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ttu
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CaReCur
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  254.   line-height: normal;
    255. 

  255.   white-space: normal;
    256. 

  256.   max-width: none;
    257. 

  257.   border: none;
    258. 

  258. }
    259. 

  259. div.rendered_html th {
    260. 

  260.   font-weight: bold;
    261. 

  261. }
    262. 

  262. div.rendered_html tbody tr:nth-child(odd) {
    263. 

  263.   background: #f5f5f5;
    264. 

  264. }
    265. 

  265. div.rendered_html tbody tr:hover {
    266. 

  266.   background: rgba(66, 165, 245, 0.2);
    267. 

  267. }
    268. 

  268. </style>
    269. 

  269. <p>This is the final compilation of logistic models through manual parameter tuning. Very few actual features were involved here!</p>
    270. 

  270. <div class="nbinput nblast docutils container">
    271. 

  271. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[659]:
    272. 

  272. </pre></div>
    273. 

  273. </div>
    274. 

  274. <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
    275. 

  275. <span></span>&quot;&quot;&quot;
    276. 

  276. 

  277. Inbound Data:
    278. 

  278. 

  279. timestamp                 datetime64[ns, US/Pacific]
    280. 

  280. solar_curtailment                            float64
    281. 

  281. solar                                        float64
    282. 

  282. net_load                                     float64
    283. 

  283. load                                         float64
    284. 

  284. generation                                   float64
    285. 

  285. renewables                                   float64
    286. 

  286. wind_curtailment                             float64
    287. 

  287. is_weekday                                      bool
    288. 

  288. t_mean                                       float32
    289. 

  289. t_wmean                                      float32
    290. 

  290. t_wmax                                       float32
    291. 

  291. t_wmin                                       float32
    292. 

  292. t_absmax                                     float32
    293. 

  293. t_absmin                                     float32
    294. 

  294. dswrf_mean                                   float32
    295. 

  295. dswrf_absmax                                 float32
    296. 

  296. dswrf_wmean                                  float32
    297. 

  297. capacity_mw                                  float64
    298. 

  298. installed_capacity                           float64
    299. 

  299. solar_capacity_factor                        float64
    300. 

  300. curtailment_event_0.01                          bool
    301. 

  301. curtailment_event_0.05                          bool
    302. 

  302. curtailment_event_0.10                          bool
    303. 

  303. 

  304. dtype: object
    305. 

  305. 

  306. &quot;&quot;&quot;
    307. 

  307. import altair as alt
    308. 

  308. import pandas as pd
    309. 

  309. 

  310. from sklearn.compose import ColumnTransformer
    311. 

  311. from sklearn.pipeline import Pipeline
    312. 

  312. from sklearn.preprocessing import StandardScaler, OneHotEncoder
    313. 

  313. from sklearn.linear_model import LogisticRegression
    314. 

  314. from sklearn.model_selection import train_test_split, GridSearchCV
    315. 

  315. 

  316. from loguru import logger
    317. 

  317. from src.conf import settings
    318. 

  318. 

  319. 

  320. INPUT_DIR = settings.DATA_DIR / &quot;processed/training/&quot;
    321. 

  321. 

  322. 

  323. def load_driven_model(df, y):
    324. 

  324.     &quot;&quot;&quot;Simple model self-contained with just load and time
    325. 

  325.     &quot;&quot;&quot;
    326. 

  326.     df[&quot;month&quot;] = df[&quot;timestamp&quot;].dt.month
    327. 

  327.     numeric = [&quot;load&quot;]
    328. 

  328.     numeric_transformer = Pipeline(
    329. 

  329.         steps=[
    330. 

  330.             (&quot;scaler&quot;, StandardScaler())
    331. 

  331.         ]
    332. 

  332.     )
    333. 

  333.     categoricals = [&quot;is_weekday&quot;, &quot;month&quot;]
    334. 

  334.     categorical_transformer = Pipeline(
    335. 

  335.         steps=[
    336. 

  336.             (&quot;onehot&quot;, OneHotEncoder(handle_unknown=&quot;ignore&quot;))
    337. 

  337.         ]
    338. 

  338.     )
    339. 

  339. 

  340.     preprocessor = ColumnTransformer(
    341. 

  341.         transformers = [
    342. 

  342.             (&quot;num&quot;, numeric_transformer, numeric),
    343. 

  343.             (&quot;cat&quot;, categorical_transformer, categoricals)
    344. 

  344.         ]
    345. 

  345.     )
    346. 

  346. 

  347.     X = df[numeric + categoricals].copy()
    348. 

  348.     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    349. 

  349.     clf = Pipeline(steps=[(&quot;preprocessor&quot;, preprocessor),(&quot;classifier&quot;, LogisticRegression())])
    350. 

  350.     clf.fit(X_train, y_train, fit_intercept=False)
    351. 

  351.     return clf, X_test, y_test
    352. 

  352. 

  353. 

  354. def load_driven_model(df, y):
    355. 

  355.     &quot;&quot;&quot;Simple model self-contained with just load and time
    356. 

  356.     &quot;&quot;&quot;
    357. 

  357.     df[&quot;month&quot;] = df[&quot;timestamp&quot;].dt.month
    358. 

  358.     numeric = [&quot;load&quot;]
    359. 

  359.     numeric_transformer = Pipeline(
    360. 

  360.         steps=[
    361. 

  361.             (&quot;scaler&quot;, StandardScaler())
    362. 

  362.         ]
    363. 

  363.     )
    364. 

  364.     categoricals = [&quot;is_weekday&quot;, &quot;month&quot;]
    365. 

