
  display: inline-block;
  box-sizing: border-box;
  border: var(--jp-border-width) solid var(--jp-border-color0);
  margin: auto 2px;
  padding: 1px 4px;
  height: calc(var(--jp-private-document-search-button-height) + 2px);
}

.jp-DocumentSearch-replace-button-wrapper:focus {
  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
}

.jp-DocumentSearch-replace-button {
  display: inline-block;
  text-align: center;
  cursor: pointer;
  box-sizing: border-box;
  color: var(--jp-ui-font-color1);

  /* height - 2 * (padding of wrapper) */
  line-height: calc(var(--jp-private-document-search-button-height) - 2px);
  width: 100%;
  height: 100%;
}

.jp-DocumentSearch-replace-button:focus {
  outline: none;
}

.jp-DocumentSearch-replace-wrapper-class {
  margin-left: 14px;
  display: flex;
}

.jp-DocumentSearch-replace-toggle {
  border: none;
  background-color: var(--jp-toolbar-background);
  border-radius: var(--jp-border-radius);
}

.jp-DocumentSearch-replace-toggle:hover {
  background-color: var(--jp-layout-color2);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.cm-editor {
  line-height: var(--jp-code-line-height);
  font-size: var(--jp-code-font-size);
  font-family: var(--jp-code-font-family);
  border: 0;
  border-radius: 0;
  height: auto;

  /* Changed to auto to autogrow */
}

.cm-editor pre {
  padding: 0 var(--jp-code-padding);
}

.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
  background-color: var(--jp-layout-color0);
  color: var(--jp-content-font-color1);
}

.jp-CodeMirrorEditor {
  cursor: text;
}

/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
    border-left: var(--jp-code-cursor-width1) solid
      var(--jp-editor-cursor-color);
  }
}

/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
  .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
    border-left: var(--jp-code-cursor-width2) solid
      var(--jp-editor-cursor-color);
  }
}

.cm-editor.jp-mod-readOnly .cm-cursor {
  display: none;
}

.jp-CollaboratorCursor {
  border-left: 5px solid transparent;
  border-right: 5px solid transparent;
  border-top: none;
  border-bottom: 3px solid;
  background-clip: content-box;
  margin-left: -5px;
  margin-right: -5px;
}

.cm-searching,
.cm-searching span {
  /* `.cm-searching span`: we need to override syntax highlighting */
  background-color: var(--jp-search-unselected-match-background-color);
  color: var(--jp-search-unselected-match-color);
}

.cm-searching::selection,
.cm-searching span::selection {
  background-color: var(--jp-search-unselected-match-background-color);
  color: var(--jp-search-unselected-match-color);
}

.jp-current-match > .cm-searching,
.jp-current-match > .cm-searching span,
.cm-searching > .jp-current-match,
.cm-searching > .jp-current-match span {
  background-color: var(--jp-search-selected-match-background-color);
  color: var(--jp-search-selected-match-color);
}

.jp-current-match > .cm-searching::selection,
.cm-searching > .jp-current-match::selection,
.jp-current-match > .cm-searching span::selection {
  background-color: var(--jp-search-selected-match-background-color);
  color: var(--jp-search-selected-match-color);
}

.cm-trailingspace {
  background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
  background-position: center left;
  background-repeat: repeat-x;
}

.jp-CollaboratorCursor-hover {
  position: absolute;
  z-index: 1;
  transform: translateX(-50%);
  color: white;
  border-radius: 3px;
  padding-left: 4px;
  padding-right: 4px;
  padding-top: 1px;
  padding-bottom: 1px;
  text-align: center;
  font-size: var(--jp-ui-font-size1);
  white-space: nowrap;
}

.jp-CodeMirror-ruler {
  border-left: 1px dashed var(--jp-border-color2);
}

/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .cm-ySelectionCaret {
  position: relative;
  border-left: 1px solid black;
  margin-left: -1px;
  margin-right: -1px;
  box-sizing: border-box;
}

.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
  white-space: nowrap;
  position: absolute;
  top: -1.15em;
  padding-bottom: 0.05em;
  left: -1px;
  font-size: 0.95em;
  font-family: var(--jp-ui-font-family);
  font-weight: bold;
  line-height: normal;
  user-select: none;
  color: white;
  padding-left: 2px;
  padding-right: 2px;
  z-index: 101;
  transition: opacity 0.3s ease-in-out;
}

.jp-CodeMirrorEditor .cm-ySelectionInfo {
  transition-delay: 0.7s;
  opacity: 0;
}

.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
  opacity: 1;
  transition-delay: 0s;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-MimeDocument {
  outline: none;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/

:root {
  --jp-private-filebrowser-button-height: 28px;
  --jp-private-filebrowser-button-width: 48px;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-FileBrowser .jp-SidePanel-content {
  display: flex;
  flex-direction: column;
}

.jp-FileBrowser-toolbar.jp-Toolbar {
  flex-wrap: wrap;
  row-gap: 12px;
  border-bottom: none;
  height: auto;
  margin: 8px 12px 0;
  box-shadow: none;
  padding: 0;
  justify-content: flex-start;
}

.jp-FileBrowser-Panel {
  flex: 1 1 auto;
  display: flex;
  flex-direction: column;
}

.jp-BreadCrumbs {
  flex: 0 0 auto;
  margin: 8px 12px;
}

.jp-BreadCrumbs-item {
  margin: 0 2px;
  padding: 0 2px;
  border-radius: var(--jp-border-radius);
  cursor: pointer;
}

.jp-BreadCrumbs-item:hover {
  background-color: var(--jp-layout-color2);
}

.jp-BreadCrumbs-item:first-child {
  margin-left: 0;
}

.jp-BreadCrumbs-item.jp-mod-dropTarget {
  background-color: var(--jp-brand-color2);
  opacity: 0.7;
}

/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/

.jp-FileBrowser-toolbar > .jp-Toolbar-item {
  flex: 0 0 auto;
  padding-left: 0;
  padding-right: 2px;
  align-items: center;
  height: unset;
}

.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
  width: 40px;
}

/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/

.jp-FileDialog.jp-mod-conflict input {
  color: var(--jp-error-color1);
}

.jp-FileDialog .jp-new-name-title {
  margin-top: 12px;
}

.jp-LastModified-hidden {
  display: none;
}

.jp-FileSize-hidden {
  display: none;
}

.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
  display: none;
}

/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/

.jp-DirListing {
  flex: 1 1 auto;
  display: flex;
  flex-direction: column;
  outline: 0;
}

.jp-DirListing-header {
  flex: 0 0 auto;
  display: flex;
  flex-direction: row;
  align-items: center;
  overflow: hidden;
  border-top: var(--jp-border-width) solid var(--jp-border-color2);
  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
  box-shadow: var(--jp-toolbar-box-shadow);
  z-index: 2;
}

.jp-DirListing-headerItem {
  padding: 4px 12px 2px;
  font-weight: 500;
}

.jp-DirListing-headerItem:hover {
  background: var(--jp-layout-color2);
}

.jp-DirListing-headerItem.jp-id-name {
  flex: 1 0 84px;
}

.jp-DirListing-headerItem.jp-id-modified {
  flex: 0 0 112px;
  border-left: var(--jp-border-width) solid var(--jp-border-color2);
  text-align: right;
}

.jp-DirListing-headerItem.jp-id-filesize {
  flex: 0 0 75px;
  border-left: var(--jp-border-width) solid var(--jp-border-color2);
  text-align: right;
}

.jp-id-narrow {
  display: none;
  flex: 0 0 5px;
  padding: 4px;
  border-left: var(--jp-border-width) solid var(--jp-border-color2);
  text-align: right;
  color: var(--jp-border-color2);
}

.jp-DirListing-narrow .jp-id-narrow {
  display: block;
}

.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
  display: none;
}

.jp-DirListing-headerItem.jp-mod-selected {
  font-weight: 600;
}

/* increase specificity to override bundled default */
.jp-DirListing-content {
  flex: 1 1 auto;
  margin: 0;
  padding: 0;
  list-style-type: none;
  overflow: auto;
  background-color: var(--jp-layout-color1);
}

.jp-DirListing-content mark {
  color: var(--jp-ui-font-color0);
  background-color: transparent;
  font-weight: bold;
}

.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
  color: var(--jp-ui-inverse-font-color0);
}

/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
  outline: 5px dashed rgba(128, 128, 128, 0.5);
  outline-offset: -10px;
  cursor: copy;
}

.jp-DirListing-item {
  display: flex;
  flex-direction: row;
  align-items: center;
  padding: 4px 12px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.jp-DirListing-checkboxWrapper {
  /* Increases hit area of checkbox. */
  padding: 4px;
}

.jp-DirListing-header
  .jp-DirListing-checkboxWrapper
  + .jp-DirListing-headerItem {
  padding-left: 4px;
}

.jp-DirListing-content .jp-DirListing-checkboxWrapper {
  position: relative;
  left: -4px;
  margin: -4px 0 -4px -8px;
}

.jp-DirListing-checkboxWrapper.jp-mod-visible {
  visibility: visible;
}

/* For devices that support hovering, hide checkboxes until hovered, selected...
*/
@media (hover: hover) {
  .jp-DirListing-checkboxWrapper {
    visibility: hidden;
  }

  .jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
  .jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
    visibility: visible;
  }
}

.jp-DirListing-item[data-is-dot] {
  opacity: 75%;
}

.jp-DirListing-item.jp-mod-selected {
  color: var(--jp-ui-inverse-font-color1);
  background: var(--jp-brand-color1);
}

.jp-DirListing-item.jp-mod-dropTarget {
  background: var(--jp-brand-color3);
}

.jp-DirListing-item:hover:not(.jp-mod-selected) {
  background: var(--jp-layout-color2);
}

.jp-DirListing-itemIcon {
  flex: 0 0 20px;
  margin-right: 4px;
}

.jp-DirListing-itemText {
  flex: 1 0 64px;
  white-space: nowrap;
  overflow: hidden;
  text-overflow: ellipsis;
  user-select: none;
}

.jp-DirListing-itemText:focus {
  outline-width: 2px;
  outline-color: var(--jp-inverse-layout-color1);
  outline-style: solid;
  outline-offset: 1px;
}

.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
  outline-color: var(--jp-layout-color1);
}

.jp-DirListing-itemModified {
  flex: 0 0 125px;
  text-align: right;
}

.jp-DirListing-itemFileSize {
  flex: 0 0 90px;
  text-align: right;
}

.jp-DirListing-editor {
  flex: 1 0 64px;
  outline: none;
  border: none;
  color: var(--jp-ui-font-color1);
  background-color: var(--jp-layout-color1);
}

.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
  color: var(--jp-success-color1);
  content: '\25CF';
  font-size: 8px;
  position: absolute;
  left: -8px;
}

.jp-DirListing-item.jp-mod-running.jp-mod-selected
  .jp-DirListing-itemIcon::before {
  color: var(--jp-ui-inverse-font-color1);
}

.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
  font-size: var(--jp-ui-font-size1);
  padding-left: 4px;
  margin-left: 4px;
  width: 160px;
  background-color: var(--jp-ui-inverse-font-color2);
  box-shadow: var(--jp-elevation-z2);
  border-radius: 0;
  color: var(--jp-ui-font-color1);
  transform: translateX(-40%) translateY(-58%);
}

.jp-Document {
  min-width: 120px;
  min-height: 120px;
  outline: none;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/

.jp-OutputArea {
  overflow-y: auto;
}

.jp-OutputArea-child {
  display: table;
  table-layout: fixed;
  width: 100%;
  overflow: hidden;
}

.jp-OutputPrompt {
  width: var(--jp-cell-prompt-width);
  color: var(--jp-cell-outprompt-font-color);
  font-family: var(--jp-cell-prompt-font-family);
  padding: var(--jp-code-padding);
  letter-spacing: var(--jp-cell-prompt-letter-spacing);
  line-height: var(--jp-code-line-height);
  font-size: var(--jp-code-font-size);
  border: var(--jp-border-width) solid transparent;
  opacity: var(--jp-cell-prompt-opacity);

  /* Right align prompt text, don't wrap to handle large prompt numbers */
  text-align: right;
  white-space: nowrap;
  overflow: hidden;
  text-overflow: ellipsis;

  /* Disable text selection */
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.jp-OutputArea-prompt {
  display: table-cell;
  vertical-align: top;
}

.jp-OutputArea-output {
  display: table-cell;
  width: 100%;
  height: auto;
  overflow: auto;
  user-select: text;
  -moz-user-select: text;
  -webkit-user-select: text;
  -ms-user-select: text;
}

.jp-OutputArea .jp-RenderedText {
  padding-left: 1ch;
}

/**
 * Prompt overlay.
 */

.jp-OutputArea-promptOverlay {
  position: absolute;
  top: 0;
  width: var(--jp-cell-prompt-width);
  height: 100%;
  opacity: 0.5;
}

.jp-OutputArea-promptOverlay:hover {
  background: var(--jp-layout-color2);
  box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
  cursor: zoom-out;
}

.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
  cursor: zoom-in;
}

/**
 * Isolated output.
 */
.jp-OutputArea-output.jp-mod-isolated {
  width: 100%;
  display: block;
}

/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
  position: relative;
}

body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
  content: '';
  position: absolute;
  top: 0;
  left: 0;
  right: 0;
  bottom: 0;
  background: transparent;
}

/* pre */

.jp-OutputArea-output pre {
  border: none;
  margin: 0;
  padding: 0;
  overflow-x: auto;
  overflow-y: auto;
  word-break: break-all;
  word-wrap: break-word;
  white-space: pre-wrap;
}

/* tables */

.jp-OutputArea-output.jp-RenderedHTMLCommon table {
  margin-left: 0;
  margin-right: 0;
}

/* description lists */

.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
  display: block;
}

.jp-OutputArea-output dl {
  width: 100%;
  overflow: hidden;
  padding: 0;
  margin: 0;
}

.jp-OutputArea-output dt {
  font-weight: bold;
  float: left;
  width: 20%;
  padding: 0;
  margin: 0;
}

.jp-OutputArea-output dd {
  float: left;
  width: 80%;
  padding: 0;
  margin: 0;
}

.jp-TrimmedOutputs pre {
  background: var(--jp-layout-color3);
  font-size: calc(var(--jp-code-font-size) * 1.4);
  text-align: center;
  text-transform: uppercase;
}

/* Hide the gutter in case of
 *  - nested output areas (e.g. in the case of output widgets)
 *  - mirrored output areas
 */
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
  display: none;
}

/* Hide empty lines in the output area, for instance due to cleared widgets */
.jp-OutputArea-prompt:empty {
  padding: 0;
  border: 0;
}

/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/

.jp-OutputArea-output.jp-OutputArea-executeResult {
  margin-left: 0;
  width: 100%;
}

/* Text output with the Out[] prompt needs a top padding to match the
 * alignment of the Out[] prompt itself.
 */
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
  padding-top: var(--jp-code-padding);
  border-top: var(--jp-border-width) solid transparent;
}

/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/

.jp-Stdin-prompt {
  color: var(--jp-content-font-color0);
  padding-right: var(--jp-code-padding);
  vertical-align: baseline;
  flex: 0 0 auto;
}

.jp-Stdin-input {
  font-family: var(--jp-code-font-family);
  font-size: inherit;
  color: inherit;
  background-color: inherit;
  width: 42%;
  min-width: 200px;

  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;

  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0 0.25em;
  margin: 0 0.25em;
  flex: 0 0 70%;
}

.jp-Stdin-input::placeholder {
  opacity: 0;
}

.jp-Stdin-input:focus {
  box-shadow: none;
}

.jp-Stdin-input:focus::placeholder {
  opacity: 1;
}

/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/

.jp-LinkedOutputView .jp-OutputArea {
  height: 100%;
  display: block;
}

.jp-LinkedOutputView .jp-OutputArea-output:only-child {
  height: 100%;
}

/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/

@media print {
  .jp-OutputArea-child {
    break-inside: avoid-page;
  }
}

/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
  .jp-OutputPrompt {
    display: table-row;
    text-align: left;
  }

  .jp-OutputArea-child .jp-OutputArea-output {
    display: table-row;
    margin-left: var(--jp-notebook-padding);
  }
}

/* Trimmed outputs warning */
.jp-TrimmedOutputs > a {
  margin: 10px;
  text-decoration: none;
  cursor: pointer;
}

.jp-TrimmedOutputs > a:hover {
  text-decoration: none;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Table of Contents
|----------------------------------------------------------------------------*/

:root {
  --jp-private-toc-active-width: 4px;
}

.jp-TableOfContents {
  display: flex;
  flex-direction: column;
  background: var(--jp-layout-color1);
  color: var(--jp-ui-font-color1);
  font-size: var(--jp-ui-font-size1);
  height: 100%;
}

.jp-TableOfContents-placeholder {
  text-align: center;
}

.jp-TableOfContents-placeholderContent {
  color: var(--jp-content-font-color2);
  padding: 8px;
}

.jp-TableOfContents-placeholderContent > h3 {
  margin-bottom: var(--jp-content-heading-margin-bottom);
}

.jp-TableOfContents .jp-SidePanel-content {
  overflow-y: auto;
}

.jp-TableOfContents-tree {
  margin: 4px;
}

.jp-TableOfContents ol {
  list-style-type: none;
}

/* stylelint-disable-next-line selector-max-type */
.jp-TableOfContents li > ol {
  /* Align left border with triangle icon center */
  padding-left: 11px;
}

.jp-TableOfContents-content {
  /* left margin for the active heading indicator */
  margin: 0 0 0 var(--jp-private-toc-active-width);
  padding: 0;
  background-color: var(--jp-layout-color1);
}

.jp-tocItem {
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.jp-tocItem-heading {
  display: flex;
  cursor: pointer;
}

.jp-tocItem-heading:hover {
  background-color: var(--jp-layout-color2);
}

.jp-tocItem-content {
  display: block;
  padding: 4px 0;
  white-space: nowrap;
  text-overflow: ellipsis;
  overflow-x: hidden;
}

.jp-tocItem-collapser {
  height: 20px;
  margin: 2px 2px 0;
  padding: 0;
  background: none;
  border: none;
  cursor: pointer;
}

.jp-tocItem-collapser:hover {
  background-color: var(--jp-layout-color3);
}

/* Active heading indicator */

.jp-tocItem-heading::before {
  content: ' ';
  background: transparent;
  width: var(--jp-private-toc-active-width);
  height: 24px;
  position: absolute;
  left: 0;
  border-radius: var(--jp-border-radius);
}

.jp-tocItem-heading.jp-tocItem-active::before {
  background-color: var(--jp-brand-color1);
}

.jp-tocItem-heading:hover.jp-tocItem-active::before {
  background: var(--jp-brand-color0);
  opacity: 1;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-Collapser {
  flex: 0 0 var(--jp-cell-collapser-width);
  padding: 0;
  margin: 0;
  border: none;
  outline: none;
  background: transparent;
  border-radius: var(--jp-border-radius);
  opacity: 1;
}

.jp-Collapser-child {
  display: block;
  width: 100%;
  box-sizing: border-box;

  /* height: 100% doesn't work because the height of its parent is computed from content */
  position: absolute;
  top: 0;
  bottom: 0;
}

/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/

/*
Hiding collapsers in print mode.

Note: input and output wrappers have "display: block" propery in print mode.
*/

@media print {
  .jp-Collapser {
    display: none;
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/

/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
  height: 0;
  width: 100%;
  padding: 0;
  margin: 0;
  border: none;
  outline: none;
  background: transparent;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/

/* All input areas */
.jp-InputArea {
  display: table;
  table-layout: fixed;
  width: 100%;
  overflow: hidden;
}

.jp-InputArea-editor {
  display: table-cell;
  overflow: hidden;
  vertical-align: top;

  /* This is the non-active, default styling */
  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
  border-radius: 0;
  background: var(--jp-cell-editor-background);
}

.jp-InputPrompt {
  display: table-cell;
  vertical-align: top;
  width: var(--jp-cell-prompt-width);
  color: var(--jp-cell-inprompt-font-color);
  font-family: var(--jp-cell-prompt-font-family);
  padding: var(--jp-code-padding);
  letter-spacing: var(--jp-cell-prompt-letter-spacing);
  opacity: var(--jp-cell-prompt-opacity);
  line-height: var(--jp-code-line-height);
  font-size: var(--jp-code-font-size);
  border: var(--jp-border-width) solid transparent;

  /* Right align prompt text, don't wrap to handle large prompt numbers */
  text-align: right;
  white-space: nowrap;
  overflow: hidden;
  text-overflow: ellipsis;

  /* Disable text selection */
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
  .jp-InputArea-editor {
    display: table-row;
    margin-left: var(--jp-notebook-padding);
  }

  .jp-InputPrompt {
    display: table-row;
    text-align: left;
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/

.jp-Placeholder {
  display: table;
  table-layout: fixed;
  width: 100%;
}

.jp-Placeholder-prompt {
  display: table-cell;
  box-sizing: border-box;
}

.jp-Placeholder-content {
  display: table-cell;
  padding: 4px 6px;
  border: 1px solid transparent;
  border-radius: 0;
  background: none;
  box-sizing: border-box;
  cursor: pointer;
}

.jp-Placeholder-contentContainer {
  display: flex;
}

.jp-Placeholder-content:hover,
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
  border-color: var(--jp-layout-color3);
}

.jp-Placeholder-content .jp-MoreHorizIcon {
  width: 32px;
  height: 16px;
  border: 1px solid transparent;
  border-radius: var(--jp-border-radius);
}

.jp-Placeholder-content .jp-MoreHorizIcon:hover {
  border: 1px solid var(--jp-border-color1);
  box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
  background-color: var(--jp-layout-color0);
}

.jp-PlaceholderText {
  white-space: nowrap;
  overflow-x: hidden;
  color: var(--jp-inverse-layout-color3);
  font-family: var(--jp-code-font-family);
}

.jp-InputPlaceholder > .jp-Placeholder-content {
  border-color: var(--jp-cell-editor-border-color);
  background: var(--jp-cell-editor-background);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/

:root {
  --jp-private-cell-scrolling-output-offset: 5px;
}

/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/

.jp-Cell {
  padding: var(--jp-cell-padding);
  margin: 0;
  border: none;
  outline: none;
  background: transparent;
}

/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/

.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
  display: flex;
  flex-direction: row;
  padding: 0;
  margin: 0;

  /* Added to reveal the box-shadow on the input and output collapsers. */
  overflow: visible;
}

/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
  flex: 1 1 auto;
}

/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/

/* Make the output collapser disappear when there is not output, but do so
 * in a manner that leaves it in the layout and preserves its width.
 */
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
  border: none !important;
  background: transparent !important;
}

.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
  min-height: var(--jp-cell-collapser-min-height);
}

/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/

/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
  margin-top: 5px;
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
  overflow-y: auto;
  max-height: 24em;
  margin-left: var(--jp-private-cell-scrolling-output-offset);
  resize: vertical;
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
  max-height: unset;
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
  content: ' ';
  box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
  width: 100%;
  height: 100%;
  position: sticky;
  bottom: 0;
  top: 0;
  margin-top: -50%;
  float: left;
  display: block;
  pointer-events: none;
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
  padding-top: 6px;
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
  width: calc(
    var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
  );
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
  left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
}

/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/

.jp-MarkdownOutput {
  display: table-cell;
  width: 100%;
  margin-top: 0;
  margin-bottom: 0;
  padding-left: var(--jp-code-padding);
}

.jp-MarkdownOutput.jp-RenderedHTMLCommon {
  overflow: auto;
}

/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
.jp-collapseHeadingButton {
  display: flex;
  min-height: var(--jp-cell-collapser-min-height);
  font-size: var(--jp-code-font-size);
  position: absolute;
  background-color: transparent;
  background-size: 25px;
  background-repeat: no-repeat;
  background-position-x: center;
  background-position-y: top;
  background-image: var(--jp-icon-caret-down);
  right: 0;
  top: 0;
  bottom: 0;
}

.jp-collapseHeadingButton.jp-mod-collapsed {
  background-image: var(--jp-icon-caret-right);
}

/*
 set the container font size to match that of content
 so that the nested collapse buttons have the right size
*/
.jp-MarkdownCell .jp-InputPrompt {
  font-size: var(--jp-content-font-size1);
}

/*
  Align collapseHeadingButton with cell top header
  The font sizes are identical to the ones in packages/rendermime/style/base.css
*/
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
  font-size: var(--jp-content-font-size5);
  background-position-y: calc(0.3 * var(--jp-content-font-size5));
}

.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
  font-size: var(--jp-content-font-size4);
  background-position-y: calc(0.3 * var(--jp-content-font-size4));
}

.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
  font-size: var(--jp-content-font-size3);
  background-position-y: calc(0.3 * var(--jp-content-font-size3));
}

.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
  font-size: var(--jp-content-font-size2);
  background-position-y: calc(0.3 * var(--jp-content-font-size2));
}

.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
  font-size: var(--jp-content-font-size1);
  background-position-y: top;
}

.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
  font-size: var(--jp-content-font-size0);
  background-position-y: top;
}

/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
  display: none;
}

.jp-Notebook.jp-mod-showHiddenCellsButton
  :is(.jp-MarkdownCell:hover, .jp-mod-active)
  .jp-collapseHeadingButton {
  display: flex;
}

/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
  margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
  margin-top: var(--jp-code-padding);
  border: 1px solid var(--jp-border-color2);
  background-color: var(--jp-border-color3) !important;
  color: var(--jp-content-font-color0) !important;
  display: flex;
}

.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
  background-color: var(--jp-border-color2) !important;
}

.jp-showHiddenCellsButton {
  display: none;
}

/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/

/*
Using block instead of flex to allow the use of the break-inside CSS property for
cell outputs.
*/

@media print {
  .jp-Cell-inputWrapper,
  .jp-Cell-outputWrapper {
    display: block;
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/

:root {
  --jp-notebook-toolbar-padding: 2px 5px 2px 2px;
}

/*-----------------------------------------------------------------------------

/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/

.jp-NotebookPanel-toolbar {
  padding: var(--jp-notebook-toolbar-padding);

  /* disable paint containment from lumino 2.0 default strict CSS containment */
  contain: style size !important;
}

.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
  border: none;
  box-shadow: none;
}

.jp-Notebook-toolbarCellTypeDropdown select {
  height: 24px;
  font-size: var(--jp-ui-font-size1);
  line-height: 14px;
  border-radius: 0;
  display: block;
}

.jp-Notebook-toolbarCellTypeDropdown span {
  top: 5px !important;
}

.jp-Toolbar-responsive-popup {
  position: absolute;
  height: fit-content;
  display: flex;
  flex-direction: row;
  flex-wrap: wrap;
  justify-content: flex-end;
  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
  box-shadow: var(--jp-toolbar-box-shadow);
  background: var(--jp-toolbar-background);
  min-height: var(--jp-toolbar-micro-height);
  padding: var(--jp-notebook-toolbar-padding);
  z-index: 1;
  right: 0;
  top: 0;
}

.jp-Toolbar > .jp-Toolbar-responsive-opener {
  margin-left: auto;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------

/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/

.jp-Notebook-ExecutionIndicator {
  position: relative;
  display: inline-block;
  height: 100%;
  z-index: 9997;
}

.jp-Notebook-ExecutionIndicator-tooltip {
  visibility: hidden;
  height: auto;
  width: max-content;
  width: -moz-max-content;
  background-color: var(--jp-layout-color2);
  color: var(--jp-ui-font-color1);
  text-align: justify;
  border-radius: 6px;
  padding: 0 5px;
  position: fixed;
  display: table;
}

.jp-Notebook-ExecutionIndicator-tooltip.up {
  transform: translateX(-50%) translateY(-100%) translateY(-32px);
}

.jp-Notebook-ExecutionIndicator-tooltip.down {
  transform: translateX(calc(-100% + 16px)) translateY(5px);
}

.jp-Notebook-ExecutionIndicator-tooltip.hidden {
  display: none;
}

.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
  visibility: visible;
}

.jp-Notebook-ExecutionIndicator span {
  font-size: var(--jp-ui-font-size1);
  font-family: var(--jp-ui-font-family);
  color: var(--jp-ui-font-color1);
  line-height: 24px;
  display: block;
}

.jp-Notebook-ExecutionIndicator-progress-bar {
  display: flex;
  justify-content: center;
  height: 100%;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*
 * Execution indicator
 */
.jp-tocItem-content::after {
  content: '';

  /* Must be identical to form a circle */
  width: 12px;
  height: 12px;
  background: none;
  border: none;
  position: absolute;
  right: 0;
}

.jp-tocItem-content[data-running='0']::after {
  border-radius: 50%;
  border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
  background: none;
}

.jp-tocItem-content[data-running='1']::after {
  border-radius: 50%;
  border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
  background-color: var(--jp-inverse-layout-color3);
}

.jp-tocItem-content[data-running='0'],
.jp-tocItem-content[data-running='1'] {
  margin-right: 12px;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.jp-Notebook-footer {
  height: 27px;
  margin-left: calc(
    var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
      var(--jp-cell-padding)
  );
  width: calc(
    100% -
      (
        var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
          var(--jp-cell-padding) + var(--jp-cell-padding)
      )
  );
  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
  color: var(--jp-ui-font-color3);
  margin-top: 6px;
  background: none;
  cursor: pointer;
}

.jp-Notebook-footer:focus {
  border-color: var(--jp-cell-editor-active-border-color);
}

/* For devices that support hovering, hide footer until hover */
@media (hover: hover) {
  .jp-Notebook-footer {
    opacity: 0;
  }

  .jp-Notebook-footer:focus,
  .jp-Notebook-footer:hover {
    opacity: 1;
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| CSS variables
|----------------------------------------------------------------------------*/

:root {
  --jp-side-by-side-output-size: 1fr;
  --jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
  --jp-private-notebook-dragImage-width: 304px;
  --jp-private-notebook-dragImage-height: 36px;
  --jp-private-notebook-selected-color: var(--md-blue-400);
  --jp-private-notebook-active-color: var(--md-green-400);
}

/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/

/* stylelint-disable selector-max-class */

.jp-NotebookPanel {
  display: block;
  height: 100%;
}

.jp-NotebookPanel.jp-Document {
  min-width: 240px;
  min-height: 120px;
}

.jp-Notebook {
  padding: var(--jp-notebook-padding);
  outline: none;
  overflow: auto;
  background: var(--jp-layout-color0);
}

.jp-Notebook.jp-mod-scrollPastEnd::after {
  display: block;
  content: '';
  min-height: var(--jp-notebook-scroll-padding);
}

.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
  contain: strict;
}

.jp-Notebook .jp-Cell {
  overflow: visible;
}

.jp-Notebook .jp-Cell .jp-InputPrompt {
  cursor: move;
}

/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
|   - Command
|   - Edit
| - Cell
|   - None
|   - Active (only one can be active)
|   - Selected (the cells actions are applied to)
|   - Multiselected (when multiple selected, the cursor)
|   - No outputs
|----------------------------------------------------------------------------*/

/* Command or edit modes */

.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
  opacity: var(--jp-cell-prompt-not-active-opacity);
  color: var(--jp-cell-prompt-not-active-font-color);
}

.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
  opacity: var(--jp-cell-prompt-not-active-opacity);
  color: var(--jp-cell-prompt-not-active-font-color);
}

/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
  background: var(--jp-brand-color1);
}

/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
  color: var(--jp-warn-color1);
}

.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
  color: var(--jp-warn-color1);
  content: '•';
}

.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
  background: var(--jp-warn-color1);
}

/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
  box-shadow: var(--jp-elevation-z2);
  background: var(--jp-brand-color1);
  opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}

/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
  background: var(--jp-brand-color0);
  opacity: 1;
}

/* Command mode */

.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
  background: var(--jp-notebook-multiselected-color);
}

.jp-Notebook.jp-mod-commandMode
  .jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
  background: transparent;
}

/* Edit mode */

.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
  box-shadow: var(--jp-input-box-shadow);
  background-color: var(--jp-cell-editor-active-background);
}

/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/

.jp-Notebook-cell.jp-mod-dropSource {
  opacity: 0.5;
}

.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
  .jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
  border-top-color: var(--jp-private-notebook-selected-color);
  border-top-style: solid;
  border-top-width: 2px;
}

.jp-dragImage {
  display: block;
  flex-direction: row;
  width: var(--jp-private-notebook-dragImage-width);
  height: var(--jp-private-notebook-dragImage-height);
  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
  background: var(--jp-cell-editor-background);
  overflow: visible;
}

.jp-dragImage-singlePrompt {
  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}

.jp-dragImage .jp-dragImage-content {
  flex: 1 1 auto;
  z-index: 2;
  font-size: var(--jp-code-font-size);
  font-family: var(--jp-code-font-family);
  line-height: var(--jp-code-line-height);
  padding: var(--jp-code-padding);
  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
  background: var(--jp-cell-editor-background-color);
  color: var(--jp-content-font-color3);
  text-align: left;
  margin: 4px 4px 4px 0;
}

.jp-dragImage .jp-dragImage-prompt {
  flex: 0 0 auto;
  min-width: 36px;
  color: var(--jp-cell-inprompt-font-color);
  padding: var(--jp-code-padding);
  padding-left: 12px;
  font-family: var(--jp-cell-prompt-font-family);
  letter-spacing: var(--jp-cell-prompt-letter-spacing);
  line-height: 1.9;
  font-size: var(--jp-code-font-size);
  border: var(--jp-border-width) solid transparent;
}

.jp-dragImage-multipleBack {
  z-index: -1;
  position: absolute;
  height: 32px;
  width: 300px;
  top: 8px;
  left: 8px;
  background: var(--jp-layout-color2);
  border: var(--jp-border-width) solid var(--jp-input-border-color);
  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}

/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/

.jp-NotebookTools {
  display: block;
  min-width: var(--jp-sidebar-min-width);
  color: var(--jp-ui-font-color1);
  background: var(--jp-layout-color1);

  /* This is needed so that all font sizing of children done in ems is
    * relative to this base size */
  font-size: var(--jp-ui-font-size1);
  overflow: auto;
}

.jp-ActiveCellTool {
  padding: 12px 0;
  display: flex;
}

.jp-ActiveCellTool-Content {
  flex: 1 1 auto;
}

.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
  background: var(--jp-cell-editor-background);
  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
  border-radius: 0;
  min-height: 29px;
}

.jp-ActiveCellTool .jp-InputPrompt {
  min-width: calc(var(--jp-cell-prompt-width) * 0.75);
}

.jp-ActiveCellTool-CellContent > pre {
  padding: 5px 4px;
  margin: 0;
  white-space: normal;
}

.jp-MetadataEditorTool {
  flex-direction: column;
  padding: 12px 0;
}

.jp-RankedPanel > :not(:first-child) {
  margin-top: 12px;
}

.jp-KeySelector select.jp-mod-styled {
  font-size: var(--jp-ui-font-size1);
  color: var(--jp-ui-font-color0);
  border: var(--jp-border-width) solid var(--jp-border-color1);
}

.jp-KeySelector label,
.jp-MetadataEditorTool label,
.jp-NumberSetter label {
  line-height: 1.4;
}

.jp-NotebookTools .jp-select-wrapper {
  margin-top: 4px;
  margin-bottom: 0;
}

.jp-NumberSetter input {
  width: 100%;
  margin-top: 4px;
}

.jp-NotebookTools .jp-Collapse {
  margin-top: 16px;
}

/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/

.jp-mod-presentationMode .jp-Notebook {
  --jp-content-font-size1: var(--jp-content-presentation-font-size1);
  --jp-code-font-size: var(--jp-code-presentation-font-size);
}

.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
  flex: 0 0 110px;
}

/*-----------------------------------------------------------------------------
| Side-by-side Mode (.jp-mod-sideBySide)
|----------------------------------------------------------------------------*/
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
  margin-top: 3em;
  margin-bottom: 3em;
  margin-left: 5%;
  margin-right: 5%;
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
  display: grid;
  grid-template-columns: minmax(0, 1fr) min-content minmax(
      0,
      var(--jp-side-by-side-output-size)
    );
  grid-template-rows: auto minmax(0, 1fr) auto;
  grid-template-areas:
    'header header header'
    'input handle output'
    'footer footer footer';
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
  grid-template-columns: minmax(0, 1fr) min-content minmax(
      0,
      var(--jp-side-by-side-resized-cell)
    );
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
  grid-area: header;
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
  grid-area: input;
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
  /* overwrite the default margin (no vertical separation needed in side by side move */
  margin-top: 0;
  grid-area: output;
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
  grid-area: footer;
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
  grid-area: handle;
  user-select: none;
  display: block;
  height: 100%;
  cursor: ew-resize;
  padding: 0 var(--jp-cell-padding);
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
  content: '';
  display: block;
  background: var(--jp-border-color2);
  height: 100%;
  width: 5px;
}

.jp-mod-sideBySide.jp-Notebook
  .jp-CodeCell.jp-mod-resizedCell
  .jp-CellResizeHandle::after {
  background: var(--jp-border-color0);
}

.jp-CellResizeHandle {
  display: none;
}

/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/

.jp-Cell-Placeholder {
  padding-left: 55px;
}

.jp-Cell-Placeholder-wrapper {
  background: #fff;
  border: 1px solid;
  border-color: #e5e6e9 #dfe0e4 #d0d1d5;
  border-radius: 4px;
  -webkit-border-radius: 4px;
  margin: 10px 15px;
}

.jp-Cell-Placeholder-wrapper-inner {
  padding: 15px;
  position: relative;
}

.jp-Cell-Placeholder-wrapper-body {
  background-repeat: repeat;
  background-size: 50% auto;
}

.jp-Cell-Placeholder-wrapper-body div {
  background: #f6f7f8;
  background-image: -webkit-linear-gradient(
    left,
    #f6f7f8 0%,
    #edeef1 20%,
    #f6f7f8 40%,
    #f6f7f8 100%
  );
  background-repeat: no-repeat;
  background-size: 800px 104px;
  height: 104px;
  position: absolute;
  right: 15px;
  left: 15px;
  top: 15px;
}

div.jp-Cell-Placeholder-h1 {
  top: 20px;
  height: 20px;
  left: 15px;
  width: 150px;
}

div.jp-Cell-Placeholder-h2 {
  left: 15px;
  top: 50px;
  height: 10px;
  width: 100px;
}

div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
  left: 15px;
  right: 15px;
  height: 10px;
}

div.jp-Cell-Placeholder-content-1 {
  top: 100px;
}

div.jp-Cell-Placeholder-content-2 {
  top: 120px;
}

div.jp-Cell-Placeholder-content-3 {
  top: 140px;
}

</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

:root {
  /* Elevation
   *
   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
   *
   * https://github.com/material-components/material-components-web
   * https://material-components-web.appspot.com/elevation.html
   */

  --jp-shadow-base-lightness: 0;
  --jp-shadow-umbra-color: rgba(
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    0.2
  );
  --jp-shadow-penumbra-color: rgba(
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    0.14
  );
  --jp-shadow-ambient-color: rgba(
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    0.12
  );
  --jp-elevation-z0: none;
  --jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
    0 1px 1px 0 var(--jp-shadow-penumbra-color),
    0 1px 3px 0 var(--jp-shadow-ambient-color);
  --jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
    0 2px 2px 0 var(--jp-shadow-penumbra-color),
    0 1px 5px 0 var(--jp-shadow-ambient-color);
  --jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
    0 4px 5px 0 var(--jp-shadow-penumbra-color),
    0 1px 10px 0 var(--jp-shadow-ambient-color);
  --jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
    0 6px 10px 0 var(--jp-shadow-penumbra-color),
    0 1px 18px 0 var(--jp-shadow-ambient-color);
  --jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
    0 8px 10px 1px var(--jp-shadow-penumbra-color),
    0 3px 14px 2px var(--jp-shadow-ambient-color);
  --jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
    0 12px 17px 2px var(--jp-shadow-penumbra-color),
    0 5px 22px 4px var(--jp-shadow-ambient-color);
  --jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
    0 16px 24px 2px var(--jp-shadow-penumbra-color),
    0 6px 30px 5px var(--jp-shadow-ambient-color);
  --jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
    0 20px 31px 3px var(--jp-shadow-penumbra-color),
    0 8px 38px 7px var(--jp-shadow-ambient-color);
  --jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
    0 24px 38px 3px var(--jp-shadow-penumbra-color),
    0 9px 46px 8px var(--jp-shadow-ambient-color);

  /* Borders
   *
   * The following variables, specify the visual styling of borders in JupyterLab.
   */

  --jp-border-width: 1px;
  --jp-border-color0: var(--md-grey-400);
  --jp-border-color1: var(--md-grey-400);
  --jp-border-color2: var(--md-grey-300);
  --jp-border-color3: var(--md-grey-200);
  --jp-inverse-border-color: var(--md-grey-600);
  --jp-border-radius: 2px;

  /* UI Fonts
   *
   * The UI font CSS variables are used for the typography all of the JupyterLab
   * user interface elements that are not directly user generated content.
   *
   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
   * is applied to a parent element. When children elements, such as headings, are sized
   * in em all things will be computed relative to that body size.
   */

  --jp-ui-font-scale-factor: 1.2;
  --jp-ui-font-size0: 0.83333em;
  --jp-ui-font-size1: 13px; /* Base font size */
  --jp-ui-font-size2: 1.2em;
  --jp-ui-font-size3: 1.44em;
  --jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
    helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
    'Segoe UI Symbol';

  /*
   * Use these font colors against the corresponding main layout colors.
   * In a light theme, these go from dark to light.
   */

  /* Defaults use Material Design specification */
  --jp-ui-font-color0: rgba(0, 0, 0, 1);
  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);

  /*
   * Use these against the brand/accent/warn/error colors.
   * These will typically go from light to darker, in both a dark and light theme.
   */

  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);

  /* Content Fonts
   *
   * Content font variables are used for typography of user generated content.
   *
   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
   * is applied to a parent element. When children elements, such as headings, are sized
   * in em all things will be computed relative to that body size.
   */

  --jp-content-line-height: 1.6;
  --jp-content-font-scale-factor: 1.2;
  --jp-content-font-size0: 0.83333em;
  --jp-content-font-size1: 14px; /* Base font size */
  --jp-content-font-size2: 1.2em;
  --jp-content-font-size3: 1.44em;
  --jp-content-font-size4: 1.728em;
  --jp-content-font-size5: 2.0736em;

  /* This gives a magnification of about 125% in presentation mode over normal. */
  --jp-content-presentation-font-size1: 17px;
  --jp-content-heading-line-height: 1;
  --jp-content-heading-margin-top: 1.2em;
  --jp-content-heading-margin-bottom: 0.8em;
  --jp-content-heading-font-weight: 500;

  /* Defaults use Material Design specification */
  --jp-content-font-color0: rgba(0, 0, 0, 1);
  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
  --jp-content-link-color: var(--md-blue-900);
  --jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
    'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
    'Segoe UI Emoji', 'Segoe UI Symbol';

  /*
   * Code Fonts
   *
   * Code font variables are used for typography of code and other monospaces content.
   */

  --jp-code-font-size: 13px;
  --jp-code-line-height: 1.3077; /* 17px for 13px base */
  --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
  --jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
  --jp-code-font-family: var(--jp-code-font-family-default);

  /* This gives a magnification of about 125% in presentation mode over normal. */
  --jp-code-presentation-font-size: 16px;

  /* may need to tweak cursor width if you change font size */
  --jp-code-cursor-width0: 1.4px;
  --jp-code-cursor-width1: 2px;
  --jp-code-cursor-width2: 4px;

  /* Layout
   *
   * The following are the main layout colors use in JupyterLab. In a light
   * theme these would go from light to dark.
   */

  --jp-layout-color0: white;
  --jp-layout-color1: white;
  --jp-layout-color2: var(--md-grey-200);
  --jp-layout-color3: var(--md-grey-400);
  --jp-layout-color4: var(--md-grey-600);

  /* Inverse Layout
   *
   * The following are the inverse layout colors use in JupyterLab. In a light
   * theme these would go from dark to light.
   */

  --jp-inverse-layout-color0: #111;
  --jp-inverse-layout-color1: var(--md-grey-900);
  --jp-inverse-layout-color2: var(--md-grey-800);
  --jp-inverse-layout-color3: var(--md-grey-700);
  --jp-inverse-layout-color4: var(--md-grey-600);

  /* Brand/accent */

  --jp-brand-color0: var(--md-blue-900);
  --jp-brand-color1: var(--md-blue-700);
  --jp-brand-color2: var(--md-blue-300);
  --jp-brand-color3: var(--md-blue-100);
  --jp-brand-color4: var(--md-blue-50);
  --jp-accent-color0: var(--md-green-900);
  --jp-accent-color1: var(--md-green-700);
  --jp-accent-color2: var(--md-green-300);
  --jp-accent-color3: var(--md-green-100);

