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    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
        0px 6px 30px 5px var(--jp-shadow-ambient-color);
    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
        0px 8px 38px 7px var(--jp-shadow-ambient-color);
    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
        0px 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-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: -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-700);

    --jp-content-font-family: -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: #111111;
    --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: 0px;
    --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: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
    --jp-toolbar-header-margin: 4px 4px 0px 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-border-color1);
    --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: #05a;
    --jp-mirror-editor-variable-3-color: #085;
    --jp-mirror-editor-punctuation-color: #05a;
    --jp-mirror-editor-property-color: #05a;
    --jp-mirror-editor-operator-color: #aa22ff;
    --jp-mirror-editor-comment-color: #408080;
    --jp-mirror-editor-string-color: #ba2121;
    --jp-mirror-editor-string-2-color: #708;
    --jp-mirror-editor-meta-color: #aa22ff;
    --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;

    /* 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);
}

[data-md-color-scheme="slate"] .jupyter-wrapper {
    /* 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
   */

    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
   * colors used in the theme as well. */
    --jp-shadow-base-lightness: 32;
    --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: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
        0px 1px 3px 0px var(--jp-shadow-ambient-color);
    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
        0px 1px 5px 0px var(--jp-shadow-ambient-color);
    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
        0px 1px 10px 0px var(--jp-shadow-ambient-color);
    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
        0px 1px 18px 0px var(--jp-shadow-ambient-color);
    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
        0px 3px 14px 2px var(--jp-shadow-ambient-color);
    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
        0px 5px 22px 4px var(--jp-shadow-ambient-color);
    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
        0px 6px 30px 5px var(--jp-shadow-ambient-color);
    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
        0px 8px 38px 7px var(--jp-shadow-ambient-color);
    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
        0px 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-700);
    --jp-border-color1: var(--md-grey-700);
    --jp-border-color2: var(--md-grey-800);
    --jp-border-color3: var(--md-grey-900);
    --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: -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(255, 255, 255, 1);
    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
    --jp-ui-font-color3: rgba(255, 255, 255, 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(0, 0, 0, 1);
    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);

    /* 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(255, 255, 255, 1);
    --jp-content-font-color1: rgba(255, 255, 255, 1);
    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
    --jp-content-font-color3: rgba(255, 255, 255, 0.5);

    --jp-content-link-color: var(--md-blue-300);

    --jp-content-font-family: -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: #111111;
    --jp-layout-color1: var(--md-grey-900);
    --jp-layout-color2: var(--md-grey-800);
    --jp-layout-color3: var(--md-grey-700);
    --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: white;
    --jp-inverse-layout-color1: white;
    --jp-inverse-layout-color2: var(--md-grey-200);
    --jp-inverse-layout-color3: var(--md-grey-400);
    --jp-inverse-layout-color4: var(--md-grey-600);

    /* Brand/accent */

    --jp-brand-color0: var(--md-blue-700);
    --jp-brand-color1: var(--md-blue-500);
    --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-700);
    --jp-accent-color1: var(--md-green-500);
    --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-700);
    --jp-warn-color1: var(--md-orange-500);
    --jp-warn-color2: var(--md-orange-300);
    --jp-warn-color3: var(--md-orange-100);

    --jp-error-color0: var(--md-red-700);
    --jp-error-color1: var(--md-red-500);
    --jp-error-color2: var(--md-red-300);
    --jp-error-color3: var(--md-red-100);

    --jp-success-color0: var(--md-green-700);
    --jp-success-color1: var(--md-green-500);
    --jp-success-color2: var(--md-green-300);
    --jp-success-color3: var(--md-green-100);

    --jp-info-color0: var(--md-cyan-700);
    --jp-info-color1: var(--md-cyan-500);
    --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(--jp-layout-color1);
    --jp-cell-editor-border-color: var(--md-grey-700);
    --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: 0px;
    --jp-cell-prompt-opacity: 1;
    --jp-cell-prompt-not-active-opacity: 1;
    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);

    /* 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: rgba(33, 150, 243, 0.24);

    /* 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: rgba(244, 67, 54, 0.28);
    --jp-rendermime-table-row-background: var(--md-grey-900);
    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);

