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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-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.
*/}.jupyter-wrapper [data-jp-theme-scrollbars=true]{scrollbar-color:rgb(var(--jp-scrollbar-thumb-color)) var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color), 0.5) rgba(0,0,0,0)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-corner{background:var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-thumb{background:rgb(var(--jp-scrollbar-thumb-color));border:var(--jp-scrollbar-thumb-margin) solid rgba(0,0,0,0);background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid 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.CodeMirror-vscrollbar::-webkit-scrollbar-thumb{background:rgba(var(--jp-scrollbar-thumb-color), 0.5);border:var(--jp-scrollbar-thumb-margin) solid rgba(0,0,0,0);background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid rgba(0,0,0,0);border-right:var(--jp-scrollbar-endpad) solid rgba(0,0,0,0)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar::-webkit-scrollbar-track:vertical{border-top:var(--jp-scrollbar-endpad) solid rgba(0,0,0,0);border-bottom:var(--jp-scrollbar-endpad) solid rgba(0,0,0,0)}.jupyter-wrapper .lm-ScrollBar[data-orientation=horizontal]{min-height:16px;max-height:16px;min-width:45px;border-top:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=vertical]{min-width:16px;max-width:16px;min-height:45px;border-left:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar-button{background-color:#f0f0f0;background-position:center center;min-height:15px;max-height:15px;min-width:15px;max-width:15px}.jupyter-wrapper .lm-ScrollBar-button:hover{background-color:#dadada}.jupyter-wrapper .lm-ScrollBar-button.lm-mod-active{background-color:#cdcdcd}.jupyter-wrapper .lm-ScrollBar-track{background:#f0f0f0}.jupyter-wrapper .lm-ScrollBar-thumb{background:#cdcdcd}.jupyter-wrapper .lm-ScrollBar-thumb:hover{background:#bababa}.jupyter-wrapper .lm-ScrollBar-thumb.lm-mod-active{background:#a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=horizontal] .lm-ScrollBar-thumb{height:100%;min-width:15px;border-left:1px solid #a0a0a0;border-right:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=vertical] .lm-ScrollBar-thumb{width:100%;min-height:15px;border-top:1px solid #a0a0a0;border-bottom:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=horizontal] 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<h1 id="Using-Online-Machine-Learning-on-a-StreamPipes-data-stream">Using Online Machine Learning on a StreamPipes data stream<a class="anchor-link" href="#Using-Online-Machine-Learning-on-a-StreamPipes-data-stream"></a></h1><p>The last tutorial (<a href="../3-getting-live-data-from-the-streampipes-data-stream">Getting live data from the StreamPipes data stream</a>) showed how we can connect to a data stream, and it would be possible to use Online Machine Learning with this approach and train a model with the incoming events at the <code>onEvent</code> method. However, the StreamPipes client also provides an easier way to do this with the use of the <a href="https://riverml.xyz">River library</a> for Online Machine Learning. We will have a look at this now.</p>
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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 dependecies for this tutorial by executing the following command</span>
<span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">river</span> <span class="n">streampipes</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-2"># you can install all required dependecies for this tutorial by executing the following command
%pip install river 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>2023-01-27 16:04:24,784 - streampipes.client.client - [INFO] - [client.py:128] [_set_up_logging] - Logging successfully initialized with logging level INFO.
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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>2023-01-27 16:04:28,212 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:163] [_make_request] - Successfully retrieved all resources.
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<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>dom</th>
<th>rev</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>
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<th>0</th>
<td>sp:spdatastream:xboBFK</td>
<td>Test</td>
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<td>None</td>
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<td>False</td>
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<td>5-558c861debc745e1ebae29a266a8bdb9</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
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<td>7</td>
<td>0</td>
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<th>1</th>
<td>urn:streampipes.apache.org:eventstream:Wgyrse</td>
<td>Test File</td>
<td></td>
<td>None</td>
<td>None</td>
<td>False</td>
<td>False</td>
<td>True</td>
<td>None</td>
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<td>4-66548b6b84287011b7cec0876ef82baf</td>
<td>1</td>
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<h2 id="How-to-use-Online-Machine-Learning-with-StreamPipes">How to use Online Machine Learning with StreamPipes<a class="anchor-link" href="#How-to-use-Online-Machine-Learning-with-StreamPipes"></a></h2><p>After we configured the client as usual, we can start with the new part.<br/>
The approach is straight forward and you can start with the ML part in just 3 steps:</p>
<ol>
<li>Create a pipeline with River and insert the preprocessing steps and model of your choice.</li>
<li>Configure the <code>OnlineML</code> wrapper to fit to your model and insert the client and required data stream ids.</li>
<li>Start the wrapper and let the learning begin.</li>
</ol>
<p>A StreamPipesFunction is then started, which trains the model for each new event. It also creates an output data stream which will send the prediction of the model back to StreamPipes. This output stream can be seen when creating a new pipeline and can be used like every other data source. So you can use it in a pipeline and save the predictions in a Data Lake.