  /* State colors (warn, error, success, info) */

  --jp-warn-color0: var(--md-orange-900);
  --jp-warn-color1: var(--md-orange-700);
  --jp-warn-color2: var(--md-orange-300);
  --jp-warn-color3: var(--md-orange-100);
  --jp-error-color0: var(--md-red-900);
  --jp-error-color1: var(--md-red-700);
  --jp-error-color2: var(--md-red-300);
  --jp-error-color3: var(--md-red-100);
  --jp-success-color0: var(--md-green-900);
  --jp-success-color1: var(--md-green-700);
  --jp-success-color2: var(--md-green-300);
  --jp-success-color3: var(--md-green-100);
  --jp-info-color0: var(--md-cyan-900);
  --jp-info-color1: var(--md-cyan-700);
  --jp-info-color2: var(--md-cyan-300);
  --jp-info-color3: var(--md-cyan-100);

  /* Cell specific styles */

  --jp-cell-padding: 5px;
  --jp-cell-collapser-width: 8px;
  --jp-cell-collapser-min-height: 20px;
  --jp-cell-collapser-not-active-hover-opacity: 0.6;
  --jp-cell-editor-background: var(--md-grey-100);
  --jp-cell-editor-border-color: var(--md-grey-300);
  --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
  --jp-cell-editor-active-background: var(--jp-layout-color0);
  --jp-cell-editor-active-border-color: var(--jp-brand-color1);
  --jp-cell-prompt-width: 64px;
  --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
  --jp-cell-prompt-letter-spacing: 0;
  --jp-cell-prompt-opacity: 1;
  --jp-cell-prompt-not-active-opacity: 0.5;
  --jp-cell-prompt-not-active-font-color: var(--md-grey-700);

  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  --jp-cell-inprompt-font-color: #307fc1;

  /* A custom blend of MD grey and orange 600
   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
  --jp-cell-outprompt-font-color: #bf5b3d;

  /* Notebook specific styles */

  --jp-notebook-padding: 10px;
  --jp-notebook-select-background: var(--jp-layout-color1);
  --jp-notebook-multiselected-color: var(--md-blue-50);

  /* The scroll padding is calculated to fill enough space at the bottom of the
  notebook to show one single-line cell (with appropriate padding) at the top
  when the notebook is scrolled all the way to the bottom. We also subtract one
  pixel so that no scrollbar appears if we have just one single-line cell in the
  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
  */
  --jp-notebook-scroll-padding: calc(
    100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
      var(--jp-code-padding) - var(--jp-cell-padding) - 1px
  );

  /* Rendermime styles */

  --jp-rendermime-error-background: #fdd;
  --jp-rendermime-table-row-background: var(--md-grey-100);
  --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);

  /* Dialog specific styles */

  --jp-dialog-background: rgba(0, 0, 0, 0.25);

  /* Console specific styles */

  --jp-console-padding: 10px;

  /* Toolbar specific styles */

  --jp-toolbar-border-color: var(--jp-border-color1);
  --jp-toolbar-micro-height: 8px;
  --jp-toolbar-background: var(--jp-layout-color1);
  --jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
  --jp-toolbar-header-margin: 4px 4px 0 4px;
  --jp-toolbar-active-background: var(--md-grey-300);

  /* Statusbar specific styles */

  --jp-statusbar-height: 24px;

  /* Input field styles */

  --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
  --jp-input-active-background: var(--jp-layout-color1);
  --jp-input-hover-background: var(--jp-layout-color1);
  --jp-input-background: var(--md-grey-100);
  --jp-input-border-color: var(--jp-inverse-border-color);
  --jp-input-active-border-color: var(--jp-brand-color1);
  --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);

  /* General editor styles */

  --jp-editor-selected-background: #d9d9d9;
  --jp-editor-selected-focused-background: #d7d4f0;
  --jp-editor-cursor-color: var(--jp-ui-font-color0);

  /* Code mirror specific styles */

  --jp-mirror-editor-keyword-color: #008000;
  --jp-mirror-editor-atom-color: #88f;
  --jp-mirror-editor-number-color: #080;
  --jp-mirror-editor-def-color: #00f;
  --jp-mirror-editor-variable-color: var(--md-grey-900);
  --jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
  --jp-mirror-editor-variable-3-color: #085;
  --jp-mirror-editor-punctuation-color: #05a;
  --jp-mirror-editor-property-color: #05a;
  --jp-mirror-editor-operator-color: #a2f;
  --jp-mirror-editor-comment-color: #408080;
  --jp-mirror-editor-string-color: #ba2121;
  --jp-mirror-editor-string-2-color: #708;
  --jp-mirror-editor-meta-color: #a2f;
  --jp-mirror-editor-qualifier-color: #555;
  --jp-mirror-editor-builtin-color: #008000;
  --jp-mirror-editor-bracket-color: #997;
  --jp-mirror-editor-tag-color: #170;
  --jp-mirror-editor-attribute-color: #00c;
  --jp-mirror-editor-header-color: blue;
  --jp-mirror-editor-quote-color: #090;
  --jp-mirror-editor-link-color: #00c;
  --jp-mirror-editor-error-color: #f00;
  --jp-mirror-editor-hr-color: #999;

  /*
    RTC user specific colors.
    These colors are used for the cursor, username in the editor,
    and the icon of the user.
  */

  --jp-collaborator-color1: #ffad8e;
  --jp-collaborator-color2: #dac83d;
  --jp-collaborator-color3: #72dd76;
  --jp-collaborator-color4: #00e4d0;
  --jp-collaborator-color5: #45d4ff;
  --jp-collaborator-color6: #e2b1ff;
  --jp-collaborator-color7: #ff9de6;

  /* Vega extension styles */

  --jp-vega-background: white;

  /* Sidebar-related styles */

  --jp-sidebar-min-width: 250px;

  /* Search-related styles */

  --jp-search-toggle-off-opacity: 0.5;
  --jp-search-toggle-hover-opacity: 0.8;
  --jp-search-toggle-on-opacity: 1;
  --jp-search-selected-match-background-color: rgb(245, 200, 0);
  --jp-search-selected-match-color: black;
  --jp-search-unselected-match-background-color: var(
    --jp-inverse-layout-color0
  );
  --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);

  /* Icon colors that work well with light or dark backgrounds */
  --jp-icon-contrast-color0: var(--md-purple-600);
  --jp-icon-contrast-color1: var(--md-green-600);
  --jp-icon-contrast-color2: var(--md-pink-600);
  --jp-icon-contrast-color3: var(--md-blue-600);

  /* Button colors */
  --jp-accept-color-normal: var(--md-blue-700);
  --jp-accept-color-hover: var(--md-blue-800);
  --jp-accept-color-active: var(--md-blue-900);
  --jp-warn-color-normal: var(--md-red-700);
  --jp-warn-color-hover: var(--md-red-800);
  --jp-warn-color-active: var(--md-red-900);
  --jp-reject-color-normal: var(--md-grey-600);
  --jp-reject-color-hover: var(--md-grey-700);
  --jp-reject-color-active: var(--md-grey-800);

  /* File or activity icons and switch semantic variables */
  --jp-jupyter-icon-color: #f37626;
  --jp-notebook-icon-color: #f37626;
  --jp-json-icon-color: var(--md-orange-700);
  --jp-console-icon-background-color: var(--md-blue-700);
  --jp-console-icon-color: white;
  --jp-terminal-icon-background-color: var(--md-grey-800);
  --jp-terminal-icon-color: var(--md-grey-200);
  --jp-text-editor-icon-color: var(--md-grey-700);
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.dates</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mdates</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">time</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">MinMaxScaler</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span><span class="p">,</span> <span class="n">Ridge</span><span class="p">,</span> <span class="n">Lasso</span><span class="p">,</span> <span class="n">ElasticNet</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <span class="n">RandomForestRegressor</span><span class="p">,</span> <span class="n">GradientBoostingRegressor</span><span class="p">,</span> <span class="n">AdaBoostRegressor</span><span class="p">,</span> <span class="n">ExtraTreesRegressor</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <span class="n">SVR</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.neighbors</span><span class="w"> </span><span class="kn">import</span> <span class="n">KNeighborsRegressor</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.tree</span><span class="w"> </span><span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.neural_network</span><span class="w"> </span><span class="kn">import</span> <span class="n">MLPRegressor</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">xgboost</span><span class="w"> </span><span class="kn">import</span> <span class="n">XGBRegressor</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean_absolute_error</span><span class="p">,</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">r2_score</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">tensorflow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">tf</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorflow.keras.models</span><span class="w"> </span><span class="kn">import</span> <span class="n">Sequential</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorflow.keras.layers</span><span class="w"> </span><span class="kn">import</span> <span class="n">LSTM</span><span class="p">,</span> <span class="n">SimpleRNN</span><span class="p">,</span> <span class="n">GRU</span><span class="p">,</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">BatchNormalization</span>
</pre></div>
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</div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'NVDA.csv'</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">columns</span>
</pre></div>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
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<pre>Index(['Date', 'Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume'], dtype='object')</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
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<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">'Date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Date'</span><span class="p">])</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre>&lt;class 'pandas.core.frame.DataFrame'&gt;
RangeIndex: 6558 entries, 0 to 6557
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype         
---  ------     --------------  -----         
 0   Date       6558 non-null   datetime64[ns]
 1   Adj Close  6558 non-null   float64       
 2   Close      6558 non-null   float64       
 3   High       6558 non-null   float64       
 4   Low        6558 non-null   float64       
 5   Open       6558 non-null   float64       
 6   Volume     6558 non-null   int64         
dtypes: datetime64[ns](1), float64(5), int64(1)
memory usage: 358.8 KB
</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">numerical_columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Adj Close"</span><span class="p">,</span> <span class="s2">"Close"</span><span class="p">,</span> <span class="s2">"High"</span><span class="p">,</span> <span class="s2">"Low"</span><span class="p">,</span> <span class="s2">"Open"</span><span class="p">,</span> <span class="s2">"Volume"</span><span class="p">]</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">numerical_columns</span><span class="p">:</span>
    <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">errors</span><span class="o">=</span><span class="s1">'coerce'</span><span class="p">)</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
<div>
<style scoped="">
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Date</th>
<th>Adj Close</th>
<th>Close</th>
<th>High</th>
<th>Low</th>
<th>Open</th>
<th>Volume</th>
</tr>
</thead>
<tbody>
<tr>
<th>count</th>
<td>6558</td>
<td>6558.000000</td>
<td>6558.000000</td>
<td>6558.000000</td>
<td>6558.000000</td>
<td>6558.000000</td>
<td>6.558000e+03</td>
</tr>
<tr>
<th>mean</th>
<td>2012-02-02 07:47:15.864592896</td>
<td>8.768532</td>
<td>8.795447</td>
<td>8.956567</td>
<td>8.618315</td>
<td>8.795850</td>
<td>5.991103e+08</td>
</tr>
<tr>
<th>min</th>
<td>1999-01-22 00:00:00</td>
<td>0.031286</td>
<td>0.034115</td>
<td>0.035547</td>
<td>0.033333</td>
<td>0.034896</td>
<td>1.968000e+07</td>
</tr>
<tr>
<th>25%</th>
<td>2005-07-29 18:00:00</td>
<td>0.257739</td>
<td>0.281042</td>
<td>0.288511</td>
<td>0.273354</td>
<td>0.280810</td>
<td>3.384780e+08</td>
</tr>
<tr>
<th>50%</th>
<td>2012-02-01 12:00:00</td>
<td>0.437176</td>
<td>0.466083</td>
<td>0.472875</td>
<td>0.459250</td>
<td>0.466584</td>
<td>5.002635e+08</td>
</tr>
<tr>
<th>75%</th>
<td>2018-08-07 18:00:00</td>
<td>4.597059</td>
<td>4.644625</td>
<td>4.724000</td>
<td>4.588750</td>
<td>4.632437</td>
<td>7.307002e+08</td>
</tr>
<tr>
<th>max</th>
<td>2025-02-14 00:00:00</td>
<td>149.429993</td>
<td>149.429993</td>
<td>153.130005</td>
<td>147.820007</td>
<td>153.029999</td>
<td>9.230856e+09</td>
</tr>
<tr>
<th>std</th>
<td>NaN</td>
<td>23.907205</td>
<td>23.904882</td>
<td>24.349618</td>
<td>23.419200</td>
<td>23.922708</td>
<td>4.307236e+08</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">numeric_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
<div>
<style scoped="">
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Adj Close</th>
<th>Close</th>
<th>High</th>
<th>Low</th>
<th>Open</th>
<th>Volume</th>
</tr>
</thead>
<tbody>
<tr>
<th>Adj Close</th>
<td>1.000000</td>
<td>1.000000</td>
<td>0.999799</td>
<td>0.999861</td>
<td>0.999613</td>
<td>-0.164438</td>
</tr>
<tr>
<th>Close</th>
<td>1.000000</td>
<td>1.000000</td>
<td>0.999799</td>
<td>0.999861</td>
<td>0.999613</td>
<td>-0.164415</td>
</tr>
<tr>
<th>High</th>
<td>0.999799</td>
<td>0.999799</td>
<td>1.000000</td>
<td>0.999803</td>
<td>0.999881</td>
<td>-0.163423</td>
</tr>
<tr>
<th>Low</th>
<td>0.999861</td>
<td>0.999861</td>
<td>0.999803</td>
<td>1.000000</td>
<td>0.999797</td>
<td>-0.165182</td>
</tr>
<tr>
<th>Open</th>
<td>0.999613</td>
<td>0.999613</td>
<td>0.999881</td>
<td>0.999797</td>
<td>1.000000</td>
<td>-0.164090</td>
</tr>
<tr>
<th>Volume</th>
<td>-0.164438</td>
<td>-0.164415</td>
<td>-0.163423</td>
<td>-0.165182</td>
<td>-0.164090</td>
<td>1.000000</td>
</tr>
</tbody>
</table>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
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<style scoped="">
    .dataframe tbody tr th:only-of-type {
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Date</th>
<th>Adj Close</th>
<th>Close</th>
<th>High</th>
<th>Low</th>
<th>Open</th>
<th>Volume</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1999-01-22</td>
<td>0.037615</td>
<td>0.041016</td>
<td>0.048828</td>
<td>0.038802</td>
<td>0.043750</td>
<td>2714688000</td>
</tr>
<tr>
<th>1</th>
<td>1999-01-25</td>
<td>0.041556</td>
<td>0.045313</td>
<td>0.045833</td>
<td>0.041016</td>
<td>0.044271</td>
<td>510480000</td>
</tr>
<tr>
<th>2</th>
<td>1999-01-26</td>
<td>0.038331</td>
<td>0.041797</td>
<td>0.046745</td>
<td>0.041146</td>
<td>0.045833</td>
<td>343200000</td>
</tr>
<tr>
<th>3</th>
<td>1999-01-27</td>
<td>0.038212</td>
<td>0.041667</td>
<td>0.042969</td>
<td>0.039583</td>
<td>0.041927</td>
<td>244368000</td>
</tr>
<tr>
<th>4</th>
<td>1999-01-28</td>
<td>0.038092</td>
<td>0.041536</td>
<td>0.041927</td>
<td>0.041276</td>
<td>0.041667</td>
<td>227520000</td>
</tr>
</tbody>
</table>
</div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">'Date'</span><span class="p">)</span>
</pre></div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"Date"</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s2">"Close"</span><span class="p">],</span><span class="n">label</span><span class="o">=</span><span class="s2">"Closing Price "</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"NVIDIA Closing Price (1999 - 2025)"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Date"</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Price (USD)"</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"Date"</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s2">"Volume"</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Tranding Volume"</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s2">"orange"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"NVIDIA Trading Volume (1999 - 2025)"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Date '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Volume '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Close'</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'green'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Closing Price Distribution '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Price (USD) '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Frequency '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
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<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">[[</span><span class="s1">'Open'</span><span class="p">,</span><span class="s1">'Close'</span><span class="p">,</span> <span class="s1">'High'</span><span class="p">,</span> <span class="s1">'Low'</span><span class="p">]])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Price Range Boxplot '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Price Type '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Price (USD) '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">scatterplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'Close'</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'Volume'</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'purple'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Close Price vs Trading Volume '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Closing Price (USD) '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Trading Volume '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Date'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'High'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'High Price '</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Date'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Low'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Low Price '</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'(NVIDIA) High &amp; Low Prices (2020-2025)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Date '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Price (USD) '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
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<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">kdeplot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Close'</span><span class="p">],</span> <span class="n">fill</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'purple'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Closing Price Density Plot'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Price (USD)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Density'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">violinplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">[[</span><span class="s1">'Close'</span><span class="p">,</span> <span class="s1">'High'</span><span class="p">,</span> <span class="s1">'Low'</span><span class="p">]],</span> <span class="n">palette</span><span class="o">=</span><span class="s1">'muted'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Price Distribution Violin Plot '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Price Type '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Price (USD) '</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">numeric_only</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'coolwarm'</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">'.2f'</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Correlation Heatmap'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">high_low_ratio</span> <span class="o">=</span> <span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">"High"</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Low"</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()]</span>
<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Average High Price"</span><span class="p">,</span> <span class="s2">"Average Low Price"</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">high_low_ratio</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">autopct</span><span class="o">=</span><span class="s2">"</span><span class="si">%.2f%%</span><span class="s2">"</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="p">[</span><span class="s2">"purple"</span><span class="p">,</span> <span class="s2">"orange"</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Proportion of High and Low Prices"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<p>ML Models</p>
</div>
</div>
</div>
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<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s2">"Open"</span><span class="p">,</span> <span class="s2">"High"</span><span class="p">,</span> <span class="s2">"Low"</span><span class="p">,</span> <span class="s2">"Volume"</span><span class="p">]]</span>
<span class="n">y</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">"Close"</span><span class="p">]</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">scaler</span><span class="o">=</span><span class="n">MinMaxScaler</span><span class="p">()</span>
<span class="n">x_scaled</span><span class="o">=</span><span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">537</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">models</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">"Linear Regression"</span><span class="p">:</span> <span class="n">LinearRegression</span><span class="p">(),</span>
    <span class="s2">"Lasso Regression"</span><span class="p">:</span> <span class="n">Lasso</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">),</span>
    <span class="s2">"ElasticNet Regression"</span><span class="p">:</span> <span class="n">ElasticNet</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">),</span>
    <span class="s2">"Random Forest"</span><span class="p">:</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
    <span class="s2">"Gradient Boosting"</span><span class="p">:</span> <span class="n">GradientBoostingRegressor</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
    <span class="s2">"AdaBoost Regressor"</span><span class="p">:</span> <span class="n">AdaBoostRegressor</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
    <span class="s2">"Extra Trees Regressor"</span><span class="p">:</span> <span class="n">ExtraTreesRegressor</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
    <span class="s2">"XGBoost Regressor"</span><span class="p">:</span> <span class="n">XGBRegressor</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
    <span class="s2">"Support Vector Regressor"</span><span class="p">:</span> <span class="n">SVR</span><span class="p">(),</span>
    <span class="s2">"K-Nearest Neighbors"</span><span class="p">:</span> <span class="n">KNeighborsRegressor</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
    <span class="s2">"Decision Tree"</span><span class="p">:</span> <span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
<span class="p">}</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">results</span><span class="o">=</span><span class="p">{}</span>

<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
    <span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
    <span class="n">y_pred</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