    /* Dialog specific styles */

    --jp-dialog-background: rgba(0, 0, 0, 0.6);

    /* Console specific styles */

    --jp-console-padding: 10px;

    /* Toolbar specific styles */

    --jp-toolbar-border-color: var(--jp-border-color2);
    --jp-toolbar-micro-height: 8px;
    --jp-toolbar-background: var(--jp-layout-color1);
    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
    --jp-toolbar-header-margin: 4px 4px 0px 4px;
    --jp-toolbar-active-background: var(--jp-layout-color0);

    /* 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-color0);
    --jp-input-hover-background: var(--jp-layout-color2);
    --jp-input-background: var(--md-grey-800);
    --jp-input-border-color: var(--jp-border-color1);
    --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: var(--jp-layout-color2);
    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
    --jp-editor-cursor-color: var(--jp-ui-font-color0);

    /* Code mirror specific styles */

    --jp-mirror-editor-keyword-color: var(--md-green-500);
    --jp-mirror-editor-atom-color: var(--md-blue-300);
    --jp-mirror-editor-number-color: var(--md-green-400);
    --jp-mirror-editor-def-color: var(--md-blue-600);
    --jp-mirror-editor-variable-color: var(--md-grey-300);
    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
    --jp-mirror-editor-variable-3-color: var(--md-green-600);
    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
    --jp-mirror-editor-property-color: var(--md-blue-400);
    --jp-mirror-editor-operator-color: #aa22ff;
    --jp-mirror-editor-comment-color: #408080;
    --jp-mirror-editor-string-color: #ff7070;
    --jp-mirror-editor-string-2-color: var(--md-purple-300);
    --jp-mirror-editor-meta-color: #aa22ff;
    --jp-mirror-editor-qualifier-color: #555;
    --jp-mirror-editor-builtin-color: var(--md-green-600);
    --jp-mirror-editor-bracket-color: #997;
    --jp-mirror-editor-tag-color: var(--md-green-700);
    --jp-mirror-editor-attribute-color: var(--md-blue-700);
    --jp-mirror-editor-header-color: var(--md-blue-500);
    --jp-mirror-editor-quote-color: var(--md-green-300);
    --jp-mirror-editor-link-color: var(--md-blue-700);
    --jp-mirror-editor-error-color: #f00;
    --jp-mirror-editor-hr-color: #999;

    /* Vega extension styles */

    --jp-vega-background: var(--md-grey-400);

    /* Sidebar-related styles */

    --jp-sidebar-min-width: 250px;

    /* Search-related styles */

    --jp-search-toggle-off-opacity: 0.6;
    --jp-search-toggle-hover-opacity: 0.8;
    --jp-search-toggle-on-opacity: 1;
    --jp-search-selected-match-background-color: rgb(255, 225, 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);

    /* scrollbar related styles. Supports every browser except Edge. */

    /* colors based on JetBrain's Darcula theme */

    --jp-scrollbar-background-color: #3f4244;
    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */

    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */

    /* hacks for setting the thumb shape. These do nothing in Firefox */

    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */

    /* 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);
}

: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: #aa00ff;--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: #ffff00;--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: #eeeeee;--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}.jupyter-wrapper{/*!

Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
Licensed under the Apache License, Version 2.0.

*//*!

Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
Licensed under the Apache License, Version 2.0.

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<h1 id="Getting-live-data-from-the-StreamPipes-data-stream">Getting live data from the StreamPipes data stream<a class="anchor-link" href="#Getting-live-data-from-the-StreamPipes-data-stream">¶</a></h1><p>In the last tutorial (<a href="../2-extracting-data-from-the-streampipes-data-lake">Extracting Data from the StreamPipes data lake</a>) we learned how to extract the stored data from a StreamPipes data lake. This tutorial is about the StreamPipes data stream and shows how to get the live data from StreamPipes into Python. Therefore, we first create the <code>client</code> instance as before.</p>
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<p><strong>Note</strong><br/>
As of now we mainly developed the support for StreamPipes functions using NATS as messaging protocol. Consequently, this setup is tested most and should work flawlessly. Visit our <a href="../../getting-started/first-steps">first-steps</a> page to see how to start StreamPipes accordingly.
Anyhow, you can also use the other <a href="../../reference/functions/broker/broker">brokers</a> that are currently supported in StreamPipes Python.
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">streampipes.client</span> <span class="kn">import</span> <span class="n">StreamPipesClient</span>
<span class="kn">from</span> <span class="nn">streampipes.client.config</span> <span class="kn">import</span> <span class="n">StreamPipesClientConfig</span>
<span class="kn">from</span> <span class="nn">streampipes.client.credential_provider</span> <span class="kn">import</span> <span class="n">StreamPipesApiKeyCredentials</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-1">from streampipes.client import StreamPipesClient
from streampipes.client.config import StreamPipesClientConfig
from streampipes.client.credential_provider import StreamPipesApiKeyCredentials</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># You can install all required libraries for this tutorial with the following command</span>
<span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">matplotlib</span> <span class="n">ipython</span> <span class="n">streampipes</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-2"># You can install all required libraries for this tutorial with the following command
%pip install matplotlib ipython streampipes</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>