  365.     categorical_transformer = Pipeline(
    366. 

  366.         steps=[
    367. 

  367.             (&quot;onehot&quot;, OneHotEncoder(handle_unknown=&quot;ignore&quot;))
    368. 

  368.         ]
    369. 

  369.     )
    370. 

  370. 

  371.     preprocessor = ColumnTransformer(
    372. 

  372.         transformers = [
    373. 

  373.             (&quot;num&quot;, numeric_transformer, numeric),
    374. 

  374.             (&quot;cat&quot;, categorical_transformer, categoricals)
    375. 

  375.         ]
    376. 

  376.     )
    377. 

  377. 

  378.     X = df[numeric + categoricals].copy()
    379. 

  379.     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    380. 

  380.     clf = Pipeline(steps=[(&quot;preprocessor&quot;, preprocessor),(&quot;classifier&quot;, LogisticRegression())])
    381. 

  381.     clf.fit(X_train, y_train)
    382. 

  382.     return clf, X_test, y_test
    383. 

  383. 

  384. 

  385. if __name__ == &quot;__main__&quot;:
    386. 

  386. 

  387.     # Step 1: read data!
    388. 

  388.     data = pd.read_parquet(INPUT_DIR / &quot;1_labeled_curtailment_events.parquet&quot;)
    389. 

  389. 

  390.     cutoff_cols = data.columns[data.columns.str.match(r&quot;curtailment_event_\d.\d\d&quot;)]
    391. 

  391.     predictions = {}
    392. 

  392.     classifiers = {}
    393. 

  393.     for col in cutoff_cols:
    394. 

  394.         y = data[col]
    395. 

  395.         clf, X_test, y_test = load_driven_model(data, y)
    396. 

  396.         p = pd.DataFrame(clf.predict_proba(X_test), columns=list(map(str,clf.classes_))).assign(true_value=y_test.values)
    397. 

  397.         predictions[col] = p
    398. 

  398.         classifiers[col] = clf
    399. 

  399. </pre></div>
    400. 

  400. </div>
    401. 

  401. </div>
    402. 

  402. <div class="nbinput docutils container">
    403. 

  403. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[660]:
    404. 

  404. </pre></div>
    405. 

  405. </div>
    406. 

  406. <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
    407. 

  407. <span></span>charts = []
    408. 

  408. for col, p in predictions.items():
    409. 

  409.     c = alt.Chart(
    410. 

  410.         p, title=f&quot;{col}&quot;,
    411. 

  411.     ).mark_bar(
    412. 

  412.         cornerRadiusTopLeft=3,
    413. 

  413.         cornerRadiusTopRight=3
    414. 

  414.     ).encode(
    415. 

  415.         alt.X(&quot;True&quot;, bin=alt.Bin(step=.05)),
    416. 

  416.         alt.Y(&quot;count(True)&quot;),
    417. 

  417.         alt.Color(&quot;true_value&quot;),
    418. 

  418.     )
    419. 

  419.     charts.append(c)
    420. 

  420. alt.hconcat(*charts)
    421. 

  421. </pre></div>
    422. 

  422. </div>
    423. 

  423. </div>
    424. 

  424. <div class="nboutput nblast docutils container">
    425. 

  425. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[660]:
    426. 

  426. </pre></div>
    427. 

  427. </div>
    428. 

  428. <div class="output_area rendered_html docutils container">
    429. 

  429. <div id="altair-viz-49fa75bfb1144e92844cf49420e720e3"></div>
    430. 

  430. <script type="text/javascript">
    431. 

  431.   (function(spec, embedOpt){
    432. 

  432.     let outputDiv = document.currentScript.previousElementSibling;
    433. 

  433.     if (outputDiv.id !== "altair-viz-49fa75bfb1144e92844cf49420e720e3") {
    434. 

  434.       outputDiv = document.getElementById("altair-viz-49fa75bfb1144e92844cf49420e720e3");
    435. 

  435.     }
    436. 

  436.     const paths = {
    437. 

  437.       "vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
    438. 

  438.       "vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
    439. 

  439.       "vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext",
    440. 

  440.       "vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
    441. 

  441.     };
    442. 

  442. 

  443.     function loadScript(lib) {
    444. 

  444.       return new Promise(function(resolve, reject) {
    445. 

  445.         var s = document.createElement('script');
    446. 

  446.         s.src = paths[lib];
    447. 

  447.         s.async = true;
    448. 

  448.         s.onload = () => resolve(paths[lib]);
    449. 

  449.         s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
    450. 

  450.         document.getElementsByTagName("head")[0].appendChild(s);
    451. 

  451.       });
    452. 

  452.     }
    453. 

  453. 

  454.     function showError(err) {
    455. 

  455.       outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
    456. 

  456.       throw err;
    457. 

  457.     }
    458. 

  458. 

  459.     function displayChart(vegaEmbed) {
    460. 

  460.       vegaEmbed(outputDiv, spec, embedOpt)
    461. 

  461.         .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
    462. 

  462.     }
    463. 

  463. 

  464.     if(typeof define === "function" && define.amd) {
    465. 

  465.       requirejs.config({paths});
    466. 

  466.       require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
    467. 

  467.     } else if (typeof vegaEmbed === "function") {
    468. 

  468.       displayChart(vegaEmbed);
    469. 

  469.     } else {
    470. 

  470.       loadScript("vega")
    471. 

  471.         .then(() => loadScript("vega-lite"))
    472. 

  472.         .then(() => loadScript("vega-embed"))
    473. 