    <span class="n">mae</span> <span class="o">=</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
    <span class="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
    <span class="n">rmse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">mse</span><span class="p">)</span>
    <span class="n">r2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
    <span class="n">train_time</span> <span class="o">=</span> <span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span>
    <span class="n">results</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"MAE"</span><span class="p">:</span> <span class="n">mae</span><span class="p">,</span> <span class="s2">"MSE"</span><span class="p">:</span> <span class="n">mse</span><span class="p">,</span> <span class="s2">"RMSE"</span><span class="p">:</span> <span class="n">rmse</span><span class="p">,</span> <span class="s2">"R2 Score"</span><span class="p">:</span> <span class="n">r2</span><span class="p">,</span> <span class="s2">"Training Time (s)"</span><span class="p">:</span> <span class="n">train_time</span><span class="p">}</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">results</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">results_df</span>
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<style scoped="">
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<th></th>
<th>MAE</th>
<th>MSE</th>
<th>RMSE</th>
<th>R2 Score</th>
<th>Training Time (s)</th>
</tr>
</thead>
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<tr>
<th>Linear Regression</th>
<td>0.080341</td>
<td>0.094662</td>
<td>0.307672</td>
<td>0.999839</td>
<td>0.003055</td>
</tr>
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<th>Lasso Regression</th>
<td>0.123431</td>
<td>0.169101</td>
<td>0.411218</td>
<td>0.999712</td>
<td>0.031215</td>
</tr>
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<th>ElasticNet Regression</th>
<td>0.118249</td>
<td>0.154090</td>
<td>0.392542</td>
<td>0.999738</td>
<td>0.023261</td>
</tr>
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<th>Random Forest</th>
<td>0.112131</td>
<td>0.166166</td>
<td>0.407635</td>
<td>0.999717</td>
<td>1.550524</td>
</tr>
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<th>Gradient Boosting</th>
<td>0.144767</td>
<td>0.200737</td>
<td>0.448037</td>
<td>0.999658</td>
<td>0.606711</td>
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<th>AdaBoost Regressor</th>
<td>2.134196</td>
<td>5.185761</td>
<td>2.277227</td>
<td>0.991169</td>
<td>0.229681</td>
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<th>Extra Trees Regressor</th>
<td>0.104615</td>
<td>0.147782</td>
<td>0.384425</td>
<td>0.999748</td>
<td>0.601528</td>
</tr>
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<th>XGBoost Regressor</th>
<td>0.230774</td>
<td>0.834200</td>
<td>0.913346</td>
<td>0.998579</td>
<td>0.071940</td>
</tr>
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<th>Support Vector Regressor</th>
<td>8.947584</td>
<td>661.369884</td>
<td>25.717113</td>
<td>-0.126229</td>
<td>0.583814</td>
</tr>
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<th>K-Nearest Neighbors</th>
<td>13.013694</td>
<td>657.503577</td>
<td>25.641833</td>
<td>-0.119645</td>
<td>0.003047</td>
</tr>
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<th>Decision Tree</th>
<td>0.140014</td>
<td>0.222315</td>
<td>0.471503</td>
<td>0.999621</td>
<td>0.025145</td>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">results_df</span><span class="p">[</span><span class="s2">"RMSE"</span><span class="p">],</span> <span class="n">hue</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">dodge</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Root Mean Squared Error"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Model Performance Comparison"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [31]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">results_df</span><span class="p">[</span><span class="s2">"Training Time (s)"</span><span class="p">],</span> <span class="n">hue</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">dodge</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Training Time (Seconds)"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Model Training Time Comparison"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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jm3GbCoa5Detm3bmOLOIFmqnk/Qz/WT3bmAwQvey/+bWgucK5zX/P8bqvaEJOn/NWMftsznkiSpDBH4XXXVVWHSpEkW/2pABMB33313bHdJkmrCNemSJJUZ9rTPYj33LbfcEtZbbz0DdEmSSpzp7pIklRmqm2+00UZxbTbri0mjnjBhQjj11FMb+9AkSdIsGKRLklRmqF5OijWF0SgUx5ZbBOrswS1Jkkqba9IlSZIkSSoRrkmXJEmSJKlEGKRLkiRJklQiKm5NOnuEfvXVV3GPUtbpSZIkSZJUn1hlPnHixNCxY8fQvPnM58orLkgnQO/UqVNjH4YkSZIkqcJ8/vnnYbHFFpvpeyouSGcGPTXOPPPM09iHI0mSJEkqcxMmTIiTxSkenZmKC9JTijsBukG6JEmSJKmh1GTJtYXjJEmSJEkqEY0apD/zzDNhm222iYvnGVG49957Z/k9U6ZMCSeffHJYYoklQuvWrcOSSy4Zrr/++gY5XkmSJEmS6lOjprtPnjw5dOnSJey7777hb3/7W42+Z5dddgnffPNNuO6668IyyywTvv7661ixXZIkSZKkpq5Rg/StttoqftTUI488Ep5++ukwevToMP/888fXmEmXJEmSJKkcNKk16ffdd19Yc801w/nnnx8WXXTRsNxyy4Vjjz02/PLLLzNNj6eSXvZDkiRJkqRS1KSquzODPnLkyNCmTZvwz3/+M4wbNy4ceuih4fvvvw833HBDtd8zYMCAcMYZZzT4sUqSJEmSVNYz6aw9p8DcbbfdFtZaa62w9dZbh4EDB4abbrpphrPpJ554Yhg/fnz+g/3RJUmSJEkqRU1qJn2RRRaJae7t2rXLv7bCCiuEXC4Xvvjii7DssstO9z1UgOdDkiRJkqRS16Rm0tddd93w1VdfhUmTJuVf++CDD0Lz5s3DYost1qjHJkmSJElSkw7SCbbfeOON+IFPPvkk/n3MmDH5VPUePXrk37/HHnuEBRZYIOyzzz7hnXfeifusH3fccXELtznnnLPRfg5JkiRJkpp8kD5q1Kiw2mqrxQ/06dMn/r1fv37xc/ZATwE75pprrvD444+Hn376KVZ533PPPcM222wTLrnkkkb7GSRJkiRJKpZmORZ0VxC2YGNNO0Xk5plnnsY+HEmSJElSmZtQizi0Sa1JlyRJkiSpnBmkS5IkSZJUIgzSJUmSJEkqEQbpkiRJkiSVCIN0SZIkSZJKhEG6JEmSJEklwiBdkiRJkqQS0bKxD0CSSkH/qzZu7EMoKaccNKKxD0GSJKkiOZMuSZIkSVKJMEiXJEmSJKlEGKRLkiRJklQiDNIlSZIkSSoRBumSJEmSJJUIg3RJkiRJkkqEQbokSZIkSSXCIF2SJEmSpBJhkC5JkiRJUokwSJckSZIkqUQYpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSSoRBuiRJkiRJJcIgXZIkSZKkEmGQLkmSJElSiTBIlyRJkiSpRBikS5IkSZJUIgzSJUmSJEkqEQbpkiRJkiSVCIN0SZIkSZJKhEG6JEmSJEklwiBdkiRJkqQSYZAuSZIkSVKJMEiXJEmSJKlEGKRLkiRJklQiDNIlSZIkSSoRBumSJEmSJJUIg3RJkiRJkkqEQbokSZIkSSXCIF2SJEmSpBJhkC5JkiRJUokwSJckSZIkqUQYpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSSkSjBunPPPNM2GabbULHjh1Ds2bNwr333lvj733uuedCy5Ytw6qrrlqvxyhJkiRJUkUE6ZMnTw5dunQJgwcPrtX3/fTTT6FHjx5h0003rbdjkyRJkiSpobUMjWirrbaKH7V18MEHhz322CO0aNFilrPvU6ZMiR/JhAkT6nSskiRJkiTVtya3Jv2GG24Io0ePDqeddlqN3j9gwIDQrl27/EenTp3q/RglSZIkSSr7IP3DDz8MJ5xwQrj11lvjevSaOPHEE8P48ePzH59//nm9H6ckSZIkSU0u3b02pk6dGlPczzjjjLDccsvV+Ptat24dPyRJkiRJKnVNJkifOHFiGDVqVHj99ddD796942vTpk0LuVwuzqo/9thjYZNNNmnsw5QkSZIkqfyD9HnmmSe89dZbVV67/PLLw/Dhw8Pdd98dllpqqUY7NkmSJEmSmnyQPmnSpPDRRx/lP//kk0/CG2+8Eeaff/6w+OKLx/XkX375Zbj55ptD8+bNQ+fOnat8f/v27UObNm2me12SJEmSpKaoUYN00tc33njj/Od9+vSJf/bs2TPceOON4euvvw5jxoxpxCOUJEmSJKnhNMuxqLuCsE86W7FR6Z0UeklC/6v+f8BQIZxy0IjGPgRJkqSKjEOb1BZskiRJkiSVM4N0SZIkSZJKhEG6JEmSJEklwiBdkiRJkqQSYZAuSZIkSVKJMEiXJEmSJKlEGKRLkiRJklQiDNIlSZIkSSoRBumSJEmSJJUIg3RJkiRJkkqEQbokSZIkSSXCIF2SJEmSpBJhkC5JkiRJUokwSJckSZIkqUQYpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSSoRBuiRJkiRJJcIgXZIkSZKkEmGQLkmSJElSiTBIlyRJkiSpRBikS5IkSZJUIgzSJUmSJEkqEQbpkiRJkiSVCIN0SZIkSZJKhEG6JEmSJEklwiBdkiRJkqQSYZAuSZIkSVKJMEiXJEmSJKlEGKRLkiRJklQiDNIlSZIkSSoRBumSJEmSJJUIg3RJkiRJkkqEQbokSZIkSSXCIF2SJEmSpBJhkC5JkiRJUokwSJckSZIkqUQYpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSSoRBuiRJkiRJJcIgXZIkSZKkEmGQLkmSJElSiWjUIP2ZZ54J22yzTejYsWNo1qxZuPfee2f6/nvuuSdsvvnmYaGFFgrzzDNP6Nq1a3j00Ucb7HglSZIkSSrbIH3y5MmhS5cuYfDgwTUO6gnSH3roofDqq6+GjTfeOAb5r7/+er0fqyRJkiRJ9a1laERbbbVV/KipQYMGVfn8nHPOCcOGDQv3339/WG211erhCCVJkiRJqpAgfXZNmzYtTJw4Mcw///wzfM+UKVPiRzJhwoQGOjpJkiRJkiqocNyFF14YJk2aFHbZZZcZvmfAgAGhXbt2+Y9OnTo16DFKkiRJklT2Qfrtt98ezjjjjHDnnXeG9u3bz/B9J554Yhg/fnz+4/PPP2/Q45QkSZIkqazT3YcMGRL233//cNddd4XNNttspu9t3bp1/JAkSZIkqdQ1uZn0O+64I+yzzz7xz+7duzf24UiSJEmSVB4z6awn/+ijj/Kff/LJJ+GNN96IheAWX3zxmKr+5Zdfhptvvjmf4t6zZ89w8cUXh7XXXjuMHTs2vj7nnHPG9eaSJEmSJDVljTqTPmrUqLh1Wto+rU+fPvHv/fr1i59//fXXYcyYMfn3X3311eH3338Phx12WFhkkUXyH0ceeWSj/QySJEmSJJXFTPpGG20UcrncDL9+4403Vvn8qaeeaoCjkiRJkiSpcTS5NemSJEmSJJUrg3RJkiRJkkqEQbokSZIkSSXCIF2SJEmSpBJhkC5JkiRJUokwSJckSZIkqUQYpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSSoRBuiRJkiRJJcIgXZIkSZKkEmGQLkmSJElSiTBIlyRJkiSpRBikS5IkSZJUIgzSJUmSJEkqEQbpkiRJkiSVCIN0SZIkSZJKhEG6JEmSJEklwiBdkiRJkqQSYZAuSZIkSVKJMEiXJEmSJKlEtKztN3zyySfh2WefDZ999ln4+eefw0ILLRRWW2210LVr19CmTZv6OUpJkiRJkipAjYP02267LVx88cVh1KhRoUOHDqFjx45hzjnnDD/88EP4+OOPY4C+5557hr59+4Yllliifo9akiRJkqRKDdKZKW/VqlXo1atX+Mc//hE6depU5etTpkwJL7zwQhgyZEhYc801w+WXXx523nnn+jpmSZIkSZIqN0g/99xzQ7du3Wb49datW4eNNtoofpx99tnh008/LeYxSpIkSZJUEWoUpM8sQC+0wAILxA9JkiRJklTP1d1fe+218NZbb+U/HzZsWNh+++3DSSedFH799dfa/nOSJEmSJKmuQfpBBx0UPvjgg/j30aNHh9122y20bds23HXXXeH444+v7T8nSZIkSZLqGqQToK+66qrx7wTmG2ywQbj99tvDjTfeGIvKSZIkSZKkBgrSc7lcmDZtWvz7E088Ebbeeuv4dyq+jxs3ro6HIUmSJEmSah2ks8Va//79wy233BKefvrp0L179/j6J598EvdPlyRJkiRJDRSkDxo0KBaP6927dzj55JPDMsssE1+/++67wzrrrFPHw5AkSZIkSTXagi1rlVVWqVLdPbngggtCixYtinVckiRJkiRVnFoH6TPSpk2bYv1TkiRJkiRVpBoF6fPNN19o1qxZjf7BH374YXaPSZIkSZKkitSypuvQk++//z4WjuvWrVvo2rVrfO2FF14Ijz76aDj11FPr70glSZIkSSpzNQrSe/bsmf/7jjvuGM4888xYOC454ogjwmWXXRa3ZDv66KPr50glSZIkSSpzta7uzoz5lltuOd3rvEaQLkmSJEmSGihIX2CBBcKwYcOme53X+JokSZIkSWqg6u5nnHFG2H///cNTTz0V1l577fjaSy+9FB555JFwzTXX1PEwJEmSJElSrYP0Xr16hRVWWCFccskl4Z577omv8fnIkSPzQbskSZIkSWqgfdIJxm+77ba6fKskSZIkSSpmkD5t2rTw0UcfhW+//Tb+PWuDDTaoyz8pSZIkSVLFq3WQ/uKLL4Y99tgjfPbZZyGXy1X5WrNmzcLUqVOLeXySJEmSJFWMWgfpBx98cFhzzTXDgw8+GBZZZJEYmEuSJEmSpEbYgu3DDz8M55xzTiwWN++884Z27dpV+aiNZ555JmyzzTahY8eOMdi/9957Z/k9VJVfffXVQ+vWrcMyyywTbrzxxtr+CJIkSZIklUeQTtE41qMXw+TJk0OXLl3C4MGDa/T+Tz75JHTv3j1svPHG4Y033ghHHXVU3A7u0UcfLcrxSJIkSZLUpNLdDz/88HDMMceEsWPHhpVXXjnMMcccVb6+yiqr1Pjf2mqrreJHTV155ZVhqaWWCn//+9+rbP120UUXhW7dutXip5AkSZIkqQyC9B133DH+ue++++ZfI1WdInL1XTjuhRdeCJtttlmV1wjOmVGfkSlTpsSPZMKECfV2fJIkSZIkNWiQTsp5Y2H2vkOHDlVe43MC719++SXMOeec033PgAEDwhlnnNGARylJkiRJUgMF6UsssURoSk488cTQp0+f/OcE9J06dWrUY5IkSZIkqShBOj7++OMwaNCg8O6778bPV1xxxXDkkUeGP/7xj6E+LbzwwuGbb76p8hqfzzPPPNXOooMq8HxIkiRJklR21d2ppE5Q/vLLL8cicXy89NJLYaWVVgqPP/54qE9du3YNTz75ZJXX+H/yuiRJkiRJFTeTfsIJJ4Sjjz46nHvuudO93rdv37D55pvX+N+aNGlSle3cWO/O1mrzzz9/WHzxxWOq+pdffhluvvnm+PWDDz44XHbZZeH444+PheuGDx8e7rzzzvDggw/W9seQJEmSJKnpz6ST4r7ffvtN9zpB8zvvvFOrf2vUqFFhtdVWix9g7Th/79evX/z866+/DmPGjMm/n+3XCMiZPWd/dbZiu/baa91+TZIkSZJUmTPpCy20UJztXnbZZau8zmvt27ev1b+10UYbxa3bZuTGG2+s9ntef/31Wv1/JEmSJEkqyyD9gAMOCAceeGAYPXp0WGeddeJrzz33XDjvvPOqVFGXJEmSJEn1HKSfeuqpYe65546p5qwZR8eOHcPpp58ejjjiiNr+c5IkSZIkqa5BerNmzWLhOD4mTpwYXyNolyRJkiRJDRykU4H9999/j2vSs8H5hx9+GOaYY46w5JJLzuYhSZIkSZJUmWpd3b1Xr17h+eefn+519krna5IkSZIkqYGCdCqrr7vuutO9/pe//CVWeJckSZIkSQ0UpLMmPa1Fzxo/fnyYOnVqHQ9DkiRJkiTVOkjfYIMNwoABA6oE5Pyd19Zbb71iH58kSZIkSRWj1oXj2A+dQP1Pf/pTWH/99eNrzz77bJgwYUIYPnx4fRyjJEmSJEkVodYz6SuuuGL417/+FXbZZZfw7bffxtT3Hj16hPfeey907ty5fo5SkiRJkqQKUOuZdHTs2DGcc845xT8aSZIkSZIqWK1n0lN6+1577RXWWWed8OWXX8bXbrnlljBy5MhiH58kSZIkSRWj1kH6P/7xj9CtW7cw55xzhtdeey1MmTIlX93d2XVJkiRJkhowSO/fv3+48sorwzXXXBPmmGOO/OvsnU7QLkmSJEmSGihIf//992N190Lt2rULP/30Ux0PQ5IkSZIk1TpIX3jhhcNHH3003eusR1966aWLdVySJEmSJFWcWgfpBxxwQDjyyCPDSy+9FJo1axa++uqrcNttt4Vjjz02HHLIIfVzlJIkSZIkVYBab8F2wgknhGnTpoVNN900/PzzzzH1vXXr1jFIP/zww+vnKCVJkiRJqgC1DtKZPT/55JPDcccdF9PeJ02aFFZcccUw11xz1c8RSpIkSZJUIeq0TzpatWoVg/MOHTqEMWPGxNl1SZIkSZLUAEH69ddfHwYOHFjltQMPPDAWi1t55ZVD586dw+effz4bhyJJkiRJUmWrcZB+9dVXh/nmmy//+SOPPBJuuOGGcPPNN4dXXnklzDvvvOGMM86or+OUJEmSJKns1XhN+ocffhjWXHPN/OfDhg0L2223Xdhzzz3j5+ecc07YZ5996ucoJUmSJEmqADWeSf/ll1/CPPPMk//8+eefj5XdE9Lex44dW/wjlCRJkiSpQtQ4SF9iiSXCq6++Gv8+bty48O9//zusu+66+a8ToLdr165+jlKSJEmSpApQ43T3nj17hsMOOywG58OHDw/LL798WGONNarMrFM8TpIkSZIk1XOQfvzxx4eff/453HPPPWHhhRcOd911V5WvP/fcc2H33Xev42FIkiRJkqRmuVwuFyrIhAkTYlr++PHjq6yxl1TZ+l+1cWMfQkk55aARjX0IkiRJFRmH1mhNeoXF8ZIkSZIkNYoaBekrrbRSGDJkSPj1119nuU3bIYccEs4999xiHZ8kSZIkSRWjRmvSL7300tC3b99w6KGHhs033zzul96xY8fQpk2b8OOPP4Z33nknjBw5MhaV6927dwzUJUmSJElSPQTpm266aRg1alQMxIcOHRpuu+228Nlnn8W90xdccMGw2mqrhR49eoQ999wzzDfffLU8BEmSJEmSVKvq7lhvvfXihyRJkiRJaqQ16ZIkSZIkqf4ZpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSmnKQ/vHHH4dTTjkl7L777uHbb7+Nrz388MNxn3RJkiRJktRAQfrTTz8dVl555fDSSy+Fe+65J0yaNCm+/uabb4bTTjutjochSZIkSZJqHaSfcMIJoX///uHxxx8PrVq1yr++ySabhBdffLHYxydJkiRJUsWodZD+1ltvhR122GG619u3bx/GjRtXrOOSJEmSJKni1DpIn3feecPXX3893euvv/56WHTRRYt1XJIkSZIkVZxaB+m77bZb6Nu3bxg7dmxo1qxZmDZtWnjuuefCscceG3r06FE/RylJkiRJUgWodZB+zjnnhOWXXz506tQpFo1bccUVwwYbbBDWWWedWPFdkiRJkiTVTcvafgPF4q655ppw6qmnhrfffjsG6quttlpYdtll63gIkiRJkiSpzvukY/HFFw9bb7112GWXXWY7QB88eHBYcsklQ5s2bcLaa68dXn755Zm+f9CgQeFPf/pTmHPOOeOM/tFHHx3+85//zNYxSJIkSZLU5GbSc7lcuPvuu8OIESPCt99+G9ekZ7F3em0MHTo09OnTJ1x55ZUxQCcA79atW3j//fdjxfhCt99+e9wG7vrrr48p9h988EHo1atXXB8/cODA2v44kiRJkiQ13Zn0o446Kuy9997hk08+CXPNNVdo165dlY/aIrA+4IADwj777BPXtxOst23bNgbh1Xn++efDuuuuG/bYY484+77FFluE3XfffZaz75IkSZIkld1M+i233BJny0l1n12//vprePXVV8OJJ56Yf6158+Zhs802Cy+88EK138Ps+a233hqD8rXWWiuMHj06PPTQQ3HgoDpTpkyJH8mECRNm+7glSZIkSSqJIJ3Z8qWXXroo//Nx48aFqVOnhg4dOlR5nc/fe++9ar+HGXS+b7311oup97///ns4+OCDw0knnVTt+wcMGBDOOOOMohyvJEmSJEklle5++umnx6D3l19+CY3hqaeeitvAXX755eG1116Ls/oPPvhgOOuss6p9P7P048ePz398/vnnDX7MkiRJkiTVy0w61dzvuOOOWNSNNeFzzDFHla8TONfUggsuGFq0aBG++eabKq/z+cILL1zt97D1G6nt+++/f/x85ZVXDpMnTw4HHnhgOPnkk2O6fFbr1q3jhyRJkiRJZRek9+zZM64j32uvvWJaOlXV64o919dYY43w5JNPhu233z6+RrV4Pu/du3e13/Pzzz9PF4gT6IP0d0mSJEmSKiZIJ7X80UcfjWvCi4Ht1wj811xzzVgIji3YmBmn2jt69OgRFl100bi2HNtss02sCL/aaqvFLds++uijOLvO6ylYlyRJkiSpIoL0Tp06hXnmmadoB7DrrruG7777LvTr1y+MHTs2rLrqquGRRx7JF5MbM2ZMlZnzU045Jc7e8+eXX34ZFlpooRign3322UU7JkmSJEmSGkOzXC1zxJlJv/TSS+N+5qxJb2rYgo0K9RSRK+Zgg6Smrf9VGzf2IZSUUw4a0diHIEmSVDZqE4fWeiadteisC//jH/8Y2rZtO13huB9++KH2RyxJkiRJkmofpLNmXJIkSZIklUh1d0mSJEmS1EhBOvnzKW+ev8+M67wlSZIkSarHIH2++eYLX3/9dWjfvn2Yd955q90bnfpzvD516tQ6HookSZIkSZWtRkH68OHDw/zzzx//PmKEFX8lSZIkSWq0IH3DDTcMSy+9dHjllVfi3yVJkiRJUvE1r+kbP/30U1PZJUmSJEkqhSBdkiRJkiSV0BZsjz76aGjXrt1M37PtttvO7jFJkiRJklSRWhZzj3Sru0uSJEmS1EDp7mPHjg3Tpk2b4YcBuiRJkiRJDRCkV7c3uiRJkiRJaoQgPZfLFfF/K0mSJEmS6hyksx59zjnnrOnbJUmSJElSfRWOu+GGG2r7b0uSJEmSpFpwn3RJkiRJkkqEQbokSZIkSSXCIF2SJEmSpBJhkC5JkiRJUlMrHJfssMMO1e6Zzmtt2rQJyyyzTNhjjz3Cn/70p2IdoyRJkiRJFaHWM+nt2rULw4cPD6+99loMzPl4/fXX42u///57GDp0aOjSpUt47rnn6ueIJUmSJEkqU7WeSV944YXjTPlll10Wmjf/b4w/bdq0cOSRR4a55547DBkyJBx88MGhb9++YeTIkfVxzJIkSZIklaVaz6Rfd9114aijjsoH6PEfad48HH744eHqq6+OM+u9e/cOb7/9drGPVZIkSZKkslbrIJ2U9vfee2+613lt6tSp8e+sTa9u3bokSZIkSSpiuvvee+8d9ttvv3DSSSeFP//5z/G1V155JZxzzjmhR48e8fOnn346rLTSSrX9pyVJkiRJqmi1DtIvuuii0KFDh3D++eeHb775Jr7G50cffXRch44tttgibLnllsU/WkmSJEmSylitg/QWLVqEk08+OX5MmDAhvjbPPPNUec/iiy9evCOUJEmSJKlC1DpIzyoMziVJkiRJUgMWjiPFnXXpHTt2DC1btowz69kPSZIkSZLUQDPpvXr1CmPGjAmnnnpqWGSRRaziLkmSJElSYwXpI0eODM8++2xYddVVi3UMkiRJkiSpLununTp1Crlcrn6ORpIkSZKkClbrIH3QoEHhhBNOCJ9++mn9HJEkSZIkSRWq1unuu+66a/j555/DH//4x9C2bdswxxxzVPn6Dz/8UMzjkyRJkiSpYrSsy0y6JEmSJEkqgSC9Z8+e9XAYkiRJkiSpRkH6hAkTwjzzzJP/+8yk90mSJEmSpHoI0uebb77w9ddfh/bt24d555232r3RqfjO61OnTq3lIUiSJEmSpBoH6cOHDw/zzz9//PuIESNsOUmSJEmSGitI33DDDav9uyRJkiRJasTCcfjpp5/Cyy+/HL799tswbdq0Kl/r0aNHsY5NkiRJkqSKUusg/f777w977rlnmDRpUiwSl12fzt8N0iVJkiRJqpvmtf2GY445Juy7774xSGdG/ccff8x//PDDD3U8DEmSJEmSVOsg/csvvwxHHHFEaNu2bf0ckSRJkiRJFarWQXq3bt3CqFGj6udoJEmSJEmqYLUO0rt37x6OO+64cPrpp4d//OMf4b777qvyUReDBw8OSy65ZGjTpk1Ye+21Y1G6mSHN/rDDDguLLLJIaN26dVhuueXCQw89VKf/tyRJkiRJTbZw3AEHHBD/PPPMM6f7GoXjpk6dWqt/b+jQoaFPnz7hyiuvjAH6oEGD4mz9+++/H9q3bz/d+3/99dew+eabx6/dfffdYdFFFw2fffZZmHfeeWv7o0iSJEmS1LSD9MIt12bXwIEDY+C/zz77xM8J1h988MFw/fXXhxNOOGG69/M6Beqef/75MMccc8TXmIWfkSlTpsSPZMKECUU9fklS9Ta9pVdjH0JJeXLvGxv7ECRJUrnuk14szIq/+uqr4cQTT8y/1rx587DZZpuFF154odrvIaW+a9euMd192LBhYaGFFgp77LFH6Nu3b2jRosV07x8wYEA444wz6vXnkCRJTU+3G25t7EMoKY/us1djH4IkqaZB+iWXXBIOPPDAuGacv88Mld9raty4cTE9vkOHDlVe5/P33nuv2u8ZPXp0GD58eNyrnXXoH330UTj00EPDb7/9Fk477bTp3s8AAOn02Zn0Tp061fgYJUmSJEkqqSD9oosuikExQTp/nxHWpNcmSK8L0u1Zj3711VfHmfM11lgjbgt3wQUXVBukU1iOD0mSJEmSyiJI/+STT6r9++xacMEFY6D9zTffVHmdzxdeeOFqv4eK7qxFz6a2r7DCCmHs2LExfb5Vq1ZFOz5JkiRJkkp6C7ZiIqBmJvzJJ5+sMlPO56w7r866664bU9yzBew++OCDGLwboEuSJEmSKq5w3BdffBELuI0ZMybOXhdWa68N1ov37NkzrLnmmmGttdaKW7BNnjw5X+29R48ecZs1CsDhkEMOCZdddlk48sgjw+GHHx4+/PDDcM4559R7mr0kSZLUkK69sWq2aaXbv1fVOlZSuap1kM4s97bbbhuWXnrpWNytc+fO4dNPPw25XC6svvrqtT6AXXfdNXz33XehX79+MWV91VVXDY888ki+mBwDAVR8Tyj69uijj4ajjz46rLLKKjGAJ2CnurskSZIkSRUVpFMt/dhjj43bms0999zhH//4RyzkRmG5Lbfcsk4H0bt37/hRnaeeemq610iFf/HFF+v0/5IkSZIkqWzWpL/77rsxBR0tW7YMv/zyS5hrrrnCmWeeGc4777z6OEZJkiRJkipCrYP0P/zhD/l16BRr+/jjj6vsey5JkiRJkhoo3f0vf/lLGDlyZNz2bOuttw7HHHNMeOutt8I999wTvyZJkiRJkhooSKd6+6RJk+LfWZfO34cOHRqWXXbZWld2lyRJkiRJdQzSp06dGrdfo6p6Sn2/8sora/NPSJIkSZKkYqxJb9GiRdhiiy3Cjz/+WJtvkyRJkiRJ9VE4jn3RR48eXdtvkyRJkiRJxQ7S+/fvH/dJf+CBB8LXX38dJkyYUOVDkiRJkiTV85p09kGnkjsV3bHtttuGZs2a5b+ey+Xi56xblyRJkiRJ9RikU8n94IMPDiNGjKjD/0aSJEmSJBUtSGemHBtuuGFNv0WSJEmSJNXXmvRserskSZIkSWrEfdKXW265WQbqP/zww+wekyRJkiRJFalWQTrr0tu1a1d/RyNJkiRJUgWrVZC+2267hfbt29ff0UiSJEmSVMFqvCbd9eiSJEmSJJVIkJ6qu0uSJEmSpEZOd582bVo9HYIkSZIkSar1FmySJEmSJKn+GKRLkiRJklQiDNIlSZIkSSoRBumSJEmSJJUIg3RJkiRJkkqEQbokSZIkSSXCIF2SJEmSpBJhkC5JkiRJUokwSJckSZIkqUQYpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSSoRBuiRJkiRJJcIgXZIkSZKkEmGQLkmSJElSiTBIlyRJkiSpRBikS5IkSZJUIgzSJUmSJEkqEQbpkiRJkiSVCIN0SZIkSZJKhEG6JEmSJEklwiBdkiRJkqQSYZAuSZIkSVKJMEiXJEmSJKlEGKRLkiRJklQiDNIlSZIkSSoRJRGkDx48OCy55JKhTZs2Ye211w4vv/xyjb5vyJAhoVmzZmH77bev92OUJEmSJKnsg/ShQ4eGPn36hNNOOy289tproUuXLqFbt27h22+/nen3ffrpp+HYY48N66+/foMdqyRJkiRJZR2kDxw4MBxwwAFhn332CSuuuGK48sorQ9u2bcP1118/w++ZOnVq2HPPPcMZZ5wRll566QY9XkmSJEmSyjJI//XXX8Orr74aNttss/8/oObN4+cvvPDCDL/vzDPPDO3btw/77bffLP8fU6ZMCRMmTKjyIUmSJElSKWrUIH3cuHFxVrxDhw5VXufzsWPHVvs9I0eODNddd1245ppravT/GDBgQGjXrl3+o1OnTkU5dkmSJEmSyi7dvTYmTpwY9t577xigL7jggjX6nhNPPDGMHz8+//H555/X+3FKkiRJklQXLUMjItBu0aJF+Oabb6q8zucLL7zwdO//+OOPY8G4bbbZJv/atGnT4p8tW7YM77//fvjjH/9Y5Xtat24dPyRJkiRJKnWNOpPeqlWrsMYaa4Qnn3yyStDN5127dp3u/csvv3x46623whtvvJH/2HbbbcPGG28c/24quyRJkiSpKWvUmXSw/VrPnj3DmmuuGdZaa60waNCgMHny5FjtHT169AiLLrpoXFvOPuqdO3eu8v3zzjtv/LPwdUmSJEmSmppGD9J33XXX8N1334V+/frFYnGrrrpqeOSRR/LF5MaMGRMrvkuSJEmSVO4aPUhH796940d1nnrqqZl+74033lhPRyVJkiRJUsNyilqSJEmSpBJhkC5JkiRJUokwSJckSZIkqUQYpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSSoRBuiRJkiRJJcIgXZIkSZKkEmGQLkmSJElSiTBIlyRJkiSpRBikS5IkSZJUIgzSJUmSJEkqEQbpkiRJkiSVCIN0SZIkSZJKhEG6JEmSJEklwiBdkiRJkqQSYZAuSZIkSVKJMEiXJEmSJKlEGKRLkiRJklQiDNIlSZIkSSoRBumSJEmSJJUIg3RJkiRJkkqEQbokSZIkSSXCIF2SJEmSpBJhkC5JkiRJUokwSJckSZIkqUQYpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSSoRBuiRJkiRJJcIgXZIkSZKkEmGQLkmSJElSiTBIlyRJkiSpRBikS5IkSZJUIgzSJUmSJEkqEQbpkiRJkiSVCIN0SZIkSZJKhEG6JEmSJEklwiBdkiRJkqQSYZAuSZIkSVKJaNnYByBJkmpm8xv/3tiHUDIe73VMYx+CJEn1wpl0SZIkSZJKhEG6JEmSJEklwiBdkiRJkqQSURJB+uDBg8OSSy4Z2rRpE9Zee+3w8ssvz/C911xzTVh//fXDfPPNFz8222yzmb5fkiRJkqSmotGD9KFDh4Y+ffqE0047Lbz22muhS5cuoVu3buHbb7+t9v1PPfVU2H333cOIESPCCy+8EDp16hS22GKL8OWXXzb4sUuSJEmSVFZB+sCBA8MBBxwQ9tlnn7DiiiuGK6+8MrRt2zZcf/311b7/tttuC4ceemhYddVVw/LLLx+uvfbaMG3atPDkk082+LFLkiRJklQ2Qfqvv/4aXn311Ziynj+g5s3j58yS18TPP/8cfvvttzD//PNX+/UpU6aECRMmVPmQJEmSJKkUNWqQPm7cuDB16tTQoUOHKq/z+dixY2v0b/Tt2zd07NixSqCfNWDAgNCuXbv8B+nxkiRJkiSVokZPd58d5557bhgyZEj45z//GYvOVefEE08M48ePz398/vnnDX6ckiRJkiTVRMvQiBZccMHQokWL8M0331R5nc8XXnjhmX7vhRdeGIP0J554IqyyyiozfF/r1q3jhyRJkiRJpa5RZ9JbtWoV1lhjjSpF31IRuK5du87w+84///xw1llnhUceeSSsueaaDXS0kiRJkiSV8Uw62H6tZ8+eMdhea621wqBBg8LkyZNjtXf06NEjLLroonFtOc4777zQr1+/cPvtt8e91dPa9bnmmit+SJIkSZLUVDV6kL7rrruG7777LgbeBNxsrcYMeSomN2bMmFjxPbniiitiVfiddtqpyr/DPuunn356gx+/JEmSJEllE6Sjd+/e8aM6Tz31VJXPP/300wY6KkmSJEmSGlaTru4uSZIkSVI5MUiXJEmSJKlEGKRLkiRJklQiDNIlSZIkSSoRBumSJEmSJJUIg3RJkiRJkkqEQbokSZIkSSXCIF2SJEmSpBJhkC5JkiRJUokwSJckSZIkqUQYpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSSoRBuiRJkiRJJcIgXZIkSZKkEmGQLkmSJElSiTBIlyRJkiSpRBikS5IkSZJUIgzSJUmSJEkqEQbpkiRJkiSVCIN0SZIkSZJKhEG6JEmSJEklwiBdkiRJkqQSYZAuSZIkSVKJMEiXJEmSJKlEGKRLkiRJklQiDNIlSZIkSSoRBumSJEmSJJWIlo19AJIkSSoP2944srEPoWTc12u9xj4ESU2UM+mSJEmSJJUIg3RJkiRJkkqEQbokSZIkSSXCIF2SJEmSpBJhkC5JkiRJUokwSJckSZIkqUQYpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSJEmSSoRBuiRJkiRJJaJlYx+AKsOoc7dq7EMoKWue8HBjH4IkSZKkEuRMuiRJkiRJJcKZdKmJuv3iTRv7EErKHkc+2diHIEmSVFG+POnpxj6EkrLoORsW5d9xJl2SJEmSpBJhkC5JkiRJUokoiSB98ODBYckllwxt2rQJa6+9dnj55Zdn+v677rorLL/88vH9K6+8cnjooYca7FglSZIkSSrbIH3o0KGhT58+4bTTTguvvfZa6NKlS+jWrVv49ttvq33/888/H3bfffew3377hddffz1sv/328ePtt99u8GOXJEmSJKmsgvSBAweGAw44IOyzzz5hxRVXDFdeeWVo27ZtuP7666t9/8UXXxy23HLLcNxxx4UVVlghnHXWWWH11VcPl112WYMfuyRJkiRJZVPd/ddffw2vvvpqOPHEE/OvNW/ePGy22WbhhRdeqPZ7eJ2Z9yxm3u+9995q3z9lypT4kYwfPz7+OWHChJke29hzr6rVz1LOFj7hoNn+Nyb95/eiHEu5mNX5VxM/26ZFbdP//GJ7Fvsc/f2XX4tyLOWiOG36n6IcSzkoTnv+UpRjKRfFaNPffplclGMpB8Voz19+mViUYykXEybMOdv/xtvnflWUYykHnU/oONv/xsQpXvM1ve7T13K5XJilXCP68ssvOcLc888/X+X14447LrfWWmtV+z1zzDFH7vbbb6/y2uDBg3Pt27ev9v2nnXZa/H/44Ycffvjhhx9++OGHH3744UdoxI/PP/98lnFy2e+Tzix9duZ92rRp4YcffggLLLBAaNasWShljLZ06tQpfP7552GeeeZp7MNp8mzP4rNNi8v2LD7btLhsz+KzTYvL9iw+27T4bNPKbM9cLhcmTpwYOnacdQZDowbpCy64YGjRokX45ptvqrzO5wsvvHC138PrtXl/69at40fWvPPOG5oSTrZSPuGaGtuz+GzT4rI9i882LS7bs/hs0+KyPYvPNi0+27Ty2rNdu3alXziuVatWYY011ghPPvlklZluPu/atWu138Pr2ffj8ccfn+H7JUmSJElqKho93Z1U9J49e4Y111wzrLXWWmHQoEFh8uTJsdo7evToERZddNEwYMCA+PmRRx4ZNtxww/D3v/89dO/ePQwZMiSMGjUqXH311Y38k0iSJEmS1MSD9F133TV89913oV+/fmHs2LFh1VVXDY888kjo0KFD/PqYMWNixfdknXXWCbfffns45ZRTwkknnRSWXXbZWNm9c+fOodyQps/+8YXp+qob27P4bNPisj2LzzYtLtuz+GzT4rI9i882LT7btLhal2F7NqN6XGMfhCRJkiRJauQ16ZIkSZIk6f8ZpEuSJEmSVCIM0iVJkiRJKhEG6ZIkSZIklQiDdEmSVJamTZuW//uvv/7aqMdSCW38888/N+qxNDXWbi7N81gqBQbpqhUfKMVjW5aOSv5dVNcxmThxYqMcSyWxQ9gw0haubNl6xx13hClTpjT2IZVtG7OVbv/+/R0MqcVzp1mzZvHvL7zwQmMfTqj030U6j2+55ZbGPpyS5DOr4Rmkq1YXaHqg/P777+G3334LlR7gFKMtmXmYPHlyYx9SRT9s0u+iEtEx+eyzz8KgQYPi53fddVfo0aNHGD9+fGMfWllLHcIXX3wxfPXVV419OGV9jT/++OPhsssuCyussEJZ7aFbSm08bNiwcNttt4W//e1voVWrVo16XE0tQD/llFPCfvvtFz799NPGPqxQ6b+LCy64IBx00EHhjTfeaOzDKrlrPT2zHnroofDKK69UbNCe+1/MM3Xq1PCf//ynXv9fBumqsXSBnnvuuWHXXXcN3bp1C88++2xFBziz25ZnnXVWbMctttgiXH755Y19WBU3Yn7rrbeGU089Ndx+++0V20FiwO2KK64IN9xwQ+jZs2e8trfbbrvQrl27xj60spTt2AwfPjxsvfXW4eabbw7fffddox5XuUnX+LXXXhs+/PDDOMu71lprNfZhlWUb33fffbEvwODemmuuGTuvmrnUbxo1alR466234nm65JJLNvZhVaRsNgMD1vfee29YddVVG/uwSrLPdMIJJ4RDDz003lN/+umnUKkDOg899FDsL3G/Y5Dt/vvvr5f/n0G6atWpHDBgQBg4cGDo1KlTHC3fbLPNwnXXXZefVVfN25J2ZHaHNuzcuXPo3bt36Nu3b6MeXyWNmPOwOeqoo8Kjjz4aTj/99HDEEUeE1157LVSali1bhtNOOy0sscQSMc1vl112Cb169Ypfs7Ndf52dSy+9NHbQSb8+//zz433UQL24fvjhh3DRRRfFe+uYMWPia2Z+FRcZN9w7eZ599NFH8bUWLVrYzjXA4ByDRxMmTAirrLJKYx9ORWOgiRn0Bx98MCy22GLxtUqdKS6U+kxnn312uOmmm+Lkxk477RTmn3/+UIltcd9994Wdd945Dqr16dMnPPPMM+H444+vl+wLg3TNUupU0slh5Ix0WFJjH3nkkRjoHHzwweHGG280UK9FW3Ixt23bNs5eEiBdeeWVcTb34osvjhe76vdh869//St8/vnn8Rx++eWXw3nnnRcD0mOOOSa8+uqroVKkjjQDbvPOO2/YfPPNwxdffBEH41Jn20C9+OffmWeeGTM4lltuuTBkyJA4MEKgfv3114dx48Y19mE2WYWB4XzzzRfuvPPOeF6Tjs0zjN+Bne/itTEZN8yid+3aNabAEuSAdjZQn7lffvklvP/+++HNN98M77zzTmMfTkVj4mnFFVcMY8eODQ8//HC+v1bJ94rsz06dGiY0GFRab731wrfffhuefvrpcMghh8TnWaUs2Rw3bly48MILwznnnBPrb+yxxx7h3XffjRlx9ZJ9kZNq4P777881a9Yst9hii+WefvrpKl877bTTcnPMMUfummuuyU2ZMqXRjrGpeOGFF2JbzjXXXLn77ruvytfuuOOOXOvWrXPHH398ox1fuRsyZEhunXXWyW2++ea5SZMm5V9/4IEHcltvvXVu4403zr366qu5cjdt2rT456hRo3KfffZZ/PzHH3/M9e7dO7f22mvnzjnnnCrv/+677xrpSMvLTz/9lFt99dVzF154YZXXuebbtm2bGzBgQO6bb75ptONrqqZOnZr/+6+//pqbOHFi/vMPP/wwt8Yaa+T+9Kc/5caNGzfd+1Uz2TbjXsHzPt1DR48enVt11VVzm222We7JJ5+c7j5T6WZ0vvE8WmGFFXK77bZb7s0332zw46pEM/pdvPPOO7k99tgjt9pqq+VuvPHGWb6/nGWv28ceeyz373//O7fhhhvmTj755Nztt9+e23nnnXPrr79+7i9/+UtulVVWyR122GEVca1PnDgxPkt4pnDPW3TRRXMHHHBA/uuPP/54fL1YnElXjTBKdOSRR4Yvv/wyjB49Or6WRslJFWZNxoEHHhhnJjVzpLYzY84oZWF69W677RZT4CheMnjw4EY7xnJGSjHF+phNz85adu/ePa61+sMf/hDXGjHDUe5p///85z/jtU3q9ffffx9n008++eTw5z//OaZ0MVoMRs8ZMbcydnHS3clOSFk1qfAM2RwbbrhhXALDsoNKXO9XjKJGtOOOO+4YunTpEjO9mO1ZZpll4oz6XHPNFWeBuO55vzO9dVuqQaYN6Z6sx+S5//zzz4ellloq3H333fH+St2aESNGxPdas6bq+UmhSNJjR44cGT+nBgjn6QcffBD7BaxPV8P8Llh7ThYjz7+vv/46FpYkw4kZ9auvvjr2xVBp94psYWP6AywDICtp3XXXjVkG++67b1h22WXjTDLr+NdYY40qSwnLTe5/v3v+ZHkPGTDPPfdcrCW11VZbxZo+IDYiG471+sX8n0tVzGzUcL/99sv94Q9/yD388MPTfe3aa6/N/fbbb/V8dOXRlsxAnH/++XFG/eKLL57u60888YRtWY/tf8stt+S6dOmS+9vf/hZHRLPuvvvu3LHHHpv7/fffc+XsoYceys0555y56667brpZcmZyaYM//vGPcZZn/vnnz7344ouNdqzldv7tsssusV2zM784+OCD42zkwgsvnM+yqYTZiWI56aSTcgsssEDu7LPPzp166qm5zp07x5ndO++8M36da52Zn3nnnTdmNKj2mEmjja+88srciSeemNt+++1jBggzSPj444/jTBOzkZWQkVQbxx13XG7ppZfOLbTQQjErkayt8ePHx69df/31sd32339/260B9OnTJ7fIIovEfsCyyy6ba9euXe6f//xn/Nrbb7+d22uvveJM8eWXX56rVF9//XVu3333zT3yyCP51z744IPp+kybbrpp7phjjsmVm2n/e/b+8ssv8c/UJ+QeSN992223ne75s/LKK+fGjBlTtGMwSNcMO5UEK6RkXnbZZbmXX345/3rPnj1jqnZ1gToMLqdvS1KnTj/99JhK/Pzzz+cmTJgQXz/33HPjxX7JJZdU+2/YlsVpfwJMlhk888wzVX4nPIQJmD766KNq/41yDdQZJOI6ptMIUlZJ9eMhw7KVr776KqZ1keZ26aWXTvdQVu3Pv9dffz33ySefxM95iNMxXG+99XL/+c9/8ucZKYSvvPJKbtddd82ttNJKjXbsTdF7772XW3755XOPPvpo/jXanLbcYostYucydcAJhMr12q5Pn3/+eRxE+sc//pF/7YsvvsgdeOCBcSDvX//6V3yN+0WvXr0qMk14RuhH0UY8/zkHR4wYEQfqCMxTMMAzqVOnTtMtNVJxMWi34IILxvsDz7mff/45d9BBB8UJqLRUg6UH3bt3jwOnlThQetNNN+VatWoV76m0UyEGlxhM2mqrreJgaLn1Vaf973fO84Q+Ij8nkzoMXDCpsc8++8T2oe/+97//PXfooYfm5p577twbb7xR1OMwSFe1mEVjtJwRMkZ9WUPJzETCCcpsRBp51Iwxwkhb7rTTTrnlllsufjCKm2YvmVFnTT+zPyo+AtEll1wy17Fjx9hJ4vfw7bffxq8xi8w6q9133z128isFM7f83ASFY8eOjWuqNtpoo3hudujQIXfkkUc29iE2adlOHfdSOt48wLfccsvczTffHF9/7rnnYied85IgkhF4MhcwaNCg2Hk3yKk5BkCYGXvwwQer/A7obHPds46ykIF67bz//vuxZgpZOFkpQ2Hw4MHTnbOew/9FEHj44YdPd87ybNp7773zrzH54XlZv7i/brLJJvHczLY169G5B6d6FqwtTudvpQXqP/zwQ+6vf/1rnEQqrJ0EXqMPwUBGygQrt/P23nvvjVlCzJzfcMMN8ZnMGnQGJhmwpB4Xz/C11lorBvJvvfVW0Y/BIF3TPUiHDRsWOzspvZVg8pRTTsmtueaasahRsuOOO8ZUQs0YaUKktWXT1/r37x9n0Bj04ObGKC4X+7rrrltxD4L6xiwwAyTMotNZp+gh5zYP6FTkkGUaK664YpVBqHJT3XlFoTwG2siKYYQ4BTFc4xSOSyleqt19NNvWzz77bJyJICDnvkr2AmnAnHPg2ifDhkCee2zq7DAIus0228RZdu8JM39epfYhG4Z7bVo+xMxO+hr3W1KzVbc2njx5cv417p1HH310Pk07IUg/6qijGvw4S1F112y3bt3iYFySAhoG6bnfEhRllVvA01iqGyQ688wzc+3bt89/zn0WZNpxDykMtsp9oGlGPx/XOIE4A0lkfxQiRkjfW24z6T/++GPMtOT6BIE57UAWVlYq8lpf/SWD9ArHQ/X777+fbpSRlLbUYQQpHoccckhcQ5Ue2JVw86oNOi6vvfbadNXaWYPGbGV6cPNn375946xlqoxLwJj9umqPNMLCjg01FDhvs6hkTnCandUgYC3XTlE6n0aOHBkDcLI40kzYl19+GQPJ7PuOOOKIOMNukD57WC7Eer5+/frlXyNbg/OR++sVV1wx3fdwn6D9mfmtrlOkqs+ciy66KFbFTx1Elg+RlZSd+eEey7pTBuxU+zZmyRsDy59++mn8nMEkzl/WpDPIBPoE7JiROrSVLNt2XMMpa+u2226Lg8H0CbLI5mLgzhoJ9fu7oM5Pmixh6Qsp2lQkz+5IxFIjliFV0r0320akdjNjTDZHWh7E/ZNrmwwDKrxX10cthzhg2rRp+Z+LviA/N0E5fSQCcWbQWdaTcD1nz5366rcbpFcw1t6wbqxwBIx1UdzA0kM5nXysSyf15aWXXiq7C7QYASJrUgrbkgfy4osvnm/LNPDBKB1pg4VpRAbodUNHnRHfbPtxo00p3YUj5gMHDowdo9SByn5POWINKRkFzM4SOHIdn3DCCfn2AJkGzDZSQMetgGqHGXJS4tL9kDXnzJrNN9980428p0CdzKRsUMNIPevbeL26NYCafhkLs15cy2nLG+6/DHJwfrOEgwEpsr3Kcc1kQ7UxRQxTnYqEzio1E1LBKDIV+LzS2zjbF+J+sMEGG8TlF7zOkgAylljyQpG4NCjH5yzB8tlfXNn2ZFKEtGTWWTM7zOAS916yPygQx/2DCRZSt+kzVGKflmud7AK2U6MPwDWdMr4IWPmcLSyLvea61DKyhg0bFgfWCcB5hp933nmxD09tgtR/Z9Jyu+22a5DlvgbpFS6dmIwKceKBi5AUWAKfNFIOUoC4gCtplLGubZmKjzAbyXpUgqPsDYFRSkbVrZhdPOkGStprCj4ZcGLNb7bQEVg7+ec//7nK+V2uCAqXWGKJ3FVXXZV/4DLbSJCecM336NEjBjPl9BBuCFzjQ4cOrZJ5lAbudthhh/iAv+uuu6Zb20sdBAZJs51JAqHCzCZNj+uaDiUzXwn315T9wT2YyrukGBOsl+uayfpEQEMbp2JwoOBpumdyTrMsg8CGgqi28f+jACe1Pe6///4qaewU59xzzz3jM4mvc7/NZi1WYnBY31Jq+1NPPVVl5pM+AkEofVqehwTxzBhX4u+CpW60Ecuy+LkZJKaGArWobr311vgeBjdYusXa63Iw9X+/X+5vqY4JPzeDkmQTcJ9LxeC23nrrKt9L34kaMqxLr28G6RUqewMihYWHBWvNUqDODHDz5s1jOhAPGmbW6PAw8lhJN6+ayBYWIfju2rVrnGHgoZAyEHggs1SAFFgqZnPREyTaoZl92TYkWGIWjXOW3wuFeRgpZxR4yJAh8T3MnlOps1JmL8h8YUYnDWAUpm2l7UIIeJjNVc0Vnj+kABMcptephUDtDory3XPPPVXeS7une4f31NqlJDKATPYCGDRm0I1n2DLLLJMvzFe4XKPSZ3lrixmklIXEQB9ZHqQCs346u4Qj2662cS6mVLPEjfonaWCDfgHBzrvvvhtf40+29qIwVXp+2XbFxX2C5xnFvtKzn0FQln2R/ZEGrdNadPrB5bq+uiaDSvTvsxhQoo9EUJ7OUQb4y6HPOvV/v2cmJFq2bBkzhRg4v+CCC+Ky1YRMF/pO3PPIMiQDhsw4Mg0aajLDIL0CFXYIuZlxE6Mjufnmm+cDdVI5GF2k0BYjaJyslTjKODPVtQMBIrML3PTSel9uAFzorOshZYh2duahftqfARAGRdLoKGlsrE0nO4Q1RpzT2dmLcgvUs1uHEKAzo7vUUkvFrA3+JEBP5xwDSZyrBufFOf9Y90ywSACZfg/cAwjUSaOsLj3Oe+nMZdsnXbOsRWcQmY4TM2GkERNUcp2zxKAwG6HcrvH6bOMUoLDbCJ1ROq1kfdFhp8ghAU52OVylt3Hhz83MHOck91buv2QZ8Mzn3svzh0H6QvYBiqPwXsoACZMmFOalDstuu+0WJ0d4jRnStERpZv9GOUs/K7VqmIArLF7IpAdBbNo6tBzO16mZGfQ555wzDlBwDVMjigkest+y6BtxDbMMjcwCnuX1UcV9RgzSK0z2BsS+nYwMpSq4XJBUMySATOvPOEEZBWbUqFJHGWvSluyTyIMgvcYDgbVmrGlJI+rc2OjYcMOzLYvb/gTkqZ2x/fbbx+JbKVAnVYtgnRk3shnKffaCwJA9X5lVZHcGtlJhKxFSrLMdS9K2yPBI2wGq5kgNTFkIBDLMojN7S0E46h2QwZEt2sesJMFO9jxVza9xZh4JEklDJBuGv9POzPCmGUqeU+ySQbEf1e05xh7dqTgsa/pZe8k5nQpJkRlGu6c6AJWscAkb5yaTHKTCEvSQRk39CQbnOEdpN5ZqqH5/F8yMMzHC/ZcAizR2BvVYd81SRJ7/3J/ZVaNS0BYzGoBgYqlNmzZxRjkbgPPc4pxNz7mmbur/fn6uRWr07LrrrvmvEXiTdUE2FhMcWbQJfUWu7+ySiYZgkF6hSBdk7QWV3NMWAilQp5NDcEmqRyWPMtYUN35SiClEkr2ZUTGc2XQ+Uup7lm1ZnNkLzmUyPehIZs9Z6gAQqDNgki2QVg6jwTPDQBAzjMyEJaT2ESAyw0t6MKntnLdUuc+uOdWscd2OGzcujrqTCsjsbbbYHgEOAWVhoE7nkJmbcj3v6hOdadbxMsjG7gxJ2s8YdKIYGGUpS6XO6s4O7gdkzTHokV1rmV02wH2UTCXauNKfX9mfn+07meBItWg+/vjj2JcaPnx4fiCYc5LZONa7qriy1zsDz/QHUhV9sm8IygpnP/l9sVNBJWKigl0F+Eg1Js4666xcixYtYiFOsu64zxIHkGFbDtf61P/9DKw7ZwadzEpmz+mbp3scKf70k8guZICi8Hsbg0F6BaKjs9BCC1Wp4JwdHaK6ITcwRpWoQq4Z44FLW2arMdORSQ9mbgB0amhLC3IVH1sD0f7MHFcX/LA+mNR3CsdVQnBEZ4RUPgrFEShmsZUSD1xmFNiSitQtq4jXHZ0YHvbMQLBlTbazmAJ12jib+p5UwrlYLBR34hpmBjcrdS75kw45GSEW4aobMuoK+wS0K4Mg6VxlezsGQbh32Mb/jwFRJjyYLS/cLSSt4yUzkbbjfuC1X3/IruFewWBJdqvghP4sg9RMnHAel2smXdaRRx4Zi8Im7H7BkiAGMihqTGFTMg/SNc5rTG4QrK611lplda2/+eabcSCCfiOYkGQJSraoIIM5LIkk+5BsucbWPKiiMDDz/vvvh7333jusssoq4aOPPgo33XRT6Nq1a9hxxx3DnXfeGbbddtuw3377hbXXXjvMM888jX3IJWvatGmx/WivVVddNfz73/8Ol19+eVhjjTVi21133XVhww03DPvvv3/YaKONwsorr9zYh1xW5/HYsWPDQw89FC655JKw3nrrha+//joMHz48HHTQQeGMM86I7xs2bFhYZpll4u+iRYsWodwtv/zyYa211goTJkwIjz76aBg/fnz+a8ccc0y47777wvPPPx//fOyxx+J5q9pd8/j9999jG7du3Tp+fsstt4RPPvkkNGvWLH7etm3b0KNHj3DAAQfE30P//v3z5y0q4VwsljfeeCN07949/PnPfw7vvPNOuPrqq+PfV1999fDggw/G38OYMWPCsssuG1555ZUwxxxzxN9P8+Z2b2qK9qON6RO89957+ecYr1100UXht99+i+f+YostFkaNGmUb/w9tcccdd8SP7bffPvzhD38In332WXzu8DUMHjw47LrrrmHixInhxRdfjNf+1KlTG/vQy84XX3wR7r333tgf2GSTTcKkSZPi74C+wDXXXBPfwzPv2GOPjfdh7hUtW7Ys69/F5MmTw7zzzhtef/31cPTRR4fRo0eHN998M4wYMSI899xz4bXXXovX/E477RTeeuut0Ldv39in4r7KPeCFF14om2v9559/Dqeddlo46aSTwsknnxxfGzlyZFh00UVDr1694s/666+/hs6dO8c4iHviCSecEF566aVGPe5mROqNegSqVzxYCy+uvfbaKzz99NPh+OOPjw+X+eefPyy11FIxeOdifPzxx+P3pA5ndf9GJaquHc4+++xw6qmnxk74kCFDYkBIB5IbHg9kOpjZgQ7bsu4K245zdeONN4431e222y5ce+218UHdrl27eH4feuihYeDAgdV+b7ng9p2u0ywetg888EDsHB5xxBHxQa3Zkz2H6NwQJIL7Jn8noDn//PPDkksuWeX7+D1stdVWBua1OJ+z5/W5554bBg0aFPbZZ5/4bFp88cXDSiutFK91OpMMjvC7mXvuueP76XTb1rW7Z9BppZ379esX/vGPf8TnGAN4H3/8cXyGPfPMM/G+mr7XNv4vOvYMxNGpZ7CIPwlyCMgZ0LjqqqvCAgssEB5++OE48UGb8dwiOFRxffXVV3HCZN9994334KFDh8ZBvf/85z/hl19+CUceeWTsEzz11FOx38C9vBJ+Fz/++GO4/vrrw6233hrat28fX/vnP/8Z5pxzzvx9gEENzlkGLgqV