<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"SP_USERNAME"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"admin@streampipes.apache.org"</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"SP_API_KEY"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"XXX"</span>

<span class="c1"># Use this if you work locally:</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"BROKER-HOST"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"localhost"</span>  
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"KAFKA-PORT"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"9094"</span> <span class="c1"># When using Kafka as message broker</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-3">import os

os.environ["SP_USERNAME"] = "admin@streampipes.apache.org"
os.environ["SP_API_KEY"] = "XXX"

# Use this if you work locally:
os.environ["BROKER-HOST"] = "localhost"  
os.environ["KAFKA-PORT"] = "9094" # When using Kafka as message broker</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client_config</span> <span class="o">=</span> <span class="n">StreamPipesClientConfig</span><span class="p">(</span>
    <span class="n">credential_provider</span><span class="o">=</span><span class="n">StreamPipesApiKeyCredentials</span><span class="p">(),</span>
    <span class="n">host_address</span><span class="o">=</span><span class="s2">"localhost"</span><span class="p">,</span>
    <span class="n">port</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span>
    <span class="n">https_disabled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">client</span> <span class="o">=</span> <span class="n">StreamPipesClient</span><span class="p">(</span><span class="n">client_config</span><span class="o">=</span><span class="n">client_config</span><span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-4">client_config = StreamPipesClientConfig(
    credential_provider=StreamPipesApiKeyCredentials(),
    host_address="localhost",
    port=80,
    https_disabled=True,
)
client = StreamPipesClient(client_config=client_config)</div>
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<pre>2022-12-14 10:43:37,664 - streampipes.client.client - [INFO] - [client.py:127] [_set_up_logging] - Logging successfully initialized with logging level INFO.
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<p>Now we can have a look at the available data streams. We can choose one or more stream to receive the data from and copy their <code>element_id</code>.</p>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client</span><span class="o">.</span><span class="n">dataStreamApi</span><span class="o">.</span><span class="n">all</span><span class="p">()</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-5">client.dataStreamApi.all().to_pandas()</div>
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<pre>2022-12-14 10:43:39,944 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:153] [_make_request] - Successfully retrieved all resources.
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<style scoped="">
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
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    .dataframe tbody tr th {
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    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>element_id</th>
<th>name</th>
<th>description</th>
<th>icon_url</th>
<th>app_id</th>
<th>includes_assets</th>
<th>includes_locales</th>
<th>internally_managed</th>
<th>measurement_object</th>
<th>index</th>
<th>...</th>
<th>uri</th>
<th>dom</th>
<th>num_transport_protocols</th>
<th>num_measurement_capability</th>
<th>num_application_links</th>
<th>num_included_assets</th>
<th>num_connected_to</th>
<th>num_category</th>
<th>num_event_properties</th>
<th>num_included_locales</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>urn:streampipes.apache.org:eventstream:HHoidJ</td>
<td>Test2</td>
<td></td>
<td>None</td>
<td>None</td>
<td>False</td>
<td>False</td>
<td>True</td>
<td>None</td>
<td>0</td>
<td>...</td>
<td>urn:streampipes.apache.org:eventstream:HHoidJ</td>
<td>None</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>7</td>
<td>0</td>
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<th>1</th>
<td>urn:streampipes.apache.org:eventstream:uPDKLI</td>
<td>Test</td>
<td></td>
<td>None</td>
<td>None</td>
<td>False</td>
<td>False</td>
<td>True</td>
<td>None</td>
<td>0</td>
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<td>urn:streampipes.apache.org:eventstream:uPDKLI</td>
<td>None</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>7</td>
<td>0</td>
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<p>2 rows × 21 columns</p>
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<p>Next we can create a StreamPipesFunction. For this we need to implement the 3 following methods:</p>
<ul>
<li><code>onServiceStarted</code> is called when the function gets started. There you can use the given meta information of the <code>FunctionContext</code> to initialize the function.</li>
<li><code>onEvent</code> is called when ever a new event arrives. The <code>event</code> contains the live data and you can use the <code>streamId</code> to identify a stream if the function is connected to multiple data streams.</li>
<li><code>onServiceStopped</code> is called when the function gets stopped.</li>
</ul>
<p>For this tutorial we just create a function that saves every new event in a <code>pandas DataFrame</code> and plots the first column of the <code>DataFrame</code> when the function gets stopped.</p>
<p>(If you want to use the same structure as in Java you can overwrite the <code>getFunctionId</code> and <code>requiredStreamIds</code> methods instead of using the <code>FunctionDefinition</code>)</p>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.function_handler</span> <span class="kn">import</span> <span class="n">FunctionHandler</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.registration</span> <span class="kn">import</span> <span class="n">Registration</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.streampipes_function</span> <span class="kn">import</span> <span class="n">StreamPipesFunction</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.utils.function_context</span> <span class="kn">import</span> <span class="n">FunctionContext</span>
<span class="kn">from</span> <span class="nn">streampipes.model.resource.function_definition</span> <span class="kn">import</span> <span class="n">FunctionDefinition</span><span class="p">,</span> <span class="n">FunctionId</span>