  473.         .catch(showError)
    474. 

  474.         .then(() => displayChart(vegaEmbed));
    475. 

  475.     }
    476. 

  476.   })({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "hconcat": [{"data": {"name": "data-227065858402a653978b6e32eceb30ef"}, "mark": {"type": "bar", "cornerRadiusTopLeft": 3, "cornerRadiusTopRight": 3}, "encoding": {"color": {"type": "nominal", "field": "true_value"}, "x": {"type": "quantitative", "bin": {"step": 0.05}, "field": "True"}, "y": {"type": "quantitative", "aggregate": "count", "field": "True"}}, "title": "curtailment_event_0.01"}, {"data": {"name": "data-d655e7bd390f77cfbf6527a0874ab966"}, "mark": {"type": "bar", "cornerRadiusTopLeft": 3, "cornerRadiusTopRight": 3}, "encoding": {"color": {"type": "nominal", "field": "true_value"}, "x": {"type": "quantitative", "bin": {"step": 0.05}, "field": "True"}, "y": {"type": "quantitative", "aggregate": "count", "field": "True"}}, "title": "curtailment_event_0.03"}, {"data": {"name": "data-adc97b50cb4ac9af831f6ad2fcadb6d8"}, "mark": {"type": "bar", "cornerRadiusTopLeft": 3, "cornerRadiusTopRight": 3}, 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    477. 

  477. </script></div>
    478. 

  478. </div>
    479. 

  479. <div class="nbinput nblast docutils container">
    480. 

  480. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[535]:
    481. 

  481. </pre></div>
    482. 

  482. </div>
    483. 

  483. <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
    484. 

  484. 

  485. <span></span>def weather_driven_model_a(df, y):
    486. 

  486.     &quot;&quot;&quot;Simple model self-contained with just load and time
    487. 

  487.     &quot;&quot;&quot;
    488. 

  488.     df[&quot;month&quot;] = df[&quot;timestamp&quot;].dt.month
    489. 

  489.     numeric = [&quot;load&quot;, &quot;t_mean&quot;, &quot;dswrf_mean&quot;]
    490. 

  490.     numeric_transformer = Pipeline(
    491. 

  491.         steps=[
    492. 

  492.             (&quot;scaler&quot;, StandardScaler())
    493. 

  493.         ]
    494. 

  494.     )
    495. 

  495.     categoricals = [&quot;is_weekday&quot;, &quot;month&quot;]
    496. 

  496.     categorical_transformer = Pipeline(
    497. 

  497.         steps=[
    498. 

  498.             (&quot;onehot&quot;, OneHotEncoder(handle_unknown=&quot;ignore&quot;))
    499. 

  499.         ]
    500. 

  500.     )
    501. 

  501. 

  502.     preprocessor = ColumnTransformer(
    503. 

  503.         transformers = [
    504. 

  504.             (&quot;num&quot;, numeric_transformer, numeric),
    505. 

  505.             (&quot;cat&quot;, categorical_transformer, categoricals)
    506. 

  506.         ]
    507. 

  507.     )
    508. 

  508. 

  509.     X = df[numeric + categoricals].copy()
    510. 

  510.     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    511. 

  511.     clf = Pipeline(steps=[(&quot;preprocessor&quot;, preprocessor),(&quot;classifier&quot;, LogisticRegression())])
    512. 

  512.     clf.fit(X_train, y_train)
    513. 

  513.     return clf, X_test, y_test
    514. 

  514. 

  515. 

  516. if __name__ == &quot;__main__&quot;:
    517. 

  517. 

  518.     # Step 1: read data!
    519. 

  519.     data = pd.read_parquet(INPUT_DIR / &quot;1_labeled_curtailment_events.parquet&quot;)
    520. 

  520. 

  521.     cutoff_cols = data.columns[data.columns.str.match(r&quot;curtailment_event_\d.\d\d&quot;)]
    522. 

  522.     predictions = {}
    523. 

  523.     classifiers = {}
    524. 

  524.     for col in cutoff_cols:
    525. 

  525.         y = data[col]
    526. 

  526.         clf, X_test, y_test = weather_driven_model_a(data, y)
    527. 

  527.         p = pd.DataFrame(clf.predict_proba(X_test), columns=list(map(str,clf.classes_))).assign(true_value=y_test.values)
    528. 

  528.         predictions[col] = p
    529. 

  529.         classifiers[col] = clf
    530. 

  530. 

  531. </pre></div>
    532. 

  532. </div>
    533. 

  533. </div>
    534. 

  534. <div class="nbinput docutils container">
    535. 

  535. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[536]:
    536. 

  536. </pre></div>
    537. 

  537. </div>
    538. 

  538. <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
    539. 

  539. <span></span>charts = []
    540. 

  540. for col, p in predictions.items():
    541. 

  541.     c = alt.Chart(
    542. 

  542.         p, title=f&quot;{col}&quot;,
    543. 

  543.     ).mark_bar(
    544. 

  544.         cornerRadiusTopLeft=3,
    545. 

  545.         cornerRadiusTopRight=3
    546. 

  546.     ).encode(
    547. 

  547.         alt.X(&quot;True&quot;, bin=True),
    548. 

  548.         alt.Y(&quot;count(True)&quot;),
    549. 

  549.         alt.Color(&quot;true_value&quot;),
    550. 

  550.     )
    551. 

  551.     charts.append(c)
    552. 

  552. alt.hconcat(*charts)
    553. 

  553. </pre></div>
    554. 

  554. </div>
    555. 

  555. </div>
    556. 

  556. <div class="nboutput nblast docutils container">
    557. 