07U+ZsyY+PwAA48MQIBJni+//DLceOONccKyVatW8b538MEHh7vuuit06tSp8Q66safyVX+y6SmkuN9yyy3x76R1kMpBug8VcVPKK9uBkCrLekvNuC1Zs0daFUgFYr0ka81Y38+aFpDaTqpQYVVMzX77P/HEE7lRo0bFv5O2xPZqFOhhbfqIESPi62wdyFYZ2e8rh3StrJRCzfoxCj5RWZw6CAlFn0ivZJ/YtGzFtbp1kz13WFfOfZItlUhlBUUJKcxH9eAPP/wwv9QiWyTKNNeatzHV2UlXZekQhbhYL036JVXd2SoJrPdlx5FsJXfP75q38U8//VSlhgdV22nPiy++OF+IjyUGLNXKPscqtY2re36Qxs7+0txnWfbC3tIsreJ+wE4uaV104j2gOGb0LN97771j0T6WdHE+P/7443G9Mf3dwkru5dYfqE66VrlHUjOJeyjnZZLWYlMojbR3tlosR9NmcM/KLndIqe+k/qcaRg1dJK46BukVgOCFwmYUkkrbqyG73UIqukMF4kp9CNcEnUXW7DC4kS0Sx1YfhW1J8QnbcvZl27Bv375xLRVrKBkg4SFDJefChwudTd5b7m1CARi2kqGGBJ0TtkvJ7vN51FFHxa3/aAs65Zo9bNey4IILxs5f4cAHxfgoIEd7swUT52laz6eaX+NsB7TZZpvFwnsMdKSAMVskjmCHeyzFIb3H1ky2negL0CllW7BNN900P7iZLbBJB9XnWPWVw6mGnYrEMalBAS62VkvtR3ux1RcDeelzFf93wdbB1Fph0iTtPsCa4sL6P/QHGKyulN9FdQMQ1EugrSgWS7HTLM5pgvR0r60kv2UCdWr2zDPPPPm16KVwrhiklzlmfelUZm9a2ZOSTiQPGKq10skv172ji1XAiFHzNIubOovZ7dQIHun0WFyn+Kg+Svtni3xkETQRKFGRlCCpnIrCVHcOMWNLNeZUII5BNzotzOgyi5CwLzoPH7dZmz1kHBF4p6qvnG9kzjDzyHmXOoj9+vWLHcJ0/pXTedhQgyDMQLJ9Itcx13yaLSdzgQJdm2yySZV7rM+rmuP8pLgWmXXMkDPoTCZY2vecDAYy77iP+hyrisFOghmy5GhDBpGyBQ3T9oAMbpCB4Mx5/U6YULCPwpxsd8csMffi7MQJ27Dxuyi3/sDMZPtGnJv0V9OWlNxHL7jggrjN2B577BEHNvg6bUR2WKVe479lzg3aImXDlQKD9DI/8eigs383uGExsstMD2mZdIK4eJl544K1UzljdAKpkkkaNUi5ZFsrZnuoKMxSAfaUJ6WIUUrbsrhIK6QjmdIH+ZyK7iw14KEDfgc77bRTvMmmjmU5dJLSg5Ot0h555JH866S5s4VIdrsk3HbbbbHqeJrpQdpmUXXHg5uZx/vuuy9WiWX/Y9p/2WWXjduxvfTSS/F92Y6O13/NESQy+5jOW9qZWZ/sLgVU1Ge7K5ayeI+t/TOM9iN4pG3BgCdbEV199dX59zD4RDbDvvvuaxtncB4SFHLfTUutWrdunT9fue7JsOM5xb7c5fQMKjV33XVXHFxKEya33357XPLGUoPk5ptvjr8HsnIq4XdBBffsBBIDSlRpZ1CJ8zZVcCdQZ0adrC+y8Fgi0KtXr4oYjJs2k8HcUr3HGaSX+QnIPr7czLhhsXaXrSfYgoE1VPydmxazE+l7y/kmVhupPbJtSkoVD+Vzzjknth+j6Dyo//a3v8XOO+1IupttWXykapOeyb6mrLtmkInZDB7CdOT5PXCTff7556tkNjR16WchKCQQTCl7KWhnO5HUSUznHQNvSy+9dD7VUrVXXUeFAIdrne1ZWH966KGHxuUG48ePj/cDBuhUtzZm2Qp7S9Op5D7KADLB4xVXXJGf3b3sssvi12hv77F1O49Z7sbAEriPZtuY5QT0E2hrvq/S2zi1XfqTgSEyPVKQSJCTBpA4f/lgCRyzuanNyuEZVIrnMQPzO+ywQ/w7+9GTopzOY+4RH330Ufz7Qw89VBG/C7LoSOlfYIEFYjYX/QX6AATm1O5g4oi+axqcYyCOOjYsg03bkZVTG037372LTAFS+Hm2JE1tEMIgvUxkTzxGxNIekRSGYZ9uCiKwDi2NtDHiyDrW7D7opgxO35bc/NJesfzJbDopr9zg2G8TL7zwQgwgs7OVtmXdVXcT5TVSuAnMCUxJdePhw+uMILP2elb/RlOTfgbSrJkZLyx8w3VOQRwCx1dffbVKuhuzOTfccEODH3M5yJ47tD2DIGRugFRWshno/KT3MTDHDLDtXTdnnHFGHOBgcIk15gzEMcNDplJ2QGq77baL2TOJ99iZy7bP4YcfHu+Z3DNI/WXmjMAm28Ys3WAgn8Kc1f0blST7c6e941kSyHIilrZkBzcIbChoyIBdVqUObtQn2ph25Z7BZBOD8vwustk2N910U1wal037roTfBc8o+gIE6hQxpg0SBpDIAiVQp55CepaRdZfaptyu9bvuuiu32GKLxSwClkNkl0I0pf6hQXoZyJ5wAwcOjIVeSGmn0mgKHLNF4ng/s+i777572V2YsyvbHqSubbzxxjHg2XzzzfNr9goLGNGWzKrblsU9l3mAsFSD2YtUyIOZijQ4kjCCXBjAlgsK4vFgZYAtiwct5yEp/vz8BDe8RttQKHKhhRZyZ4EiFCkkVZA10qz9J3sjddjT+lNmbBgEJYW4XGYhGvIaZ7C4Y8eOsRo291LamIwRAsqEmTECJNq5KXWuSuU8fvrpp2P2B4NNvE4mGOc0S9yynXj6DSwVqvQ2zv78DMqTNZPajYCQtGqeTdksL2okFN6jVdzfBVlk3BvIBuGc5u983Hnnnfn3MDlFf6x37965SmwjAnWW/NEuqThcuhekQP0Pf/hDlTYrp0GMaf/7WTlH/vSnP8V6W/SLeJ4sscQSVQYumsp9ziC9jJx44olx1IhZ3ocffjheqASPabaczg4jwTyILRI3cwR9BDqk/6XKlxQmSdvT8TAgzcoCRvVbFIaaCnQeyQTJpnoToJLWxQO5XIvC8FAlaGFknKyBhPQ0RohTJdZ77rkn7spAhgGdcdZIE/So7iicRSDDrCIPfIpGEiQyaMd5B2bSeI3Zx0pY81hs3D/pNJ177rn517iOmfXgPGaQmUG6DTfcsMrzqql0rkpl5pE1p9TuSBhYYiaddNi99torZiHRxp07d7aNM6jhQ6DDun0wSM/yQfoCfI1zlTo09KfIpCnHZ1CpYPCZ+wRbhSX0cxlAYRkMac1kONAfoEZQ+l1UYn+MGjVc12TKpAy71A5kfe25556xkGy5ev755+P9jpox6Txg8CL1m5paoG6QXiZIBySITOtTebBQ5TkVhAE3Mh7Iu+66qwVhalDAKD0QGIkrLGDEjC4pV/vss49tWWTDhg2Lo56pEBdrq3kYZ2cvKBRD54gKxOUcIBGck8JGcEilVnZrYN0ug3BZtEFaf0Uam+qGzgznEbOMzKJlsb6RgJx0bDAQQqBZCWsei93GDEDRiWQgmYG4wnOZbBBmz8kQYZmL99ja11LhGcX9cb755ouBehaBOjuRcD6TUUfWiG38/yhQyn7SZMhkl7GxzRqDxmQ3MZBE4VgGlcr5GdTY0oQTuzykjDqwPCZlN5DpxGQJGY+V8rvIBphkfTKomfY9JyjdfvvtY18hZX+lewPt0xSC07qYPHlyzKLgnkeGYVYK1KkfxYRmU2GQ3kQVXmSsNeehAVJguXFdeeWV+XQsAp9U2bHSC8LMrC1Zx0QHhlk0Xqdjnm1LZnCZZUtr1G3L4p/LtC8j4mldEetTs0VhGJDie9jbt5yKxM0IqX2sx6XgE51DaiCAc6+6Aoeafcw2kJ1Q2K4McvJ7KDzfvP5nLtuObI0EMrzIgqHTRMXswrbOnt+wjes2q0SBLbYLI4tuViq1jQuLxLGumU4+A0kMdmRRuJBtAEmlpfieg3T12x9gJx3Wn/Ps4/eC7H1h9OjR8ZnI+yqhP1DYRgwW9+nTJw5ksPNFGqQgKKXfQCYefabCdiunQH1a5ufiZ03nS7bmBsiKY/nkSiutFLekbQr9JoP0MtjzlFRMLkjSfLhIebCkoBJ0gChslt0rvSmcnI3RlqTC0Ilklpat6bLb04AHAVt6pO0sYFsWB7OS3EQJyJldo0BXtkBPCtq5yWbrApTTwyYre15RMIsZHGZuLOxUPDM6d9jJgdS4bEE+sLc091I66qp9G5ONRAZS2jaQWiksZWE2MnUk4Xld9zYmyyhbI4XnP9k4BJ0sjUkIZGznqrJbWBGIM4jEuUlaO2bUXpU6uFFs2fblWZ/d/pLq+q1atcoXPktZT4XKtT8wo2WBLG9j9pisGJa8MZCcTfPm2ieATxXvy/F8mTx5cn5wIg3csPsK69KJj7Iopp2WrTYFBulNTPYGxHoz0oLpwDMrQZVrikJkK12zBoUK0IymV9LNqy4FjOgsklLMBc+aPVKs077oqVAUa1AtYFQc2TZkcISRT2YtSGlLRWFuvPHG/Hv4vZC+efDBB1dM5zL7czIwxMg4qe9keFT3HtXt/OO6Z3kFM48JQc0yyywTlw7R2SEwpwZF2vpHtWtjtsHh3GVLULZQShXzCdR5jhUG6qp9G7M8hmCmZcuWsZOa7g30EXbccce49pzgU9O3HWuaC585BIq02aabbhrTqythlrYUfhcsOeR3wXLChCCTwXuWHqZAvZL6YYXPeZZeMCGXlgCQ6s4gMoUNmWBK1e0ZED3hhBPKbiBp2v/ag2wWljnw/OA6HTlyZP4cov9OoM7SnqbKIL2JYu05wQrFMxJOTmZ5+GAmiG1BOGktCDNzBDyse2R9U8KMLiOTbPnFg4KiZXTaLWBUfBThYU0VN9uE0U8eNrxOKhvBEwE6684qrShMYaVmAnUeSixr0exj/TMDdFQZZw0fxfpYIsR1zv2TQlF8jSwGZtYsEll7DBzTiaLyMBlfDMhxbafAh0CdNcAUMyvHGZ+GQMorO5EQpLOWmsKbDNyn85T+AUs4qF3DfaTSZa9fMg8JbBjcYLb2mmuuyX+NStgMLnHP/eyzzxrpaCvnd8G6Yc5hBvMI1KmhlJ1RpyAY9+nCCuXljLoRhQOYDCAxi84SwCzOZdqNGKCwr1RuA0z0GeknMgjB84RlkiztSbPn9C2pLUMRaAYwmiKD9CaGi42LlRkeZs2pfpvFbFvqdFJ454ADDrAgTIF0wyLIpigMFzU3NR4MWXQgaV8qYTJ7xkPctiwuRoNpewp9kN6enTW/9NJL43oqOpurr756XIJQKUVhqlufmzArRqeRlNbCB7Rqh0J8nGMMAqU90SlQxIBQwqwNs2sUMHT9ae2RYs3sF8utyOxKASV1P+hYpRl1UhCZ7S33a7u+imul8xjMotG2aU/0dP9gpp2dS2zj/0cRKfoArHfmfsCznr5VdskgARGTHax1Vf1hGzvuFY8//njMYKIPwOfcF7KBOgOp2Xt0uWNXgey+72AGvXnz5vndB9I1zpJM2ox+FUsDy8W3BQVxyWzlHMjuXAEGcXiGkxkDCucREzXVwV+D9CaguhkbHhqMiDNyzpqzQgQ5WXYqp2/LFOC88847cZaMhzAPh1mxg1N3hdkHzKDRaWQGgwdy4e+ImQu2vOLBXK6jwennYh0VayKza6uqex/ojKd1vao7smSyS1pSWjbFChmUq47Xf+0Q/FDUlCA823aso5xzzjlj6nvhuWwb1w6DSMw8kgGS8HcCUHZ5oeNaWOTUNs7FteZkZ2V3DuE+TGYd7XbDDTfkX2cAzzarPwzgsZtDduKJwJRBUmrTsK1YwrK4SshkLDzfqOnBRBx9INLbCd5ZjpGtes+gJ9c7hQ1Zo56d/GjK9aKOPfbYKgMVnC/swnThhRfmP0/SJEYyoz5VU9A8qKQxkNKsWbP49zvuuCOcfPLJ8e877bRTOP3008O0adPC5ZdfHl577bX890ydOjW0bdu2yr/RsmXLUOloq9SWt912W+jXr1/49ttvwworrBBuueWW2G4XX3xxeO655/Lfw2uFWrRo0aDHXS44D5s3/+8t58477ww//PBDmG+++cKxxx4bjjjiiHDUUUeFe++9N/6O+F3x/sUXXzx07tw5LLPMMvnXy+1c5ue65557QteuXcM222wTVlllldgOkydPnu59tAl472KLLdZIR9z0/f777/Ha/uijj+J5mEyZMiUsvfTS4dRTTw3PPPNM+PHHH6e7B3j9z1g6P/83AZBva+6znL+03c8//xxfP+aYY+K1fNVVV4UHH3wwtnNqa9t4xlK7Zv++xBJLhDnnnLNKP6Bdu3Zh//33D/PMM0944oknwiGHHFKlbW3j/95TP/nkkzBp0qT8a0sttVQ46KCDwrLLLhv23XffcMMNN8TXN9lkk9hm1fUJNPto288//zx8/PHH+ddatWoVunXrFvbcc8/YZ9t7773j6506dYp9CfoD5azwGj3vvPPCPvvsE6/zNm3ahIMPPjhe31zb1157bXjggQdCr1694nOte/fu8Vn27rvvhqZupZVWCj179oznQ3p+tG7dOsw///zxZ06f8/zGmmuuGX799df8988xxxyhqTJIbyJB5csvvxxuvfXWGKhfcskl8bWdd945dnTeeeed+Nrrr79e7YWd/o1Kb8sUIL711lvh+uuvj4HQ1VdfHcaNGxdvAkOHDo0PCG6EKVC3I1P8c/mrr74Ku+22Wzj66KNjEDTvvPOG0047LfTu3TsOPg0bNiz/uyo0o9ebKjrZtMfZZ58dTjnllPDII4+EFVdcMfTt2zcMGTKkSucRXst189RTT4UrrrginHnmmbGTTXDItU2H5umnnw733Xdf/kEPOkB8ncDHe0Dtr/H0OQh0Fl100fDXv/41fp4GkH/55ZfY6d54443DSSedFMaOHWtb17KNU0eU59cf/vCH2A/Idso519dbb72w1157hVdeeSW89NJLoVJVF9C1b98+bLXVVmHkyJHhs88+y79OgL766quH9ddfPw7YpUAAnqP187vgnkwA+q9//Ss89NBDVQKs5ZdfPuy6667hscceC8cdd1zZ9gdmNBiXMKDE4Bv3zVGjRuXvnRtuuGE48sgjwwknnBDvq7Rfhw4dYgA/99xzh6Zul112iZM1w4cPD8cff3z497//HV8/8cQTwxdffBEOPPDAKs9vBoX52X/77bdq27FJaeypfM0aaR4URGBNLutz2Vf23HPPzX+dVK211147pgpRKEEzL2C0/vrr57bffvu4VyIFSEivYm06WO9P2nvXrl3jWhbNvmyaNmlLVB2mmjNrptgehJ0JQPVsfj8UlbrjjjtylVIXgbVVFDfJri/v2bNn3I+bAijZ7eZUexSBYs0p1z21D1gilLDUZY899ohfY4cHsH8q9TxIJbQ4XN22WaPQEffYU045Jb5GOibnc9pCkA+eZ+xHz/eyhjItd9Gs25iCsaT/8gy7+OKLY4oray7pH7CbC2urWavKdqG8j/WcpG9XahsX7jLA1orpvpoKcNEPSIXh+BrPJu6/FOqktg8ps94Pivu7IHV7yJAhsc4KW2NRB4i+Lm2eCqPSPyB1mfOcNessT0gFJyvlfCW9nyV/4BykDThn2ZUkoU1YPpjdno3aVFQ5L6elU3PPPXfsL7EsheUAbJFM0dF11lknFpDjfkdNCZZJlgOD9BLHnqd0YLgYWXNB1XE68HQ0zz///Pz7eJjsu+++FbFOp67ohNNJf+211/JrW1gXScV2KmHSOQcPcDrutmVxUciIQREqC7Ommoczn/NAzgbqFDoiaCp3VCal2jU7CLBzQHY9KajMTCf8sssus0BcHVH8iXV5FC+jfRmEo6gMReIS7gfcUwlieNizfppOkFXca69v3765RRZZJHfqqafmrrjiijgQxy4knL+0PUEjX6diPruQ8Ezja7T5sGHDGvvwmwQ6ogw6sZafwKVdu3axajvefvvtWPiM85gP7qMMAoLq+mzbWmmy1y+FtHjeU7SQTj0TIDznBw0aFAfnaSM6+fSvGFAC528lPI8aGsW82M2Bey3tznPwvffeiwW/GOBbbLHF4vZZyy+/fHwOggrdBKfff/99rlImNWgXgm3+5JpP7+H8pG2oSZWt08OAKBMhVDTn2VYObTFmzJj834mJFl100VhLhlomXL/sAMSgGgM87AZQLgE6DNJLHPtH88DIBoyM9jJazrZAbLNWWGTC4LJ6FNLgBk8Bo+yNkOJRjM4RqKcZ9exMp2Yf7UhHkpnyLIJ1BqG4saYHL7MY5d7uPFQIHpmhIWuDNqADmR0JBx1uiqMUBvCq2aAcQWJ2T3nakY4f1cWZLWf/1AkTJsQiPAyEMtM4dOhQq7jXAe3H/TVt70XBIgrDZatkp+rDzO6keyyFIwko3d5q1l566aXYxqlYLAEN95Gbb745/x4GPnjGMdOUUECOjm05zarVZZCYATp2FAEVwwlkGJQH9wm2WmXWlvtDKkRFtgf7czfl4lOlhtlPBprSntZnnHFG3PouDdQRlDHLTiYO701tT/BJBg737HJHmzCJQdbRu+++GyeOeJ4xEAfun2y1yMRTeg1M5DFAmmbem6r0fLjvvvviIBnnQTZ7OAXqZBpkldsz2yC9RKVOIh0ctlHhpoUUvDBaNs8888Q0d0aBE2d9pm+H1JZUwGUGJ1USTrPpfM72NalSJB12Ffd3we+AYJQU4sLfC8EpD59s9gLfU66BOrMF55xzTkxZTagkzvnHoFxhQF7uqX31gdlDZscJ/tKeqWnQg5Rg0uV48NMxpM0Lt7eBlZxrJrUTmSFp9vGf//xnrMicAnTO6cKZcpYTse0lHdFsZoNmPrjHjBrIRKKNWV4AAhcqkKeZczCTxjI4BvSb+qxaXfEcoW3YromBejz66KOx7VLHn3O4cE9p0q95NhEEMbCk2e+LpWf6QQcdFDNCQFo7kyRXXXVV/JzMGgLNLIJUBk7o8xbuF16O91Lul8wKp3smFe4ZyE/307R7E21L5mHhs6op952y58s999yTa9OmTYxxOAeyGJjkvsazvJxmzgsZpJeIwosqnaiMkPEwYUuQ7LZqjEDS4SQlln28eaCo+rZMD19GxkmvYgYte1Ojs8hoOW3J/vMGRbNnRg8ItrPhpsoawCw6maQY0lk//PDDc+WMUV+2TCFQJI09i0CdkXHW3RXOqKtubU1nkMEh1qUzQERqZXbknaUW1PgwU6F2WOPM9lXZDhX7oDPwweATne7sDDqz64U1U+h0cQ0waKXp8RxinXn2XsCsGlutMeBMmvvgwYPzXyPwZHY4O4PG74eU+EpvYwYuWFL0wQcfxD3l6VMx25j6BdwfyOpK5zPLr/bbb7+4DMMBpOJjcI7+QBosSb8L+mW8zkcaOOU10rxZKlOudYIKzzEyOsn6IMuDjKTC85WaVGTVlNOgMoF29mdg8qxLly75QUj68VzHDAaTKZRm1AniWWZVrpkuBuklFtQwssuIIR0aTkY8+OCDMaWN1A4uWDo3BJqcmBQ+YhaSkTZVbUs6gHvvvXdu8803zxfa46ZHmgwDG6TR0HkkfYq0Y24CFC1LN0PNXvvToSSdmJFyUi8ZIWcwZL311ssXhiPFnXOdGzGdetah0TEtV5xjpLGxxozzsnCtOWskGUiiU2JWTHH2nqdDSAYN61DTYGYa8GSEnvWQ6aGvWWNNILO5BIt0olK6MIMf7E9LdgJr0hMyk7p3757bbbfdpjunm3rHsr6w9pY2JsOLtfsUTEqoY8Ezv3///vnX6LizBI6vZbORKjXbgH7Urbfemh+coE147tCmzEgSlCcss+C85f1ZDELxobojCKc/S+0f7hsJfVdmxQk+U3ZD6g9suummMdOs8LlZrgPXDKoxaE/tg+w9k74SA0W0U8oySPdZ+kyp0Gk5YJkZfXJqEiX0A5dccsnYR+f6ZfKCOhK0B5M9aTDyzjvvjINv5cogvYRQjZGTj9lEPngQp84OgTrVcfk6naPVV189jirRuVxxxRXjg0m5Kg8B2op0NW5wtCVtSlDExc3sGsEQbUkniLbkaxQoya5hVd3PZdqXegqkGdLOpAwyWsqACDdashZ4DwV7wEOH9ZapgF85qK6jTIeDoo+kBjPwln0wgXM2u55Us9f2rMPlnCNLIY3Kp98DHUKyOCo1oKktBtJYZ85sOYEQRbi4Z6ZzmOcUAyLUn6Ct6ZjTxrwvZTQ15VTMhmxj6s2keyJtnDJASGnfZJNN4usMdtLBJTWW96TZpEptYwI+ChOuuuqqMZuDjn+acWT9fqoCDWZqOW+33nrrmN2UHTDyfjD7uD+QHcfAEQXOWIOezfygOBwDp/THSG9n5pQJEwZSsuuKy/13wcAE91Ou5yOOOCL/+oABA2K/lYmmtASTDA/OVwaVymmAkzpEKegmiyDNmm+77bYx7uGaJuuNNiHmoa0K6xuVK4P0EhpxpHOT1o0xO8EFmh195EZGoMNDJ924CEZ58Jii/f8YsKBNUlES/mzZsmWVEVvaj9E3bgypLSlSQuqrBYxmD7PnPJDTwBGdTc5lRjzTQ2nUqFGxkA8zRKljydoiOp/lknqczivqRzAKzAw6a6zAA5bsDmpKUAynMFBX7cwoKEm/g08++STOqDNrngJ1Zh4Z4Cxci6rqkYJKRhfZXAnndPPmzau8xnIWZs3poBMksR1busbLrahPsbHOkvYkyysb7HD/ZAAkoQ/A4BID0aRxU/w0tW2ltjHtRCYca/XJLKAWxRxzzBGfMykQ4NnELg4M2BHoMLue3cmhnAKfxkTbk1GTnncMzpPBSNCVCvKx9IXnH4E6g/gE53xeib8L+kRsm0j/k6yDhECU+yiZswx2FJ6v5TAYl/09UxCTiZ2UJfD222/HgR3ahsmb9IwmYM/W4ipnBuklgq1RuBBBYJ4tCEPQUli4hDUsVMSmOqlrpv4rXcCsOUuj5bRrtoARI5HsH5vFjYAODw8L27Lu0gODtdXMpGcLSKUCPXSUClMIeVgToLPGstzWnKXzjw4hAWLakop0ax5OBO88eKmHUAkVa+tbdW2YTX1nRp3MGTqMjNAbPNYM90060Ay+ZbFkg3OamSBSiLO1UZjxyK4TtI1njHOUczdlFrG+PyG1lTamo8pzLFskiYAn21Gv1DZmuR9tlK3zQXo0+yVTkDR7HvK8YVkRhcvoY1X64EaxMSnCYB79gKyUEcIkSHaZF787lncwUVUpu2qQyl2YLUedD65xJusYuE8YWGIyjtT3gQMHlvX5SqzDABqZriz3/b1goIavk11M3JOtb1LODNIbAbNmhSm9jDwyQsb6XVKBs2mZpLQRRKZ1kzzQSeHkwq30qqO0Y6p8nzBzSXolBXNoy+wac2Z8SL/M3iCZZSNlsLB6pOqGgJs1ZcwGZQue0Jlk5pyZ9ZS+RcohXyf1rdwCdM4xHrjZn58BJGZy0kOYziMZHAQ7hRVtNWuk/qb6BsxAsFSguhmY7Iw6qdi0twF6zXHuki5M5kEaAGW2nKCSpRtso8ZgEzNiDEjxe8gOxpmlUDMMEhPIsOcvgTqzZ2wZyBZq9BEYWGKmid8DqbHZZ1YltzGzbZyLBN/pvOOZQuBOgV0yOpiVpNBUdQWmKmnWtr6R5UFKNvfYlP3B+cwSDgacmDFneQFBJ8X6Cosel/vvIm0NSgo3/XoGlrLZmwTqXOcUPZ2RcmmjdM/i2cIWnmCwkuuV8+Tee+/N/6wM5jCZQd2iStqpwiC9gZGKxRpdOu9sEcRNCnTQSVvj4mUbsIR1GdzYODkLH8LlkOoyO8g4YHaSlD9G3igwwQOY1H/Sqqj6SOcxITCkLelcFrZdpbdlXbB2n2CTNVPZba5YN0QhlGyAnmY2OPfZjz6L2aCmnu5NpkC2OjCY8WLZBcUds+cYI8SktKbaBzyELFxWtxRBOn8EjaS/Mfgxs+15slsrpd+FAXrt0DliRoxUVmZ8CwebyRw5/vjj47PMe2rtpPYiUCcwp7ozdTvSlqFpJolZOAIc+gTl0lkvBgbaqfNBoTKeM6xLZ1aX653nEDvkMLNOcJSeQZ6j9Reos9MAA3YMKvF74bzlHkzmIhl2bDnKOc5AVCUNMrH8gvOQzCRmjVniR1+JySMykpjcIAYgUGews1yl3zeDFvTh99133/yy3RSor7322vlt6AjkySRo6vu/15ZBegMiVY1iJqRrEMgwskjnkuCRhy0FzigIxwgwo0qs56GQhkV3qm9LbnSs62UdHzc0Rt5SGhXrInkwsISA2Vs+eHDblsXBucpaKQY8GCAh6MyuEeK8JouBThKjxBQ9orgRD+xyKwrDz0D6NMVxeJBkt09k0I00PnCN8zXOUQKcwi3YVHvUlaDdaee09rSm55XXf91QL4UlA6usskq+ndM600K2ce2k9mKAj9lytp1KM0yz+p5Klf35yZ5jEImla4XL2kAHn9oKDm7UP9YXk8VAPyGbGZpw76A2TSX+Lsg0JAhlWSBZsWSEUd+DbBAGl+abb744mMRzLVtLqdwMHz48Dq5zTaaJinQ9p0B9nXXWibEQ50slnisG6Q2EmUaKmmQLwrBmh4swrYNOxU7Ya5YAnpFGZooqsZDGzKSCMKTCJKztpS1JJ85mLbCNBcEi6VXZtWm2Zd2x9pSZtFTcg44PMz7cTNM2Kaw9T+mvbHlDxgNfL7f2T0EKafs8XAm+mUVIAxGcc/zc2T1NeQgxoFRdx0W1a3dmZ8iaYQCOBzrprEm5nGOl2PbMqDMwynWdlq4Utnc5DMA1htRJJd2dGXUGPFPGHaxCPvNAnQF8lg6y7Cpt50k7FWbNeH+ofwxaM6POvTkVkUPaA73SfhfZ8/S0006LfQZqI6QAlcCUtdZnnXVWbpdddokZSeWc7cXPTtHL7DmQJjNSe3Tp0iVmGdCnrEQG6fWMk42CEASQBN/ZwkaceKlqK6mvpLYnzD5SrCedrOV8odYGFyoj5eyfmPY6zhYwIu2YhzSpsOmi5waYvfBty7pj5JuiMGwdlsVsJqndnLfZnQYYTScFlk5muaYYp1lEzk3agBQ/fu40Uswe0Qy4MahEO1BLgv2P05ZKqrkZzRpSz4BCmgwMZXfEQLnur9vYCNS57hmESoG6amZWwXU29Z02ZrC+upnhSjSjgK5wRp1MOrZd9T5bf2qSxcGzkECdiRLS3Ctdts3OPPPMGKjTJyisrZRVbn2mhExXdlmp7r5IhgGImdLfK5FBegOh6AEz6Yzuku7KOhwCTdalk/5OZWtSCJkVYqaYvQKTSk9nK0SxvCWWWCJe4HQOaUuCI0YmSSMiTZDRN2YrGZFMo+lw5mH2EAxxzpLqmmbS6UCSrcAyDdZX0fYURCEoLXzwlNu5nM6noUOHxqrtzBgwWEQHMRU3oXPdq1evuA0gHW7Oz0oqfFIs2WuX9Lj+/fvHzngKEMlWIFDnd8ASGDDDzj1BNZO2rQTtm93XuDoEkWTKUDVfNZO9B1LIMDs4P6MZddqYWadKlx2YL9wlpLBtKcDF8ipqprhFbf2iGCx93JkF6hTs5NlH7aBKUZMBJdLc6S9wfX/11VfTvbdc+6y0AbEP2QIsW8u+zvXKUsrX7CcZpDeEdEFyE2PtLkW1CHKyBWEYKSKoYVaCYKfcgplioxgX64ApDkeKcWEwSHGuQw45JM6w25bFxXrJAw88MD5wGQWm+EnaDoNZC9ZXEaynfVHL3TPPPBOXpzC4Rmfk2WefjWmqhcE4bUOmgUXiai97DVPBmUwEOuAMzlHHI3XeCdQpqMUyC9Kxs9usaeboIFLng1kvlmJRzCgVPZwZOliVkq5azPO4X79+8fnEwMiMztH0fpYUVXobU9sjDVTwbOfZU10GR7aNGaBjgNQ+QHFl25P6NKyfJrV9Vs9JCvlWynmcrdNR3TO/cEad5xnndTn2D9JAA88YYp00CckgL88Zfu70rOFeSGFHllB+lql6X6kM0htIuiAff/zxGKizVpqU7OpGytLn5TqCVixsP8N2NQxsUPV2ZmlBPqSLiyraBOrM8GQLxmX3OSUorYQHMhVZWZub7WhTrZ4AkQCSwL1c09UaGh0Y7p2cf8xAst0inXXaOgXq7733XlxCRGG+ct5Ttr6KwpHxRccpbe9Z02u4Eq71YiHYZLCeAqcshyuUffZn/17JbUybMblBoVIG6bjOZyT7vE/tZx+gfpa/saQgZS7VVLmfx4899ljMRAIZdqT6VzcQVzj4zIBSufX708/DUgcmLuizU82dFH/67U888UQc5GGpGtnE7MBEv9JZ9P8ySG9A6YKkA0mgzvZV2T0iszcuHyi1m1EnrTXb2ckOdJTbTa/UZtRXWGGF/F7VKHwYlesDOZ1XzIiRxp6k9NVHHnkkpr7TsST40exhxoatK5l9TFt/8TugbQnUyV6oLnW4XM+/Ysk+a8hEICuEXUhIN6zuPfCeWncMLJHpkZYWMONG6jYd+zQwourPNTK0uKeyvCJd6zM6F2c00KHi4Pwlk5Hsm+wWrJWOeyWBOSnsBOdUt2dCaWbvL/cJOgJxBn7T8jSWoXIdU9w5DfZQSJelKRSBntkAXKUxSC+y7AVXXTXC7F7JFOBiFLK6dSiqXaBOp4ft6rKDHqq7mm53kWbUCdTTDbfSsNUaQU3h2meKxpHuT9E4ixfNHu6b1EAgJZAZyGx14BSos+yCEfgZbQemmT+vCBCZ2aBQDwUO2caK1HcV14gRI2LdjtGjR8ft1ZhBoxgqg83MJs1qy7VKPT95HrHXOc8b7qnsIZ3SZh2Iaxxk0RGk77777hVd3Ks6ZHwQiFKHKqm0AaX0szAhycAFWOZLKvtBBx3UyEfXNDQPKqrmzf/bpEcffXS44IILwqRJk6b7+rRp00L37t3DfffdFy677LJwxx13NNLRli7aqKZWWGGF8Nhjj4UnnnginHvuufV6XJXi22+/DS1atIh/f/jhh8MXX3xR7ftWXnnlcPjhh4cNN9wwHHroofF3UK4Y1MQbb7wRbrvttvDqq6+G77//Pqy00kqhb9++4dprrw1nn312fA/XPW2x1FJLheeffz4svfTSjXz0Tfv6577JPbN///5hjjnmCFtssUX+a82aNQurr756GDx4cNh+++1Dy5YtG+GIm2Ybp+fVKaecEq9fztU2bdqEtdZaKwwZMiQ89dRTYbfddst/D++58cYbG/Gom/5zbNFFFw2jR48Oe+65Z9h4443D+PHj43lNP+DLL78M33zzTaMcaymfn9ddd1149tlnw4UXXhiuuuqqeP3zXLr44ovDd999l39W0a5quP7YkUceGU499dR4n+C+8NVXX4VKlW2jKVOmhM6dO8d754svvhjOOeecMHHixPismjp16nTfy+vV/b2ptkHh+cI1ut5664VffvklrL322mGTTTYJV1xxRfzanXfeGUaMGNEox9skNPYoQbnIjn5RDIFR8ezepjMaISb1zfWSMx49J+2FGYeajNLyHkfUZx/nJKnDVHJn5oKZy6+//nqm38P6ofPOO6/s25/ZXCrZU7CMNLbevXvHWXKyZgYMGBBn1Nm1gWKGrqua/eufFGDWO7LHbqrmTAEpzk+2sJyRcj8Pi4kZyQ4dOuTuu+++6barI02R9b8rr7xynBnivPd5VfvzmLoUnMupuBbPKqqPs/SN3V5Au5INwpaVlS7bnzr++OPjMheqYGefQ5y3tBfPKLKZNttss5gOr/o7j0lrZwaU5x41PxLSlxdbbLH4O6rESvrZNuJ5RQyQ0FZkgJFpl92CeVZ9qqb486frNtWISsgYZkkgGa/8PS2J5E+WVbFk0OdK9QzSi+yCCy6I2wpQFKE2DyJP0OnbhIcwnXFu/hSWoG2rW0JQyA767K8148ZJm88333z5yvk1TcMqt/ZPPzftQFGTq6++OhYpY3sqAhcKmaVt/vhz4MCBueuuuy5WZFbd0Tnn2mffeR7wpAIT1PD74E+WWLA+XXXHWsCllloqVl4G91fOW5au8LV0Th922GGxMnN6TpXbNV5s2XslBc8YuCPFncEQUoOp55GwRpMisizXIo3btv1/F154YVx2kc7F6qq3s/aXzj/FO7PLYFT8+zG/i/322y8OkNLmbIObsJaYrXEZNEk1QyrtWqffnwaUUo0kzlfSvdkSmG2CqVjOYBK7OJWDdD2ynSSDNTynOQ/22GOP3K233poflKSvxPM8FXjlPkcfn/MouwWbqjJILyIKmeyyyy5xHcp2223X2IfTpDErywwOsw/MnFFwgjX8ffr0aexDqwgnnnhiPI+pmp1mfyq5CB/rRBkR32GHHap0QG666ab48OGBROaBircPOhVgX3jhhXwFfYoUMdsLgsWHH344DiIdccQRjXbMTR0zPgSPBOmc48xycM0vvfTSMXhnJr2QA8o1d+mll8ZMJLKTUsDetm3b/F7RdFQJbtZee+0qO0QYqP93T3T21qYN02ARWQbMmO+zzz75Oh/cd9n2MruziIqL+zAB1lNPPRU/ZzCEfhmv/e1vf8u/75RTTonBZyX2E84999z8gFLhjgJ8TgBPRhJtxmBcOQwopZ+P2kTU1WAAkpoRVLbn+cEznElLcO2SYciAJdd19+7dcwsttJDZhrNgkD4bqrsRkepDR4dtbLiJzeh9mjEesn/961/jaGTWsGHDYuB4yy23NNqxlat0jtLR4cbLXufMphF88mBJD+dK7TzSkeaBQnZB4Qw5gTpVXJlltzpz7aXAO4t9upl9SA93lhhcccUV8XPSg0kV5DxlSVGlnpO1Vd2OIQQ6bItDkNiqVatY3IelBaQPUzG/tlsrqaoePXrkn2MslWnXrl3+PKbAIc86lnSdf/75BpnV2HrrrWNK+7333huD84022ihWdmfgIxscJt4LinuvSH9y7vLs+/HHH/PvYUCJLQTJdmSQpNwrlM8MW65SJJYMOrBE8/7774/9WDILUoFDBkKJC8rhWk/nxhtvvBErt5NpkT0/3n///VitnX5T2qaX1xis2H///WOfymzDWTNIL0KHh79nKwpzQfJwZsQ83bwq6YZVW4Vtww2PUbnTTz89377pZsZ6KNICaW+3qSuObDtSHT+t/QVVs6nwzDZiKSUWpDFV2q4ErMFjhvGQQw6ZrkYCKfCcl5W4Hm92kO5WuDcsf+c6Z0sWZiDpAKTAhs7NNddcEz+y7JzX/Bon1ZrsmLSMhT8ZkHv88cfzM7m8n3WUDECp9jgfeWaRFkzGB2vSOY+vvPLK+HXamY4ryzYKv68SzehZzgwueyeTVUeqcKrzw/2WVOvqtlzU7MkGWimTicFnZoDZ67pwkI/aKwTxWZXW3+XnXWeddeJWwGy9yuASad/MLHPdM7BUqByudYJsMtzIoEDh4APZL/SL2BvdlPa6MUifzQcKqVgEMd26dYtrppNx48bFUSS2p0j7oVbajau2bcmalnRxH3fccTHtkhmd7PvYria7DkqzJ3tOMihCGlb79u3jOmCKxHDTpYPJGnV+H7xG+zO7Xq6DJKlNSLcsrIFAShtrIJnpZW1ZVmGxFM0a6/ZSYJgttkNRLbJmWrZsWWVrPwbwCHxSp0C1Xx/NekHSENkPnVkeZjcSgh6yFOhYMYNZDh3JhjCjeyHtS4FJMuvS+kxQoI97bLbPUKkKi27RZqTIZgM/tm3KYladtdEqflYTSwmYaCIjtHnz5rFeAvcE2pw05WxBZPq5ZNyQ5Vip13r6nKWZtAVZX9TvSJMaLN1k+Ws5pLcX/twsi2SmnOWoSXpmpOcO7cB5RIZWlvFQzRikzwY6PHR2CCgpcEKnkpSPVK2VGxizRLzuetWZ3+xICdx1113jjQ6s2aOjSAox+6CnDiSFokiVUXGdeeaZMTjnRspDmQ46xbpIxQQzb4wGU92ZID0FVuV2o00/zwMPPBDX1rGfJ9d0dsYrFSviumdgSbVHcMI6toS0SUbb00wjON/IRiJFkE4jszYMhjLD25TTBBsLHSnWTDJjzgwIA26kunM/Tdc55/YGG2wQZy9dH123Ku6s409r+clM4pwlA4cBKdqS13i2scTAtv1/3E/p8DPpQZ0PztXsDCQDdNyHaU8Gicv1GdSYmCnnd0CdCrIXssu3mGzidc5dCoQR0BO4E5hWynmcvdavuuqqOKBBvzVVuue5lDKUEorEUXizHJE5yD7w3MvY3SbbTtnJDs4pCu2q9gzS64hOJQFLSgciuGTWh+JmXLipgiEPZm5odipnjDUqPJB5QGS3pWB0lqCcba1IHSLlmsITPpyLh5spARCdIs5pDB8+PGaAkFJY2M68Nz2oyvWc5rwjOGS2lqCRjghFnW677bb8exgdpwBKtuK1ambEiBExIKfIZkqBY4kFgyKs7Sd4TKlyzNywVprZXwZGDB7rhmuY4IcMkCzW+7NdKJkL4PfB0oJyWDPZGM8xBj0IyKnkzGAHs2cElpzXpL0S0DAASsaS53HVewJrntPyQAJyMmhYw08QkAZAWGpE8c50Xnp+Fkf2GU+KNjOf3C8Ks8VeeumlWLeCitwMlpLWXYnnMQP3TNCR2Uk9CSbiOE9TGzBRR0zAgAYDSuk8Lcc+K312iuoSqJNpmKS2SIM7LJ1U7Rmk1wEnH2lZqeooD2HW5dC5ZHSRGxwXb2GqrA+U6VFEgw54SnXlhk+HnRQZOjjc7K6//voYDNGR9OE8+wh+suvOGEjiJsrAEudydg0wr9H+hWuwyzXVnRlFBoLSjC7ZG4wCUxyHh1A29Zqt1igQo9pLxfZ22mmn/JIWHvZ0DFnbR3X3hM4OAycULyz3AaL6QJvxzGK9ZKqEn0295FlFDZA0sJxUUqd7drG+nJlHAknQWaXjnlJeyU7iPspsEmncDoJUxVIAzsFsbR/+zmwl917uy5yzZC6lQMe2K47CZzn1Pjif2UqMTIY0m54tLkvfloyQSvxdMJDEQFy6tlmDzkBytk4KXyOLlkzQNIhRzm00o0AdDAzz7OEeqNozSK+B6qpVkspOB50AZ/XVV48zaykAYi9UHtDMoKuqwpFEiuoQFLHmjLR2CkkRtDO7w2xbdet87TzWve2pxMksMQMezFaAhwhtzUwmMxdpBh2kxVJRl0ql5WRGI9rMHDAjxgOFc5KHMVXGGQVmSxFmc9NMr2ovGxwSsLC+nNnyNKNOMUICdWbMC4vDlfsAUbHMqH0Y6GS9ZFrfm+6jDDaRkul9te73DzrkaWCTSuS0c7qPzqi4me39/+3IbBv9prQ1XcLzikw6lmhU930q3r2CJUgs4UxBJUvfKBZHoP7uu+/m30dQWkm/i8L7KQPGZB6mNsoWhKS/mrZb5JlWSYPK1QXqxEBsk0qxUtWNQXotLlDWmpDuS2CeMAvESG/a649ZYDr1bg0087ZkLQs3Lm5opLGTGkjK+7777hs754xWMpJb3fZMmj2sj2ILG6pnM9iUZjJIN2QbkYTOJXtZku5dTudyOg/52ZklyK6N5udM1zf7fbIF3YQJE/JpgHRaaCMexuXeOSm2bHux73nPnj3jIAiZRwTqqfYEgToz7My0p61bVPt7LOc1tSRSYTjObdqUQVBmJjmvf/nllzhQwrpK1ewczrYxz32eYwxycD9l0JlOO39Pbc4giFXyZz6ARKYWgQ/P/2z9Hu4FpAunNf6qv3oATIwQXDHRlLAEkfsFg1AEpPQH6CdwHVTa849nEVvTssR1/fXXj+vQGUBKg3Mp64vJjuwygUoaVE6BOu3Dkh8qv5vmPnsM0mciexOisBYnHTOOFNTiYQyKGbEWvV+/fnE0mKJadHoqMQ2oNkXiqHzP+qY0MknhPR4IVL0FgyGs3+OmqOLIphKSgkR6Jp1J0lxpd7IYWLbB3p577bVX7NBnC/SUQ6CezkNGdlkXyrpyOiHVbZHCz0+l4YSg/aKLLoppfqo7Zmzo3LC0gg459wPWNxKYpwJmPOypfs0a1ErrDBarijvPKdqZazgF4QRDFN4im4bX+bDOR8199913+b+TcUOWER1y7p3cL5hBTwE6uFewbjdb/bhSZfsADFqwpRr31NQPIMDhnCWThsCHderUpHGXgfpFlhyBd7Zqe/Y+wNf5HaTJlEq5V2TPV2bKaSOKmPKMokYSOzbw7EoY8GQQg75TubfNzPDspi4XRXezO7aobgzSa4CHCQENFZ+ZlWDUnGAmrUe97rrrcnPMMUfcoopUj0q5idUFHRuqiA8dOrTafbZpO2YyCRRZm+rDefYRkNKuhVuAsD6VVCQ6lXydmTUGn9guhM5TtuBhOQw2pYcuKZQUxiMApyNIZgFryrKzXwxcMJDEeci6SArF8JDO7iGv2uF+yEARA5ncB7JoY5YTEEym1HcyHdLvzHtpzZGlwLZfFIBkjTQBEQNRBDwJRRA538lacn10zXCO0oaci2R9ELCkQmfcY8n8YmCZv9Om3Cs41yk66XPs/3HfJWuO9bq0F/0BJjl4BnHOkrHEsiuWFjHhUU6DxKU6Q8yAaPY+Wzj7Sy0Alr5VUvp2wkwws8O33HJL/rU77rgjZh706NEjvk6dCTIOs0XiKmkGvRB9eCczisMgfRbSfqZpH0jWm6XAJntT4yZG6nsl3sRq6qGHHoo3tjS6RltxIdORTGnXVMpkpsfqt8VBdgL1Eejw7LnnnvHhkk3vZj/aNPszo32+y6n96WiQgpXdZ5vBNoL07Kx5KmpIdVZ2cSCDJi1p0exhxpzOTSFG30kVpmOeLchXyZ2d2uKeSZBz+umnV7l+yfIiUGewqdyv8frATBr3UQYxqedBsM6yl7QUBswIU/GZwJMMHQaZfY5VxXpm2ig7w8ZgMNkcPPvTjCSZdAxymJFY/0hxZ4IpFZNNbc55y2RKNgOv0u7HFICbc845Y0ZStpgpbrzxxrjTAM8ssmhYsuW1rmJrGTRTEyZMCK+99lpYbbXVwhNPPBH22muvcMEFF4SDDz44/Pzzz+Hvf/972GeffcKSSy6Z/55p06aFli1t2kK///57WGyxxULHjh3DO++8E4YMGRJuueWWMMccc4QOHTqEhx9+OHTu3Dk0a9YsHHXUUbEN+R7bsm44D3/88cfQunXr8Nlnn4XNNtss9O7dO8w777zx97DjjjuGI444InzzzTfxPKadt99++7DQQgvF72cQj99FixYtQrm0x/XXXx/mnnvusMACC+Rf5zz87bffwocffhgGDRoU5p9//rDLLruELbbYImy88cbhhx9+iG2w4IILNurxNzXp/Cn8fNlllw1Dhw4N//rXv8Iqq6yS//pyyy0XunTpEtZee+2wxBJL5F9v3rx5gx97U0T7cg1zrU+dOjX/OufuOuusE7bbbrvw1ltvxXOde25WuVzj9eH2228Phx56aLj33nvDlltuGSZPnhz7AvPNN1/49NNPw8orrxzbfq211grPPPNMePvtt8NHH30Ull9++fh+2tbn2H9NnDgxzDXXXGHhhReO5yhtc8opp8S+1IUXXhgOPPDA0K5du9CmTZv899ifKg7asbp76UorrRTGjx8fz2/6BDwf8euvv4ZLLrkk/PTTT/H3Uon34/XXXz/0798/nHrqqeHJJ58Mm266aejUqVP8Ws+ePcPuu+8exo0bF8/Ztm3bxueb17qKquhhfxM2o5RKUjApckSKbLbiMLM9bC3A7LpmXRGfNaiMorNuh22tKEZCBVwKklBAKqUOJo5Gzj62sCPdlfVTpLUxWs7oMLNtpGGSFssaQGaJeE+5n8sULGQ/U5alkD3ArgxkxjCzzp7RjIyzPpqMD5a1WLiwbrKzLVQTZ51adts/2pg9pSkcyTpfZmsouMMs5YxSLlUV90ueR5zH2WKmzEhSHZ/U4SwKmFGci3uCaob25d7IWmmWC6VzkkJmFDyk0Bnn9sxU6nOMJUOF5xqpwTz7U+ZcqnxPFhf34XLbRaRUZO+lZNNxL6BuRSoSx5Ivlh1QL4TMxlGjRsWMRu7TlZLFMLPnzTnnnBP7rmR9ZJdpFsYMLstSsRmkV3OBsv1StkAMHXgC9GyKJmluqUhcpT6Ea9KWPKSza6Fvv/32mIp555135ts47dPN+mAV/2HDg4NCPHQ2KYCYfR/pm6RykpZJEaRKOJdTBVJ2ZaDoY7Y4YeqQXHrppTH9Ou0Rq7qdf1zrBIZ0zFluQfpkSmklHZh16AzQke7KPsmp/e3szDp4pNPIMhZSVVmSlQZBWBNN55r0SwZG07It1kxS1Eg1w32RWjMMbnKOkuKeXWdJjRoCdQKc7Oueu7n4fGfwl4E4Bo1SUE46MNc66cGFy5C4/tP2Vaq/Ku7UVqHeCs98lnKlLUUpJkuBOPoJLNnIFokr935B9pnFwDz9Jep2ZKu0UySO3V369+8/y4E5qVgM0guwRpd9z+k8ZtetEqCvsMIKceacStB0MKl2mW5izvrkpmsHKmHTXnQODzrooPzrqc3okNOxpPotMz/l/iBojBFzZiYYKOF1Ch+1aNEinuOF+J2kzmUl/B7oVFM4j2uYnQWS7IBSpcwg1BfOMwpuUheB9f0MarJWN7u2j848+6UzKJLauxLOv9nBdczg0l133RXvnxQtIlBP+5+DrdfoZDMQxVp0nmnZnRoMJGeOwWTamCwvUG+GDjrPM9ZLJ9xfuacefvjh1RZCrdTzk3W6FIekXQj6CHgSMrm4DzCQxCAxA0lk1xE0eu0XF2v/0445DJByDlM4NdUI4ndDBlmSagRVUn2l7L2Qc7ZDhw5x0JNtahloymZ3EKhzP+V9aeBJqk8VH6RnHwqkDTI7wXYpXIxsU7PbbrvlC2cwe7HffvvF1HdG08qp8nWxkUrFTW7AgAFxVJJqrlQNT21FKhypQ8xO8LCulBHbhh4xZ5Q8jfrSxilQz24dkm3zShpsSjPqpL5TPCfxep79zg5ZMcyipW19yFagAA8zaMze3HzzzdX+G17/M0e6cGHHmqwugnEKH7JkIAWWLO0gICJVk/b2eVVzt956a9wSLHtPpKJ7dYE6M+r8TqiqX+l4vrBsioGjhD4Uz3oGkVJxUjKUuBd06tQpP/lhH6D4AToBJ0E3GIzeZZdd8oNQ2e0Cx48fH4sfFw7eVVJ/gEklzse0JSADx/SVODcZaE7SfdaBTjWEig3SCy+wF154IV6kzE4kpF4xIsya9IkTJ1b77/hAmR6V8LMddD5nuQCDHtkUKh7kbG9n57G4WOfPAAl7ehaen8wUp45UYTXzSpQCdTI52AZIdZPtzDGjyOzNySefHK9pKjqT7s6AEXvMElAS7GT3k9ascS2zvIp6CSmABFXGeY1Udr5OCjb3gBn9G6q7GQXq9BUq/flFH4rBissuu6zK66ROMxDP858BUTJnEtZEU8W9UmZtGxoZNtSfwYknnhgnmFhvTrXy7P2XPgEDKSxDqkQMHh166KFxiUvqm7LNMpNNLM9gyVba4WlGNZek+lCRQTpr9bJbgJDaw8OFD/Y8z158BJrc0OgAuQ6lZhjoOPvss+PfSWWjOBkPZgoZscaPUcjCh7Gdx7ohrbUQ2R7777//TEfEmfVZf/31fcj8L1CniCHLMkxhmz2kAbI0iM4eRaE47yjGR8CezkE+Z5kB6yI9/2qHWXMCcTqNjz/+eL4tKWKa7qGkaLJnd3Z7MBU3UGfGje0ZC1PcKznIpK9EsU0G4VK7/O1vf4vr+RmoY/aWASWWEqYisdnrv5JmbetbuhcwWMeECWv+Cc4JPOnnkimScJ9mKRKD1ZXcXrQPA2/U9OCcpRYFOG+ZrGOAKVu/xmeXGkJFBukUMEprT9OFRoogwTjBTRpNTF9LI8TZFGHNHAU36CRSQZwRWvDg5gFOW7KuX7OHwmaFwThZCswKsxawcPCDrzHjw1IDOBqcq7IWL1v8STWTPXfopNMhTOmCIAOJ2ZxUsJC0SjKTWB/p+Vc76VrmvkqqMOmqrO1NFZpTkEMtAAbgKnVWrCG8++67cc36UUcd1diHUnKDxixhY0kLg54UIkvnJ8gAIdMjOyup+vPBBx/ELCaWHYJlmmTdsAQmrT8nQKdwb6UU7SwcDEqfp5+bNHfunzyr0n7o1E3q06ePA0lqcBW1mV/al/PWW2+Nnw8ePDisuOKKYYMNNgg77LBD3BeSfdDZQ/mss86Ke8kykPGXv/wl7unLvqea+f6baS/kxRdfPLz//vth7NixoXv37vFrvM5esrQ/+85r9gwYMCDu6Q32611yySXjOcv5fM0114TDDz887kmdfPHFF3GfcPb8XX311ePvo3Av60rVoUOHxj6EJimdOxdddFEYM2ZM2GijjeI1Ds4t7gsbbrhhePDBB+P+3M8991yYNGlS2GmnneL3zmjvXv1Xtn3SXubsY/zAAw+EnXfeOXz99dfxPsveva1atYrPuJdffjkss8wyVfaaVnHRF2Av9MUWWyxUsn//+9/hyy+/jNd6t27dwpprrhmfS+wtPWzYsDBixIjwxz/+MfznP/+J5yPPqM6dO3tuNsB9Azz/+/TpEy6//PK4p3evXr3iffeCCy4IAwcODB07dox71r/yyitxb+/UR66E+yn9/3fffTe8/fbb4YgjjghdunTJn6uTJ08O7733Xlh11VXDPffcE89t3lP4b0j1LleB0ogZMzykBY4cOTI/S0FFbEbISdusbmSxktPZstgnNs2Qo7oRRioPM2rL8oKnn346jqyTqpnea4p73WWrkLNEg5FwKuWC1Daqk1LRmQI9vJdRcyrosiuBo8GaXYWzLSwXIEOGlMDsfuggtZXMGdalkv7qjhg1k20fUqxJx2QWPbU9Mz3MqK+11lpxWRHPpr/+9a8xm6FSZsVmVzHOwUp9jpEGzPOEZSvZHTJS9iHLAUht53kEzklmJJml9NovLrYNowAfhSUpAJdF8Uj6uqnCPvdftr3ldWqEVGI9gOOPPz4W1SPriK3n5ptvvrgmnTYgu4C2ZEtAMpVYk+6uGGosFR2kg9RgUrPoSGYDdQprHXzwwRX7AJ4Zqt0feOCBcUsf9kBNsg/e1MZUxWTLimWWWSY+nO2gFxcPW9ZUE6QzCMK6f/AApsPOecwDh447qYe2v2YXA250yqlrkK3TQZEdAnWKELHOMYvzjvTrdF+opA7h7D6j2AqUNZIMeLL7CFvYpTW/BOobbbRRDJboTLKcyCrZNZO9B/L8p1jUa6+9lk9z1Yxdf/31sRgsfaXs1n/ZNbsULqXAHucuKe8M1nt+1s+94qmnnoptTTDOfeKCCy6oUq+GgRSCzhmphP5AuqfSR2Lw6NVXX82fp4XbBFKzivsstZQsbKzGVFFBevZGlL3gmP0pDNR5CFGJ3JGz6rG9z5FHHjnd9lWFN3s65ml7j0ocsS02OpKcm2A9JAUNQUeJIJyZNYIoMINOET+CpiFDhuTPbdtfdXXTTTfFjiDnHudVIQY227RpE2d0smuiqxvAU/WywQtr+QnM2dMY22yzTSxaxuBoNlDn2l9ttdXyAZDX+Mxlz0EGlwhsll9++bgrxhFHHJF78803G/X4ShnPlzRYlMX2XgQ72Qw7AiDWPPM6QaLnZ/2i5gy1k9h+lcF5+gfUT6Bvu95668X7d6WZNGlSlc+5l9K3T9kg2Ur33EvTlnVZDiipsYRKTssuDNSZ7c2mvid2Kmu3z3RqY75Okb4777xzuq+p9ij4xrZp7N1J6jozGW+88Ub+6ylQZ2aN8746PmxUV+y1zV7n7NFNNk3C1pXZa/yggw6K2y1RQbhwRl0zVrjFT9pLmlTW1Lls165dXDJEx/K8886L21eBAREHQWuP5xYBehrYPPbYY/O7uTCrntgH+P82oOgYVdrZZjFbxJT+E30tdnDJFtklQGR7S2ck60/hc517B4Oo/E4I1umjUUBu3333zVUSMjnZdi4tuQAz5mQdsUSI+2l2S0AKSLN0q3DXBqmxhEpPy04juyn1nU6oo+h1C9RTBVFwk6M9GT33oVw8jPSy5VJ2xoLzOa1RJ1Bn7S+p76lzL80u1kRzH037yCaksHIusv6UDk5yyCGHxNfZekmzRuYB2yOxhCAhAGemh2ub5SvM8l5xxRXxayxlYRkRaypZ8pI4CDpz2fbh2UWNBNo+DZLQaaeTTrYCuxBkt2rVf5FWzfmXAne2rWQmkuw6PmeGvXBGPbEvUBw1HTTifGcbNnaB4XdCtk0lDTg98MADscYUGV4pUCcuYNch2oNK7gmvc14zsVRJbaTSVtZBek3TsrN/Z+sqZxvrHqgzGMJDm8JlrIN2/dnsy56fzF4w8MQaM7ZgSqnvSLObBOoUPDnssMMa5XhVfh599NF4TpE6mc5HCu0wU8OMBINCZHewvCJh3bqd8pqhA0naNSnXzJAn3EvBDBgd7dSe++23X2x7BknsUNZMtp1IC6ZtR4wYkfv+++/j+t3FFlssd8kll8Svn3TSSbGYFMsLGKCqdCllmDZkFp3ne1b2+c62i6S4M7vuuVm/sjVBChX2uTjn02vl/Hvh+cTPl55TPLsYAOX+mQL1e++9Nw5YkPZOFg2z6ww0M8Nu0U2VkrIP0muSlk3la0bN77///vzXDCpr38YMcNDGzEaQYuX6s+IG6JyfpLcTjNOxPO6442JqZjZQ58FCde2ffvrJc1hFw/66CyywQJXXyJZJRaMIZMicoYDZ6NGjq7zP63/m0nXKc+j000+PgXo2BZP7KDM8DLqljBmeV6yddK/52t9HWTJEijuZCiwhAhkJ22+/fX6g8+yzz47nM21e6dkJZMgwaJGwbzSzkNdcc02VczSdgzybmG1P63xV3Jo0L730Uvw7z38G77JLj6pTeG8o9/txde3BUiECddqLZxb3XF5jsGnBBReM/dbddtvNSSWVnIoI0mFadsO0cY8ePeIougF6/RQ3IjUzVR/mYcODmhl1UtroTDLzk51B92GjYqDwIOvMH3vssem+loIYZoDZYqmwUI9qdo2zhp+0TGZw6VBmUzEpZsaaUlIx//znP8dB0HRtV3oQWRvMnrMUo7BmB4UQWev/6aefxs932GGHuNQg/X4quY0JbOgjJe+//36se0JmTWHxuO+++y7eA7p27eqzp8io77HTTjvlmjdvnttjjz1iTRqXZlbFPZTZcO6daflVGthk6RX3Vdaof/bZZ/nv+fjjj+Mzy51HVIqa8Z9QIcaOHRvOPvvs8Morr4Qdd9wx7LvvvmHnnXcO33zzTXjjjTfCHHPMEaZOnRpatGjR2IfaZP3444+hXbt2oXnz5uH3338PLVu2bOxDavI4Zy+99NLwz3/+M6y22mqhTZs2+a/99NNP4fzzzw/nnntuWGGFFcK0adPCv/71r3guS8UyevToeO5tvvnm4e9//3tYYoklqnx94sSJYY899gjLLrtsGDhwYKMdZ1N10kknhWuvvTb0798/TJkyJdx///2xzQ866KBw3HHHxfcce+yxYdy4cfGeesUVV/i8qgHuhzyLcOONN4YDDjgg3ieHDBkSVlxxxfz7br311nD66aeH+eefP0yePDl+31tvvRXbmi5Ss2bNQqVJ59Y111wTP15++eX814YPHx5OOeWU8Oabb4bdd989dO3aNXz55Zfh8ccfj/cC+lien8WTzkHac/nllw+ffvppPJ/33HNP+1mZvme3bt3CqFGjwlprrRX7+/PNN1/44x//GA4++OCw9tprh3fffTdsueWWYe+994731uw9AJV6rauE5SqMadkNo5JnHoqJWXNmeC677LL4OSma7InKej/2Qk1VSJ9//vlYfdtt1lRfmFls3bp1nMXJFtRi9nHzzTfPdenSxfV8dUBGDG3HntPJBx98EGd3F1988fw6aWRnJ73Ga/4MIgWWGWBmeVu1apXfZinbhrQ/yzpIfU+vOxuci+nVLKvKVrvHqFGjcqecckosYEiWB+t7jz76aKu413O2zUorrRTXT/M74bkP+1v/bYMXXnghbjVHG7HtL/0maihQb4JztG/fvnHHjLnmmisWiEzLtaRSVVEz6QkjbH379g3fffddGDZsWBzxdTRSpYZL84cffggbb7xx2H777cNKK60U7rnnnvD111+H//znP3FUfdNNNw3nnHNOlXPX2QvVB86rG264IRx66KGhQ4cOoXPnzvG+ycwZnn32WWfP6oBrnLY84YQTwhFHHJF//aOPPgrdu3cP33//fTjyyCPDqaee2qjH2ZSMGDEijBkzJvTs2TPOmIHsA9p0v/32i7O+zz//fFh44YXDb7/9Vm3mUaX2CR555JHw6quvxlnJBRZYIF7P/J1sA2bMC2cbJ0yYEM9h2jJleXkPKH4mCNc//dXbb789ZjIdeOCB4YEHHojZC3/5y1/y38N5v/jii4dKxLnJuUuGLBkHtFerVq3Cv//975h9cPPNN8fsQ9ps3XXXDU8//XS+faVSVJFBOkzLVik/kLMuuOCCcNlll4Xx48eHww47LGyxxRZhww03DL169YrnLWmyUkNhaRDn3AcffBA7g6uvvnoMhOiUey+t2TWe/ZMBN9IvF1xwwXDGGWfEYCfh9ffeey8O0DFAYirmzNGd+fnnn8Muu+wSg0ee8SNHjowDSCuvvHJ8D4F6jx49wrfffhuee+65OODkeftftBltR6D366+/xgmN9ddfPzz66KPhr3/9a1yW0bZt27DKKqvM8N8wZbj4CDCZWGKAiec/OH+POuqo8NBDD4X77rsv/PnPf459go4dO4aLLrooVALundnlfwkp75zH3EufeeaZ/LXNdc5994knnogTHDyzZtTvkkpBxQbpiReoSu08ZIaHzhKdoQ022CC+9s4778SH0dJLL53/HmY3mIFjjbDU2Jw9mznWQT/22GNxxnzRRRcNf/jDH/Jfu+2222KGwjHHHBP22Wef0KlTpzBp0qTY6WY2nT8JfAyAZi61D+vKCVoY4DjrrLPCySefXOV9BOrMsrPGn1l3Ahv9Vxqw+OKLL+IaXmbJWX/+8ccfxxlcPmeWkvW+/Mk6fwaRVD/IAOnXr19YbLHFwh133BHbPCFQP/744+MMcZcuXeJ5z6xxJdSkIbODn79Pnz7xugd/pn7U66+/HnbaaaewyCKLxGucNmHgiZn1xGeWSl3FB+lSY8t2vJmpIKWdFGI6RHzwYE6YTechTIr7Z599Fh9EzgCpoRks1g6DbmQc8CezOxQ2YoaSQDEZPHhwOPPMM+OMLwHQ559/HmeKSN+kI2mbz1y2fei877///jFoSRkJDHRk30egvtVWW8WCiHfeeWejHnspqe48Y7kFwQ2DSF999VUsIsdgMu+lgJmBTv0hM2S99daLGUx33XVX2GGHHaabWKKoLH0DzvNyz2jinCO4ZhCODC6KwjHJwTnLB6ns3De32WabeO/cbbfd4iAcs+eVMHih8uIUstTIUoeICu3XX399uO6662J620YbbRSGDh0a0wwTqubSUeJB9dprr8UHMQ8sqSEZLNYOs+akXzKrS1DDbBipqlRnpqI766JZynLvvffG4B2sM+V6p9PNNW6bz1xqHwYwuX+SAkzwPffcc8elArR79n1kM5AKmx0EVfXXNhXx77777hioM9hEYMTs7S233JI/PzX7CDYLkVHHgAi/A2bUGZhP0hwbgTuDUOl3Ua4Bejo/GYRgbXnr1q3zr/HBYAU7NzGwgTXWWCNmMNFXYqBJamoM0qVGwGw5D5mE9b2kZBGkU9DkySefjNuuHXLIIXErIArHYbPNNguXXHJJ3KIpFTx0FkMqbVyjBN9sp0YHmu3UKADJ9kB0vJkVYitFtgA77bTT4ozZoEGDvMZridlEBjY/+eST+DmFz8hQoF0JKK+++uq4xR0DoBTiIhXWIHPmCAQZVGIgKc3gpvbia3x4fhZ3yRvPfwaV2OouLXVjwI7fARkiBJ1IS2CyKuV3weAb9SRSO7CUiG1AqeGz66675t9HoM4adfpTUlNjkC41MAq9sFbqyiuvjOmvWG655eIDhlkKRs15ELPWnA4ma1KZFWKfT9ChT0WnynnEXConpFaTjnrVVVfFz+l4/+Mf/wjbbbdd3H+edExmy8ikSeiAe43XfOaRQnHzzjtvDG7AAAcz5hTeXGihheLAB21M4Vhm3CstsKkLAiCyOhgwevHFF6u0V5rB1OxLAToDedwnyKyjPgX9AjJCmFEn5f2XX36Jld1feuml+P5KaX8GLsg6AuciS1kI1BMKb5Ixk3ZzQBrA+NOf