<span class="k">class</span> <span class="nc">ExampleFunction</span><span class="p">(</span><span class="n">StreamPipesFunction</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">function_definition</span><span class="p">:</span> <span class="n">FunctionDefinition</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">function_definition</span><span class="p">)</span>
        <span class="c1"># Create the Dataframe to save the live data</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">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="k">def</span> <span class="nf">onServiceStarted</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">:</span> <span class="n">FunctionContext</span><span class="p">):</span>
        <span class="c1"># Get the name of the timestamp field</span>
        <span class="k">for</span> <span class="n">event_property</span> <span class="ow">in</span> <span class="n">context</span><span class="o">.</span><span class="n">schema</span><span class="p">[</span><span class="n">context</span><span class="o">.</span><span class="n">streams</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">event_schema</span><span class="o">.</span><span class="n">event_properties</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">event_property</span><span class="o">.</span><span class="n">property_scope</span> <span class="o">==</span> <span class="s2">"HEADER_PROPERTY"</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">timestamp</span> <span class="o">=</span> <span class="n">event_property</span><span class="o">.</span><span class="n">runtime_name</span>

    <span class="k">def</span> <span class="nf">onEvent</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">streamId</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
        <span class="c1"># Convert the unix timestamp to datetime</span>
        <span class="n">event</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">timestamp</span><span class="p">]</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">fromtimestamp</span><span class="p">(</span><span class="n">event</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">timestamp</span><span class="p">]</span> <span class="o">/</span> <span class="mi">1000</span><span class="p">)</span>
        <span class="c1"># Add every value of the event to the DataFrame</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span>
            <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="n">key</span><span class="p">:</span> <span class="p">[</span><span class="n">event</span><span class="p">[</span><span class="n">key</span><span class="p">]]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">event</span><span class="o">.</span><span class="n">keys</span><span class="p">()})</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">timestamp</span><span class="p">)]</span>
        <span class="p">)</span>