  557. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[536]:
    558. 

  558. </pre></div>
    559. 

  559. </div>
    560. 

  560. <div class="output_area rendered_html docutils container">
    561. 

  561. <div id="altair-viz-aa15f74fb512468cbf4b4d2a01df0a02"></div>
    562. 

  562. <script type="text/javascript">
    563. 

  563.   (function(spec, embedOpt){
    564. 

  564.     let outputDiv = document.currentScript.previousElementSibling;
    565. 

  565.     if (outputDiv.id !== "altair-viz-aa15f74fb512468cbf4b4d2a01df0a02") {
    566. 

  566.       outputDiv = document.getElementById("altair-viz-aa15f74fb512468cbf4b4d2a01df0a02");
    567. 

  567.     }
    568. 

  568.     const paths = {
    569. 

  569.       "vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
    570. 

  570.       "vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
    571. 

  571.       "vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext",
    572. 

  572.       "vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
    573. 

  573.     };
    574. 

  574. 

  575.     function loadScript(lib) {
    576. 

  576.       return new Promise(function(resolve, reject) {
    577. 

  577.         var s = document.createElement('script');
    578. 

  578.         s.src = paths[lib];
    579. 

  579.         s.async = true;
    580. 

  580.         s.onload = () => resolve(paths[lib]);
    581. 

  581.         s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
    582. 

  582.         document.getElementsByTagName("head")[0].appendChild(s);
    583. 

  583.       });
    584. 

  584.     }
    585. 

  585. 

  586.     function showError(err) {
    587. 

  587.       outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
    588. 

  588.       throw err;
    589. 

  589.     }
    590. 

  590. 

  591.     function displayChart(vegaEmbed) {
    592. 

  592.       vegaEmbed(outputDiv, spec, embedOpt)
    593. 

  593.         .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
    594. 

  594.     }
    595. 

  595. 

  596.     if(typeof define === "function" && define.amd) {
    597. 

  597.       requirejs.config({paths});
    598. 

  598.       require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
    599. 

  599.     } else if (typeof vegaEmbed === "function") {
    600. 

  600.       displayChart(vegaEmbed);
    601. 

  601.     } else {
    602. 

  602.       loadScript("vega")
    603. 

  603.         .then(() => loadScript("vega-lite"))
    604. 

  604.         .then(() => loadScript("vega-embed"))
    605. 

  605.         .catch(showError)
    606. 

  606.         .then(() => displayChart(vegaEmbed));
    607. 

  607.     }
    608. 

  608.   })({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "hconcat": [{"data": {"name": "data-6da7409d06c605cf395abdb5f3b13adc"}, "mark": {"type": "bar", "cornerRadiusTopLeft": 3, "cornerRadiusTopRight": 3}, "encoding": {"color": {"type": "nominal", "field": "true_value"}, "x": {"type": "quantitative", "bin": true, "field": "True"}, "y": {"type": "quantitative", "aggregate": "count", "field": "True"}}, "title": "curtailment_event_0.01"}, {"data": {"name": "data-9a96ec9546523ccd1e745085ad51f97e"}, "mark": {"type": "bar", "cornerRadiusTopLeft": 3, "cornerRadiusTopRight": 3}, "encoding": {"color": {"type": "nominal", "field": "true_value"}, "x": {"type": "quantitative", "bin": true, "field": "True"}, "y": {"type": "quantitative", "aggregate": "count", "field": "True"}}, "title": "curtailment_event_0.03"}, {"data": {"name": "data-e7566aa1c64d1b49d2468fe5ce2fc94a"}, "mark": {"type": "bar", "cornerRadiusTopLeft": 3, "cornerRadiusTopRight": 3}, "encoding": {"color": {"type": "nominal", "field": "true_value"}, "x": {"type": "quantitative", "bin": true, "field": "True"}, "y": {"type": "quantitative", "aggregate": "count", "field": "True"}}, "title": "curtailment_event_0.05"}, {"data": {"name": "data-465643bf1ba1812e78ab643fe1962b9b"}, "mark": {"type": "bar", "cornerRadiusTopLeft": 3, "cornerRadiusTopRight": 3}, "encoding": {"color": {"type": "nominal", "field": "true_value"}, "x": {"type": "quantitative", "bin": true, "field": "True"}, "y": {"type": "quantitative", "aggregate": "count", "field": "True"}}, "title": "curtailment_event_0.10"}], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json", "datasets": {"data-6da7409d06c605cf395abdb5f3b13adc": [{"False": 0.1882877769635125, "True": 0.8117122230364875, "true_value": true}, {"False": 0.9056311313944561, "True": 0.094368868605544, "true_value": false}, {"False": 0.46449977416708055, "True": 0.5355002258329195, "true_value": true}, {"False": 0.6873057832881695, "True": 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"true_value": false}, {"False": 0.9583929591605316, "True": 0.0416070408394684, "true_value": false}, {"False": 0.928022650653386, "True": 0.071977349346614, "true_value": false}, {"False": 0.9404004201706264, "True": 0.05959957982937353, "true_value": false}, {"False": 0.9427981601675747, "True": 0.057201839832425355, "true_value": false}, {"False": 0.9475472973072212, "True": 0.05245270269277882, "true_value": false}, {"False": 0.8608637981019039, "True": 0.13913620189809606, "true_value": false}, {"False": 0.9907564542687752, "True": 0.009243545731224751, "true_value": false}, {"False": 0.9927572605680143, "True": 0.007242739431985755, "true_value": false}, {"False": 0.5076468857654957, "True": 0.4923531142345044, "true_value": false}, {"False": 0.9627803184993676, "True": 0.03721968150063242, "true_value": false}, {"False": 0.9315695349333668, "True": 0.06843046506663314, "true_value": true}, {"False": 0.9340918291068093, "True": 0.0659081708931907, "true_value": true}, {"False": 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{"False": 0.9661205056499546, "True": 0.033879494350045496, "true_value": false}, {"False": 0.6337583358726958, "True": 0.3662416641273042, "true_value": true}, {"False": 0.9644980964487847, "True": 0.035501903551215276, "true_value": false}, {"False": 0.9927241632759363, "True": 0.0072758367240637785, "true_value": false}, {"False": 0.956008972887078, "True": 0.04399102711292204, "true_value": false}, {"False": 0.9410156227229227, "True": 0.058984377277077255, "true_value": false}, {"False": 0.9729083056600775, "True": 0.02709169433992255, "true_value": false}, {"False": 0.825433573272482, "True": 0.17456642672751804, "true_value": false}, {"False": 0.9602114531062513, "True": 0.03978854689374869, "true_value": false}]}}, {"mode": "vega-lite"});
    609. 