/uRgnJokn/5SA9t6661jAM5sGg8RHirM9KT1qXQomTGnAjGYbWMmnfdkC51Y8FBqWhiEoyNJkEh1Ydae33TTTWGeeeaJ24JRiZx0zaRSOuB1le6BzJwTxDCzRgEtApnC1HYykJiB/Oabb8Jee+1V9mt3i4l2pJAhywhUfyggyQw6advUreB8ZVkBS9wITElrJ92d85lB/jRwX+7IfmGggkEilg5R5Z7XyCzI3lv5yCq8fzoYp6bGwnFSA2L0Oz1Y6TSyLvXss8+OlZ2ZBQKFTkaPHh2L8/AQ4nOqvKc1VVYklZouOt+kX3JNs+yFwbdCBo8zR/utueaa8e+slWa9PxXxV1xxxRiwk/bOYAgVsak8zn00W9UZ3kdrH0CSRux5WX/Y+YEdCahNkV0KR0FJZo45p8kUoUo5524lnb8ULGRJEH2oddZZJy75o6YHs+lk1KT16QTvLCXiXOVeKzVlBulSA2Hkm4ct1UfTljXZQJ315zyAH3jggXD00UeHueaaK3aIKILy5ptvxr9b4VlqmtK1y9ZB5513XpwxY72k13Td7qOktLNWmi3BCGSo6EwmAnt8X3vttaF9+/ZhzjnnjLPttC9tns1SUN04gFR/6Acw6ERqd3bwjiUwbLX29ttvxzoKlTrQRPBNGz311FNxrT5ZhgTpDGCkCu8sI6LtWD7keaqmzjNYagAULKJ6Mw/g7J6yRxxxRHy4sNcnCNg32WSTMHDgwPDoo4/GBxAVoVMV90p6IEvlJAXiG2+8cexwM1hHkG6AXrf7aNovmg45a6ZBQThm0t9///1YGG7ZZZcNTz/9dPj+++/j2n/NPgOf4haJyyIThEGnYcOGxbo1ac015zqZIswSZ1Vaf4ABilNOOSX+3ExocL2ndepkF3Bu0q5p4NM+k5o6Z9KlekahqN69e8eqw3/729/yr1MgjrSt7Iw6Dxxm0ZkBynL2QiofVBo+44wz4hZgdMxV9/so61RTkI6JEyeGpZdeOg50UoAry/uoSilAJ62dtf4Ek5yrvM7z//bbbw8nnnhiXBJDQbQDDjggnrvMDjuo998ZdQo/kvrOOn3aCixrSXuhm6GkcuDTSqpHPIRJY2dkfJtttsm/zqwORaNWWWWVmNbOjDoPFB7QPLRZm0YxqcSOpVRexSNZV51mgzV791GKxTGwSbDD7CP3VQrEFfI+qsZE4JgCdJ71LHnp2LFjLBrJzgMM1l900UVxfTVfox9AZXI+f+6552IfYUaz8JU2o37yySfHQJ3lgaS7M8GRAnQYoKsc+MSS6gmpaaSsM6uT9u0FaWwffvhh3IqNAD2lZB1++OFx7/RHHnkkrruSVJ5YS0kn3JTM4txHU+ZRakcGONmuCs6oqVSk8/CLL76ImXQjRowISyyxRHxt2223jQNRFOhj+7VevXqFsWPHxnOajDt3I6hq4YUXDieddFJcOvTtt996nassme4u1XNaFgWLSMuiSjuFjShyxBYrdDrTgyU7Op5e86EjSbW/j5IC/+c//9nBD5UcZsqZ/WU3FwJyZsk5Zxmsoxo5a60pHFfIwbzqsYafNsuuRZfKhUG6VM8YDWdm/MEHH4xbA/3rX/+K+5xm10917949dO7cOXZEU5VSHzaSVPP76FZbbRUDHdb7w8BGpYQdCC6++OKY2r7AAgvEau1gB5e2bdvGIofUWyC1nTR3+wA15zIAlSPPaKkB0rKoSMpayqWWWirccccd8XU6lnQiCdA/+uijfJXStGWQJKnm99HRo0fH9yQG6GrswDGL7cF69uwZ11OTCXLsscfG1wnQwYATSzWYXbcPUDsG6CpHzqRLDTwT9PLLL4edd945PqApfMR2QW+99VbsbLrmTJJmzPuomtrMLucmATiz45yfzKhfccUVsTDcQQcdFKu3897jjjsuTJgwIe76YNApySBdauAOJhVJX3311Th7zloqUt7sWEpSzXgfVVPB9mA33XRTPC85PwnE99hjjzD//PPHrRjJ/CCAP/jgg+N5zXsphGj6tiTvAFIjVCRdZpllwhprrGHHUpJqyfuomkKK+3333RduuOGGcOWVV4bHHnss7LnnnuGqq64KAwcOjFut7r///rEOzUILLRSXZtx5550xQGem3QBdkjPpUiP48ccfY3VXHsR2LCWp9ryPqlQRnBNss4d3WnsOAnQqvFNAjiJxbB82ZMiQcPrpp4fevXuHM/+vvTsL0bps4wB8i58p4pqOmJFoiOGaqKAiRKSoBBmmTEIGmnjg5JIYSgcuKO4UpaIgQy5gLiB1kmDkgjaCgoiEihsTOnWQghJCmqgfz/Mxw4zLfGMu8+pcFwzMf3mH/3vyMr/3fu77WbSoXp8bKBxCOtQjS9oAHo/PUQrJlStXYtCgQVFeXh4lJSWxdu3aGl8ipVkKv//+e94rvfLLprQd24wZM3JIrz78EGi4fO0M9cg/lgCPx+cohfSFUfv27eP777+Pzz77LHbv3p0DedousHIf7759++ZtBCu/XGrbtm3uU08tG2+//XZ9vwWgQKikAwDAY0jDDG/evJm3WHvppZfi5MmTOXynML5r167ce556zkeMGJHnKqQe9OqsCAGqU0kHAIDHkCrhaal6ixYtYubMmdGrV6/47rvvYsKECTF48ODo2rVr9OnTJ65duxZ79+7Nr6msricCOlCdSjoAANTRw6re69atywPgli1bFrNmzcoV9bT7QPr9yJEjUVZWloN6YtghUBshHQAAHtGpU6eiZ8+eNc6lQXFpCFwK6qkvvWnTpjmop6XvKdingXHNmze3vB2olU8HAAD4P1LPeaV9+/ZF796982T26lIlPe1/Pm/evCgtLY2///4737dt27a8H3qPHj3yPukCOlAbnxAAAFCLn376KVavXh1Hjx7Nx++8807Mnj07pkyZknvPq3vvvffykLjp06fnoXFJ6lH/9ttv47XXXsv7owPURjMMAAA8xMaNG3NlfPTo0TW2SVu1alWuiE+cODEfpyXtSVrinirq/fr1izFjxlTd/+abb+YKfOpVB6iNkA4AAA+wffv2HLhTUB81alS0atWqxvW0tP327dvx8ccfx7lz53LFfMuWLXly+5IlS+4bEiegA3VhcBwAANzj8uXLUVxcHOPGjYtPP/206nzqKU9D41I4HzJkSD63cuXKWLNmTd6CrUOHDvHzzz/nbdkA/g2VdAAAeIDUP/7qq69WHa9fvz4vWU+95q+88kq8/vrrcfDgwZgzZ058+OGHOZh37NgxL4O3zRrwbxkcBwAAD/DXX3/Fjz/+mIN5qqinkF5UVBR79uyJb775Jv74449YvHhxvrdz587RqVOnHNDTFmsCOvBv+fQAAIB7pDC+adOmGDt2bA7pLVu2jK+//joPgGvXrl1cvXo196inQJ40atSo6rW2WAMeh5AOAAAPMGzYsDwQLvWhd+3a9b7rKbin6jnAk2RwHAAAPOJQuUmTJsWVK1eirKwsGjduXN+PBLxAVNIBAKAOUigvLS2NX375JQ+VqwzoadK7oA48KRpmAACgDioqKnIw79atWxw+fDhPc09T3AV04Emy3B0AAOro2rVr0bp16zwoTgUdeBqEdAAAeETpX+jqE90BnhTL3QEA4BEJ6MDTIqQDAABAgRDSAQAAoEAI6QAAAFAghHQAAAAoEEI6AAAAFAghHQAAAAqEkA4AAAAFQkgHgAZk4sSJeX/n5cuX1zj/ww8/2PcZAAqAkA4ADUyzZs1ixYoVcfXq1fp+FADgHkI6ADQww4cPj44dO8ayZcsees+uXbuiV69e0bRp0+jSpUt8+eWXNa6nc0uXLo1PPvkkWrZsGZ07d44NGzbUuOfSpUtRXFwcbdq0iZdffjnef//9+O23357a+wKAF4GQDgANTOPGjXPAXrNmTVRUVNx3/dixYzlcjx8/Pn799ddYuHBhzJs3LzZt2lTjvhTcBw4cGMePH4+SkpKYOnVqnDlzJl+7detWjBw5Mgf4Q4cORVlZWbRo0SJGjRoV//zzzzN7rwDwvBHSAaABGjNmTPTr1y8WLFhw37Wvvvoqhg0bloN59+7dcx/7tGnTYtWqVTXue/fdd3M479atW8ydOzfat28f+/fvz9d27NgRd+7cidLS0ujTp0/06NEjNm7cGBcvXowDBw48s/cJAM8bIR0AGqjUl7558+Y4ffp0jfPpeOjQoTXOpeNz587F7du3q8717du36vc0dC4tof/zzz/z8YkTJ+L8+fO5kp4q6OknLXm/ceNGXLhw4am/NwB4Xv2nvh8AAKgfb731Vl6S/sUXX+Rq+aNq0qRJjeMU1FP1PLl+/XoMGDAgtm7det/rioqKHuOpAeDFJqQDQAOWtmJLy97feOONqnNpaXrqIa8uHael76mfvS769++fl7x36NAhWrVq9cSfGwBeVJa7A0ADlvrFP/roo1i9enXVudmzZ8fevXtj8eLFcfbs2bwkfu3atfH555/X+e+mv5l61NNE9zQ4rry8PPeiz5gx44HD6gCA/xHSAaCBW7RoUdUy9coq+M6dO2P79u3Ru3fvmD9/fr7nUZbEN2/ePA4ePJi3Zvvggw9ydX7y5Mm5J11lHQAertHdu3fv1nIdAAAAeEZU0gEAAKBACOkAAABQIIR0AAAAKBBCOgAAABQIIR0AAAAKhJAOAAAABUJIBwAAgAIhpAMAAECBENIBAACgQAjpAAAAUCCEdAAAAIjC8F/pTBELxFQVlAAAAABJRU5ErkJggg=="/>
</div>
</div>
</div>
</div>
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
<div class="jp-InputPrompt jp-InputArea-prompt">In [32]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">results_df</span><span class="p">[</span><span class="s2">"R2 Score"</span><span class="p">],</span> <span class="n">hue</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">dodge</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"R2 Score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Model Accuracy Comparison"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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7/+eqrX9sknnxS22GKLwuyzzx5/D9vhsX1XdW320Ucfxa3YeC3t27cvLLbYYrENf/nll6l+L9uGsWUbv78u2B6NLdSWWWaZuHXZjDPOGLdaGzRoUOHzzz+v8tyRI0fGrdt4LXPOOWdhp512mur/xxZsHAul2Oasuq3NSo+/dNw+8sgjhT333LMwxxxzFGaZZZb4//rmm2+q/OwTTzxRWGWVVeL2bhyrhx12WOG+++6LPz9q1Kg//H9XtwUb29qxjRrHNH/nn//858Khhx5amDBhQpWfe/755ws9e/aMr412Y0vDMWPG1Ooc5LWVvkZJUj604j/NPVAgSZJaPirbs56ebIaWjJR9ZuwpDFjd9nOSJDUm16RLkqTpxrr1F198Maa9S5Kk+nNNuiRJqjcK8Y0dOzacddZZcQ9xCulJkqT6cyZdkiTV2z//+c+YGk4ROqrZs0+4JEmqP9ekS5IkSZKUE86kS5IkSZKUEwbpkiRJkiTlRMUVjpsyZUr47LPPwqyzzhpatWrV3C9HkiRJklTmCoVC+P7770OnTp1C69bTniuvuCCdAL1z587N/TIkSZIkSRVm3LhxYcEFF5zmcyouSGcGPTXObLPN1twvR5IkSZJU5iZOnBgni1M8Oi0VF6SnFHcCdIN0SZIkSVJTqc2SawvHSZIkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlRLMG6Y8++mjYZJNNQqdOnUKrVq3Cbbfd9oc/M3r06LD88suH9u3bh8UXXzxcffXVTfJaJUmSJEkq6yB90qRJoXv37uHCCy+s1fM/+OCD0KtXr7DuuuuGF198MRx00EFh9913D/fdd1+jv1ZJkiRJkhpb29CMNtpoo/hRW5dccklYdNFFw1lnnRW/XmqppcLjjz8ezj777NCzZ89GfKWSJEmSJDW+FrUm/cknnww9evSo8hjBOY/X5JdffgkTJ06s8iFJkiRJUh4160x6XX3xxRdhvvnmq/IYXxN4//TTT2GmmWaa6meGDBkSjj/++Dr/vz4/4YLpeq3l5E+D95/u3/HcqbXPmKgEKx5xz3T/juHnrt8gr6Uc7HjgQ9P9O066dN0GeS3l4ui9Rk3371j/ur4N8lrKxUO7WEMlb3pedX1zv4Rcua/fztP9Oza9+vEGeS3l4I6+azT3S5Aa3adHPtLcLyFXFjhl7cqbSa+PQYMGhQkTJhQ/xo0b19wvSZIkSZKklj+TPv/884cvv/yyymN8Pdtss1U7iw6qwPMhSZIkSVLetaggfdVVVw133313lcceeOCB+LgkSeVsg6v/WzRV//VA34Ob+yVIktQomjXd/YcffohbqfGRtljj848//riYqt67d+/i8/fee+/w/vvvh8MOOyy8+eab4aKLLgo33XRTGDBgQLP9DZIkSZIklUWQ/txzz4XlllsufmDgwIHx88GDB8evP//882LADrZfu+uuu+LsOfursxXbFVdc4fZrkiRJkqSy0Kzp7uuss04oFAo1fv/qq6+u9mdeeOGFRn5lkiRJkiQ1vbKv7i5JkiRJUkthkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBPNHqRfeOGFYZFFFgkzzjhjWHnllcMzzzwzzeefc8454a9//WuYaaaZQufOncOAAQPCzz//3GSvV5IkSZKksgzSR44cGQYOHBiOPfbY8Pzzz4fu3buHnj17hq+++qra5w8fPjwcccQR8flvvPFGuPLKK+PvOPLII5v8tUuSJEmSVFZB+tChQ8Mee+wR+vXrF7p06RIuueSS0KFDhzBs2LBqnz9mzJiw+uqrhx133DHOvm+44YZhhx12+MPZd0mSJEmSWoJmC9J//fXXMHbs2NCjR4//fzGtW8evn3zyyWp/ZrXVVos/k4Ly999/P9x9991h4403rvH/88svv4SJEydW+ZAkSZIkKY/aNtf/ePz48WHy5Mlhvvnmq/I4X7/55pvV/gwz6PzcGmusEQqFQvj999/D3nvvPc109yFDhoTjjz++wV+/JEmSJEllVziuLkaPHh1OOeWUcNFFF8U17Lfccku46667woknnljjzwwaNChMmDCh+DFu3Lgmfc2SJEmSJOV+Jn3uuecObdq0CV9++WWVx/l6/vnnr/ZnjjnmmLDLLruE3XffPX69zDLLhEmTJoU999wzHHXUUTFdvlT79u3jhyRJkiRJeddsM+nt2rULK6ywQnjooYeKj02ZMiV+veqqq1b7Mz/++ONUgTiBPkh/lyRJkiSpJWu2mXSw/VqfPn3CiiuuGFZaaaW4Bzoz41R7R+/evcMCCywQ15Vjk002iRXhl1tuubin+rvvvhtn13k8BeuSJEmSJLVUzRqkb7fdduHrr78OgwcPDl988UVYdtllw7333lssJvfxxx9XmTk/+uijQ6tWreK/n376aZhnnnligH7yySc3418hSZIkSVIZBOnYf//940dNheKy2rZtG4499tj4IUmSJElSuWlR1d0lSZIkSSpnBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk40e5B+4YUXhkUWWSTMOOOMYeWVVw7PPPPMNJ//3Xffhf322y/86U9/Cu3btw9/+ctfwt13391kr1eSJEmSpMbSNjSjkSNHhoEDB4ZLLrkkBujnnHNO6NmzZ3jrrbfCvPPOO9Xzf/3117DBBhvE7/3zn/8MCyywQPjoo4/C7LPP3iyvX5IkSZKksgnShw4dGvbYY4/Qr1+/+DXB+l133RWGDRsWjjjiiKmez+P/+c9/wpgxY8IMM8wQH2MWXpIkSZKkctBs6e7Mio8dOzb06NHj/19M69bx6yeffLLan7njjjvCqquuGtPd55tvvtC1a9dwyimnhMmTJ9f4//nll1/CxIkTq3xIkiRJkpRHzRakjx8/PgbXBNtZfP3FF19U+zPvv/9+THPn51iHfswxx4SzzjornHTSSTX+f4YMGRI6duxY/OjcuXOD/y2SJEmSJJVF4bi6mDJlSlyPftlll4UVVlghbLfdduGoo46KafI1GTRoUJgwYULxY9y4cU36miVJkiRJyv2a9Lnnnju0adMmfPnll1Ue5+v555+/2p+hojtr0fm5ZKmllooz76TPt2vXbqqfoQI8H5IkSZIk5V2zzaQTUDMb/tBDD1WZKedr1p1XZ/XVVw/vvvtufF7y9ttvx+C9ugBdkiRJkqSWpFnT3dl+7fLLLw/XXHNNeOONN8I+++wTJk2aVKz23rt375iunvB9qrsfeOCBMTinEjyF4ygkJ0mSJElSS9esW7Cxpvzrr78OgwcPjinryy67bLj33nuLxeQ+/vjjWPE9oejbfffdFwYMGBC6desW90knYD/88MOb8a+QJEmSJKkMgnTsv//+8aM6o0ePnuoxUuGfeuqpJnhlkiRJkiQ1rRZV3V2SJEmSpHJmkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSS05SL/uuuvC6quvHjp16hQ++uij+Ng555wTbr/99oZ+fZIkSZIkVYw6B+kXX3xxGDhwYNh4443Dd999FyZPnhwfn3322WOgLkmSJEmSmihIP//888Pll18ejjrqqNCmTZvi4yuuuGJ45ZVX6vkyJEmSJElSnYP0Dz74ICy33HJTPd6+ffswadKkhnpdkiRJkiRVnDoH6Ysuumh48cUXp3r83nvvDUsttVRDvS5JkiRJkipO27r+AOvR99tvv/Dzzz+HQqEQnnnmmXDjjTeGIUOGhCuuuKJxXqUkSZIkSRWgzkH67rvvHmaaaaZw9NFHhx9//DHsuOOOscr7ueeeG7bffvvGeZWSJEmSJFWAOgXpv//+exg+fHjo2bNn2GmnnWKQ/sMPP4R555238V6hJEmSJEkVok5r0tu2bRv23nvvmOqODh06GKBLkiRJktRcheNWWmml8MILLzTU/1+SJEmSJNV3Tfq+++4bDj744PDJJ5+EFVZYIcw888xVvt+tW7e6/kpJkiRJklSfID0Vh+vfv3/xsVatWsVK7/w7efLkhn2FkiRJkiRViDoH6R988EHjvBJJkiRJkipcnYP0hRdeuHFeiSRJkiRJFa7OQTree++9cM4554Q33ngjft2lS5dw4IEHhj//+c8N/fokSZIkSaoYda7uft9998Wg/JlnnolF4vh4+umnw9JLLx0eeOCBxnmVkiRJkiRVgDrPpB9xxBFhwIAB4dRTT53q8cMPPzxssMEGDfn6JEmSJEmqGHWeSSfFfbfddpvq8V133TW8/vrrDfW6JEmSJEmqOHUO0ueZZ57w4osvTvU4j80777wN9bokSZIkSao4dU5332OPPcKee+4Z3n///bDaaqvFx5544olw2mmnhYEDBzbGa5QkSZIkqSLUOUg/5phjwqyzzhrOOuusMGjQoPhYp06dwnHHHRf69+/fGK9RkiRJkqSKUOcgvVWrVrFwHB/ff/99fIygXZIkSZIkNXGQ/sEHH4Tff/89LLHEElWC83feeSfMMMMMYZFFFpnOlyRJkiRJUmWqc+G4vn37hjFjxkz1OHul8z1JkiRJktREQfoLL7wQVl999akeX2WVVaqt+i5JkiRJkhopSGdNelqLnjVhwoQwefLkuv46SZIkSZJU3yB9rbXWCkOGDKkSkPM5j62xxhp1/XWSJEmSJKm+hePYD51A/a9//WtYc80142OPPfZYmDhxYnj44Yfr+uskSZIkSVJ9Z9K7dOkSXn755bDtttuGr776Kqa+9+7dO7z55puha9eudf11kiRJkiSpvjPp6NSpUzjllFPq86OSJEmSJGl6Z9LHjx8fPvrooyqPvfbaa6Ffv35xVn348OG1/VWSJEmSJGl6gvQDDjggnHfeecWvSXVnTfqzzz4bfvnll7hH+nXXXVfbXydJkiRJkuobpD/11FNh0003LX597bXXhjnnnDPujX777bfH9PcLL7ywtr9OkiRJkiTVN0j/4osvwiKLLFL8mkruW265ZWjb9r/L2gng33nnndr+OkmSJEmSVN8gfbbZZgvfffdd8etnnnkmrLzyysWvW7VqFdPeJUmSJElSIwfpq6yySlyTPmXKlPDPf/4zbr223nrrFb//9ttvh86dO9fzZUiSJEmSpFpvwXbiiSeG9ddfP1x//fXh999/D0ceeWSYY445it8fMWJEWHvttRvrdUqSJEmSVPZqHaR369YtvPHGG+GJJ54I888/f5VUd2y//fahS5cujfEaJUmSJEmqCLUO0jH33HOHzTbbrNrv9erVq6FekyRJkiRJFanWa9IbE1u3UTl+xhlnjDP0FKWrDVLsKVi3+eabN/prlCRJkiSp7IP0kSNHhoEDB4Zjjz02PP/886F79+6hZ8+e4auvvprmz3344YfhkEMOCWuuuWaTvVZJkiRJkso6SB86dGjYY489Qr9+/eKa9ksuuSR06NAhDBs2rMafmTx5cthpp53C8ccfHxZbbLFp/n62hZs4cWKVD0mSJEmS8qhZg/Rff/01jB07NvTo0eP/X1Dr1vHrJ598ssafO+GEE8K8884bdttttz/8fwwZMiR07Nix+OE2cZIkSZKkvGrWIH38+PFxVny++ear8jhff/HFF9X+zOOPPx6uvPLKcPnll9fq/zFo0KAwYcKE4se4ceMa5LVLkiRJktRsQfpvv/0WDjvssLD44ouHlVZaaap09C+//DK0adMmNKbvv/8+7LLLLjFAp9J8bbRv3z7MNttsVT4kSZIkSWrRW7CdfPLJ4dprr43F2r777rtY7O3pp58Ol156afE5hUKhTv9zAm0CewL8LL5mL/ZS7733XiwYt8kmmxQfmzJlyn//kLZtw1tvvRX+/Oc/1+k1SJIkSZLU4mbSb7jhhnDFFVfEIP2kk04Kzz33XHj44YdjwbcUnLMdWl20a9curLDCCuGhhx6qEnTz9aqrrjrV85dccsnwyiuvhBdffLH4semmm4Z11103fu56c0mSJElSRcykf/rpp6Fr167Fr0l7Hz16dFhvvfViCvrpp59erxfAjHyfPn3CiiuuGNPozznnnDBp0qQY/KN3795hgQUWiAXg2Ec9+xow++yzx39LH5ckSZIkqWyDdNLPSTdfZJFFio8RPI8aNSrOZPft27deL2C77bYLX3/9dRg8eHAsFrfsssuGe++9t1hM7uOPP44V3yVJkiRJKne1DtKZMR8+fHhYf/31qzzeqVOnmPa+zjrr1PtF7L///vGjOszWT8vVV19d7/+vJEmSJEktMkg/5phjwptvvlnt95hRf+SRR8IDDzzQkK9NkiRJkqSKUusgfeGFF44fNWFGfdttt22o1yVJkiRJUsVpkMXev/zySzjrrLPCoosu2hC/TpIkSZKkitS6LoH4oEGDYhX21VZbLdx2223x8auuuioG51RlHzBgQGO+VkmSJEmSylqt092pvn7ppZeGHj16hDFjxoRtttkmbpP21FNPhaFDh8av27Rp07ivVpIkSZKkMlbrIP3mm28O1157bdh0003Dq6++Grp16xZ+//338NJLL4VWrVo17quUJEmSJKkC1Drd/ZNPPgkrrLBC/Lxr166hffv2Mb3dAF2SJEmSpCYO0idPnhzatWtX/Lpt27ZhlllmaaCXIUmSJEmSap3uXigUQt++feMMOn7++eew9957h5lnnrnK82655ZaGf5WSJEmSJFWAWgfpffr0qfL1zjvv3BivR5IkSZKkilXrIJ2t1iRJkiRJUg7WpEuSJEmSpMZlkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5UQugvQLL7wwLLLIImHGGWcMK6+8cnjmmWdqfO7ll18e1lxzzTDHHHPEjx49ekzz+ZIkSZIktRTNHqSPHDkyDBw4MBx77LHh+eefD927dw89e/YMX331VbXPHz16dNhhhx3CqFGjwpNPPhk6d+4cNtxww/Dpp582+WuXJEmSJKmsgvShQ4eGPfbYI/Tr1y906dIlXHLJJaFDhw5h2LBh1T7/hhtuCPvuu29Ydtllw5JLLhmuuOKKMGXKlPDQQw81+WuXJEmSJKlsgvRff/01jB07NqasF19Q69bxa2bJa+PHH38Mv/32W5hzzjmr/f4vv/wSJk6cWOVDkiRJkqQ8atYgffz48WHy5Mlhvvnmq/I4X3/xxRe1+h2HH3546NSpU5VAP2vIkCGhY8eOxQ/S4yVJkiRJyqNmT3efHqeeemoYMWJEuPXWW2PRueoMGjQoTJgwofgxbty4Jn+dkiRJkiTVRtvQjOaee+7Qpk2b8OWXX1Z5nK/nn3/+af7smWeeGYP0Bx98MHTr1q3G57Vv3z5+SJIkSZKUd806k96uXbuwwgorVCn6lorArbrqqjX+3Omnnx5OPPHEcO+994YVV1yxiV6tJEmSJEllPJMOtl/r06dPDLZXWmmlcM4554RJkybFau/o3bt3WGCBBeLacpx22mlh8ODBYfjw4XFv9bR2fZZZZokfkiRJkiS1VM0epG+33Xbh66+/joE3ATdbqzFDnorJffzxx7Hie3LxxRfHqvBbb711ld/DPuvHHXdck79+SZIkSZLKJkjH/vvvHz+qM3r06Cpff/jhh030qiRJkiRJaloturq7JEmSJEnlxCBdkiRJkqScMEiXJEmSJCknDNIlSZIkScoJg3RJkiRJknLCIF2SJEmSpJwwSJckSZIkKScM0iVJkiRJygmDdEmSJEmScsIgXZIkSZKknDBIlyRJkiQpJwzSJUmSJEnKCYN0SZIkSZJywiBdkiRJkqScMEiXJEmSJCknDNIlSZIkScoJg3RJkiRJknLCIF2SJEmSpJwwSJckSZIkKScM0iVJkiRJygmDdEmSJEmScsIgXZIkSZKknDBIlyRJkiQpJwzSJUmSJEnKCYN0SZIkSZJywiBdkiRJkqScMEiXJEmSJCknDNIlSZIkScoJg3RJkiRJknLCIF2SJEmSpJwwSJckSZIkKScM0iVJkiRJygmDdEmSJEmScsIgXZIkSZKknDBIlyRJkiQpJwzSJUmSJEnKCYN0SZIkSZJywiBdkiRJkqScMEiXJEmSJCknDNIlSZIkScoJg3RJkiRJknLCIF2SJEmSpJwwSJckSZIkKScM0iVJkiRJygmDdEmSJEmScsIgXZIkSZKknDBIlyRJkiQpJwzSJUmSJEnKCYN0SZIkSZJywiBdkiRJkqScMEiXJEmSJCknDNIlSZIkScoJg3RJkiRJknLCIF2SJEmSpJwwSJckSZIkKScM0iVJkiRJygmDdEmSJEmScsIgXZIkSZKknDBIlyRJkiQpJwzSJUmSJEnKCYN0SZIkSZJywiBdkiRJkqScMEiXJEmSJCknDNIlSZIkScqJXATpF154YVhkkUXCjDPOGFZeeeXwzDPPTPP5N998c1hyySXj85dZZplw9913N9lrlSRJkiSpbIP0kSNHhoEDB4Zjjz02PP/886F79+6hZ8+e4auvvqr2+WPGjAk77LBD2G233cILL7wQNt988/jx6quvNvlrlyRJkiSprIL0oUOHhj322CP069cvdOnSJVxyySWhQ4cOYdiwYdU+/9xzzw1///vfw6GHHhqWWmqpcOKJJ4bll18+XHDBBU3+2iVJkiRJKpsg/ddffw1jx44NPXr0+P8X1Lp1/PrJJ5+s9md4PPt8MPNe0/N/+eWXMHHixCofkiRJkiTlUatCoVBorv/5Z599FhZYYIGYwr7qqqsWHz/ssMPCI488Ep5++umpfqZdu3bhmmuuiSnvyUUXXRSOP/748OWXX071/OOOOy5+r9SECRPCbLPN1qB/jyRJktRQrrh66r5tJdu973zT/TtePuHTBnkt5aDb4AWa+yVUlIkTJ4aOHTvWKg5t9nT3xjZo0KDYEOlj3Lhxzf2SJEmSJEmqVtvQjOaee+7Qpk2bqWbA+Xr++eev9md4vC7Pb9++ffyQJEmSJCnvmnUmndT1FVZYITz00EPFx6ZMmRK/zqa/Z/F49vl44IEHany+JEmSJEktRbPOpIPt1/r06RNWXHHFsNJKK4VzzjknTJo0KVZ7R+/eveO69SFDhsSvDzzwwLD22muHs846K/Tq1SuMGDEiPPfcc+Gyyy5r5r9EkiRJkqQWHqRvt9124euvvw6DBw8OX3zxRVh22WXDvffeG+ab77+FIT7++ONY8T1ZbbXVwvDhw8PRRx8djjzyyLDEEkuE2267LXTt2rUZ/wpJkiRJklp4dfe8V9WTJEmSmovV3auyunvDsrp707K6uyRJkiRJLZBBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEmSJEk5YZAuSZIkSVJOGKRLkiRJkpQTBumSJEmSJOWEQbokSZIkSTlhkC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5UTb5n4BkiRJktQUug1eoLlfgvSHnEmXJEmSJCknDNIlSZIkScoJ090lSZKkHNq973zN/RIkNQNn0iVJkiRJygmDdEmSJEmScsIgXZIkSZKknDBIlyRJkiQpJwzSJUmSJEnKCYN0SZIkSZJywiBdkiRJkqScMEiXJEmSJCknDNIlSZIkScoJg3RJkiRJknLCIF2SJEmSpJwwSJckSZIkKScM0iVJkiRJygmDdEmSJEmScsIgXZIkSZKknDBIlyRJkiQpJwzSJUmSJEnKCYN0SZIkSZJywiBdkiRJkqScaBsqTKFQiP9OnDixuV+KJEmSJKkCTPxf/Jni0WmpuCD9+++/j/927ty5uV+KJEmSJKnC4tGOHTtO8zmtCrUJ5cvIlClTwmeffRZmnXXW0KpVq5D30RYGE8aNGxdmm2225n45LZ7t2fBs04ZlezY827Rh2Z4NzzZtWLZnw7NNG5btWbltWigUYoDeqVOn0Lr1tFedV9xMOg2y4IILhpaEgy3PB1xLY3s2PNu0YdmeDc82bVi2Z8OzTRuW7dnwbNOGZXtWZpt2/IMZ9MTCcZIkSZIk5YRBuiRJkiRJOWGQnmPt27cPxx57bPxX08/2bHi2acOyPRuebdqwbM+GZ5s2LNuz4dmmDcv2bHjty7BNK65wnCRJkiRJeeVMuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEkqS1OmTCl+/uuvvzbra6mENv7xxx+b9bW0JNZtzu9xLOWBQbrqxJtKw7Et86OS34vqOibff/99s7yWSmKHsGm0bv3fbs6RRx4ZbrzxxvDLL78090sq2zYePHhwOOmkkxwMqeU9p1WrVvHzJ598srlfTsXj/UjH8XXXXdfcLyd3vF81D4N01ekkTTeV33//Pfz222+h0gOchmhLZh4mTZrU3C+pom846b2oRHRMPvroo3DOOefEr2+++ebQu3fvMGHChOZ+aWUtdQifeuqp8NlnnzX3yynrc/yBBx4IF1xwQVhqqaXKag/dPLXx7bffHm644Yaw5ZZbhnbt2jXr62pJAfrRRx8ddtttt/Dhhx8298uqWNn344wzzgh77bVXePHFF5v7ZeXqPE/3q7vvvjs8++yzFR20F/4X80yePDn8/PPPjfr/MkhXraWT9NRTTw3bbbdd6NmzZ3jssccqOsCZ3rY88cQTYztuuOGG4aKLLmrul1VxI+bXX399OOaYY8Lw4cMrtpPEgNvFF18crrrqqtCnT594bm+22WahY8eOzf3SylK2c/Pwww+HjTfeOFx77bXh66+/btbXVW7SOX7FFVeEd955J87yrrTSSs39ssqyje+4447YF2Bwb8UVV4ydV9Us9Zmee+658Morr8RjdJFFFmnul1WxshkNDFjfdtttYdlll23ul5W7/tIRRxwR9t1333g9/e6770IlD+jcfffdsb/E9Y6Btn//+9+N8v8zSFedOpVDhgwJQ4cODZ07d46j5T169AhXXnllcVZdtW9L2pHZHdqwa9euYf/99w+HH354s76+Shox54Zz0EEHhfvuuy8cd9xxoX///uH5558PlaZt27bh2GOPDQsvvHBM89t2221D37594/fsbDdeh+f888+PnXTSr08//fR4HTVQb1j/+c9/wtlnnx2vrR9//HF8zMyvhkXGDddO7mfvvvtufKxNmza28x9gYI6Bo4kTJ4Zu3bo198upeAw0MYN+1113hQUXXDA+VsmzxUnqL5188snhmmuuiRMbW2+9dZhzzjlDpbbHHXfcEbbZZps4sDZw4MDw6KOPhsMOO6xRsi8M0vWHUqeSTg6jZ6TDkhp77733xkBn7733DldffbWBeh3akpO5Q4cOcfaSAOmSSy6Js7nnnntuPNnVuDecl19+OYwbNy4ew88880w47bTTYkB68MEHh7Fjx4ZKkTrSDLjNPvvsYYMNNgiffPJJHIxLnW0D9YY//k444YSYwfGXv/wljBgxIg6MEKgPGzYsjB8/vrlfZotVGhjOMccc4aabborHNenY3MN4D+x8N1wbk3HDLPqqq64a02AJckA7G6jX7KeffgpvvfVWeOmll8Lrr7/e3C+n4jHx1KVLl/DFF1+Ee+65p9hfq9RrRfbvpkYNkxkMKq2xxhrhq6++Co888kjYZ5994r2skpZrjh8/Ppx55pnhlFNOifU3dtxxx/DGG2/EjLhGyb4oSLXw73//u9CqVavCggsuWHjkkUeqfO/YY48tzDDDDIXLL7+88MsvvzTba2wpnnzyydiWs8wyS+GOO+6o8r0bb7yx0L59+8Jhhx3WbK+v3I0YMaKw2mqrFTbYYIPCDz/8UHz8zjvvLGy88caFddddtzB27NhCuZsyZUr897nnnit89NFH8etvv/22sP/++xdWXnnlwimnnFLl+V9//XUzvdLy8t133xWWX375wplnnlnlcc75Dh06FIYMGVL48ssvm+31tVSTJ08ufv7rr78Wvv/+++LX77zzTmGFFVYo/PWvfy2MHz9+querdrJtxrWC+326hr7//vuFZZddttCjR4/CQw89NNV1ppLVdKxxL1pqqaUK22+/feGll15q8tdVqWp6P15//fXCjjvuWFhuueUKV1999R8+v1xlz9n777+/8NprrxXWXnvtwlFHHVUYPnx4YZtttimsueaahVVWWaXQrVu3wn777Vcx5/n3338f7yXcU7jmLbDAAoU99tij+P0HHnggPt5QnElXrTBKdOCBB4ZPP/00vP/++/GxNEpOqjBrMvbcc884M6lpI7WdGXNGKkvTq7fffvuYBkfxkgsvvLDZXmM5I6WYYn3MpmdnLXv16hXXW80888xxrRGzHOWe9n/rrbfGc5vU62+++SbOph911FHhb3/7W0zpYrQYjKAzam5l7IZJdyc7IWXVpMIzZHOsvfbacQkMyw4qdc3f9BY2oh232mqr0L1795jpxYzP4osvHmfUZ5llljgTxHnP853prd9SDTJtSPdkPSb3/TFjxoRFF100/POf/4zXV+rWjBo1Kj630mvWZI9NikSSGvv444/Hr6n/wTH69ttvxz4B69PVdO8Ha8/JYuT+9/nnn8fCkmQ4MaN+2WWXxb4YKulakS1qTF+AJQBkJK2++uoxw2DXXXcNSyyxRJxFZg3/CiusUGUZYTkq/O+951+W95AF88QTT8RaUhtttFGs6QNiI7LhWLPfkP9zqYppjRrutttuhZlnnrlwzz33TPW9K664ovDbb7818qsrj7ZkBuL000+PM+rnnnvuVN9/8MEHbctGbP/rrruu0L1798KWW24ZR0Sz/vnPfxYOOeSQwu+//14oZ3fffXdhpplmKlx55ZVTzZIzk0sb/PnPf44zPXPOOWfhqaeearbXWm7H37bbbhvbNTvzi7333jvORs4///zFLJtKmaFoCEceeWRhrrnmKpx88smFY445ptC1a9c4s3vTTTfF73OuM/sz++yzx4wG1R2zabTxJZdcUhg0aFBh8803jxkgzCDhvffeizNNzEZWQkZSbR166KGFxRZbrDDPPPPEjEQytiZMmBC/N2zYsNhmu+++u23WRAYOHFj405/+FPsBSyyxRKFjx46FW2+9NX7v1VdfLey8885xtviiiy4qVKLPP/+8sOuuuxbuvffe4mNvv/32VP2l9ddfv3DwwQcXytGU/917f/rpp/hv6hNyDaTvvummm051/1lmmWUKH3/8cYO9BoN01dipJFghJfOCCy4oPPPMM8XH+/TpE1O1qwvUYXA5dVuSOnXcccfFVOIxY8YUJk6cGB8/9dRT48l+3nnnVfs7bMuGaX8CTJYZPProo1XeE27CBEzvvvtutb+jXAN1Bok4j+k4gpRVUv24ybBs5bPPPotpXaS6nX/++VPdmFX34++FF14ofPDBB/FrbuJ0DNdYY43Czz//XDzOSCN89tlnC9ttt11h6aWXbrbX3hK9+eabhSWXXLJw3333FR+jzWnLDTfcMHYwUwecYKhcz+3GNG7cuDiI9K9//av42CeffFLYc88940Deyy+/HB/jetG3b9+KSxOuCX0o2od7P8ffqFGj4iAdgXkKBLgfde7ceaplRmp4DNrNPffc8frAfe7HH38s7LXXXnECKi3VYPlBr1694sBppQ2UXnPNNYV27drF6yltVIrBJQaTNtpoozgQWo791Cn/e8+5n9BH5G9lUofBCyY1+vXrF9uIvvtZZ51V2HfffQuzzjpr4cUXX2zQ12GQrmoxi8ZoOaNkjPyyhpKZiYQDlNmINPKomjHKSFtuvfXWhb/85S/xg1HcNHvJjDpr+pn9UcMjEF1kkUUKnTp1ih0l3oevvvoqfo9ZZNZa7bDDDrGTXymYueXvJij84osv4pqqddZZJx6b8803X+HAAw9s7pfYomU7dVxL6XxzA//73/9euPbaa+PjTzzxROyoc1wSRDICT+YCzjnnnNiBN8ipPQZAmBm76667qrwHdLY571lLWcpAvW7eeuutWDOFLJyslKFw4YUXTnXMegwXYgB4wAEHTHW8cl/aZZddio8x8eEx2fi4vq633nrx2My2N+vRuQanehasLU7HbyUF6v/5z38K//jHP+IEUmndJPAY/QcGMVIWWDket7fddlvMEmLm/Kqrror3ZNagMzDJgCX1uLiHr7TSSjGQf+WVVxr8NRika6ob6e233x47Oym9lWDy6KOPLqy44oqxqFGy1VZbxVRC1YxUIVLbsilsJ510UpxBY9CDCxyjuJzsq6++ekXdCJoCs8AMkDCLTmedoocc29ygU5FDlml06dKlyiBUuanuuKJQHgNtZMUwQpyCGM5xCselFC/V7TqabevHHnsszkYQkHNdJXuBNGCOOXDuk2FDIM81NnV4GATdZJNN4iy714Rp369S+5ANw7U2LR9idid9j+stqdmqXxtPmjSp+BjXzgEDBhRTtROC9IMOOqhQ6ao7X3v27BkH4pIU0DBAz7WWoCirHAOe5lLdINEJJ5xQmHfeeYtfc50FmXZcQ0qDrXIeaKrpb+P8JhBnIInsj1LEB+lny3Em/dtvv42ZlpyjIDCnLcjCykpFXhurv2SQXuG4qX7zzTdTjTKS0pY6jCDFY5999onrqNINu9wvXnVFx+X555+fqlo769CYrUw3b/49/PDD46xlqoxLwJj9vuqOVMLSzg01FDhus6hkTnCandkgYC3XjlE6nh5//PEYgJPFkWbCPv300xhIZp/Xv3//OMNukD59WC7Emr7BgwcXHyNbg+OR6+vFF1881c9wnaD9mfmtrmOkqvecs88+O1bFT51Elg+RlZSd/eEay7pTBuxU9zZmyRsDyx9++GH8msEkjl/WpDPIBPoE7JiROrSVKttunL8pY+uGG26IA8H0B7LI5GLQzvoIjf9+UOcnTZaw9IU0baqSZ3ckYqkRy5Aq5dqbbR/SupktJpsjLQ3i2sl5TXYBFd6r65+WSwwwZcqU4t9GX5C/naCcPhKBODPoLOtJOKezx05j9dsN0isYa29YN1Y6CsbaKC5g6aacDj7WpZP+8vTTT5flSTq9ASJrUkrbkpvyQgstVGzLNPDBKB1pg6WpRAbo9UNHnVHfbPtxoU0p3aUj5kOHDo2do9SJyv5MOWINKRkFzM4SOHIeH3HEEcX2AJkGzDZSQMftgOqGGXJS4tL1kDXnzJzNMcccU428p0CdzKRsUMNIPevbeLy6dYCaehkLs16cy2nLG66/DHJwfLOEgwEpsr3Kdd1kU7QxRQxTnYqEzio1E1LRKDIV+LqS2zjbD+JasNZaa8WlFzzOcgCylVjuQpG4NCDH1yy/8r7f8LJtyqQIacmstWaGmMElrr1kf1AgjusHEyykb9NnqLQ+Lec5mQVsp8b9n/M5ZXsRrPI121c29HrrPGZk3X777XFgnQCce/hpp50W+/DUJkj9dyYtN9tssyZZ7muQXuHSgcmoEAceOBFJgSXwSSPlIAWIk7hSRhmnpy1T8RFmI1mPSnCUvSAwUsnIuhWzG066gJL2moJPBpxY85stdATWTv7tb3+rcnyXK4LChRdeuHDppZcWb7rMNhKkJ5zzvXv3jsFMud2IGxvn+MiRI6tkHqWBuy222CLe4G+++eap1vZSB4FB0mxnkkCoNLNJU+O8plPJzFfC9TVlf3ANpvIuacYE6+W8brKxENDQxqkYHCh4mq6ZHNMsyyCwoSCqbfxfFN+krse///3vKmnsFObcaaed4v2I73OtzWYsVlpg2FRSavvo0aOrzHzSRyAQpU/L/ZAgnlnjSns/WOZG+7Aki7+ZAWJqKFCH6vrrr4/PYWCDZVusuy4Xk//3/nJ9S3VM+NsZlCSjgOtcKga38cYbV/lZ+k7UkGFdemMzSK9Q2QsQaSzcMFhrlgJ1ZoBbt24d04G42TCzRoeHkcdKuXjVVrawCMH3qquuGmcYuCmkDARuyiwVIAWWitmc9ASJld6haQjZNiRYYhaNY5b3heI8jJQzEjxixIj4HGbPqdRZKTMYZL4wq5MGMErTttJ2IQQ8zOaq9kqPH1KACQ7T49RCoHYHRfluueWWKs+l3dO1w2tq3VISGUAmewEMGjPoxj1s8cUXLxbmK12uUcmzvPXBDFLKQmKgjywPUoFZQ51dwpFt10pvY9KpWd5G7ZM0qEGfgGDnjTfeiI/xL9t6UZQq3bsqvd0aA9cJ7mcU+0r3fgZBWfZF9kcatE5r0ekHl/Ma62kNKtG3z2JAif4RQXk6RhncL5f+6uT/vc9MSLRt2zZmCjFwfsYZZ8RlqwnZLvSduOaRZUgWDJlxZBs01WSGQXoFKu0QcjHjIkZHcoMNNigG6qRyMLpIoS1G0ThYK22U8Y9U1w4EiMwucOFL6325AHCis7aHtCHa2ZmHxml/BkAYFEmjo6SxsTad7BDWGHFMZ2cwyi1Qz24dQoDOjO6iiy4aszb4lwA9HXMMJHGsGpw3zPHHumeCRQLI9D5wDSBQJ42yuvQ4r6XTlm2fdM6yFp1BZDpOzISRSkxQyXnOEoPSbIRyO8cbs41TgMJuI3RG6bSS9UWnnSKHBDjZ5XCV3MalfzOzchyPXFe59pJhwP2e6y73HgboS3n/bzil11IGSZg0oTAvdVi23377ODnCY8yQpiVK0/od5Sr9ndSpYfKttHghEx4EsGnb0HI5XidnZtBnmmmmOEjBeUyNKCZ4yH7Lom/EecwyNLILuJc3RhX3mhikV5jsBYi9OxkZSlVwOSmpZkgAmdafcYAyEsyoUSWOMta2LdknkRtBeowbAuvNWNOSRtW5uNGx4aJnWzZs+xOQp3bG5ptvHotvpUCddC2CdWbcyGYo9xkMAkP2fGVWkd0Z2E6FrURIsc52LknbIsMjbQeo2iM9MGUhEMgwi87sLQXhqHdABke2aB+zkgQ72eNUtT/HmX0kSCQNkWwYPqedmeFNs5Tcp9glg2I/qt99jH26U3FY1vSz9pJjOhWTIjOMdk91ACpV6fI1jksmOEiDJeghhZraEwzMcXzSZizTUOO/H8yMMzHC9ZcAizR2BvVYe81SRO7/XJ/ZVaMS0A41DT4wqTTjjDPG2eRsAM49i2M23ePKweT/tQHnIzV6tttuu+L3CLzJuiAbiwmOLNqFviLneHbJRFMwSK9QpAuy9oJK7mkLgRSo08khuCTVo1JHGeuCCz8pxBQiyV7QqBjObDofKfU9y7ZsmBkMjmUyPehIZo9Z6gAQqDNgki2QVi4jwjVhIIgZRmbCElL7CBCZ4SU9mNR2jluq3GfXnOqPcd6OHz8+jrqTDsjsbbbYHgEOAWVpoE7nkJmbcj3uGhOdadbyMsjG7gxJ2s8YdKIYGGUpSyXO6k4vrgdkzTHokV1rmV02wHWUTCXauJLvX9m/na07mdxIdWjee++92I96+OGHi4PAHI/MxLHWVQ0ve74z8Ex/IFXSJ/uGoKx09pP3jJ0KKg2TFOwqwEeqL3HiiScW2rRpE4twknHHNZYYgOzacjnPJ//v72DdOTPoZFYye07fPF3jSPOnn0R2IYMUpT/bHAzSKxAdnXnmmadKBefs6BDVDbmAMapEFXLVjJsubZmtxkxHJt2cuQDQqaEtLcjV8NgaiPZn5ri64If1waS+UziuEoIjOiOk8lEojkAxi62UuOkyo8CWVKRuWUW8/ujIcLNnFoJta7KdxRSo08bZ1PekEo7FhkJxJ85hZnCzUgeTf+mQkxFiIa76IaOutE9AuzIIko5VtrdjEIRrh238XwyGMtnBbHnpTiFpHS9ZibQb1wLP+8ZFdg3XCgZMslsFJ/RnGaRm4oTjuFwz6ZIDDzwwFoRN2PmC5UAMYlDQmKKmZB2k85vHmNggUF1ppZXK7jx/6aWX4mAE/UYwIckylGxRQQZzWBJJ9iHZcs2tdVBFYWDmrbfeCrvsskvo1q1bePfdd8M111wTVl111bDVVluFm266KWy66aZht912CyuvvHKYbbbZmvsl59aUKVNi+9Feyy67bHjttdfCRRddFFZYYYXYdldeeWVYe+21w+677x7WWWedsMwyyzT3Sy6r4/iLL74Id999dzjvvPPCGmusET7//PPw8MMPh7322iscf/zx8Xm33357WHzxxeN70aZNm1DullxyybDSSiuFiRMnhvvuuy9MmDCh+L2DDz443HHHHWHMmDHx3/vvvz8et6rbOY/ff/89tnH79u3j19ddd1344IMPQqtWreLXHTp0CL179w577LFHfB9OOumk4nGLSjgWG8qLL74YevXqFf72t7+F119/PVx22WXx8+WXXz7cdddd8X34+OOPwxJLLBGeffbZMMMMM8T3p3Vruze1RfvRxvQJ3nzzzeJ9jMfOPvvs8Ntvv8Vjf8EFFwzPPfecbRxCbIcbb7wxfmy++eZh5plnDh999FG85/A9XHjhhWG77bYL33//fXjqqafieT958uTmfull6ZNPPgm33XZb7A+st9564YcffojvA32Byy+/PD6He94hhxwSr8NcK9q2bVu278ekSZPC7LPPHl544YUwYMCA8P7774eXXnopjBo1KjzxxBPh+eefj+f71ltvHV555ZVw+OGHx/4U11TO/yeffLKszvMff/wxHHvsseHII48MRx11VHzs8ccfDwsssEDo27dv/Ht//fXX0LVr1xgHcU084ogjwtNPP92sr7sVkXqzvgI1Km6spSfYzjvvHB555JFw2GGHxRvMnHPOGRZddNEYvHNCPvDAA/FnUoezut9Riaprh5NPPjkcc8wxsRM+YsSIGBDSgeSix02ZDmZ2oMO2rL/StuNYXXfddeNFdbPNNgtXXHFFvFF37NgxHt/77rtvGDp0aLU/Wy64fKfzNIsb7p133hk7iP379483a02f7DFEB4cgEVw3+ZyA5vTTTw+LLLJIlZ/jfdhoo40MzOtwPGeP61NPPTWcc845oV+/fvHetNBCC4Wll146nut0KBkc4b2ZddZZ4/PpdNvWdbtm0GmlnQcPHhz+9a9/xfsYA3jvvfdevIc9+uij8bqaftY2DrFTzyAcHXoGiviXIIeAnMGMSy+9NMw111zhnnvuiZMetBf3LAJDNbzPPvssTpjsuuuu8Ro8cuTIOKj3888/h59++ikceOCBsU8wevTo2G/gWl7u78e3334bhg0bFq6//vow77zzxsduvfXWMNNMMxWvAQxocMwyaFGq3M7zjz/+ON4/wMAjgxBgkufTTz8NV199dZywbNeuXbzu7b333uHmm28OnTt3br4X3dxT+Wo82RQVUtyvu+66+DlpHaRykO5DRdyU8sqWIKTKst5SNbcla/ZIqwLpQKyXZL0Z6/tZ0wJS20kXKq2Mqelv/wcffLDw3HPPxc9JW2J7NYr0sDZ91KhR8XG2DmSrjOzPlUvKVpJSqFlDRsEnKotTByGh6BMpluwTm5atuFa3frLHDuvKuU6yrRLprKAoIYX5qB78zjvvFJdaZAtFmepa+zamOjvpqiwdohgX66VJwaSqO1slgTW/7DiSreTu8V37Nv7uu++q1PCgajvtee655xYL8bHEgKVa2ftYJbZxdfcO0tjZX5prLEte2FuaZVVcC9jFJa2JTjz/G05N9/JddtklFu5jSRfH8wMPPBDXG9PfLa3kXm79gVLpPOX6SL0krp8cl0lah02RNNLe2WaxXE2p4ZqVXe6QUt9J/081jJq6SFx1DNIrAMELhc0oJJW2V0N2y4VUdIcKxJV4E64tOous22FwI1skjq0+StuS4hO25fTLtuHhhx8e11OxhpIBEm40VHIuvcHQ2eS55d4mFIFhKxlqSNA5YcuU7D6fBx10UNz6j7agU67pw3Ytc889d+z8lQ58UIyPAnK0N9swcZymNX2q/TnOlkA9evSIhfcY6EgBY7ZIHAEP11iKQ3qNrZ1sO9EXoFPK1mDrr79+cXAzW2CTDqr3samrhlMNOxWJY0KDAlxsrZbajrZimy8G8dLXapz3g62DqbXCpEnafYA1xaX1f+gPMFhdCe9HdYMP1EugnSgUS6HTLI5pgvR0na00v2UCdWr2zDbbbMW16Hk4VgzSyxyzvnQqsxet7EFJJ5KbDNVa6eSX697RDVXAiJHzNIubOovZ7dQIHun0WFyn4VGBlPbPFvnIImgiUKIqKUFSORWFqe4YYsaWasypQByDbnRamNFlFiFhX3RuPm6zNn3IOCLwTlVfOd7InGHmkeMudRAHDx4cO4Tp+Cun47CpBkGYhWT7RM5jzvk0W07mAkW61ltvvSrXWO9XtcfxSXEtMuuYIWfQmUywtO85GQxk3nEd9T72/xjoJJghQ472YwApW8wwbQ3IwAbZB86cN/6ECUX7KMzJlnfMFHMtzk6csA0b70e59Qdqku0XcWzSV03bUXINPeOMM+IWYzvuuGMc1OD7tA+ZYZV8fv+WOTZoj5QNlwcG6WV+4NFBZ/9ucMFidJeZHtIy6QRxAjPzxklrp7JmdAKplEkaNUi5ZFsrZnuoKMxSAfaUJ62IkUrbsmGRWkhHMqUQ8jUV3VlqwI0HvAdbb711vMimjmU5dJTSzZOt0u69997i46S5s4VIdrsk3HDDDbHqeJrtQdpmUfXHjZuZxzvuuCNWiWUPZNp/iSWWiNuxPf300/F52c6O53/tESQyA5mOW9qZmZ/sLgVU1GfLK5ayeI2t+z2M9iOApG3BgCdbEV122WXF5zD4RDbDrrvuahv/D8cgASHX3LTMqn379sVjlXOe7DruUezJXU73nzy6+eab4+BSmjAZPnx4XPLGcoPk2muvje8FWTnl/n5QwT07ecSAElXaGVTiuE0V3AnUmVEn44sMPJYH9O3bt2IG4qZMYzA3r9c4g/QyPwDZx5eLGRcs1u6y9QTbMLCOis+5aDE7kX62XC9idZXaI9umpFRxYz7llFNi+zGSzs16yy23jJ132pGUN9uy4ZGqTXom+5qy7ppBJmY0uAnTked94CI7ZsyYKpkNLV36WwgKCQRTyl4K2tlOJHUU03HHwNtiiy1WTLdU3VXXWSHA4VxnexbWoO67775xucGECRPi9YABOtWvjVm2wv7SdCy5jjKATPB48cUXF2d3L7jggvg92ttrbP2OY5a7MbAErqPZNmY5Af0E2pqfq+Q2Tu2W/mVQiCyPFCAS5KTBI45dPlj+xkxuaq9yuP/k9ThmYH6LLbaIn7MnPSnK6TjmGvHuu+/Gz+++++6yfz/IoCOdf6655oqZXPQVuP8TmFO3g0kj+q1pYI5BOGrYsAQ2bUVWbu0z5X/XLrIFSOPn3pK0tIEIg/QykT3wGBVLe0RSGIZ9uimIwDq0NNrGiCPrWLP7oJsyOHVbcgFMe8XyL7PppLxykWO/TTz55JMxgMzOVtqW9VfdRZTHSOEmMCcwJdWNGxCPM4rM2us/+h0tTfobSLNmZry08A3nOQVxCBzHjh1bJeWNGZ2rrrqqyV9zOcgeO7Q9gyBkboB0VrIZ6ACl5zEwxwyw7V0/xx9/fBzgYHCJNeYMxDHLQ6ZSdkBqs802i9kzidfYacu2zwEHHBCvmVwzSP1l9ozAJtvGLN1gIJ/CnNX9jkqR/ZvTvvEsB2QpEctasgMbBDYUM2SwLqsSBzaaAu1M23LNYLKJQXnej2y2zTXXXBOXxmVTv8v9/eD+RD+AQJ0Cxvz9CQNIZIASqFNPId3HyLhL7VKO5/nNN99cWHDBBWMmAcshskshWlL/0CC9DGQPuKFDh8ZCL6S0U200BY7ZInE8n1n0HXbYoSxPzumRbQ/S19Zdd90Y8GywwQbFNXulBYxoS2bVbcuGPZa5ibBUgxmMVMiD2Yo0OJIwilwawJYLCuJxc2WALYubLcchKf78/QQ3PEbbUChynnnmcWeBBihSSLoga6RZ+0/2Ruq0pzWozNgwCEoKcTnNRDTVOc5gcadOnWJFbK6ltDEZIwSUCTNjBEm0c0vqXOXlOH7kkUdi9geDTTxOJhjHNEvcsh15+g0sFarkNs7+7QzIkzGT2oxgkJRq7kvZDC/qI5Ren9Xw7wdZZFwbyAbhmOZzPm666abic5icoj+2//77FyqtfQjUWe5Hm6TicOk6kAL1mWeeuUp7ldsAxpT//b0cI3/9619jvS36RdxPFl544SqDFy3lOmeQXkYGDRoUR42Y5b3nnnviyUrwmGbL6ewwGsyN2CJx00bQR6BD+l+qfklhkrQ9HTcD0qwsYNS4RWGoqUDnkUyQbKo3ASqpXdyQy7UoDDdWghZGx8kaSEhRY4Q4VWO95ZZb4q4MZBjQGWeNNEGP6o/CWQQyzCpyw6doJEEig3Ycd2A2jceYfSz3NY+NgesnnaZTTz21+BjnMbMeHMcMMjNIt/baa1e5X7WUzlVeZh5Zd0rtjoSBJWbSSYndeeedYxYSbdy1a1fb+H+o30Ogw5p9MEDP0kH6AXyP45QaNPSlyKIpx/tPnjD4zHWC7cIS+rkMorAMhrRmshzoD1AjKL0fldYfoz4N5zRZMim7LrUBGV877bRTLCJbzsaMGROvd9SMSccBAxip39TSAnWD9DJBOiBBZFqfys2FKs+pIAy4kHFD3m677SwIU4sCRumGwEhcaQEjZnRJuerXr59t2cBuv/32OOqZCnGxtpqbcXYGg0IxdJCoQFzOARLBOWlsBIdUa2W3BtbtMgiXRRuk9Veksql+6NBwHDHLyExaFusbCchJxwYDIQSa5b7msTHamAEoOpIMJDMQV3oskw3C7DkZIixz8Rpb91oq3KO4Ps4xxxwxUM8iUGcnEo5nMurIGrGN/4vipOwnTXZMdgkb26wxYExmE4NIFI1lQKmc7z95kCac2OUhZdSB5TEpw4FMJyZLyHishPcjG1yS8cmAZtr3nIB08803j/2ElPmVrgu0TUsITOtr0qRJMYuCax4ZhlkpUKd+FBOaLYVBegtVeqKx1pwbB0iB5cJ1ySWXFFOyCHxSdcdKLgjzR23JOiY6MMyi8Tgd82xbMoPLLFtao25bNvyxTPsyIp7WFbE+NVsUhgEpfoa9fcupSFxNSO1jPS4Fn+ggUgMBHHvVFTjU9GPGgeyE0nZlkJP3ofR48/yftmw7sjUSyPAiC4ZOE1WzS9s6e3zDNq7frBIFttgyjCy6P1KJbVxaJI41zXTwGURioCOLooVsAUgaLYX3HKBr/P4AO+mw/px7H+8NsteF999/P94TeV4l9Aey7cNA8cCBA+MgBrtepAEKAlL6DGTh0V8qbbNyC9SnZP42/t50vGRrboCsOJZPLr300nFL2pbQbzJIL4M9T0nF5KQkzYcTlZtLCipBB4jCZtm90lvCwdkcbUkqDJ1IZmnZmi67PQ24EbClR9rSArZlw2BWkosoATmzaxToyhbpSUE7F9lsXYByu+FUd1xRMItZHGZvKr2wU0Oq6dhhJwdS47IF+cDe0lxL6ayr7m1MNhIZSGnbQGqlsJSFGcnUmYTHdf3bmCyjbI0U7v9k4xB4sjQmIZCxnf9fdgsrAnEGkDguSWtHTW1ViQMbjSXbxtzrs9tfUmG/Xbt2xeJnKeupVLn2B6pbEsjSNmaOyYhhuRuDyNkUb857AvhU7b5cj5dJkyYVByjSwA27r7Aunfgoi2LaadlqS2CQ3sJkL0CsNyMtmA48sxJUuaYwRLbSNetQqADNaHqlXLzqW8CIziIpxZzwrNkjxTrti54KRbEG1QJGDSPbhgyOMPLJzAUpbakozNVXX118Du8L6Zt77713xXQus38nA0OMjpP6ToZHdc9R/Y4/znuWVzDzmBDULL744nHpEB0eAnNqUKStf1S3NmYbHI5dtgRlC6VUMZ9AnftYaaCuurcxy2MIZtq2bRs7qenaQB9hq622imvPCUBVtd1Yz1x6vyFIpL3WX3/9mFpd7jO0eXo/WHLI+8FywoRAk8F7lh6mQL1S+mGl93iWXjAZl9L/SXVnAJnChkwupcr2DIYeccQRZTmQNOV/bUJGC8scuH9wrj7++OPFY4j+O4E6S3taKoP0Foq15wQrFM9IODiZ5eGDmSC2BuGgtSDMtBHwsO6R9U0JM7qMTrLlFzcKipbRabeAUcOjEA/rqrjYJox+csPhcVLZCJ4I0Fl3VmlFYUorNROoc1NiWYumH+ufGaCjyjjr+CjWxxIhznOunxSL4ntkMTC7ZpHIumPgmE4U1YfJ+GJAjnM7BT8E6qwDpphZuc76NDbSXtmJhCCd9dQU3mTgPh2n9A9YwkHtGq4jlSx77pJ1SGDDwAYztZdffnnxe1TCZmCJ6+1HH33UTK+2st4P1g1zDDOYR6BODaXsjDoFwbhOl1YpL1fUjCgdvGQAiVl0lv9lcSzTZvT/S/tJ5TjARJ+RfiIDEdxPWCbJ0p40e07fktoyFIFmEKMlMkhvYTjhOGGZ4WHWnOq3Wcy2pU4nhXf22GMPC8KUSBctgmwKw3BSc2HjxpBFB5L2pRoms2fcyG3LhsWIMG1PoQ/S27Oz5ueff35cU0Vnc/nll49LECqhKAyqW5+bMCtGx5GU1tKbtOqGQnwcYwwCpT3RKVDEgFDCrA0zbBQwdA1q3ZFizewXy63I7EoBJXU/6FilGXVSEJntLfdzu7GKa6XjGMyk0bZpT/R0/WCmnZ1LbOP/ooAU93/WOnMt4D5Pvyq7XJCAiIkO1rmqcbGVHdeKBx54IGYw0Qfga64L2UCdgdTsNbqcsatAds93MIPeunXr4u4D6fxmOSbtRZ+KZYHl5KuSgrhktnIMZHeuAIM43MPJjgHF84iJWurgr0F6C1DdjA03DkbEGTlnzVkpgpwsO5VTt2UKcF5//fU4S8aNmJvDH7GDU3+l2QfMoNFpZBaDG3Lpe8TsBVtecWMu1xHh9Hexjop1kdm1VdU9D3TG07pe1R9ZMtklLSktm2KFDMpVx/O/bgiAKGpKEJ5tO9ZSzjTTTDH1vfRYto3rhkEkZh7JAEn4nCCUXV7ouJYWOa30NmatOZlZ2V1DuAaTVUebXXXVVcXHGbyr9PZqbAzgsZtDduKJ4JRBUmrTsLVYwrK4cs9kLD3eqOfBJBz9H9LbCd5ZjpGteM+AJ+c6hQ1Zo56d+Gjp9aIOOeSQKoMVHC/swnTmmWcWv07SJEZSU5+qJWgdlGsMpLRq1Sp+fuONN4ajjjoqfr711luH4447LkyZMiVcdNFF4fnnny/+zOTJk0OHDh2q/I62bduGSkdbpba84YYbwuDBg8NXX30VllpqqXDdddfFdjv33HPDE088UfwZHivVpk2bJn3d5YLjsHXr/15ybrrppvCf//wnzDHHHOGQQw4J/fv3DwcddFC47bbb4nvEe8XzF1poodC1a9ew+OKLFx8vt2OZv+uWW24Jq666athkk01Ct27dYjtMmjRpqufRJuC5Cy64YDO94pbv999/j+f2u+++G4/D5JdffgmLLbZYOOaYY8Kjjz4avv3226muAZ7/NUvH5/8mAIptzXWW45e2+/HHH+PjBx98cDyXL7300nDXXXfFdk5tbRvXLLVr9vOFF144zDTTTFX6AR07dgy77757mG222cKDDz4Y9tlnnyptW+ltzPH4wQcfhB9++KH42KKLLhr22muvsMQSS4Rdd901XHXVVfHx9dZbL7ZXdf0BNQzad9y4ceG9994rPtauXbvQs2fPsNNOO8U+2y677BIf79y5c+xL0B8oV6Xn52mnnRb69esXz/EZZ5wx7L333vHc5ry+4oorwp133hn69u0b72m9evWK97E33ngjlIOll1469OnTJx4P6f7Rvn37MOecc8a/O33N/Rsrrrhi+PXXX4s/P8MMM4SWyiC9hQSVzzzzTLj++utjoH7eeefFx7bZZpvY0Xn99dfjYy+88EK1J3f6HZXelilAfOWVV8KwYcNiIHTZZZeF8ePHx4vAyJEj4w2Ci2EK1Cu9I9MYx/Jnn30Wtt9++zBgwIAYBM0+++zh2GOPDfvvv38cfLr99tuL71Wpmh5vqehk0x4nn3xyOProo8O9994bunTpEg4//PAwYsSIKh1IeC7Xz+jRo8PFF18cTjjhhNjRJjjk3KZT88gjj4Q77rijeKMHnSC+T+DjNaDu53j6GgQ7CyywQPjHP/4Rv04DyD/99FPsdK+77rrhyCOPDF988YVtXcc2Th1R7l8zzzxz7AdkO+Yc62ussUbYeeedw7PPPhuefvrpUImqC+bmnXfesNFGG4XHH388fPTRR8XHCdCXX375sOaaa8bBuhQEwOOz8d4PrskEoS+//HK4++67qwRYSy65ZNhuu+3C/fffHw499NCy7Q9UNxCXMKDEwBvXzOeee6543Vx77bXDgQceGI444oh4TaXt5ptvvhjAzzrrrKEcbLvttnGy5uGHHw6HHXZYeO211+LjgwYNCp988knYc889q9y/GRTm7//tt9+qbcsWpbmn8vXHSPOgIAJrclmfy76yp556avH7pGutvPLKMVWIQgmadgGjNddcs7D55pvHvRIpQEJ6FWvTwXp/0t5XXXXVuJZF0y+bpk3aElWHqebMuim2CGFnAlA9m/eHolI33nhjoVLqIrC2iuIm2fXlffr0iftxUwAlu92c6o5CUKw75byn9gFLhBKWuuy4447xe+zwAPZPpZ4H6YQWh6vfNmsUO+Iae/TRR8fHSMnkeE5bCPLB/Yz96PlZ1lGm5S764zamYCzpv9zDzj333JjmyppL+gfs5sL6atarsl0oz2M9JyncldjGpTsMsK1iuqamAlz0AVJhOL7HfYlrL0U6qetDuqzXgoZ/P0jfHjFiRKyzwtZY1AGir0u7p8Ko9A9IXeY4Z806SxRSwclKOF5J7We5HzgG+fs5ZtmRJKE9WDqY3Z6NulRUOC+3pVOzzjpr7C+xNIUlAWyRTNHR1VZbLRaQ43pHXQmWSZYDg/ScY89TOjCckKy5oOo4HXg6mqeffnrxedxQdt1117JfpzM96ITTSX/++eeLa1tYF0nFdqph0jkHN3E67rZlw6KQEYMiVBZmTTU3Z77mhpwN1Cl0RNBU7qhMSrVrdhBg54DselJQmZlO+AUXXGCBuHqiABRr8yheRvsyCEdRGYrEJVwPuKYSxHCzZ/00HSGruNfd4YcfXvjTn/5UOOaYYwoXX3xxHIhjFxKOX9qeoJHvUzGfXUi4p/E92vz2229v7pffItARZdCJtfwELh07doxV2/Hqq6/G4mccx3xwHWUQEFTXZ9vWSpI9dymkxb2egoV06Jn84B5/zjnnxIF52ocOPn0rBpPAsVsJ96LmQDEvdnPgWkvbcx988803Y8EvBvgWXHDBuH3WkksuGe+DoEI3Aeo333xTqIQJDdqEYJt/Od/Tczg+aRfqUWVr9DAYyiQI1cy5r5VLe3z88cfFz4mJFlhggVhLhlomnMPsAMTAGgM87AZQLgE6DNJzjv2juWlkA0ZGfBktZ1sgtlkrLTRhcFk9imlwgaeAUfZiSPEoRucI1NOMenamU9OPdqQjyUx5FsE6g1BcWNONl5mMcm93bioEj8zSkLVBG9CJzI6Ggw43xVFKA3jVblCOIDG7pzztSMeP6uLMlrN/6sSJE2MhHgZCmWkcOXKkVdzrgfbj+pq296JoEYXhspWyUwViZnjSNZbCkQSUbnH1x55++unYxqlYLAEN15Frr722+BwGPrjHMdOUUECOjm25zazVZYCYwTl2EwHVwglkGJAH1wi2WWXGlmtDKkJFpgd7c7fkwlN5xOwnA01pT+vjjz8+bn+XBuoIyphlJxOH56b2JwAlA4drdjmjPZjAIOPojTfeiJNG3MsYhAPXTrZZZNIpPQYm8RgcTTPvLVm6P9xxxx1xoIzjIJs9nAJ1sg2yyu2ebZCeU6mTSAeHbVS4aCEFL4yYzTbbbDHNnZHgxFmfqdshtSUVcJnBSZWE02w6X7N9TaoUSYddDfte8B4QjJJCXPq+EJxyA8pmL/Az5RqoM1twyimnxJTVhEriHH8MypUG5OWc2tdYmD1kdpzgL+2ZmgY9SAkmXY4bPx1D2rx0ixtYzbl2UjuRGZJmIG+99dZYkTkF6BzTpTPlLCdi20s6o9nMBk17cI9ZNZCJRBuzvAAELlQhTzPnYDaNZXAM6JfDzFpdcQ+hXdiqiUF63HfffbHdUqef47d0T2lSr7kvEQQxqKSG6Yule/pee+0VM0JAWjuTJJdeemn8mswags0sAlUGT+jzlu4ZXm7XUa6VzAin6yXV7RnET9fStHMT7UrWYel9qqX3m7LHyy233FKYccYZY4zDMZDFwCTXNe7l5TRzXsogPSdKT6x0oDJKxg2FbUGy26oxAkmHk5RY9vHmpqLq2zLdgBkdJ72KGbTshY3OIiPmtCX7zxsUTZ+abhJsacNFlXWAWXQySTOks37AAQcUyhmjvmybQqBIGnsWgTqj46y7K51RV/3ams4gg0OsS2eAiNTK7Mg7Sy2o8WGmQt2wxpktrLIdKvZBZ+CDwSc63dkZdGbXS2um0OniHGDQSlPjPsQ68+y1gJk1tlpjwJk09wsvvLD4PYJPZoizs2i8P6TEV3IbM2jBcqK333477idPf4rZxtQn4NpARlc6lll6tdtuu8UlGA4eNQ4G5+gPpAGT9H7QL+NxPtLAKY+R6s1SmXKsE1R6jJHNSdYHWR5kI5Uer9SjIqOm3AaUCbSzfweTZ927dy8OQtKP51xmMJhMoTSjThDPMqtyzXYxSM9ZUMPoLiOGdGg4GHHXXXfFlDZSOzhp6dwQaHJgUviIWUhG21S1LekA7rLLLoUNNtigWGiPCx9pMgxskEZD55H0KdKOuQhQtCxdEDV97U+HknRiRspJvWSEnMGQNdZYo1gYjhR3jnUuxHTqWYdGx7RccYyRysY6M47L0rXmrJNkIIlOiVkxDbP3PB1CMmhYi5oGM9OAJyP0rIdMN339MdYEMptLsEgnKqUMM/jB/rRkJ7AmPSEzqVevXoXtt99+qmO6HDqXjYG1t7QxGV6s3adgUkIdC+75J510UvExOu8sgeN72WykSsw0oA91/fXXFwcmaA/uObQnM5IE5QlLLDhmeX4WA1B8aPoQhNOfpfYP142Eviuz4gSgKcMh9QfWX3/9mGlWet8sx4FrBtQYsKf2QfZ6ST+JgSLaKGUYpGss/aVU5LRcsMyMPjk1iRL6gYssskjso3MOM3lBLQnahMmeNBh50003xQG4cmWQniNUZOTgYzaRD27EqbNDoE51XL5P52j55ZePo0p0Lrt06RJvTipUuQnQVqSscZGjLWlTgiJObmbXCIZoSzpBtCXfo0BJdg2r6n8s077UUyDVkHYmbZDRUgZEuNCStcBzKNoDbjyst0wF/MpBdR1lOhwUfSQ1mIG37I0JHLPZ9aSavrZnHS7HHFkKaVQ+vQ90CMniqMSApj4YSGOdObPlBEMU4uKamY5h7lMMiFB/gramY04b87yU0dTS0zGbqo2pN5OuibRxygAhpX299daLjzPYSQeX9Fiek2aTKrGNCfYoSrjsssvGTA46/WnGkbX7qQI0mKXlmN14441jZlN2sMhrQcPg+kB2HANHFDljDXo284PicAyc0h8jvZ2ZUyZMGEzJrisu5/eDQQmupZzL/fv3Lz4+ZMiQ2GdlkiktvyTDg+OVQaVyG9ykDlEKuskkSLPmm266aYx7OK/JeqNdiHlor9L6RuXKID1HI450btK6MWYnOEmzo49cyAh0uPGkCxfBKDcfU7T/HwMWtEkqSsK/bdu2rTJiS/sx+saFIbUlRUpIfbWA0fRh9pwbcho4orPJscyIZ7oxPffcc7GYDzNEqWPJ2iI6n+WSepyOK+pHMArMDDprrMBNluwOakpQDKc0UFfd1BSUpPfggw8+iDPqzJqnQJ2ZRwY4S9ejqnqkoJLRRTZXwjHdunXrKo+xnIVZczroBEpsx5bO8XIr6tPQWGdJe5LllQ12uH4yAJLQB2BwiYFoUrkpfprathLbmDYiC451+mQVUIdihhlmiPeYFARwX2IHBwbrCHSYXc/u4lBugU9zov3JqEn3OwbnyWAk6EpF+Vj6wv2PQJ1BfIJzvq6094P+EFsm0vck4yAhCOUaStYsAx2lx2u5DMRl32cKYjKxkzIFXn311TiwQ/sweZPu0QTs2Vpc5cwgPSfYGoWTEQTm2YIwBC2lxUtYx0JFbCqUum7qv9IJzLqzNGJOu2YLGDEayf6xWVwI6PBws7At6y/dNFhbzUx6toBUKtJDZ6k0jZCbNQE6ayzLbc1ZOv7oFBIgpi2pSLfm5kTwzs2XegjlXrG2KVTXhtnUd2bUyZyhw8gIvcFj7XDdpAPN4FsWSzY4ppkNIo04WxuFGY/sOkHbuGYcoxy7KbOI9f0J6a20MR1V7mPZIkkEPNnOeiW2MUv9aJ9sjQ9So9krmWKk2WOQew1LiihaRv+qkgc2GguTIgzm0Q/IShkhTIJkl3nx/rG8g4mqSthVgzTu0kw5anxwfjNRx6B9wsASE3Gkvg8dOrTsj1diHQbRyHRlue/vJQM1fJ/sYuKebH2TcmaQ3gyYNStN6WXkkVEy1u+SCpxNyySljSAyrZvkhk4KJydvpVcepR1T5fuEmUvSKymYQ1tm15gz40P6ZfYiySwbKYOl1SNVPwTcrCljNihb9ITOJDPnzKynFC7SDvk+qW/lFqBzjHHTzf79DCAxm5NuxHQgyeAg2CmtaKs/Rupvqm/ALARLBaqbgcnOqJOKTXsboNcexy4pw2QepAFQZssJKlm6wTZqDDYxI8aAFO9DdjDOLIXaYZCYQIY9fwnUmUFjy0C2UKOPwMASM028D6THZu9ZldrGzLRxHBJ8p2OO+wmBO8V1yeZgVpIiU9UVl6qUGdumQpYHadlcY1P2B8czSzgYcGLGnCUGBJ4U7CstelzO70faFpT0bfr0DCxlMzcJ1DnHKXhak3Jqn3TN4t7CFp5gsJJzluPktttuK/69DOYwmUHdokraqcIgvYmRjsUaXTrvbBHERQp00Elb4wRmG7CEdRlc2Dg4S2/C5ZLuUl9kHDA7ScofI28UmOAmTOo/aVVUfaTzmBAY0pZ0LkvbrtLbsj5Yu0+wybqp7DZXrBuiGEo2QE+zGxz77EefxWxQS0/3JlMgWyEYzHix7ILijtljjBFiUlpT7QNuQhYuq1+aIJ0/gkbS3xj8mNb2PNntldJ7YYBeN3SOmBEjlZUZ39LBZjJHDjvssHgv85paN6m9CNQJzKnwTN2OtGVomkliJo4Ahz5BOXXYpweD7NT4oEgZ9xjWpTOjy7nOPYjdcZhZJzhK9x+Pz8YN1NlpgAE7BpV4bzhuuQaTuUiGHVuOcowzEFUpg0wsv+A4JCuJGWOW99FPYuKIbCQmNuj/E6gz0FnO0vvNwAV9+F133bW4bDcF6iuvvHJxKzoCebIJymEP+LowSG9CpKpR0IR0DQIZRhbpXBI8crOlwBkF4RgFZlSJ9TwU0rDoTvVtycWOdb2s4+OixshbSqNiXSQ3BpYQMHvLBzdv27JhcKyyXooBDwZICDqza4Q4rslioKPESDFFjyhuxA273IrC8DeQPk1xHG4k2e0TGXQjjQ+c43yPY5QAp3QLNtUddSVod9o5rT+t7XHl+V8/1EthyUC3bt2K7ZzWmZayjesmtRcDfMyWs+1UmmH6o5+pRNm/ncw5BpBYtla6pA107qmr4MBG02B9MZkM9BOymaEJ1w5q01Ta+0GWIQEoSwLJiCUbjNoeZIMwuDTHHHPEwSTuadk6SuXo4YcfjoPrnJdpoiKd0ylQX2211WIsxPFSaccKDNKbCDONFDbJFoRhzQ4nYloHnQqesNcsATwjjcwUVVohjT+SisKQCpOwtpe2JJ04m7XAVhYEi6RXZden2Zb1x9pTZtJScQ86P8z4cDFN26Sw9jylv7LtDRkPfL/c2j8FKaTtc4Ml+GYWIQ1EcMzxd2f3NeUmxIBSdR0X1a3dmZ0ha4YBOG7opLQm5XKM5bHtmVFnYJTzOi1dKW3vchiAaw6pk0q6OzPqDHimjDtYibzmQJ3Be5YNsuQqbeVJG5VmzHhtaBoMWjOjzrU5FZFD2gO9kt6P7HF67LHHxv4CtRFScEpQyjrrE088sbDtttvGbKRyz/Ti76foZfYYSJMZqU26d+8eMw3oU1Yig/RGxsFGUQgCSILvbGEjDrxUtZXUV1LbE2YfKdaTDtZyP1lrixOV0XL2T0x7HWcLGJF2zI2aVNh00nMRzJ74tmX9MfJNURi2DstiNpPUbo7b7E4DjKaTAksns1xTjNMsIscmbUCKH393Gilmj2gG3BhUoh2oJcH+x2lLJdVeTbOG1DOgkCYDQ9kdMVCO++vmAYE65z2DUClQV+38UXCdTX2njRmsr252uNLUFMyVzqiTRceWq15jG1dtsji4FxKoM1FCmnsly7bXCSecEAN1+gOldZWyyq2/lEWmK7usVHddJMsAxEzp80pkkN5EKHrATDojvKS7sg6HQJN16aS/U9maFEJmhZgpZq/ApJLT2apDsbyFF144nuB0DmlLgiNGJ0klIk2Q0TdmKxmVTCPqcOZh+hAMccyS6ppm0ulAkq3AMg3WWNH2FEUhKC29+ZTbsZyOp5EjR8aq7cwYMFhEJzEVN6Fz3bdv37gNIB1ujs9KKnzSULLnLulxJ510UuyQpwCRbAUCdd4DlsCAGXauCaqdtG0laN/svsbVIYgkU4aq+aqd7DWQQobZwfmaZtRpY2adKll2UL50h5DSdqUAF0urqJfi9rSNj2Kw9HGnFahTsJN7H7WDKkFtBpRIc6evwLn92WefTfXccu6v0g7EPmQMsGwt+zjnLEspn7efZJDeFNJJyUWMtbsU1SLIyRaEYaSIoIZZCYKdcgtmGhrFuFgHTHE4UoxLg0GKc+2zzz5xht22bFisl9xzzz3jDZeRYAqgpO0wmLlgjRXBetoXtdw9+uijcXkKg2t0Rh577LGYploajNM2ZBpYJK7usucwVZzJRKATzuAcdTxSB55AnYJaLLMgHTu7zZqmjU4idT6Y9WIpFgWNUtHDaaGDVQnpqg19HA8ePDjenxgYqekYTc9nSVEltzF1PdIgBfd17jvVZW9k25fBOQZHvf83vGybUp+GNdSktv/RfZJCvpVwHGdrdFR3vy+dUedexnFdrn2DNNjAPYZYJ01CMsjLfYa/Pd1ruBZS3JEllB9lKt9XKoP0JpJOygceeCAG6qyVJiW7utGy9HU5j6I1BLafYbsaBjaoejut1CBv1A2LKtoE6szwZAvGZfc5JSithBsyVVlZm5vtaFOtngCRAJLAvZxT1poSnRiunRx/zECy3SIddto6BepvvvlmXEJEYb5y31e2MYrCkfFFxylt71nbc7gSzvWGQsDJYD0FTlkOVyp7789+XqltTHsxsUGRUgboOMdrkr3Xp7bz/t94y99YVpAyl2qrnI/j+++/P2Yhgew60vyrG4QrHXhmQKkc+/zpb2KpAxMX9Nmp5k6aP/32Bx98MA7ysFSNbGJ2YKJf6Sz6fxmkN6F0UtKBJFBn+6rsHpHZC5c3lbrNqJPWmu3sZAc6yvHCl6cZ9aWWWqq4VzVKb0jlekNOxxUzYqSxJyl99d57742p73QuCX40fZixYetKZh/T1l+8B7QtgTrZC9WlDpfr8ddQsvcaMhHICmEXEtINq3sOvKbWHwNLZHqkpQXMupG+Tec+DYxo6uOM7CyupyytSOd5TcdhTYMcajgcv2Qykn2T3YK1knGdJDAnhZ3gnMr2TCZN6/mVMDlHIM7Ab1qexjJUzmWKO6fBHgrpsjyFItDTGoSrNAbpDSx70lVXjTC7VzIFuBiFrG4tiuoWqNPpYbu67KCH6q+2212kGXUC9XTBrTRstUZQU7r2maJxpPtTNM4CRtOH6yY1EEgLZAYyWx04Beosu2AEvqbtwDTt+xUBIjMbFOqhwCFbWZH6roY1atSoWLfj/fffj9urMYtGMVQGm5lN+qMt1yrx2ORexF7n3Gu4nrKHdEqZdRCu+ZBFR5C+ww47VHRxr1JkfBCEUoMqqcQBpfT3MCHJ4AVY5ksq+1577dXMr65laB3UoFq3/m+TDhgwIJxxxhnhhx9+mOr7U6ZMCb169Qp33HFHuOCCC8KNN97YTK82v2ij2lpqqaXC/fffHx588MFw6qmnNurrqhRfffVVaNOmTfz8nnvuCZ988km1z1tmmWXCAQccENZee+2w7777xvegXDGoiRdffDHccMMNYezYseGbb74JSy+9dDj88MPDFVdcEU4++eT4HM572mLRRRcNY8aMCYsttlgzv/qWff5z3eSaedJJJ4UZZpghbLjhhsXvtWrVKiy//PLhwgsvDJtvvnlo27ZtM7ziltnG6X519NFHx/OXY3XGGWcMK620UhgxYkQYPXp02H777Ys/w3OuvvrqZnzVLf8+tsACC4T3338/7LTTTmHdddcNEyZMiMc1/YBPP/00fPnll6HSZY/NK6+8Mjz22GPhzDPPDJdeemk897knnXvuueHrr78u3qdoUzVtf+zAAw8MxxxzTLxOcF347LPPQqW3zy+//BK6du0ar5tPPfVUOOWUU8L3338f71OTJ0+e6md5vLrPW3I7lB4vnKdrrLFG+Omnn8LKK68c1ltvvXDxxRfH7910001h1KhRzfJ6W4TmHiUoF9kRMIohMCqe3du0plFiUt9cL1nzCDppL8w41GaUluc4qj79OCZJHaaSO7MXzFx+/vnn0/wZ1g+ddtppZd/+zOZSyZ6CZaSy7b///nGWnKyZIUOGxBl1dm2gmKHrqqb//CcFmPWO7LGbKjpTRIrjky0sa1Lux2FDYlZyvvnmK9xxxx1TbVdHmiJrgJdZZpk4O8Rx7/2q7scxdSk4llNxLe5VVCBn6Ru7vYB2JRuELSsrWbYvddhhh8UlLlTBzt6DOGZpK+5PZDL16NEjpsOrcY9j0tqZAeW+R82PhPTlBRdcML5PlVZNP9s+3Kvo/ye0E9lfZNllt1/+o/5US22DdO6mGlEJGcMsCSTjlc/Tkkj+ZVkVSwa9r1TPIL2BnXHGGXFbAYoi1OVm5AE6dZtwI6YzzsWfwhK0bXVLCErZQZ/+tWZcOGnzOeaYo1g5v7apWOXW/unvph0oanLZZZfFImVsT0XgQiGztM0f/w4dOrRw5ZVXxorMqj866Jz77DvPDZ5UYIIa3g/+ZYkF69NVf6wFXHTRRWPlZXB95bhl6QrfS8f0fvvtFyszp/tUuZ3jDS17raToGQN3pLgzGEJqMPU8EtZoUkSW5Vqkctu2/3XmmWfGJRfpOKyuejtrf+n4U7gzuwRGjXM95v3Ybbfd4gAp7c42uAlridkal4GTVDOkks5z+vxpQCnVR+J4JdWb7YDZIphq5QwmsYNTuUjnJNtJMljDfZrjYMcddyxcf/31xUFJ+krcz1OBV65z9PE5jrJbsKkqg/QGRDGTbbfdNq5F2WyzzZr75bRozMoyg8PsAzNnFJxgDf/AgQOb+6VVhEGDBsXjmKrZafankovwsU6UUfEtttiiSgfkmmuuiTcfbkhkHqjh9kGnAuyTTz5ZrKBPkSJme0GweM8998RBpP79+zfba27pmPUheCRI5xhnloNzfrHFFovBOzPppRxQrr3zzz8/ZiKRnZQC9g4dOhT3iqajSnCz8sorV9khotIDdTry7KtN+6WBIjIMmDHv169fscYH11y2vMzuKqKGx3WYAGv06NHxawZE6Jfx2JZbbll83tFHHx0D0ErrJ5x66qnFAaXSHQX4mgCebCTai4G4chlQSn8jtYmoq8EAJHUjqG7P/YN7OJOW4Pwlw5ABS87tXr16FeaZZx6zDf+AQfp0qO5CRKoPHR22seEiVtPzVDNutP/4xz/iiGTW7bffHgPH6667rtleW7lKxyidHS687HXObBrBJzeXdHOu1M4jHWluKGQXlM6QE6hTyZVZdqsz110KvLPYp5sZiHRzZ4nBxRdfHL8mPZh0QY5TlhRV6jFZV9XtGEKww7Y4BInt2rWLxX1YWkAKMRXz67q1kqrq3bt38T7GUpmOHTsWj2MKHHKvY0nX6aefbqBZYuONN44p7bfddlsMztdZZ51Y2Z1Bj2xgmHgdaPhrRfqXY5d737ffflt8DgNKbCFItiMDJZVQpbw6bLdKgViy58DyzH//+9+xD0tWQSpwyCAoMUG5nOfp2HjxxRdj5XYyLbLHx1tvvRWrtdNvStv08hgDFrvvvnvsU5lt+McM0hugw8Pn2YrCnJTcnBkxTxevSrlg1Udp23DRY1TuuOOOK7ZvuqCxHoq0QNrbbeoaRrYdqY6f1v6CqtlUeGYbsZQSC9KYKm1XAtbgMcO4zz77TFUjgRR4jstKW483vUh3K90fls85z9mShRlIOgApsKGDc/nll8ePLDvotT/HSbUmOyYtY+FfBuQeeOCB4kwuz2ctJQNQqjuOR+5ZpAWT8cGadI7jSy65JH6fdqbjyrKN0p+rNDXdx5m9Zd9kMupIFU41frjWkmZd3XaLmn7ZQCtlMjH4zCwwe12XDvJRe4UgPquS+rv8rauttlrcBphtVxlcIuWbWWXOeQaWSpXLeU6QTYYbGRQoHYAgA4Z+EXujm9JePwbp03lTIR2LIKZnz55xzXQyfvz4OIrE9hRpP9RKunDVpy1Z05JO7kMPPTSmXTKjk30e29Vk10Fp+mSPSQZFSMWad9554zpgisRw0aWDyRp13g8eo/2ZXS/XQZLUJqRcltZAIK2NdZDM9LK+LKu0WIr+GGv3UmCYLbhDUS2yZtq2bVtlaz8G8Ah8UqdAdV8fzXpB0hDZD52ZHmY3EgIfshToWDGLWS6dycZW07WQ9qXAJJl1aX0mKNDHNTbbZ6hEpUW3aC/SY7NBH1s2ZTGrzrpoNU5WE8sJmGgiI7R169axXgLXBNqdNOVsQWT6uWTckOVYied5+pplmbQDGV/U7kgTGizbZOlruaS3l/7tLItkppzlqEm6Z6T7Dm3BcUSGVpbxUO0YpE8HOjx0dggoKXJCp5KUj1StlQsYs0Q87nrVaV/wSAncbrvt4sUOrNmjo0gKMfugpw4khaJIlVHDOuGEE2JwzoWUmzIddIp1kYoJZt4YEaa6M0F6CqzK7UKb/p4777wzrq1jP0/O6eyMVypYxHnPwJLqjuCEdWwJaZOMtqeZRnC8kY1EmiCdRmZtGAxlhrelpwo2BzpSrJtkxpwZEAbcSHXneprOc47ttdZaK85guj66flXcWcef1vKTmcQxSwYOA1K0JY9xb2OJgW37X1xL6ewz4UGND47T7Awkg3Ncg2lLBojL9f7T3Jgp532gTgUZDNnlW0w28TjHLgXCCOgJ3AlOK+E4zp7nl156aRzMoM+aqtxzT0rZSQlF4ii6Wa7IHGQveK5l7G6TbavsZAfHFIV2VXcG6fVEp5KAJaUDEVwy60NxM07eVMGQGzMXNDuVNWONCjdlbhDZrSkYnSUoZ1sr0odIuabwhDfohsPFlACIjhHHNB5++OGYAUJaYWk789x0syrXY5rjjuCQ2VqCRjoiFHW64YYbis9hhJwCKNmK16qdUaNGxYCcIpspBY4lFgyKsLaf4DGlyjFzw1ppZn8ZGDF4rB/OYQIgMkCyWO/PdqFkLoD3g6UF5bJusqnvYwx6EJBTzZnBDmbQCC45rkl9JaBhAJSMJY/j/78esN45LQ0kICd7hvX7BABp8INlRhTuTMekx2bDyd7jSdNm5pPrRWm22NNPPx3rVlCRm8FSUrsr7Thm0J7JObI6qSXBJBzHafr7maQjHmAwgwGldJyWa3+VPjtFdQnUyTRMUnukwR2WTqruDNLrgYOP1KxUeZSbMOty6FwyusgFjhO4NFXWm8rUKKRBBzylunLBp8NOigwdHC54w4YNi8EQHUlv0NOP4Ce77oyBJC6iDCxxLGfXAPMY7V+6BrtcU92ZUWQgKM3okr3BKDDFcbgJZVOv2WqNIjGqu1Rsb+utty4uaeFmT8eQ9X1Ud0/o8DBwQvHCch8gagy0Gfcs1kymSvjZ9EvuVdQASQPLSaV0uhsC68uZeSSYBJ1VOu8p7ZXsJK6jzCaRyu0gyP9jGQDHX7auD58zW8l1l2syxytZSynQsd0aTum9nHofHM9sJ0Y2Q5pNzxaXpW9LRkilvR8MJDEIl85r1qAziJytkcL3yKAlCzQNYJR7+9QUqIOBYe49XANVdwbptVBdtUpS2emgE+Asv/zycWYtBUDshcoNmhl0VVU6mkhRHYIi1p2R1k4hKYJ2ZneYbatuna+dx/q3PZU4mSVmwIMZC3Ajoa2ZyWT2Is2gg7RYqupSrbSc1DSqzcwBM2LcUDgmuSFTZZxRYLYUYTY3zfSq7rLBIQEL68uZLU8z6hQjJFBnxry0OFy5DxA1lJrah4FO1kymNb7pOspgE2mZXlfrf/2gU54GNqlGTjun62hNBc4qvb1TGzLTRp8pbUuXcK8ii47lGdX9nBr2WsESJJZwpsCSpW8UiyNQf+ONN4rPIzCtlPej9FrKYDFZh6l9ssUg6aumrRa5n1XagHJ1gToxENukUqxU9WOQXoeTlPUmpPsSmCfMAjHam/b6YxaYTr1bA027LVnLwsWLixpp7KQGkvK+6667xs45I5aM5Fa3PZOmD2uk2MaG6tkMNqXZDFIO2UokoXPJXpake5fTsZyOQ/52Zgmya6P5O9P5zX6fbEE3ceLEYhognRbaiBtyOXdOGkO2vdj3vE+fPnEQhMwjAvVUe4JAnRl2ZtrT1i2q+zWW45paEqkwHMc2bcogKLOTHNc//fRTHChhbaVqdwxn25j7PvcxBjm4njLoTMedz1ObMwhilfyaB4/I0iLw4d6frd3DdYB04bS+X41bE4CJEYIrJpoSliByvWAQiqCU/gD9BM6DSrr/cR9iW1qWt6655ppxHToDSGlgLmV8MdGRXSJQaQPKKVCnjVjyQ+V309ynj0H6NGQvQhTW4qBjxpGCWtyMQTEj1qIPHjw4jghTVItOT6WlAdW1SByV71nflEYnKbzHDYGqt2AwhPV7XBjVMLLphKQgkZ5JZ5I0V9qdLAaWbbC/58477xw79NkiPeUQqKfjkJFd1oWyrpxOSHXbpPD3U204IWg/++yzY5qf6o8ZGzo4LK2gU871gPWNBOapgBk3e6pfsw61kjqDDVnFnfsU7cw5nIJwAiKKb5FNw+N8WOej9r7++uvi52TckGVEp5xrJ9cLZtBTgA6uFazbzVY/rvT7PwMWbKnG9TT1AQhwOF7JoiHwYZ069WjcYaDxkSVH4J2t2p69DvB93oc0mVIJ14rs8cpMOe1DAVPuT9RHYrcG7lsJg50MYNBvKud2qQ3u3dTlouhudscW1Y9Bei1wQyGgoeIzsxKMmhPMpPWoV155ZWGGGWaIW1SR6lEJF7H6omNDFfGRI0dWu882bcdMJoEia1O9QU8/AlLatXQbENankopEp5LvM7PG4BNbhtCByhY8LIfBpnTjJY2SwngE4HQGySxgXVl29ouBCwaSOA5ZG0mxGG7U2T3kVTdcDxkoYiCT60AWbcxyAoLJlPpOpkN6z7yW1h5ZCmz7RQFI1kgTFDEQRdCTUASR452sJddH1w7HKG3IsUjWBwFLKnbGNZbMLwaW+Zw25VrBsU7RSe9j/8U1l4w51uvSVvQFmODg/sPxSrYSS65YVsRkRzkNEOd5lpgB0ex1tnQGmHoALH2rtBRuZoGZGb7uuuuKj914440x66B3797xcWpMkG2YLRJXaTPopejDO5nRMAzS/0DazzTtA8l6sxTYZC9qXMRIfa+0i1hd3H333fHilkbXaCtOZDqSKe2aapnM9Fj9tmGQnUB9BDo9O+20U7zBZNO72ZM2zf7UtM93ObU/HQ1SsLL7bDPYRpCenTVPRQ2p0MouDmTQpCUtmj7MmNPBKcXoO6nCdM6zBfkqvcNTF1wzCXSOO+64KucvWV4E6gw2lfs53hiYTeM6yiAm9TwI1ln2kpbCgFlhqj4TfJKhwyCz97FClbXMtE92do2BYDI5uO+nGUmy6BjgMBuxaZDizgRTKiab2p3jlsmUbAZeJV2PKQA300wzxWykbCFTXH311XGnAe5XZNCwXMvzXI2hbdA0TZw4MTz//PNhueWWCw8++GDYeeedwxlnnBH23nvv8OOPP4azzjor9OvXLyyyyCLFn5kyZUpo29amLfX777+HBRdcMHTq1Cm8/vrrYcSIEeG6664LM8wwQ5hvvvnCPffcE7p27RpatWoVDjrooNiG/IxtWT8ch99++21o3759+Oijj0KPHj3C/vvvH2afffb4Pmy11Vahf//+4csvv4zHMe28+eabh3nmmSf+PIN4vBdt2rQJ5dIew4YNC7POOmuYa665io9zHP7222/hnXfeCeecc06Yc845w7bbbhs23HDDsO6664b//Oc/sQ3mnnvuZn39LU06fkq/XmKJJcLIkSPDyy+/HLp161b8/l/+8pfQvXv3sPLKK4eFF164+Hjr1q2b/LW3RLQv5zDn+uTJk4uPc+yuttpqYbPNNguvvPJKPNa55maVyzneGIYPHx723XffcNttt4W///3vYdKkSbEvMMccc4QPP/wwLLPMMrHtV1pppfDoo4+GV199Nbz77rthySWXjM+nbb2PhfD999+HWWaZJcw///zx+KRdjj766NiPOvPMM8Oee+4ZOnbsGGacccbiz9iXaji0ZXXX0qWXXjpMmDAhHt/0Cbg/4tdffw3nnXde+O677+J7U2nX4zXXXDOcdNJJ4ZhjjgkPPfRQWH/99UPnzp3j9/r06RN22GGHMH78+HjMdujQId7bPM/V4Bol9G+hakqpJAWTIkekyGYrDjPbw9YCzK7rjyviswaVkXTW7rCtFcVIqIBLQRIKSKXUwcQRyenHFnaku7KGirQ2RssZIWa2jTRM0mJZB8gsEc8p92OZgoXsacqyFLIH2JWBzBhm1tkzmtFx1keT8cGyFgsX1k92toVq4qxTy277RxuzpzSFI1nny2wNRXeYpawp5VJVcb3kfsRxnC1myqwk1fFJH86igBkFurgmqHZoX66NrJdmuVA6JilmRsFDip1xbE9LJd7HWC5UepyRGsx9P2XNpar3ZHBxDS63HUTyJHstJZuOawF1K1KROJZ8sfSAeiFkNj733HMxo5HrdCVkMkzrXnPKKafEfitZH9klmqXxgkuy1BgM0qs5Sdl+KVsghg48AXo2RZM0t1QkrhJvwrVtS27U2bXQw4cPj6mYN910U7GN0z7drA9Ww99wuHlQjIfOJgUQs88jfZNUTtIyKYJUCcdyqkDKrgwUfcwWJ0wdkvPPPz+mX6c9YlW/449zncCQzjnLLUifTGmtpAOzDp0BOlJe2Ss5tb8dnj8OHuk4soyFVFWWZKVBENZE07kmBZOB0bRsi3WTFDZS7XBdpNYMg5sco6S4Z9dZUqOGQJ0AJ/t4pR+73NsZ+GUQjgGjFJSTDsx5Tnpw6RIkzv20fZUat4o7tVWot8I9n6VcaUtRislSII5+Aks2skXiyrlfkL1fMShPX4maHdkq7RSJY2eXk0466Q8H5aSGZJBegjW67HtO5zG7bpUAfamllooz51SCpoNJtct0EXPWpzBVO1AJm/aic7jXXnsVH09tRoecjiXVb5n5KecbQXONmDM7wUAJj1P4qE2bNvEYL8V7kjqXlfA+0KmmcB7nMDsLJNkBpUqYQWhMHGcU3KQuAuv7GdRkrW52fR8devZLZ1AktXclHH/Tg/OYwaWbb745Xj8pXESgnvY/B1uv0clmIIq16NzTsjs1VHog+UcYTKaNyfIC9WbopHM/Y810wvWVa+oBBxxQbSHUSjw2WadLYUjahICPgCchi4trAINIDBAziERmHQGj533DY/1/2jGHAVKOYQqnphpBvD9kkCWpRlCl1FfKXgc5Zuebb7444MkWtQw0ZbM7CNS5lvK8NPAkNbaKD9KzNwbSBpmdYLsUTki2qdl+++2LhTOYvdhtt91i6jsjauVU+bqhkUrFhW7IkCFxZJKKrlQNT21FOhzpQ8xOcMOuhBHb5hgxZ5Q8jfzSxilQz24fkm3zShpsSjPqpL5TPCfxfJ7+Dg9ZMcykpW19yFagCA+zaMzeXHvttdX+Ds//aSNluLRjTVYXwTiFD1kykAJLlnYQFJGuSXt7v6q966+/Pm4Llr0mUtG9ukCdGXXeE6rqVzLuLSyZYtAoof/EfZ4BpFSYlOwkrgOdO3cuTnx4/2+cAJ2gk6AbDEZvu+22xUGo7HaBEyZMiMWPSwfvKqU/wIQSx2PaEpBBY/pJHJsMMifpGusgp5pKxQbppSfZk08+GU9UZicS0q8YFWZN+vfff1/t7/GmMjUq4Wc76HzNcgEGPbIpVNzM2d7OzmPDYp0/AyTs61l6fDJTnDpTpdXMK1EK1MnkYCsg1U+2M8eMIrM3Rx11VDynqepMujsDRuwzS0BJsJPdT1p/jHOZ5VXUS0gBJKgyzmOksvN9UrC5BtT0O1R/NQXq9BUq+f5F/4mBigsuuKDK46RNMwjPvZ/BULJmEtZDU8W9EmZsmwsZNtSfwaBBg+IEE+vNqVievf7SJ2AwhWVIlYbBo3333Tcub0n9UrZYZqKJ5Rks10q7O9VUb0lqLBUZpLNWL7sNCKk93GD4YM/z7AlIoMkFjQ6Qa1Fqh4GOk08+OX5OOhvFybg5U8iINX6MRJbekO081g9praXI9th9992nOSLOrM+aa67pjeZ/gTpFDFmWYRrb9CEVkKVBdPYoDMVxRzE+AvZ0DPI1ywxYF+nxVzfMmhOI03F84IEHim1JEdN0DSVNkz27s9uDqWEDdWbd2J6xNMW9UgNN+kkU2mQALrXJlltuGdfyM0jHzC2DSSwjTAVis+d+pczYNpV0LWCwjgkT1v0TnBN80s8lUyThOs1SJAarK7WtaBsG3ajnwTFLHQpw3DJRxwBTtnaN9y01lYoM0ilglNaeppONFEGCcYKbNJqYvpdGibMpwpo2im7QSaSCOCO04ObNTZy2ZF2/pg+FzUqDcbIUmBVmPWDp4AffY8aHpQZwRLhQZS1etviTaid77NBRp0OYUgZBBhKzOalgIWmVZCaxPtLjr27Sucx1lXRh0lVZ35sqNKdAh1oADMBV4qxYU3njjTfimvWDDjqouV9KrgaMWb7GchYGPClClo5NkP1Blkd2VlKN6+23345ZTCw7BMs0ybphCUxaf06ATuHeSijaWToYlL5OfzNp7lw7uU+l/dCpmTRw4EAHktQsKmpDv7Q35/XXXx+/vvDCC0OXLl3CWmutFbbYYou4LyT7oLOH8oknnhj3kmUgY5VVVol7+rLvqaa9/2baC3mhhRYKb731Vvjiiy9Cr1694vd4nL1kaX/2ndf0GTJkSNzTG+zXu8gii8RjluP58ssvDwcccEDckzr55JNP4j7h7Pm7/PLLx/ejdC/rSjXffPM190tokdKxc/bZZ4ePP/44rLPOOvEcB8cW14W111473HXXXXF/7ieeeCL88MMPYeutt44/W9PevfqvbPukvczZx/jOO+8M22yzTfj888/jdZb9e9u1axfvcc8880xYfPHFq+w3rYZFX4C90BdccMFQqV577bXw6aefxvO8Z8+eYcUVV4z3JPaWvv3228OoUaPCn//85/Dzzz/HY5H7U9euXT0um+i6Ae7/AwcODBdddFHc17tv377xunvGGWeEoUOHhk6dOsV965999tm4v3fqI5f7tZS+/xtvvBFeffXV0L9//9C9e/fisTpp0qTw5ptvhmWXXTbccsst8djmOaW/Q2oShQqURs2Y4SEt8PHHHy/OUlARmxFy0jarG1ms1HS2UuwTm2bIUd0oI5WHGbVlecEjjzwSR9dJ1UzPNcW9/rJVyFmiwUg41XJBahsVSqnoTJEensuoOVV02ZXAEWFNr9LZFpYLkCFDWmB2P3SQ3krmDGtTSYF1R4zaybYPKdakZDKLntqe2R5m1FdaaaW4rIh70z/+8Y+YzVAJs2INoSGOwUq8j5EGzL2EJSvZ3TFS5iFLAUht514EjkdmJJml9LxveGwdRhE+CktSAC6L4pH0dVOVfa6/bHvL49QIqbSaAIcddlgsqEfGEdvOzTHHHHFNOn8/mQW0I1sCkqXEmnR3xFBzquggHaQGk55FRzIbqFNYa++9967IG/Afodr9nnvuGbf0YR/UJHvzTW1MZUy2rVh88cXjDdoOesPiZsuaaoJ0BkFY9w9uwHTYOY656dBxJ/3Q9tf0YsCNjjl1DbJ1Oii0Q6BOESLWOWZx3JF+na4LldIhbIh7FFuBsk6SAU92H2ELu7Tul0B9nXXWiQETHUqWE1kpu3ay10Du/xSMev7554uprqresGHDYiFY+knZbf+ya3YpWkpxPY5bUt4ZqPfYbLxrxejRo2N7E4xznTjjjDOq1KthMIXAsybl3h9I11P6RwwejR07tniclm4TSL0qrrHUUbKosZpbRQXp2QtR9qRj9qc0UOdGRCVyR8+qx/Y+Bx544FTbV5Ve7OmYp+09Km3EtjHQkeTYBOshKWgIOksE4cysEUSBGXSK+BE0jRgxonhs2/6qr2uuuSZ2BDn2OK5KMbA544wzxhmd7Jro6gbwVL1sAMNafgJz9jTGJptsEouWMTiaDdQ595dbbrliEOQ5Pm3ZY5DBJQKbJZdcMu6K0b9//8JLL73UrK8vr7i3pIGiLLb2ItjJZtcRALHemccJED02Gx81Z6idxParDM7TP6B+An3bNdZYI16/K8kPP/xQ5Wuuo/TrUzZItso919G0XV2WA0pqTqGS07JLA3Vme7Op74mdyrrtM53amO9TpO+mm26a6nuqOwq+sW0a+3eSus5sxosvvlj8fgrUmVnjuK+ONxzVF3tts9c5e3STTZOwdWX2HN9rr73ilktUEC6dUVfNSrf5SftJk8qaOpgdO3aMS4boXJ522mlxCyswIOIgaN1x3yJATwObhxxySHE3F2bVk0rvA6S/n4JjVGlni8VsAVP6TvSz2L0lW2CX4JCtLZ2RbFyl93WuHQyi8r4QrNNHo4DcrrvuWqgUZHGy5VxacgFmzMk4YnkQ19LsloAUj2bZVumODVJzCpWelp1Gd1PqO51QR9HrF6inCqLgQkd7MoLujbnhMNrLlkvZWQuO57RGnUCdtb+kvqfOvTS9WBPNdTTtJZuQxsqxyBpUOjnJPvvsEx9n+yX9MTIP2B6JJQQJATizPZzbLF9hlvfiiy+O32MpC8uIWFfJkpfEQdBpy7YP9y5qJND2aZCEjjsddbIV2IUgu1WrCjGlmmMvBe5sWclMJJl1fM0Me+mMemI/oOHUdtCI451t2NgFhveFbJtKGXC68847Y30psrtSoE5MwI5DtAWV3BMe57hmUqlS2kctQ1kH6bVNy85+ztZVzjbWP1BnMIQbN4XLWAftGrTplz0+mcFg4Ik1ZmzBlFLfkWY3CdQperLffvs1y+tV+bnvvvviMUXqZDoeKbbDTA2zEgwKkd3B8oqEdet2zGuHTiRp16RcM0OecC0FM2B0tFN77rbbbrHtGSSxU1k72XYiLZi2HTVqVOGbb76J63cXXHDBwnnnnRe/f+SRR8aCUiwvYICqkqWUYdqPWXTu7VnZeztbLpLizuy6x2Xjy9YEKVXa5+KYT4+V63vDvYm/Ld2juG8x+Mm1MwXqt912WxysIO2dDBpm1xlkZobdgpvKm7IP0muTlk3la0bN//3vfxe/Z1BZ9zZmgIM2ZjaCFCvXoDVsgM7xSXo7wTgdy0MPPTSmZmYDdW4uVNf+7rvvPIbVYNhfd6655qryGNkyqXAUgQyZMxQwe//996s8z/N/2tJ5yn3ouOOOi4F6Ng2T6yizPAy6pYwZ7lesn3Sv+bpfR1kyRIo7mQosIQIZCZtvvnlxoPPkk0+OxzNtXsnZCWTHMGCRsG80s5CXX355leMzHX/cl5htT+t81fA1aZ5++un4Ofd/Bu+yS4+qU3ptKOfrcXVtwTIhAnXaivsV11seY7Bp7rnnjn3W7bff3gkl5VJFBOkwLbtp2rh3795xJN0AvXGKG5GamaoPc8PhRs2MOiltdCaZ+cnOoHvDUUOg8CDrzO+///6pvpeCGGaA2WaptFiPaneOs4af1ExmcOlUZtMxKWbGmlLSMf/2t7/FQdB0bldyEFlXzJ6zFKO0ZgeFEFnr/+GHH8avt9hii7jUIL0/ldrGBDb0j5K33nor1jwhq6a0eNzXX38dz/9VV13V+04joL7H1ltvXWjdunVhxx13jDVpXJpZqHL9ZDac62ZaepUGNVl2xTWVNeofffRR8Wfee++9eL9y1xHlVSv+EyrEF198EU4++eTw7LPPhq222irsuuuuYZtttglffvllePHFF8MMM8wQJk+eHNq0adPcL7XF+vbbb0PHjh1D69atw++//x7atm3b3C+pxeOYPf/888Ott94alltuuTDjjDMWv/fdd9+F008/PZx66qlhqaWWClOmTAkvv/xyPJalhvL+++/HY2+DDTYIZ511Vlh44YWrfP/7778PO+64Y1hiiSXC0KFDm+11tlRHHnlkuOKKK8JJJ50Ufvnll/Dvf/87tvlee+0VDj300PicQw45JIwfPz5eUy+++GLvV7XA9ZB7Ea6++uqwxx57xOvkiBEjQpcuXYrPu/7668Nxxx0X5pxzzjBp0qT4c6+88kpsa7pIrVq1CpUkHVeXX355/HjmmWeK33v44YfD0UcfHV566aWwww47hFVXXTV8+umn4YEHHojXAfpXHpsNKx2DtOmSSy4ZPvzww3g877TTTvaz/tfv7NmzZ3juuefCSiutFPv6c8wxR/jzn/8c9t5777DyyiuHN954I/z9738Pu+yyS7yuZs9/VOJ5rhagUGFMy24alTrz0NCYNWeG54ILLohfk6LJnqis+WMv1FSJdMyYMbH6ttusqbEws9i+ffs4i5MtqMXs4wYbbFDo3r27a/rqgYwY2o59p5O33347zu4utNBCxXXSyM5Qeo7X/h5EGiyzwMz0tmvXrrjVUrYNaX+WdZD6nh6v9BlhUqtZUpWtdI/nnnuucPTRR8fihWR4sL53wIABVnFvgmybpZdeOq6h5n3hvo9K72/x9z/55JNxmznahy1/6TNRQ4FaExyjhx9+eNwtY5ZZZonFIdNSLSnPKmomPWGU7fDDDw9ff/11uP322+Oor6ORyhtOzf/85z9h3XXXDZtvvnlYeumlwy233BI+//zz8PPPP8dR9fXXXz+ccsopVY5dZzDUGDiurrrqqrDvvvuG+eabL3Tt2jVeN5k9w2OPPeYMWj1wjtOWRxxxROjfv3/x8XfffTf06tUrfPPNN+HAAw8MxxxzTLO+zpZk1KhR4eOPPw59+vSJs2Yg+4A23W233eLM75gxY8L8888ffvvtt2ozjyqxT3DvvfeGsWPHxlnJueaaK57LfE6mATPmpbONEydOjMcv7ZgyvDz/GycThPOf/urw4cNjJtOee+4Z7rzzzpjBsMoqqxR/huN+oYUWCpWGY5Njl+xYsg1oq3bt2oXXXnstZh5ce+21MfOQ9lp99dXDI488UmxbKa8qMkiHadnK8w0564wzzggXXHBBmDBhQthvv/3ChhtuGNZee+3Qt2/feNySJis1FZYGccy9/fbbsTO4/PLLx0CIjrnX0tqd49l/GXAjBXPuuecOxx9/fAx4Eh5/88034wAdAySmY04b3Zkff/wxbLvttjGA5B7/+OOPxwGkZZZZJj6HQL13797hq6++Ck888UQccPK4/W/ATbsR5P36669xMmPNNdcM9913X/jHP/4Rl2R06NAhdOvWrcbfYcpw4yDIZGKJASbu/+D4Peigg8Ldd98d7rjjjvC3v/0t9gk6deoUzj777FDuuG5ml/4lpLxzHHMdffTRR4vnNec419wHH3wwTm5wv6qpzyXlRcUG6YknqfJ2HDLDQ4eJDtFaa60VH3v99dfjDWmxxRYr/gwzHMzAsUZYam7OoE0b66Dvv//+OGO+wAILhJlnnrn4vRtuuCFmKBx88MGhX79+oXPnzuGHH36InW5m0/mX4McgaNpS+7CunKCFAY4TTzwxHHXUUVWeR6DOLDtr/Jl1J7DR/2cPfPLJJ3ENL7PkrD9/77334uwtXzNLyXpf/mWNPwNIajxkgAwePDgsuOCC4cYbb4ztnhCoH3bYYXGWuHv37vG4Z+a43GvSkNnB3z5w4MB4zoN/Ux/qhRdeCFtvvXX405/+FM9v2oOBJ2bWE+9XagkqPkiXmlu2481sBSntpBDTKeKDG3PCbDo3YVLcP/roo3gzqvQZIDU9g8W6YdCNjAP+ZYaH4kbMUhIoJhdeeGE44YQT4owvQdC4cePibBEpnHQmbfNpy7YPHfjdd989Bi0pI4GBjuzzCNQ32mijWBDxpptuatbXnhfVHWMstSC4YQDps88+i0XkGEjmuRQvM9BpXGSGrLHGGjGD6eabbw5bbLHFVBNLFJWlb8BxXs4ZTRxzBNcMwJG9RVE4Jjg4ZvkglZ1r5iabbBKvm9tvv30cgGP2vNwHLlSenEKWmlnqFFGhfdiwYeHKK6+M6W3rrLNOGDlyZEw1TKicS2eJm9Xzzz8fb8TctKSmZLBYN8yak4LJrC6BDbNhpKpSnZmK7qyLZinLbbfdFoN3sM6U851ON+e4bT5tqX0YwOT6SQowwfess84alwrQ7tnnkc1AOmx2ELTSVXeMUQ3/n//8ZwzUGWgiMGLm9rrrrisem2oYBJylyKhjUIT3gRl1BuaTNMdG4M4gVHo/yjFAT8cnAxCsLW/fvn3xMT4YqGDXJgY1sMIKK8TsJfpJDDRJLZFButQMmC3nRpOwvpe0LIJ0ipo89NBDcdu1ffbZJ24FROE49OjRI5x33nlxi6ZU8NCZDCnfOEcJvtlOjQ4026lRAJItguh4MzPEVopsAXbsscfGGbNzzjnHc7yOmE1kYPODDz6IX1P8jAwF2pWg8rLLLotb3DEASiEu0mENNGtGEMiAEoNIafY2tRXf48Njs+GXvHH/Z1CJ7e7SUjcG7HgfyBAh8ERaApNVCe8HA2/UkkhtwDIitgClfs92221XfB6BOmvU6UtJLZFButTEKPTCeqlLLrkkpr/iL3/5S7zJMFPBqDk3Ytaa08FkTSqzQuz1CTr0qehUuY6YS+WG1GrSUS+99NL4NR3vf/3rX2GzzTaL+8+TkslsGZk0CR1wz/HazzxSKG722WePwQ0Y4GDGnMKb88wzTxz4oI0pHMuMeyUFNvVBAERGB4NFTz31VJW2SjOYahgpQGcgj+sEmXXUp6BfQEYIM+qkvP/000+xsvvTTz8dn18J7wGDFmQcgWORZSwE6glFN8mWSTs5IA1e/PWvf3UgTi2Wd3+piW288cYxAGc2jRsJNxZmetL6VDqUzJhTgRjMtjGTznOyxU4seCi1LAzC0ZkkSKTCMGvPr7nmmjDbbLPFbcGoRE7KZlIJHfDpka6BzJwTxDC7RgEtApnS1HYykJiB/PLLL8POO+9c1mt3GxJtSBFDlhCocVFAkhl0UrepW8HxytIClrgRnJLWTro7xzOD/GngvpyR+cIgBYNELBuiwj2PkVWQva7ykVV67XQgTi2RheOkJsTod7qx0mlkXerJJ58cKzszCwSKnbz//vuxQA83Ir6myntaV2VVUqnlovNNCibnNMteGHwrZfA4bbTfiiuuGD9nvTTr/amI36VLlxiwk/bOYAgVsak+znU0W9kZXkfrFjySRuwx2bjY+YEdCahNkV0KR0FJZo85pskUoVI5x26lHL8ULGQ5EP2n1VZbLS73o54Hs+lk06T16QTvLCPiWOU6K7V0BulSE2Hkm5stFUjTtjXZQJ3159yA77zzzjBgwIAwyyyzxE4RhVBeeuml+LkVnqWWKZ27bB902mmnxRkz1kx6TtfvOkpKO+ul2RaMQIaqzmQisM/3FVdcEeadd94w00wzxdl22pc2z2YpqO4cPGpc9AMYdCK9Ozt4xxIYtlp79dVXYx2FShxoIvimfUaPHh3X6ZNhSJDO4EWq8M4SItqNpUMepyoHHsVSE6BgEdWbuQFn95Xt379/vMGw3ycI2Ndbb70wdOjQcN9998WbEBWhUxX3SrkhS+UmBeLrrrtu7HAzWEeQboBev+to2i+aTjnrpkFBOGbS33rrrVgYbokllgiPPPJI+Oabb+Laf00fA5+GLxKXRSYIg0633357rFuT1l1zrJMpwkxxViX1BxicOProo+PfzGQG53pap05mAccmbZoGPe0vqRw4ky41MgpF7b///rHq8JZbbll8nAJxpG5lZ9S56TCLzgxQljMYUvmg2vDxxx8ftwCjY676X0dZq5qCdHz//fdhscUWiwOdFODK8jqqPAXopLWz3p+AkmOVx7n/Dx8+PAwaNCguiaEo2h577BGPXWaIK31Qjxl1ij6S+s4afdoJLGlJe6GbnaRy4d1KakTchEljZ2R8k002KT7OrA5Fo7p16xbT2plR56bCDZqbNmvTKCaV2LGUyqt4JOuq02ywpu86SrE4BjYJdph95LpKgbhSXkfVnAgeU4DOvZ4lL506dYpFI9l5gMH6s88+O66x5nv0A6hOztdPPPFE7CPUNAtfSTPqRx11VAzUWRpIujuTGylAhwG6yoV3LKmRkJpGyjqzOmnfXpDG9s4778St2AjQU1rWAQccEPdOv/fee+PaK0nlifWUdMJNy2yY62jKPErtyAAn21XBWTXlRToOP/nkk5hJN2rUqLDwwgvHxzbddNM4EEWRPrZf69u3b/jiiy/iMU3GnbsR/L/5558/HHnkkXHZ0FdffeU5rrJlurvUyKlZFCwiNYsq7RQ2osgRW6zQ6Uw3l+zoeHrMG48k1f06Sgr83/72Nwc/lDvMlDMDzG4uBOTMknPMMlhHRXLWW1M4rpSDeVNj/T7tlV2LLpUTg3SpkTEazsz4XXfdFbcGevnll+M+p9k1VL169Qpdu3aNHdFUqdQbjiTV/jq60UYbxUCH9f4wsFGesAPBueeeG1Pb55prrlitHezg0qFDh1jkkHoLpLaT5m4foHYqfQmAypdHtdQEqVlUJWUt5aKLLhpuvPHG+DgdSzqRBOjvvvtusVJp2jJIklT76+j7778fn5MYoKu5g8cstgjr06dPXFNNJsghhxwSHydABwNOLNVgdt0+QO0ZoKtcOZMuNfFM0DPPPBO22WabeIOm8BHbBb3yyiuxs+maM0mqmddRtbTZXY5NAnBmxzk+mVG/+OKLY2G4vfbaK1Zv57mHHnpomDhxYtz1wcBTkkG61MQdTKqSjh07Ns6es56KlDc7lpJUO15H1VKwRdg111wTj0uOTwLxHXfcMcw555xxK0YyPwjg995773hc81wKIZrCLckrgNQMVUkXX3zxsMIKK9ixlKQ68jqqlpDifscdd4SrrroqXHLJJeH+++8PO+20U7j00kvD0KFD41aru+++e6xDM88888SlGTfddFMM0JlpN0CX5Ey61Ay+/fbbWN2VG7EdS0mqO6+jyiuCc4Jt9vFOa89BgE6FdwrIUSSOLcRGjBgRjjvuuLD//vuHE044oVlft6T8MEiXmpEpbZI0fbyOKk/Gjx8fVl555fDBBx+EfffdN1xwwQVVBpGopfDpp5/GvdLTYBPbsfXv3z8G6dnih5Iql8POUjOyYylJ08frqPI0YDT33HOHW2+9NRx00EHh7rvvjgE52wWmvby7desWtxFMg0tzzDFHXKfOko111lmnuf8ESTnhTLokSZI0HShm+Msvv8Qt1tq1axdee+21GHwTjP/rX/+Ka89Zc77hhhvGugqsQc8yI0RSljPpkiRJ0nRgJpxU9VlmmSUceOCBYemllw7Dhw8PO++8c1hllVXCoosuGpZZZpnw3XffhYceeij+TJpdhwG6pCxn0iVJkqRaqmnW+6KLLooF4IYMGRIGDBgQZ9TZfYDPn3766fDEE0/EQB0WO5Q0LQbpkiRJUh29/vrroUuXLlUeo1AcReAI1FmX3r59+xiok/pOYE/BuA4dOpjeLmmavDpIkiRJf4A158nDDz8cunbtGiuzZzGTzv7nxxxzTLjiiivCTz/9FJ934403xv3Ql1pqqbhPugG6pGnxCiFJkiRNw/333x/OO++88Mwzz8Sv11tvvXDwwQeHPfbYI649z9pkk01ikbgDDjggFo0Da9SHDRsWOnfuHPdHl6RpcTGMJEmSVIOrrroqzoxvuummVbZJO+OMM+KMeN++fePXpLSDFHdm1JdddtmwxRZbFJ/fvXv3OAPPWnVJmhaDdEmSJKkaI0aMiAE3gfrf//73MNtss1X5PqntkydPDrvsskt455134oz5tddeGyu3n3zyyVMViTNAl1QbFo6TJEmSSnz99ddh2223DVtvvXXYb7/9io+zppyicQTnq666anzs9NNPD+eff37cgm3eeecNDz74YNyWTZLqw5l0SZIkqRqsH19ggQWKX1988cUxZZ215n/605/CYostFh599NFw2GGHhe222y4G5vPPP39Mg3ebNUn1ZeE4SZIkqRoTJ04Md911VwzMmVEnSJ9nnnnCfffdF84999zw2WefhRNPPDE+d6GFFgqdOnWKATpbrBmgS6ovrx6SJElSCYLxq6++Omy11VYxSJ911lnDOeecEwvAzTXXXOHbb7+Na9QJyNGqVaviz7rFmqTpYZAuSZIkVWP99dePBeFYh77oootO9X0Cd2bPJakhWThOkiRJqmNRuX79+oXx48eHJ554IrRp06a5X5KkMuJMuiRJklQLBOVXXHFFePzxx2NRuRSgU+ndQF1SQ3HBjCRJklQLn3zySQzMF1988TBmzJhYzZ0q7gbokhqS6e6SJElSLX333XehY8eOsVCcM+iSGoNBuiRJklRHdKGzFd0lqaGY7i5JkiTVkQG6pMZikC5JkiRJUk4YpEuSJEmSlBMG6ZIkSZIk5YRBuiRJkiRJOWGQLkmSJElSThikS5IkSZKUEwbpkiRJkiTlhEG6JEkVpG/fvnF/51NPPbXK47fddpv7PkuSlAMG6ZIkVZgZZ5wxnHbaaeHbb79t7pciSZJKGKRLklRhevToEeaff/4wZMiQGp/zr3/9Kyy99NKhffv2YZFFFglnnXVWle/z2CmnnBJ23XXXMOuss4aFFlooXHbZZVWeM27cuLDtttuG2WefPcw555xhs802Cx9++GGj/V2SJJUDg3RJkipMmzZtYoB9/vnnh08++WSq748dOzYG19tvv3145ZVXwnHHHReOOeaYcPXVV1d5HoH7iiuuGF544YWw7777hn322Se89dZb8Xu//fZb6NmzZwzgH3vssfDEE0+EWWaZJfz9738Pv/76a5P9rZIktTQG6ZIkVaAtttgiLLvssuHYY4+d6ntDhw4N66+/fgzM//KXv8R17Pvvv38444wzqjxv4403jsH54osvHg4//PAw99xzh1GjRsXvjRw5MkyZMiVcccUVYZlllglLLbVUuOqqq8LHH38cRo8e3WR/pyRJLY1BuiRJFYp16ddcc0144403qjzO16uvvnqVx/j6nXfeCZMnTy4+1q1bt+LnFJ0jhf6rr76KX7/00kvh3XffjTPpzKDzQcr7zz//HN57771G/9skSWqp2jb3C5AkSc1jrbXWiinpgwYNirPldTXDDDNU+ZpAndlz/PDDD2GFFVYIN9xww1Q/N88880zHq5YkqbwZpEuSVMHYio2097/+9a/Fx0hNZw15Fl+T+s569tpYfvnlY8r7vPPOG2abbbYGf92SJJUr090lSapgrBffaaedwnnnnVd87OCDDw4PPfRQOPHEE8Pbb78dU+IvuOCCcMghh9T69/I7WaNORXcKx33wwQdxLXr//v2rLVYnSZL+yyBdkqQKd8IJJxTT1NMs+E033RRGjBgRunbtGgYPHhyfU5eU+A4dOoRHH300bs225ZZbxtn53XbbLa5Jd2ZdkqSatSoUCoVpfF+SJEmSJDURZ9IlSZIkScoJg3RJkiRJknLCIF2SJEmSpJwwSJckSZIkKScM0iVJkiRJygmDdEmSJEmScsIgXZIkSZKknDBIlyRJkiQpJwzSJUmSJEnKCYN0SZIkSZJywiBdkiRJkqSQD/8Htf6FIdwzKBkAAAAASUVORK5CYII="/>
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<p>DL Models</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
<span class="n">df_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">[[</span><span class="s1">'Close'</span><span class="p">]])</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">create_sequences</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">):</span>
    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">-</span> <span class="n">seq_length</span><span class="p">):</span>
        <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">seq_length</span><span class="p">])</span>
        <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="n">seq_length</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>