You can also stop and start the training with the method <code>set_learning</code>. To stop the whole function use the <code>stop</code> methode and if you want to delete the output stream entirely, you can go to the <code>Pipeline Element Installer</code> in StreamPipes and uninstall it.</p>
<p>Now let's take a look at some examples. If you want to execute the examples below you have to create an adapter for the <code>Machine Data Simulator</code>, select the <code>flowrate</code> sensor and insert the stream id of this stream.</p>
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<h2 id="KMeans">KMeans<a class="anchor-link" href="#KMeans"></a></h2>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">river</span> <span class="kn">import</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">compose</span><span class="p">,</span> <span class="n">preprocessing</span>
<span class="kn">from</span> <span class="nn">streampipes.function_zoo.river_function</span> <span class="kn">import</span> <span class="n">OnlineML</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.utils.data_stream_generator</span> <span class="kn">import</span> <span class="n">RuntimeType</span>
<span class="n">k_means</span> <span class="o">=</span> <span class="n">compose</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span>
<span class="p">(</span><span class="s2">"drop_features"</span><span class="p">,</span> <span class="n">compose</span><span class="o">.</span><span class="n">Discard</span><span class="p">(</span><span class="s2">"sensorId"</span><span class="p">,</span> <span class="s2">"timestamp"</span><span class="p">)),</span>
<span class="p">(</span><span class="s2">"scale"</span><span class="p">,</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">StandardScaler</span><span class="p">()),</span>
<span class="p">(</span><span class="s2">"k_means"</span><span class="p">,</span> <span class="n">cluster</span><span class="o">.</span><span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">)),</span>
<span class="p">)</span>
<span class="n">clustering</span> <span class="o">=</span> <span class="n">OnlineML</span><span class="p">(</span>
<span class="n">client</span><span class="o">=</span><span class="n">client</span><span class="p">,</span> <span class="n">stream_ids</span><span class="o">=</span><span class="p">[</span><span class="s2">"sp:spdatastream:xboBFK"</span><span class="p">],</span> <span class="n">model</span><span class="o">=</span><span class="n">k_means</span><span class="p">,</span> <span class="n">prediction_type</span><span class="o">=</span><span class="n">RuntimeType</span><span class="o">.</span><span class="n">INTEGER</span><span class="o">.</span><span class="n">value</span>
<span class="p">)</span>
<span class="n">clustering</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-6">from river import cluster, compose, preprocessing
from streampipes.function_zoo.river_function import OnlineML
from streampipes.functions.utils.data_stream_generator import RuntimeType
k_means = compose.Pipeline(
("drop_features", compose.Discard("sensorId", "timestamp")),
("scale", preprocessing.StandardScaler()),
("k_means", cluster.KMeans(n_clusters=2)),
)
clustering = OnlineML(
client=client, stream_ids=["sp:spdatastream:xboBFK"], model=k_means, prediction_type=RuntimeType.INTEGER.value
)
clustering.start()</div>
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<pre>2023-01-27 16:04:35,599 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:163] [_make_request] - Successfully retrieved all resources.
2023-01-27 16:04:35,599 - streampipes.functions.function_handler - [INFO] - [function_handler.py:64] [initializeFunctions] - Create output data stream "sp:spdatastream:cwKPoo" for the function "65cf8b86-bcdf-433e-a1c7-3e920eab55d0"
2023-01-27 16:04:37,766 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:163] [_make_request] - Successfully retrieved all resources.