    <span class="k">def</span> <span class="nf">onServiceStopped</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="c1"># Plot the first column of the Dataframe</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">5</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="bp">self</span><span class="o">.</span><span class="n">timestamp</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="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</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="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</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 class="clipboard-copy-txt" id="cell-6">from typing import Dict, Any
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
from streampipes.functions.function_handler import FunctionHandler
from streampipes.functions.registration import Registration
from streampipes.functions.streampipes_function import StreamPipesFunction
from streampipes.functions.utils.function_context import FunctionContext
from streampipes.model.resource.function_definition import FunctionDefinition, FunctionId

class ExampleFunction(StreamPipesFunction):
    def __init__(self, function_definition: FunctionDefinition) -&gt; None:
        super().__init__(function_definition)
        # Create the Dataframe to save the live data
        self.df = pd.DataFrame()

    def onServiceStarted(self, context: FunctionContext):
        # Get the name of the timestamp field
        for event_property in context.schema[context.streams[0]].event_schema.event_properties:
            if event_property.property_scope == "HEADER_PROPERTY":
                self.timestamp = event_property.runtime_name

    def onEvent(self, event: Dict[str, Any], streamId: str):
        # Convert the unix timestamp to datetime
        event[self.timestamp] = datetime.fromtimestamp(event[self.timestamp] / 1000)
        # Add every value of the event to the DataFrame
        self.df = pd.concat(
            [self.df, pd.DataFrame({key: [event[key]] for key in event.keys()}).set_index(self.timestamp)]
        )

    def onServiceStopped(self):
        # Plot the first column of the Dataframe
        plt.figure(figsize=(10, 5))
        plt.xlabel(self.timestamp)
        plt.ylabel(self.df.columns[0])
        plt.plot(self.df.iloc[:, 0])
        plt.show()</div>
</div>
</div>
</div>
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<p>Now we can start the function. First we create an instance of the <code>ExampleFunction</code> and insert the <code>element_id</code> of the stream which data we want to consume. Then we have to register this function and we can start all functions by initializing the <code>FunctionHandler</code>. (it's also possible to register multiple functions with <code>.register(...).register(...)</code>)</p>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">example_function</span> <span class="o">=</span> <span class="n">ExampleFunction</span><span class="p">(</span>
    <span class="n">FunctionDefinition</span><span class="p">(</span>
        <span class="n">function_id</span><span class="o">=</span><span class="n">FunctionId</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="s2">"example-function"</span><span class="p">),</span>
        <span class="n">consumed_streams</span><span class="o">=</span><span class="p">[</span><span class="s2">"urn:streampipes.apache.org:eventstream:uPDKLI"</span><span class="p">]</span>
    <span class="p">)</span>
<span class="p">)</span>

<span class="n">registration</span> <span class="o">=</span> <span class="n">Registration</span><span class="p">()</span>
<span class="n">registration</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">example_function</span><span class="p">)</span>

<span class="n">function_handler</span> <span class="o">=</span> <span class="n">FunctionHandler</span><span class="p">(</span><span class="n">registration</span><span class="p">,</span> <span class="n">client</span><span class="p">)</span>
<span class="n">function_handler</span><span class="o">.</span><span class="n">initializeFunctions</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-7">example_function = ExampleFunction(
    FunctionDefinition(
        function_id=FunctionId(id="example-function"),
        consumed_streams=["urn:streampipes.apache.org:eventstream:uPDKLI"]
    )
)

registration = Registration()
registration.register(example_function)

function_handler = FunctionHandler(registration, client)
function_handler.initializeFunctions()</div>
</div>
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<pre>2022-12-14 10:43:42,810 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:153] [_make_request] - Successfully retrieved all resources.
2022-12-14 10:43:42,812 - streampipes.functions.function_handler - [INFO] - [function_handler.py:82] [initializeFunctions] - Using NatsBroker for &lt;__main__.ExampleFunction object at 0x000001F2EF298D90&gt;
</pre>
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<p>The <code>DataFrame</code> of the <code>ExampleFunction</code> gets updated whenever a new event arrives. Let's test this by executing the cell below.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">asyncio</span>
<span class="kn">from</span> <span class="nn">asyncio.exceptions</span> <span class="kn">import</span> <span class="n">CancelledError</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">clear_output</span>