<span class="n">seq_length</span> <span class="o">=</span> <span class="mi">60</span> 
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">create_sequences</span><span class="p">(</span><span class="n">df_scaled</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">train_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="n">train_size</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">train_size</span><span class="p">:]</span>
<span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="n">train_size</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">train_size</span><span class="p">:]</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">build_lstm_model</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
        <span class="n">LSTM</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">seq_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
        <span class="n">BatchNormalization</span><span class="p">(),</span>
        <span class="n">LSTM</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
        <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
        <span class="n">Dense</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span>
        <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="p">])</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">'adam'</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'mse'</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'mae'</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="k">def</span><span class="w"> </span><span class="nf">build_rnn_model</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
        <span class="n">SimpleRNN</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">seq_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
        <span class="n">BatchNormalization</span><span class="p">(),</span>
        <span class="n">SimpleRNN</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
        <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
        <span class="n">Dense</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span>
        <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="p">])</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">'adam'</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'mse'</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'mae'</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="k">def</span><span class="w"> </span><span class="nf">build_gru_model</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
        <span class="n">GRU</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">seq_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
        <span class="n">BatchNormalization</span><span class="p">(),</span>
        <span class="n">GRU</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
        <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
        <span class="n">Dense</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span>
        <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="p">])</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">'adam'</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'mse'</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'mae'</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">models</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">"LSTM"</span><span class="p">:</span> <span class="n">build_lstm_model</span><span class="p">(),</span>
    <span class="s2">"RNN"</span><span class="p">:</span> <span class="n">build_rnn_model</span><span class="p">(),</span>
    <span class="s2">"GRU"</span><span class="p">:</span> <span class="n">build_gru_model</span><span class="p">()</span>
<span class="p">}</span>