2023-01-27 16:04:37,767 - streampipes.functions.function_handler - [INFO] - [function_handler.py:78] [initializeFunctions] - Using NatsBroker for RiverFunction
2023-01-27 16:04:37,791 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:48] [_makeConnection] - Connected to NATS at localhost:4222
2023-01-27 16:04:37,791 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:48] [_makeConnection] - Connected to NATS at localhost:4222
2023-01-27 16:04:37,792 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:58] [createSubscription] - Subscribed to stream: sp:spdatastream:xboBFK
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">clustering</span><span class="o">.</span><span class="n">set_learning</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-7">clustering.set_learning(False)</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">clustering</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
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<div class="clipboard-copy-txt" id="cell-8">clustering.stop()</div>
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<pre>2023-01-27 16:04:57,303 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:82] [disconnect] - Stopped connection to stream: sp:spdatastream:xboBFK
2023-01-27 16:04:57,304 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:82] [disconnect] - Stopped connection to stream: sp:spdatastream:cwKPoo
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<h2 id="HoeffdingTreeRegressor">HoeffdingTreeRegressor<a class="anchor-link" href="#HoeffdingTreeRegressor"></a></h2>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">from</span> <span class="nn">river</span> <span class="kn">import</span> <span class="n">compose</span><span class="p">,</span> <span class="n">tree</span>
<span class="kn">from</span> <span class="nn">streampipes.function_zoo.river_function</span> <span class="kn">import</span> <span class="n">OnlineML</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.utils.data_stream_generator</span> <span class="kn">import</span> <span class="n">RuntimeType</span>
<span class="n">hoeffding_tree</span> <span class="o">=</span> <span class="n">compose</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span>
<span class="p">(</span><span class="s2">"drop_features"</span><span class="p">,</span> <span class="n">compose</span><span class="o">.</span><span class="n">Discard</span><span class="p">(</span><span class="s2">"sensorId"</span><span class="p">,</span> <span class="s2">"timestamp"</span><span class="p">)),</span>
<span class="p">(</span><span class="s2">"hoeffding_tree"</span><span class="p">,</span> <span class="n">tree</span><span class="o">.</span><span class="n">HoeffdingTreeRegressor</span><span class="p">(</span><span class="n">grace_period</span><span class="o">=</span><span class="mi">5</span><span class="p">)),</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">draw_tree</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">streamId</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Draw the tree and save the image."""</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">n_nodes</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span><span class="o">.</span><span class="n">render</span><span class="p">(</span><span class="s2">"hoeffding_tree"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"png"</span><span class="p">,</span> <span class="n">cleanup</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">save_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Save the trained model."""</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"hoeffding_tree.pkl"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
<span class="n">regressor</span> <span class="o">=</span> <span class="n">OnlineML</span><span class="p">(</span>
<span class="n">client</span><span class="o">=</span><span class="n">client</span><span class="p">,</span>
<span class="n">stream_ids</span><span class="o">=</span><span class="p">[</span><span class="s2">"sp:spdatastream:xboBFK"</span><span class="p">],</span>
<span class="n">model</span><span class="o">=</span><span class="n">hoeffding_tree</span><span class="p">,</span>
<span class="n">prediction_type</span><span class="o">=</span><span class="n">RuntimeType</span><span class="o">.</span><span class="n">FLOAT</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
<span class="n">supervised</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">target_label</span><span class="o">=</span><span class="s2">"temperature"</span><span class="p">,</span>
<span class="n">on_event</span><span class="o">=</span><span class="n">draw_tree</span><span class="p">,</span>
<span class="n">on_stop</span><span class="o">=</span><span class="n">save_model</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">regressor</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-9">import pickle
from river import compose, tree
from streampipes.function_zoo.river_function import OnlineML
from streampipes.functions.utils.data_stream_generator import RuntimeType
hoeffding_tree = compose.Pipeline(
("drop_features", compose.Discard("sensorId", "timestamp")),
("hoeffding_tree", tree.HoeffdingTreeRegressor(grace_period=5)),
)
def draw_tree(self, event, streamId):
"""Draw the tree and save the image."""
if self.learning:
if self.model[1].n_nodes != None:
self.model[1].draw().render("hoeffding_tree", format="png", cleanup=True)
def save_model(self):
"""Save the trained model."""