  609. </script></div>
    610. 

  610. </div>
    611. 

  611. <div class="nbinput nblast docutils container">
    612. 

  612. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[654]:
    613. 

  613. </pre></div>
    614. 

  614. </div>
    615. 

  615. <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
    616. 

  616. <span></span>from sklearn import svm
    617. 

  617. 

  618. def weather_driven_model_b(df, y):
    619. 

  619.     &quot;&quot;&quot;Simple model self-contained with just load and time
    620. 

  620.     &quot;&quot;&quot;
    621. 

  621.     df[&quot;month&quot;] = df[&quot;timestamp&quot;].dt.month
    622. 

  622.     numeric = [&quot;load&quot;,&quot;t_mean&quot;, &quot;t_wmin&quot;, &quot;t_wmax&quot;, &quot;dswrf_wmean&quot;, &quot;dswrf_absmax&quot;]
    623. 

  623.     numeric_transformer = Pipeline(
    624. 

  624.         steps=[
    625. 

  625.             (&quot;scaler&quot;, StandardScaler())
    626. 

  626.         ]
    627. 

  627.     )
    628. 

  628.     categoricals = [&quot;is_weekday&quot;, &quot;month&quot;]
    629. 

  629.     categorical_transformer = Pipeline(
    630. 

  630.         steps=[
    631. 

  631.             (&quot;onehot&quot;, OneHotEncoder(handle_unknown=&quot;ignore&quot;))
    632. 

  632.         ]
    633. 

  633.     )
    634. 

  634. 

  635.     preprocessor = ColumnTransformer(
    636. 

  636.         transformers = [
    637. 

  637.             (&quot;num&quot;, numeric_transformer, numeric),
    638. 

  638.             (&quot;cat&quot;, categorical_transformer, categoricals)
    639. 

  639.         ]
    640. 

  640.     )
    641. 

  641. 

  642.     X = df[numeric + categoricals].copy()
    643. 

  643.     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    644. 

  644.     clf = Pipeline(steps=[(&quot;preprocessor&quot;, preprocessor),(&quot;classifier&quot;, svm.SVC(probability=True))])
    645. 

  645.     clf.fit(X_train, y_train)
    646. 

  646.     return clf, X_test, y_test
    647. 

  647. 

  648. 

  649. 

  650. if __name__ == &quot;__main__&quot;:
    651. 

  651. 

  652.     # Step 1: read data!
    653. 

  653.     data = pd.read_parquet(INPUT_DIR / &quot;1_labeled_curtailment_events.parquet&quot;)
    654. 

  654. 

  655.     cutoff_cols = data.columns[data.columns.str.match(r&quot;curtailment_event_\d.\d\d&quot;)]
    656. 

  656.     predictions = {}
    657. 

  657.     classifiers = {}
    658. 

  658.     for col in cutoff_cols:
    659. 

  659.         y = data[col]
    660. 

  660.         clf, X_test, y_test = weather_driven_model_b(data, y)
    661. 

  661.         p = pd.DataFrame(clf.predict_proba(X_test), columns=list(map(str,clf.classes_))).assign(true_value=y_test.values)
    662. 

  662.         predictions[col] = p
    663. 

  663.         classifiers[col] = clf
    664. 

  664. </pre></div>
    665. 

  665. </div>
    666. 

  666. </div>
    667. 

  667. <div class="nbinput docutils container">
    668. 

  668. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[655]:
    669. 

  669. </pre></div>
    670. 

  670. </div>
    671. 

  671. <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
    672. 

  672. <span></span>charts = []
    673. 

  673. for col, p in predictions.items():
    674. 

  674.     c = alt.Chart(
    675. 

  675.         p, title=f&quot;{col}&quot;,
    676. 

  676.     ).mark_bar(
    677. 

  677.         cornerRadiusTopLeft=3,
    678. 

  678.         cornerRadiusTopRight=3
    679. 

  679.     ).encode(
    680. 

  680.         alt.X(&quot;True&quot;, bin=alt.Bin(step=.05)),
    681. 

  681.         alt.Y(&quot;count(True)&quot;),
    682. 

  682.         alt.Color(&quot;true_value&quot;),
    683. 

  683.     )
    684. 

  684.     charts.append(c)
    685. 

  685. alt.hconcat(*charts)
    686. 

  686. </pre></div>
    687. 

  687. </div>
    688. 

  688. </div>
    689. 