<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span>
    <span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
    
    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="n">mae</span> <span class="o">=</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
    <span class="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
    <span class="n">rmse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">mse</span><span class="p">)</span>
    <span class="n">r2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
    <span class="n">train_time</span> <span class="o">=</span> <span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span>

     <span class="c1"># Calculate accuracy</span>
    <span class="n">threshold</span> <span class="o">=</span> <span class="mf">0.05</span>  <span class="c1"># 5% tolerance level</span>
    <span class="n">correct_predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">((</span><span class="n">y_pred</span> <span class="o">-</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">/</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">threshold</span>
    <span class="n">accuracy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">correct_predictions</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span>
    
    <span class="n">results</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"MAE"</span><span class="p">:</span> <span class="n">mae</span><span class="p">,</span> <span class="s2">"MSE"</span><span class="p">:</span> <span class="n">mse</span><span class="p">,</span> <span class="s2">"RMSE"</span><span class="p">:</span> <span class="n">rmse</span><span class="p">,</span> <span class="s2">"R2 Score"</span><span class="p">:</span> <span class="n">r2</span><span class="p">,</span> <span class="s2">"Training Time (s)"</span><span class="p">:</span> <span class="n">train_time</span><span class="p">,</span> <span class="s2">"Accuracy (%)"</span><span class="p">:</span> <span class="n">accuracy</span><span class="p">}</span>
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<pre>e:\miniconda3\envs\StockMarketForcastinAI\Lib\site-packages\keras\src\layers\rnn\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)
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<pre>Epoch 1/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">7s</span> 28ms/step - loss: 9.4855e-05 - mae: 0.0052 - val_loss: 0.1272 - val_mae: 0.2470
Epoch 2/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 6.1383e-06 - mae: 0.0015 - val_loss: 0.1086 - val_mae: 0.2223
Epoch 3/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 24ms/step - loss: 5.4644e-06 - mae: 0.0015 - val_loss: 0.0983 - val_mae: 0.2050
Epoch 4/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 5.1618e-06 - mae: 0.0015 - val_loss: 0.0835 - val_mae: 0.1789
Epoch 5/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 3.2602e-06 - mae: 0.0013 - val_loss: 0.0595 - val_mae: 0.1317
Epoch 6/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3089e-06 - mae: 0.0013 - val_loss: 0.0636 - val_mae: 0.1356
Epoch 7/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3729e-06 - mae: 0.0013 - val_loss: 0.0559 - val_mae: 0.1266
Epoch 8/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 4.5938e-06 - mae: 0.0015 - val_loss: 0.0517 - val_mae: 0.1108
Epoch 9/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.6815e-06 - mae: 0.0013 - val_loss: 0.0559 - val_mae: 0.1170
Epoch 10/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.8079e-06 - mae: 0.0013 - val_loss: 0.0593 - val_mae: 0.1286
Epoch 11/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 24ms/step - loss: 2.7033e-06 - mae: 0.0012 - val_loss: 0.0590 - val_mae: 0.1378
Epoch 12/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.5238e-06 - mae: 0.0013 - val_loss: 0.0549 - val_mae: 0.1201
Epoch 13/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.1838e-06 - mae: 0.0013 - val_loss: 0.0587 - val_mae: 0.1293
Epoch 14/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.0659e-06 - mae: 0.0013 - val_loss: 0.0622 - val_mae: 0.1351
Epoch 15/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3384e-06 - mae: 0.0013 - val_loss: 0.0536 - val_mae: 0.1186
Epoch 16/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3072e-06 - mae: 0.0013 - val_loss: 0.0585 - val_mae: 0.1298
Epoch 17/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3371e-06 - mae: 0.0013 - val_loss: 0.0580 - val_mae: 0.1272
Epoch 18/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 24ms/step - loss: 2.9714e-06 - mae: 0.0012 - val_loss: 0.0602 - val_mae: 0.1339
Epoch 19/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.0614e-06 - mae: 0.0013 - val_loss: 0.0580 - val_mae: 0.1232
Epoch 20/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.6552e-06 - mae: 0.0014 - val_loss: 0.0631 - val_mae: 0.1372
Epoch 21/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.8774e-06 - mae: 0.0014 - val_loss: 0.0610 - val_mae: 0.1316
Epoch 22/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 24ms/step - loss: 2.9697e-06 - mae: 0.0013 - val_loss: 0.0603 - val_mae: 0.1292
Epoch 23/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.3506e-06 - mae: 0.0011 - val_loss: 0.0634 - val_mae: 0.1327
Epoch 24/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 24ms/step - loss: 3.0668e-06 - mae: 0.0013 - val_loss: 0.0685 - val_mae: 0.1529
Epoch 25/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.4264e-06 - mae: 0.0014 - val_loss: 0.0673 - val_mae: 0.1358
Epoch 26/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.5840e-06 - mae: 0.0012 - val_loss: 0.0723 - val_mae: 0.1428
Epoch 27/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.5981e-06 - mae: 0.0012 - val_loss: 0.0684 - val_mae: 0.1353
Epoch 28/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 24ms/step - loss: 2.7093e-06 - mae: 0.0012 - val_loss: 0.0725 - val_mae: 0.1437
Epoch 29/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 24ms/step - loss: 2.8892e-06 - mae: 0.0012 - val_loss: 0.0723 - val_mae: 0.1422
Epoch 30/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 24ms/step - loss: 2.6894e-06 - mae: 0.0012 - val_loss: 0.0770 - val_mae: 0.1468
Epoch 31/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 24ms/step - loss: 2.2396e-06 - mae: 0.0011 - val_loss: 0.0746 - val_mae: 0.1423
Epoch 32/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.7156e-06 - mae: 0.0012 - val_loss: 0.0830 - val_mae: 0.1584
Epoch 33/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.2127e-06 - mae: 0.0013 - val_loss: 0.0716 - val_mae: 0.1409
Epoch 34/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 24ms/step - loss: 2.7709e-06 - mae: 0.0012 - val_loss: 0.0738 - val_mae: 0.1494
Epoch 35/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9245e-06 - mae: 0.0012 - val_loss: 0.0695 - val_mae: 0.1364
Epoch 36/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 2.8758e-06 - mae: 0.0012 - val_loss: 0.0694 - val_mae: 0.1389
Epoch 37/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9860e-06 - mae: 0.0013 - val_loss: 0.0711 - val_mae: 0.1406
Epoch 38/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9757e-06 - mae: 0.0012 - val_loss: 0.0735 - val_mae: 0.1365
Epoch 39/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.5030e-06 - mae: 0.0013 - val_loss: 0.0746 - val_mae: 0.1360
Epoch 40/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 2.8294e-06 - mae: 0.0012 - val_loss: 0.0897 - val_mae: 0.1546
Epoch 41/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 3.1242e-06 - mae: 0.0013 - val_loss: 0.0855 - val_mae: 0.1458
Epoch 42/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 6.5044e-06 - mae: 0.0018 - val_loss: 0.0931 - val_mae: 0.1704
Epoch 43/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 3.1385e-06 - mae: 0.0013 - val_loss: 0.0931 - val_mae: 0.1629
Epoch 44/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.7047e-06 - mae: 0.0012 - val_loss: 0.0951 - val_mae: 0.1627
Epoch 45/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.1481e-06 - mae: 0.0011 - val_loss: 0.0917 - val_mae: 0.1585
Epoch 46/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.7932e-06 - mae: 0.0012 - val_loss: 0.0950 - val_mae: 0.1604
Epoch 47/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.7157e-06 - mae: 0.0012 - val_loss: 0.0984 - val_mae: 0.1648
Epoch 48/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.1848e-06 - mae: 0.0013 - val_loss: 0.0960 - val_mae: 0.1630
Epoch 49/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.7822e-06 - mae: 0.0012 - val_loss: 0.0898 - val_mae: 0.1564
Epoch 50/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.4764e-06 - mae: 0.0011 - val_loss: 0.0921 - val_mae: 0.1616
<span class="ansi-bold">41/41</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">1s</span> 14ms/step
Epoch 1/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 11ms/step - loss: 0.0413 - mae: 0.1302 - val_loss: 0.1202 - val_mae: 0.2284
Epoch 2/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 3.5178e-04 - mae: 0.0124 - val_loss: 0.0437 - val_mae: 0.1576
Epoch 3/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.2553e-04 - mae: 0.0070 - val_loss: 0.0102 - val_mae: 0.0851
Epoch 4/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 7.8376e-05 - mae: 0.0055 - val_loss: 0.0296 - val_mae: 0.0952
Epoch 5/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 5.9381e-05 - mae: 0.0046 - val_loss: 0.0542 - val_mae: 0.1218
Epoch 6/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 4.3462e-05 - mae: 0.0040 - val_loss: 0.0289 - val_mae: 0.0924
Epoch 7/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 4.0900e-05 - mae: 0.0037 - val_loss: 0.0296 - val_mae: 0.0949
Epoch 8/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 2.5603e-05 - mae: 0.0028 - val_loss: 0.0242 - val_mae: 0.0868
Epoch 9/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 3.1076e-05 - mae: 0.0032 - val_loss: 0.0237 - val_mae: 0.0900
Epoch 10/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 2.5748e-05 - mae: 0.0027 - val_loss: 0.0263 - val_mae: 0.0902
Epoch 11/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 2.3802e-05 - mae: 0.0026 - val_loss: 0.0108 - val_mae: 0.0577
Epoch 12/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.6894e-05 - mae: 0.0023 - val_loss: 0.0271 - val_mae: 0.0951
Epoch 13/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.6254e-05 - mae: 0.0023 - val_loss: 0.0139 - val_mae: 0.0665
Epoch 14/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.0487e-05 - mae: 0.0019 - val_loss: 0.0219 - val_mae: 0.0825
Epoch 15/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.8734e-05 - mae: 0.0023 - val_loss: 0.0052 - val_mae: 0.0381
Epoch 16/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.7175e-05 - mae: 0.0022 - val_loss: 0.0045 - val_mae: 0.0386
Epoch 17/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.1918e-05 - mae: 0.0019 - val_loss: 0.0045 - val_mae: 0.0383
Epoch 18/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.2536e-05 - mae: 0.0020 - val_loss: 0.0127 - val_mae: 0.0656
Epoch 19/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.0896e-05 - mae: 0.0019 - val_loss: 0.0048 - val_mae: 0.0409
Epoch 20/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 8.8737e-06 - mae: 0.0018 - val_loss: 0.0042 - val_mae: 0.0386
Epoch 21/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.2907e-05 - mae: 0.0020 - val_loss: 0.0073 - val_mae: 0.0541
Epoch 22/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 8.8176e-06 - mae: 0.0017 - val_loss: 0.0058 - val_mae: 0.0400
Epoch 23/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.0806e-05 - mae: 0.0018 - val_loss: 0.0054 - val_mae: 0.0440
Epoch 24/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 9.0638e-06 - mae: 0.0017 - val_loss: 0.0103 - val_mae: 0.0497
Epoch 25/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 7.7337e-06 - mae: 0.0016 - val_loss: 0.0188 - val_mae: 0.0740
Epoch 26/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.1375e-05 - mae: 0.0018 - val_loss: 0.0101 - val_mae: 0.0569
Epoch 27/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 6.4286e-06 - mae: 0.0015 - val_loss: 0.0090 - val_mae: 0.0510
Epoch 28/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.0390e-05 - mae: 0.0019 - val_loss: 0.0093 - val_mae: 0.0487
Epoch 29/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 5.9338e-06 - mae: 0.0015 - val_loss: 0.0155 - val_mae: 0.0666
Epoch 30/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 7.1072e-06 - mae: 0.0016 - val_loss: 0.0078 - val_mae: 0.0559
Epoch 31/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.2098e-05 - mae: 0.0019 - val_loss: 0.0209 - val_mae: 0.0796
Epoch 32/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 6.6445e-06 - mae: 0.0016 - val_loss: 0.0360 - val_mae: 0.1023
Epoch 33/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 6.1517e-06 - mae: 0.0015 - val_loss: 0.0302 - val_mae: 0.0896
Epoch 34/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 4.8530e-06 - mae: 0.0014 - val_loss: 0.0248 - val_mae: 0.0854
Epoch 35/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 6.8652e-06 - mae: 0.0015 - val_loss: 0.0308 - val_mae: 0.0880
Epoch 36/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 8.2672e-06 - mae: 0.0016 - val_loss: 0.0422 - val_mae: 0.1101
Epoch 37/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 6.9593e-06 - mae: 0.0016 - val_loss: 0.0463 - val_mae: 0.1098
Epoch 38/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 4.9729e-06 - mae: 0.0014 - val_loss: 0.0576 - val_mae: 0.1387
Epoch 39/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 6.2529e-06 - mae: 0.0015 - val_loss: 0.0506 - val_mae: 0.1262
Epoch 40/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 5.3620e-06 - mae: 0.0015 - val_loss: 0.0441 - val_mae: 0.1141
Epoch 41/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 4.4055e-06 - mae: 0.0014 - val_loss: 0.0411 - val_mae: 0.1082
Epoch 42/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 6.9062e-06 - mae: 0.0015 - val_loss: 0.0378 - val_mae: 0.0951
Epoch 43/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 5.6326e-06 - mae: 0.0016 - val_loss: 0.0386 - val_mae: 0.1100
Epoch 44/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 4.1266e-06 - mae: 0.0014 - val_loss: 0.0380 - val_mae: 0.1110
Epoch 45/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 1.3028e-05 - mae: 0.0020 - val_loss: 0.0599 - val_mae: 0.1440
Epoch 46/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 6.7642e-06 - mae: 0.0015 - val_loss: 0.0615 - val_mae: 0.1467
Epoch 47/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 4.8022e-06 - mae: 0.0014 - val_loss: 0.0526 - val_mae: 0.1236
Epoch 48/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 5.0821e-06 - mae: 0.0015 - val_loss: 0.0539 - val_mae: 0.1310
Epoch 49/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 4.5982e-06 - mae: 0.0015 - val_loss: 0.0581 - val_mae: 0.1372
Epoch 50/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 10ms/step - loss: 3.6360e-06 - mae: 0.0013 - val_loss: 0.0799 - val_mae: 0.1581
<span class="ansi-bold">41/41</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 7ms/step
Epoch 1/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">7s</span> 27ms/step - loss: 4.8260e-05 - mae: 0.0041 - val_loss: 0.1237 - val_mae: 0.2471
Epoch 2/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 4.8317e-06 - mae: 0.0015 - val_loss: 0.0950 - val_mae: 0.2136
Epoch 3/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 6.2272e-06 - mae: 0.0017 - val_loss: 0.0642 - val_mae: 0.1775
Epoch 4/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 3.6686e-06 - mae: 0.0013 - val_loss: 0.0317 - val_mae: 0.1209
Epoch 5/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.8887e-06 - mae: 0.0014 - val_loss: 0.0123 - val_mae: 0.0657
Epoch 6/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8334e-06 - mae: 0.0012 - val_loss: 0.0090 - val_mae: 0.0507
Epoch 7/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.6984e-06 - mae: 0.0014 - val_loss: 0.0080 - val_mae: 0.0523
Epoch 8/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 3.0453e-06 - mae: 0.0012 - val_loss: 0.0121 - val_mae: 0.0622
Epoch 9/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3211e-06 - mae: 0.0013 - val_loss: 0.0076 - val_mae: 0.0448
Epoch 10/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.0397e-06 - mae: 0.0013 - val_loss: 0.0089 - val_mae: 0.0454
Epoch 11/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9286e-06 - mae: 0.0012 - val_loss: 0.0115 - val_mae: 0.0528
Epoch 12/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 4.5252e-06 - mae: 0.0015 - val_loss: 0.0162 - val_mae: 0.0694
Epoch 13/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.5903e-06 - mae: 0.0011 - val_loss: 0.0086 - val_mae: 0.0451
Epoch 14/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 3.0315e-06 - mae: 0.0013 - val_loss: 0.0135 - val_mae: 0.0619
Epoch 15/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3303e-06 - mae: 0.0013 - val_loss: 0.0096 - val_mae: 0.0525
Epoch 16/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.1975e-06 - mae: 0.0013 - val_loss: 0.0099 - val_mae: 0.0488
Epoch 17/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.1529e-06 - mae: 0.0013 - val_loss: 0.0230 - val_mae: 0.0881
Epoch 18/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.6185e-06 - mae: 0.0012 - val_loss: 0.0141 - val_mae: 0.0635
Epoch 19/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.2779e-06 - mae: 0.0013 - val_loss: 0.0132 - val_mae: 0.0606
Epoch 20/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9076e-06 - mae: 0.0012 - val_loss: 0.0152 - val_mae: 0.0662
Epoch 21/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.1630e-06 - mae: 0.0011 - val_loss: 0.0163 - val_mae: 0.0705
Epoch 22/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.2820e-06 - mae: 0.0013 - val_loss: 0.0121 - val_mae: 0.0576
Epoch 23/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8287e-06 - mae: 0.0012 - val_loss: 0.0150 - val_mae: 0.0594
Epoch 24/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.7456e-06 - mae: 0.0012 - val_loss: 0.0118 - val_mae: 0.0525
Epoch 25/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.4633e-06 - mae: 0.0013 - val_loss: 0.0163 - val_mae: 0.0933
Epoch 26/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.7141e-06 - mae: 0.0012 - val_loss: 0.0093 - val_mae: 0.0439
Epoch 27/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.2161e-06 - mae: 0.0013 - val_loss: 0.0166 - val_mae: 0.0688
Epoch 28/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9710e-06 - mae: 0.0013 - val_loss: 0.0142 - val_mae: 0.0608
Epoch 29/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8807e-06 - mae: 0.0012 - val_loss: 0.0187 - val_mae: 0.0777
Epoch 30/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.6052e-06 - mae: 0.0012 - val_loss: 0.0164 - val_mae: 0.0695
Epoch 31/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.1430e-06 - mae: 0.0011 - val_loss: 0.0166 - val_mae: 0.0691
Epoch 32/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8517e-06 - mae: 0.0012 - val_loss: 0.0228 - val_mae: 0.0867
Epoch 33/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9402e-06 - mae: 0.0012 - val_loss: 0.0132 - val_mae: 0.0565
Epoch 34/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.1820e-06 - mae: 0.0013 - val_loss: 0.0127 - val_mae: 0.0543
Epoch 35/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.4087e-06 - mae: 0.0013 - val_loss: 0.0189 - val_mae: 0.0733
Epoch 36/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.1895e-06 - mae: 0.0013 - val_loss: 0.0163 - val_mae: 0.0750
Epoch 37/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.3370e-06 - mae: 0.0011 - val_loss: 0.0213 - val_mae: 0.0878
Epoch 38/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.6497e-06 - mae: 0.0012 - val_loss: 0.0172 - val_mae: 0.0669
Epoch 39/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.6968e-06 - mae: 0.0014 - val_loss: 0.0184 - val_mae: 0.0699
Epoch 40/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9497e-06 - mae: 0.0013 - val_loss: 0.0210 - val_mae: 0.0823
Epoch 41/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3451e-06 - mae: 0.0013 - val_loss: 0.0202 - val_mae: 0.0770
Epoch 42/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3208e-06 - mae: 0.0013 - val_loss: 0.0198 - val_mae: 0.0785
Epoch 43/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8921e-06 - mae: 0.0013 - val_loss: 0.0234 - val_mae: 0.0823
Epoch 44/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8071e-06 - mae: 0.0012 - val_loss: 0.0244 - val_mae: 0.0831
Epoch 45/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.5250e-06 - mae: 0.0011 - val_loss: 0.0203 - val_mae: 0.0696
Epoch 46/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3453e-06 - mae: 0.0013 - val_loss: 0.0212 - val_mae: 0.0763
Epoch 47/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.4188e-06 - mae: 0.0011 - val_loss: 0.0303 - val_mae: 0.0997
Epoch 48/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.1078e-06 - mae: 0.0011 - val_loss: 0.0192 - val_mae: 0.0669
Epoch 49/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.5105e-06 - mae: 0.0012 - val_loss: 0.0241 - val_mae: 0.0802
Epoch 50/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8502e-06 - mae: 0.0012 - val_loss: 0.0245 - val_mae: 0.0756
<span class="ansi-bold">41/41</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">1s</span> 12ms/step
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<div class="jp-InputPrompt jp-InputArea-prompt">In [39]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">results</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
<span class="n">results_df</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[39]:</div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>MAE</th>
<th>MSE</th>
<th>RMSE</th>
<th>R2 Score</th>
<th>Training Time (s)</th>
<th>Accuracy (%)</th>
</tr>
</thead>
<tbody>
<tr>
<th>LSTM</th>
<td>0.161630</td>
<td>0.092133</td>
<td>0.303534</td>
<td>-0.261974</td>
<td>206.351229</td>
<td>0.000000</td>
</tr>
<tr>
<th>RNN</th>
<td>0.158111</td>
<td>0.079903</td>
<td>0.282672</td>
<td>-0.094460</td>
<td>83.743238</td>
<td>2.307692</td>
</tr>
<tr>
<th>GRU</th>
<td>0.075626</td>
<td>0.024468</td>
<td>0.156422</td>
<td>0.664856</td>
<td>207.883645</td>
<td>34.076923</td>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [40]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">results_df</span><span class="p">[</span><span class="s2">"RMSE"</span><span class="p">],</span> <span class="n">hue</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">dodge</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Root Mean Squared Error"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Model Performance Comparison"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [41]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">results_df</span><span class="p">[</span><span class="s2">"Training Time (s)"</span><span class="p">],</span> <span class="n">hue</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">dodge</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Training Time (Seconds)"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Model Training Time Comparison"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
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"/>
</div>
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</div>
</div>
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
<div class="jp-InputPrompt jp-InputArea-prompt">In [42]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">results_df</span><span class="p">[</span><span class="s2">"R2 Score"</span><span class="p">],</span> <span class="n">hue</span><span class="o">=</span><span class="n">results_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">dodge</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"R2 Score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Model Accuracy Comparison"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [43]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">build_gru_model</span><span class="p">()</span>
<span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span>
<span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
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<pre>Epoch 1/50
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<pre>e:\miniconda3\envs\StockMarketForcastinAI\Lib\site-packages\keras\src\layers\rnn\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)
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<pre><span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">7s</span> 27ms/step - loss: 8.2738e-05 - mae: 0.0048 - val_loss: 0.1222 - val_mae: 0.2451
Epoch 2/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 4.8725e-06 - mae: 0.0015 - val_loss: 0.1025 - val_mae: 0.2238
Epoch 3/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 4.3879e-06 - mae: 0.0014 - val_loss: 0.0598 - val_mae: 0.1665
Epoch 4/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 4.6173e-06 - mae: 0.0014 - val_loss: 0.0355 - val_mae: 0.1282
Epoch 5/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.9100e-06 - mae: 0.0014 - val_loss: 0.0123 - val_mae: 0.0543
Epoch 6/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.2521e-06 - mae: 0.0012 - val_loss: 0.0130 - val_mae: 0.0570
Epoch 7/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8651e-06 - mae: 0.0012 - val_loss: 0.0088 - val_mae: 0.0526
Epoch 8/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.8010e-06 - mae: 0.0014 - val_loss: 0.0091 - val_mae: 0.0455
Epoch 9/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8084e-06 - mae: 0.0012 - val_loss: 0.0122 - val_mae: 0.0588
Epoch 10/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8078e-06 - mae: 0.0012 - val_loss: 0.0112 - val_mae: 0.0528
Epoch 11/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.2325e-06 - mae: 0.0013 - val_loss: 0.0144 - val_mae: 0.0652
Epoch 12/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.1148e-06 - mae: 0.0012 - val_loss: 0.0163 - val_mae: 0.0708
Epoch 13/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.5967e-06 - mae: 0.0014 - val_loss: 0.0096 - val_mae: 0.0464
Epoch 14/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9402e-06 - mae: 0.0013 - val_loss: 0.0079 - val_mae: 0.0414
Epoch 15/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8597e-06 - mae: 0.0012 - val_loss: 0.0122 - val_mae: 0.0566
Epoch 16/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.5433e-06 - mae: 0.0011 - val_loss: 0.0092 - val_mae: 0.0475
Epoch 17/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9397e-06 - mae: 0.0013 - val_loss: 0.0099 - val_mae: 0.0487
Epoch 18/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8228e-06 - mae: 0.0012 - val_loss: 0.0119 - val_mae: 0.0665
Epoch 19/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.4636e-06 - mae: 0.0013 - val_loss: 0.0117 - val_mae: 0.0528
Epoch 20/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.0672e-06 - mae: 0.0013 - val_loss: 0.0111 - val_mae: 0.0516
Epoch 21/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.3122e-06 - mae: 0.0011 - val_loss: 0.0090 - val_mae: 0.0440
Epoch 22/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.1790e-06 - mae: 0.0013 - val_loss: 0.0102 - val_mae: 0.0475
Epoch 23/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.4939e-06 - mae: 0.0011 - val_loss: 0.0118 - val_mae: 0.0636
Epoch 24/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.8459e-06 - mae: 0.0013 - val_loss: 0.0119 - val_mae: 0.0540
Epoch 25/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.3888e-06 - mae: 0.0011 - val_loss: 0.0139 - val_mae: 0.0572
Epoch 26/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.4827e-06 - mae: 0.0014 - val_loss: 0.0141 - val_mae: 0.0636
Epoch 27/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.0234e-06 - mae: 0.0012 - val_loss: 0.0120 - val_mae: 0.0531
Epoch 28/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.5915e-06 - mae: 0.0012 - val_loss: 0.0107 - val_mae: 0.0529
Epoch 29/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3440e-06 - mae: 0.0014 - val_loss: 0.0125 - val_mae: 0.0521
Epoch 30/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.6797e-06 - mae: 0.0012 - val_loss: 0.0140 - val_mae: 0.0589
Epoch 31/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.3416e-06 - mae: 0.0013 - val_loss: 0.0199 - val_mae: 0.0801
Epoch 32/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.1069e-06 - mae: 0.0013 - val_loss: 0.0116 - val_mae: 0.0490
Epoch 33/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.6427e-06 - mae: 0.0012 - val_loss: 0.0177 - val_mae: 0.0723
Epoch 34/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.4365e-06 - mae: 0.0011 - val_loss: 0.0221 - val_mae: 0.0813
Epoch 35/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.8140e-06 - mae: 0.0013 - val_loss: 0.0159 - val_mae: 0.1026
Epoch 36/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.0448e-06 - mae: 0.0013 - val_loss: 0.0135 - val_mae: 0.0530
Epoch 37/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.0877e-06 - mae: 0.0013 - val_loss: 0.0177 - val_mae: 0.0619
Epoch 38/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 3.4025e-06 - mae: 0.0014 - val_loss: 0.0211 - val_mae: 0.0770
Epoch 39/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.4824e-06 - mae: 0.0011 - val_loss: 0.0213 - val_mae: 0.0684
Epoch 40/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.5971e-06 - mae: 0.0012 - val_loss: 0.0169 - val_mae: 0.0592
Epoch 41/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.9068e-06 - mae: 0.0012 - val_loss: 0.0240 - val_mae: 0.0854
Epoch 42/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.6018e-06 - mae: 0.0012 - val_loss: 0.0219 - val_mae: 0.0734
Epoch 43/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.7140e-06 - mae: 0.0012 - val_loss: 0.0198 - val_mae: 0.0724
Epoch 44/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.6817e-06 - mae: 0.0012 - val_loss: 0.0193 - val_mae: 0.0677
Epoch 45/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.1955e-06 - mae: 0.0011 - val_loss: 0.0225 - val_mae: 0.0786
Epoch 46/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 25ms/step - loss: 2.4500e-06 - mae: 0.0011 - val_loss: 0.0204 - val_mae: 0.0729
Epoch 47/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 2.9400e-06 - mae: 0.0012 - val_loss: 0.0215 - val_mae: 0.0681
Epoch 48/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 27ms/step - loss: 2.7293e-06 - mae: 0.0012 - val_loss: 0.0213 - val_mae: 0.0686
Epoch 49/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 26ms/step - loss: 2.4060e-06 - mae: 0.0012 - val_loss: 0.0210 - val_mae: 0.0694
Epoch 50/50
<span class="ansi-bold">163/163</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">5s</span> 30ms/step - loss: 2.6696e-06 - mae: 0.0012 - val_loss: 0.0215 - val_mae: 0.0772
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<div class="jp-InputPrompt jp-InputArea-prompt">In [44]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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<pre><span class="ansi-bold">41/41</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">1s</span> 12ms/step
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<div class="jp-InputPrompt jp-InputArea-prompt">In [45]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">future_dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">"Date"</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">periods</span><span class="o">=</span><span class="mi">36</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">'ME'</span><span class="p">)</span>
<span class="n">future_inputs</span> <span class="o">=</span> <span class="n">df_scaled</span><span class="p">[</span><span class="o">-</span><span class="n">seq_length</span><span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">monthly_predictions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">36</span><span class="p">):</span>
    <span class="n">next_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">future_inputs</span><span class="p">)</span>
    <span class="n">monthly_predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">next_pred</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">future_inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">future_inputs</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:,</span> <span class="p">:],</span> <span class="n">next_pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="n">weekly_dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">"Date"</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">periods</span><span class="o">=</span><span class="mi">156</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">'W'</span><span class="p">)</span>
<span class="n">future_inputs</span> <span class="o">=</span> <span class="n">df_scaled</span><span class="p">[</span><span class="o">-</span><span class="n">seq_length</span><span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">weekly_predictions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">156</span><span class="p">):</span>
    <span class="n">next_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">future_inputs</span><span class="p">)</span>
    <span class="n">weekly_predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">next_pred</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">future_inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">future_inputs</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:,</span> <span class="p">:],</span> <span class="n">next_pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 26ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 28ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 25ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 26ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 26ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 27ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 26ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 26ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 27ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 22ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 26ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 24ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 23ms/step
<span class="ansi-bold">1/1</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 25ms/step
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<div class="jp-InputPrompt jp-InputArea-prompt">In [46]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">monthly_predictions_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">monthly_predictions</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="n">weekly_predictions_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">weekly_predictions</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="n">y_test_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="n">y_pred_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">y_pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [47]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"Date"</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_size</span><span class="o">+</span><span class="n">seq_length</span><span class="p">:],</span> <span class="n">y_test_scaled</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Actual Prices'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"Date"</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_size</span><span class="o">+</span><span class="n">seq_length</span><span class="p">:],</span> <span class="n">y_pred_scaled</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Predicted Prices (GRU)'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">future_dates</span><span class="p">,</span> <span class="n">monthly_predictions_scaled</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Future Monthly Predictions'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'dotted'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'green'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">weekly_dates</span><span class="p">,</span> <span class="n">weekly_predictions_scaled</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Future Weekly Predictions'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'dashed'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'purple'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Stock Price Predictions vs Actual Prices (GRU Model)"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Date"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Stock Price"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<ol>
<li>The NVIDIA stock price dataset is loaded and converted into a time-series format.</li>
<li>The closing prices are normalized using MinMaxScaler to improve training efficiency.</li>
<li>A sequence length of 60 days is chosen to capture trends in stock prices.</li>
<li>A function generates input (X) and output (y) pairs, where X contains past stock prices, and y is the next predicted price.</li>
<li>The dataset is divided into 80% training and 20% testing to train the model effectively.</li>
<li>This ensures that the model learns from past data while being evaluated on unseen data.</li>
<li>A GRU (Gated Recurrent Unit) model is built with the following layers:</li>
</ol>
<ul>
<li>GRU layer (128 units) with return sequences enabled.</li>
<li>Batch Normalization to stabilize learning.</li>
<li>GRU layer (64 units) to refine predictions.</li>
<li>Dropout (20%) to prevent overfitting.</li>
<li>Dense layers for final prediction.</li>
</ul>
<ol start="8">
<li>The model is compiled with Adam optimizer and MSE loss function.</li>
<li>The GRU model is trained using 50 epochs and a batch size of 32.</li>
<li>Validation is performed on the test set during training to monitor performance.</li>
<li>The trained model predicts stock prices for the test set.</li>
<li>The results are inverse-transformed to original price values.</li>
<li>The model's accuracy is measured using MAE, MSE, RMSE, R2 Score, and Accuracy (%).</li>
<li>These metrics help in understanding how close the predictions are to real values.</li>
<li>The model generates weekly and monthly predictions until the end of 2027.</li>
<li>The predicted prices are converted back to their original scale.</li>
<li>The results are plotted:</li>
</ol>
<ul>
<li>Bar charts compare RMSE, training time, and R2 score.</li>
<li>A line plot compares actual vs. predicted stock prices, along with future projections.</li>
</ul>
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