with open("hoeffding_tree.pkl", "wb") as f:
pickle.dump(self.model, f)
regressor = OnlineML(
client=client,
stream_ids=["sp:spdatastream:xboBFK"],
model=hoeffding_tree,
prediction_type=RuntimeType.FLOAT.value,
supervised=True,
target_label="temperature",
on_event=draw_tree,
on_stop=save_model,
)
regressor.start()</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">regressor</span><span class="o">.</span><span class="n">set_learning</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-10">regressor.set_learning(False)</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">regressor</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-11">regressor.stop()</div>
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<h2 id="DecisionTreeClassifier">DecisionTreeClassifier<a class="anchor-link" href="#DecisionTreeClassifier"></a></h2>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">from</span> <span class="nn">river</span> <span class="kn">import</span> <span class="n">compose</span><span class="p">,</span> <span class="n">tree</span>
<span class="kn">from</span> <span class="nn">streampipes.function_zoo.river_function</span> <span class="kn">import</span> <span class="n">OnlineML</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.utils.data_stream_generator</span> <span class="kn">import</span> <span class="n">RuntimeType</span>
<span class="n">decision_tree</span> <span class="o">=</span> <span class="n">compose</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span>
<span class="p">(</span><span class="s2">"drop_features"</span><span class="p">,</span> <span class="n">compose</span><span class="o">.</span><span class="n">Discard</span><span class="p">(</span><span class="s2">"sensorId"</span><span class="p">,</span> <span class="s2">"timestamp"</span><span class="p">)),</span>
<span class="p">(</span><span class="s2">"decision_tree"</span><span class="p">,</span> <span class="n">tree</span><span class="o">.</span><span class="n">ExtremelyFastDecisionTreeClassifier</span><span class="p">(</span><span class="n">grace_period</span><span class="o">=</span><span class="mi">5</span><span class="p">)),</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">draw_tree</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">streamId</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Draw the tree and save the image."""</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">n_nodes</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span><span class="o">.</span><span class="n">render</span><span class="p">(</span><span class="s2">"decicion_tree"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"png"</span><span class="p">,</span> <span class="n">cleanup</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">save_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Save the trained model."""</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"decision_tree.pkl"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
<span class="n">classifier</span> <span class="o">=</span> <span class="n">OnlineML</span><span class="p">(</span>
<span class="n">client</span><span class="o">=</span><span class="n">client</span><span class="p">,</span>
<span class="n">stream_ids</span><span class="o">=</span><span class="p">[</span><span class="s2">"sp:spdatastream:xboBFK"</span><span class="p">],</span>
<span class="n">model</span><span class="o">=</span><span class="n">decision_tree</span><span class="p">,</span>
<span class="n">prediction_type</span><span class="o">=</span><span class="n">RuntimeType</span><span class="o">.</span><span class="n">BOOLEAN</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
<span class="n">supervised</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">target_label</span><span class="o">=</span><span class="s2">"sensor_fault_flags"</span><span class="p">,</span>
<span class="n">on_event</span><span class="o">=</span><span class="n">draw_tree</span><span class="p">,</span>
<span class="n">on_stop</span><span class="o">=</span><span class="n">save_model</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">classifier</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-12">import pickle
from river import compose, tree
from streampipes.function_zoo.river_function import OnlineML
from streampipes.functions.utils.data_stream_generator import RuntimeType
decision_tree = compose.Pipeline(
("drop_features", compose.Discard("sensorId", "timestamp")),
("decision_tree", tree.ExtremelyFastDecisionTreeClassifier(grace_period=5)),
)
def draw_tree(self, event, streamId):
"""Draw the tree and save the image."""
if self.learning:
if self.model[1].n_nodes != None:
self.model[1].draw().render("decicion_tree", format="png", cleanup=True)
def save_model(self):
"""Save the trained model."""
with open("decision_tree.pkl", "wb") as f:
pickle.dump(self.model, f)
classifier = OnlineML(
client=client,
stream_ids=["sp:spdatastream:xboBFK"],
model=decision_tree,
prediction_type=RuntimeType.BOOLEAN.value,
supervised=True,
target_label="sensor_fault_flags",
on_event=draw_tree,
on_stop=save_model,
)
classifier.start()</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">classifier</span><span class="o">.</span><span class="n">set_learning</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-13">classifier.set_learning(False)</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">classifier</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-14">classifier.stop()</div>
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