  689. <div class="nboutput nblast docutils container">
    690. 

  690. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[655]:
    691. 

  691. </pre></div>
    692. 

  692. </div>
    693. 

  693. <div class="output_area rendered_html docutils container">
    694. 

  694. <div id="altair-viz-2fc4db490e6a4939bfabfdb228bc4bd8"></div>
    695. 

  695. <script type="text/javascript">
    696. 

  696.   (function(spec, embedOpt){
    697. 

  697.     let outputDiv = document.currentScript.previousElementSibling;
    698. 

  698.     if (outputDiv.id !== "altair-viz-2fc4db490e6a4939bfabfdb228bc4bd8") {
    699. 

  699.       outputDiv = document.getElementById("altair-viz-2fc4db490e6a4939bfabfdb228bc4bd8");
    700. 

  700.     }
    701. 

  701.     const paths = {
    702. 

  702.       "vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
    703. 

  703.       "vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
    704. 

  704.       "vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext",
    705. 

  705.       "vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
    706. 

  706.     };
    707. 

  707. 

  708.     function loadScript(lib) {
    709. 

  709.       return new Promise(function(resolve, reject) {
    710. 

  710.         var s = document.createElement('script');
    711. 

  711.         s.src = paths[lib];
    712. 

  712.         s.async = true;
    713. 

  713.         s.onload = () => resolve(paths[lib]);
    714. 

  714.         s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
    715. 

  715.         document.getElementsByTagName("head")[0].appendChild(s);
    716. 

  716.       });
    717. 

  717.     }
    718. 

  718. 

  719.     function showError(err) {
    720. 

  720.       outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
    721. 

  721.       throw err;
    722. 

  722.     }
    723. 

  723. 

  724.     function displayChart(vegaEmbed) {
    725. 

  725.       vegaEmbed(outputDiv, spec, embedOpt)
    726. 

  726.         .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
    727. 

  727.     }
    728. 

  728. 

  729.     if(typeof define === "function" && define.amd) {
    730. 

  730.       requirejs.config({paths});
    731. 

  731.       require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
    732. 

  732.     } else if (typeof vegaEmbed === "function") {
    733. 

  733.       displayChart(vegaEmbed);
    734. 

  734.     } else {
    735. 

  735.       loadScript("vega")
    736. 

  736.         .then(() => loadScript("vega-lite"))
    737. 

  737.         .then(() => loadScript("vega-embed"))
    738. 

  738.         .catch(showError)
    739. 

  739.         .then(() => displayChart(vegaEmbed));
    740. 

  740.     }
    741. 

  741.   })({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "hconcat": [{"data": {"name": "data-560753ff61b89f9ec6bd4584f1230ec0"}, "mark": {"type": "bar", "cornerRadiusTopLeft": 3, "cornerRadiusTopRight": 3}, "encoding": {"color": {"type": "nominal", "field": "true_value"}, "x": {"type": "quantitative", "bin": {"step": 0.05}, "field": "True"}, "y": {"type": "quantitative", "aggregate": "count", "field": "True"}}, "title": "curtailment_event_0.01"}, {"data": {"name": "data-339a9a166ef6ce2cc9324a6ce536fa62"}, "mark": {"type": "bar", "cornerRadiusTopLeft": 3, "cornerRadiusTopRight": 3}, "encoding": {"color": {"type": "nominal", "field": "true_value"}, "x": {"type": "quantitative", "bin": {"step": 0.05}, "field": "True"}, "y": {"type": "quantitative", "aggregate": "count", "field": "True"}}, "title": "curtailment_event_0.03"}, {"data": {"name": "data-f5992622b4defa8ee8bf04c82643b136"}, "mark": {"type": "bar", "cornerRadiusTopLeft": 3, "cornerRadiusTopRight": 3}, 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    742. 

  742. </script></div>
    743. 

  743. </div>
    744. 

  744. <div class="nbinput docutils container">
    745. 

  745. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[505]:
    746. 

  746. </pre></div>
    747. 

  747. </div>
    748. 

  748. <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
    749. 

  749. <span></span>predictions[&quot;curtailment_event_0.01&quot;][&quot;true_value&quot;].value_counts()
    750. 

  750. </pre></div>
    751. 

  751. </div>
    752. 

  752. </div>
    753. 

  753. <div class="nboutput nblast docutils container">
    754. 

  754. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[505]:
    755. 

  755. </pre></div>
    756. 

  756. </div>
    757. 

  757. <div class="output_area docutils container">
    758. 

  758. <div class="highlight"><pre>
    759. 

  759. True     49
    760. 

  760. False    22
    761. 

  761. Name: true_value, dtype: int64
    762. 

  762. </pre></div></div>
    763. 

  763. </div>
    764. 

  764. <div class="nbinput docutils container">
    765. 

  765. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[474]:
    766. 

  766. </pre></div>
    767. 

  767. </div>
    768. 

  768. <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
    769. 

  769. <span></span>alt.Chart(
    770. 

  770.     data.assign(pct_curtailed=data.eval(&quot;solar_curtailment/solar&quot;), curtailed_per_mw_installed=data.eval(&quot;solar_curtailment/installed_capacity&quot;))
    771. 

  771. ).mark_bar().encode(
    772. 

  772.     alt.X(&quot;yearmonth(timestamp)&quot;),
    773. 

  773.     alt.Y(&quot;mean(pct_curtailed)&quot;)
    774. 

  774. )
    775. 

  775. </pre></div>
    776. 

  776. </div>
    777. 

  777. </div>
    778. 

  778. <div class="nboutput nblast docutils container">
    779. 