<span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
    <span class="n">clear_output</span><span class="p">(</span><span class="n">wait</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">display</span><span class="p">(</span><span class="n">example_function</span><span class="o">.</span><span class="n">df</span><span class="p">)</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="k">await</span> <span class="n">asyncio</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="k">except</span> <span class="n">CancelledError</span><span class="p">:</span>
        <span class="k">break</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-8">import asyncio
from asyncio.exceptions import CancelledError
from IPython.display import clear_output

while True:
    clear_output(wait=True)
    display(example_function.df)
    try:
        await asyncio.sleep(1)
    except CancelledError:
        break</div>
</div>
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<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>mass_flow</th>
<th>density</th>
<th>volume_flow</th>
<th>sensor_fault_flags</th>
<th>temperature</th>
<th>sensorId</th>
</tr>
<tr>
<th>timestamp</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2022-12-14 10:43:43.357</th>
<td>10.955496</td>
<td>47.546290</td>
<td>1.001985</td>
<td>False</td>
<td>44.993413</td>
<td>flowrate02</td>
</tr>
<tr>
<th>2022-12-14 10:43:44.371</th>
<td>6.499040</td>
<td>44.392069</td>
<td>2.034402</td>
<td>False</td>
<td>41.232352</td>
<td>flowrate02</td>
</tr>
<tr>
<th>2022-12-14 10:43:45.382</th>
<td>10.168300</td>
<td>41.192146</td>
<td>9.724287</td>
<td>False</td>
<td>46.812779</td>
<td>flowrate02</td>
</tr>
<tr>
<th>2022-12-14 10:43:46.395</th>
<td>10.849059</td>
<td>50.086308</td>
<td>5.832691</td>
<td>False</td>
<td>45.860412</td>
<td>flowrate02</td>
</tr>
<tr>
<th>2022-12-14 10:43:47.410</th>
<td>3.081855</td>
<td>47.254246</td>
<td>8.860531</td>
<td>False</td>
<td>50.505801</td>
<td>flowrate02</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>2022-12-14 10:44:43.920</th>
<td>1.803572</td>
<td>41.978894</td>
<td>10.294002</td>
<td>False</td>
<td>47.820239</td>
<td>flowrate02</td>
</tr>
<tr>
<th>2022-12-14 10:44:44.932</th>
<td>1.967062</td>
<td>42.212883</td>
<td>3.237440</td>
<td>False</td>
<td>49.047258</td>
<td>flowrate02</td>
</tr>
<tr>
<th>2022-12-14 10:44:45.934</th>
<td>4.457819</td>
<td>47.561256</td>
<td>0.315024</td>
<td>False</td>
<td>40.223413</td>
<td>flowrate02</td>
</tr>
<tr>
<th>2022-12-14 10:44:46.949</th>
<td>8.745343</td>
<td>46.346891</td>
<td>7.439090</td>
<td>False</td>
<td>41.982529</td>
<td>flowrate02</td>
</tr>
<tr>
<th>2022-12-14 10:44:47.950</th>
<td>5.828744</td>
<td>47.679720</td>
<td>6.307405</td>
<td>False</td>
<td>42.100354</td>
<td>flowrate02</td>
</tr>
</tbody>
</table>
<p>65 rows × 6 columns</p>
</div>
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<p>The <code>while</code> loop just displays the the <code>DataFrame</code> every second until the cell is stopped. We could achieve the same result manually by executing <code>example_function.df</code> repeatedly.</p>
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<p>You can stop the functions whenever you want by executing the command below.</p>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">function_handler</span><span class="o">.</span><span class="n">disconnect</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-9">function_handler.disconnect()</div>
</div>
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<pre>2022-12-14 10:44:53,309 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:67] [disconnect] - Stopped connection to stream: urn:streampipes.apache.org:eventstream:uPDKLI
</pre>
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"/>
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<p>That's enough for this tutorial. Now you can try to write your own <code>StreamPipesFunction</code>. All you need to do is creating a new class, implementing the 4 required methods and registering the function.</p>
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<p>Want to see more exciting use cases you can achieve with StreamPipes functions in Python? Then don't hesitate and jump to our <a href="../4-using-online-machine-learning-on-a-streampipes-data-stream">next tutorial</a> on applying online machine learning algorithms to StreamPipes data streams with <a href="https://riverml.xyz">River</a>.</p>
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<p>How do you like this tutorial?
We hope you like it and would love to receive some feedback from you.
Just go to our <a href="https://github.com/apache/streampipes/discussions">GitHub discussion page</a> and let us know your impression.
We'll read and react to them all, we promise!</p>
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