  779. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[474]:
    780. 

  780. </pre></div>
    781. 

  781. </div>
    782. 

  782. <div class="output_area rendered_html docutils container">
    783. 

  783. <div id="altair-viz-0b56de43032c497599187d367796584f"></div>
    784. 

  784. <script type="text/javascript">
    785. 

  785.   (function(spec, embedOpt){
    786. 

  786.     let outputDiv = document.currentScript.previousElementSibling;
    787. 

  787.     if (outputDiv.id !== "altair-viz-0b56de43032c497599187d367796584f") {
    788. 

  788.       outputDiv = document.getElementById("altair-viz-0b56de43032c497599187d367796584f");
    789. 

  789.     }
    790. 

  790.     const paths = {
    791. 

  791.       "vega": "https://cdn.jsdelivr.net/npm//vega@5?noext",
    792. 

  792.       "vega-lib": "https://cdn.jsdelivr.net/npm//vega-lib?noext",
    793. 

  793.       "vega-lite": "https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext",
    794. 

  794.       "vega-embed": "https://cdn.jsdelivr.net/npm//vega-embed@6?noext",
    795. 

  795.     };
    796. 

  796. 

  797.     function loadScript(lib) {
    798. 

  798.       return new Promise(function(resolve, reject) {
    799. 

  799.         var s = document.createElement('script');
    800. 

  800.         s.src = paths[lib];
    801. 

  801.         s.async = true;
    802. 

  802.         s.onload = () => resolve(paths[lib]);
    803. 

  803.         s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
    804. 

  804.         document.getElementsByTagName("head")[0].appendChild(s);
    805. 

  805.       });
    806. 

  806.     }
    807. 

  807. 

  808.     function showError(err) {
    809. 

  809.       outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
    810. 

  810.       throw err;
    811. 

  811.     }
    812. 

  812. 

  813.     function displayChart(vegaEmbed) {
    814. 

  814.       vegaEmbed(outputDiv, spec, embedOpt)
    815. 

  815.         .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
    816. 

  816.     }
    817. 

  817. 

  818.     if(typeof define === "function" && define.amd) {
    819. 

  819.       requirejs.config({paths});
    820. 

  820.       require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
    821. 

  821.     } else if (typeof vegaEmbed === "function") {
    822. 

  822.       displayChart(vegaEmbed);
    823. 

  823.     } else {
    824. 

  824.       loadScript("vega")
    825. 

  825.         .then(() => loadScript("vega-lite"))
    826. 

  826.         .then(() => loadScript("vega-embed"))
    827. 

  827.         .catch(showError)
    828. 

  828.         .then(() => displayChart(vegaEmbed));
    829. 

  829.     }
    830. 

  830.   })({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "data": {"name": "data-d5df16cf6362eb3b1a3cf983e551444b"}, "mark": "bar", "encoding": {"x": {"type": "temporal", "field": "timestamp", "timeUnit": "yearmonth"}, "y": {"type": "quantitative", "aggregate": "mean", "field": "pct_curtailed"}}, "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json", "datasets": {"data-d5df16cf6362eb3b1a3cf983e551444b": [{"timestamp": "2017-02-01T00:00:00-08:00", "solar_curtailment": 706.7219553159325, "solar": 53045.72953195965, "net_load": 556654.2380033325, "load": 617066.3903664342, "generation": 446776.94578821707, "renewables": 103435.90740989875, "wind_curtailment": 5.389166666666666, "is_weekday": false, "t_mean": 282.79998779296875, "t_wmean": 279.9640197753906, "t_wmax": 286.2569580078125, "t_wmin": 272.8097229003906, "t_absmax": 303.79998779296875, "t_absmin": 265.29998779296875, "dswrf_mean": 188.18710327148438, "dswrf_absmax": 690.0, "dswrf_wmean": 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"t_wmean": 286.5802001953125, "t_wmax": 293.6330871582031, "t_wmin": 281.1843566894531, "t_absmax": 316.21380615234375, "t_absmin": 271.28924560546875, "dswrf_mean": 308.0048522949219, "dswrf_absmax": 1020.0, "dswrf_wmean": 317.0162048339844, "capacity_mw": 12338.859999999999, "installed_capacity": 12338.859999999999, "solar_capacity_factor": 0.21362384196023532, "curtailment_event_0.01": true, "curtailment_event_0.05": true, "curtailment_event_0.10": true, "month": 5, "pct_curtailed": 0.1307710234264984, "curtailed_per_mw_installed": 0.6704594025945726}, {"timestamp": "2019-05-27T00:00:00-07:00", "solar_curtailment": 37029.533523878, "solar": 64339.70782113601, "net_load": 315589.0777697107, "load": 472691.80604852946, "generation": 375217.42870437406, "renewables": 204472.71358563768, "wind_curtailment": 1566.368776940905, "is_weekday": false, "t_mean": 291.40679931640625, "t_wmean": 291.2173156738281, "t_wmax": 298.18524169921875, "t_wmin": 284.2032775878906, "t_absmax": 315.056396484375, "t_absmin": 275.52716064453125, "dswrf_mean": 446.1672058105469, "dswrf_absmax": 1050.0, "dswrf_wmean": 427.90814208984375, "capacity_mw": 12338.859999999999, "installed_capacity": 12338.859999999999, "solar_capacity_factor": 0.21726651888537524, "curtailment_event_0.01": true, "curtailment_event_0.05": true, "curtailment_event_0.10": true, "month": 5, "pct_curtailed": 0.5755315772775946, "curtailed_per_mw_installed": 3.0010498152890954}, {"timestamp": "2019-05-28T00:00:00-07:00", "solar_curtailment": 5296.214228672795, "solar": 99557.7273390503, "net_load": 357923.63582077716, "load": 536411.8336654371, "generation": 418286.9385383355, "renewables": 225126.88097539102, "wind_curtailment": 77.08074581120133, "is_weekday": false, "t_mean": 295.2656555175781, "t_wmean": 296.42926025390625, "t_wmax": 305.25006103515625, "t_wmin": 287.40667724609375, "t_absmax": 324.0306701660156, "t_absmin": 277.74481201171875, "dswrf_mean": 510.8817138671875, "dswrf_absmax": 1020.0, "dswrf_wmean": 516.6630249023438, "capacity_mw": 12338.859999999999, "installed_capacity": 12338.859999999999, "solar_capacity_factor": 0.3361930226234106, "curtailment_event_0.01": true, "curtailment_event_0.05": true, "curtailment_event_0.10": true, "month": 5, "pct_curtailed": 0.053197419931415205, "curtailed_per_mw_installed": 0.42923043366022434}, {"timestamp": "2019-05-29T00:00:00-07:00", "solar_curtailment": 6063.860798092374, "solar": 103321.7750488151, "net_load": 418084.88405153353, "load": 563512.6630264802, "generation": 417508.56194762053, "renewables": 192472.70318809405, "wind_curtailment": 888.1668807302367, "is_weekday": false, "t_mean": 297.8746032714844, "t_wmean": 296.6529235839844, "t_wmax": 305.2181396484375, "t_wmin": 289.2449951171875, "t_absmax": 327.0476989746094, "t_absmin": 278.13177490234375, "dswrf_mean": 511.92901611328125, "dswrf_absmax": 1020.0, "dswrf_wmean": 517.62109375, "capacity_mw": 12338.859999999999, "installed_capacity": 12338.859999999999, "solar_capacity_factor": 0.348903704261763, "curtailment_event_0.01": true, "curtailment_event_0.05": true, "curtailment_event_0.10": true, "month": 5, "pct_curtailed": 0.05868908848330819, "curtailed_per_mw_installed": 0.4914441689177424}, {"timestamp": "2019-05-30T00:00:00-07:00", "solar_curtailment": 638.4546206535263, "solar": 107952.10996715259, "net_load": 421902.9539542025, "load": 572102.3381833098, "generation": 424514.5190400252, "renewables": 196591.2082727267, "wind_curtailment": 1145.2549229239671, "is_weekday": false, "t_mean": 297.6970520019531, "t_wmean": 298.2995300292969, "t_wmax": 306.864013671875, "t_wmin": 290.5595703125, "t_absmax": 326.49737548828125, "t_absmin": 276.90740966796875, "dswrf_mean": 508.2204284667969, "dswrf_absmax": 1040.0, "dswrf_wmean": 523.2311401367188, "capacity_mw": 12338.859999999999, "installed_capacity": 12338.859999999999, "solar_capacity_factor": 0.36453972100864196, "curtailment_event_0.01": true, "curtailment_event_0.05": true, "curtailment_event_0.10": false, "month": 5, "pct_curtailed": 0.005914239386778024, "curtailed_per_mw_installed": 0.05174340422482517}, {"timestamp": "2019-05-31T00:00:00-07:00", "solar_curtailment": 4035.8954376499883, "solar": 103672.76914138845, "net_load": 440371.55543453124, "load": 577493.2705836142, "generation": 420032.7014634957, "renewables": 184935.12174783298, "wind_curtailment": 75.78673430940336, "is_weekday": false, "t_mean": 297.62664794921875, "t_wmean": 299.09295654296875, "t_wmax": 307.90704345703125, "t_wmin": 292.068115234375, "t_absmax": 326.33502197265625, "t_absmin": 280.3343505859375, "dswrf_mean": 538.7109985351562, "dswrf_absmax": 1030.0, "dswrf_wmean": 541.9086303710938, "capacity_mw": 12338.859999999997, "installed_capacity": 12338.859999999997, "solar_capacity_factor": 0.35008896399055667, "curtailment_event_0.01": true, "curtailment_event_0.05": true, "curtailment_event_0.10": true, "month": 5, "pct_curtailed": 0.03892917562707188, "curtailed_per_mw_installed": 0.3270881943429125}]}}, {"mode": "vega-lite"});
    831. 

  831. </script></div>
    832. 

  832. </div>
    833. 

  833. <div class="nbinput docutils container">
    834. 

  834. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[466]:
    835. 

  835. </pre></div>
    836. 

  836. </div>
    837. 

  837. <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
    838. 

  838. <span></span>data[[&quot;timestamp&quot;, &quot;solar_curtailment&quot;, &quot;solar&quot;, &quot;installed_capacity&quot;]]
    839. 

  839. </pre></div>
    840. 

  840. </div>
    841. 

  841. </div>
    842. 

  842. <div class="nboutput nblast docutils container">
    843. 

  843. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[466]:
    844. 

  844. </pre></div>
    845. 

  845. </div>
    846. 

  846. <div class="output_area rendered_html docutils container">
    847. 

  847. <div>
    848. 

  848. <style scoped>
    849. 

  849.     .dataframe tbody tr th:only-of-type {
    850. 

  850.         vertical-align: middle;
    851. 

  851.     }
    852. 

  852. 

  853.     .dataframe tbody tr th {
    854. 

  854.         vertical-align: top;
    855. 

  855.     }
    856. 

  856. 

  857.     .dataframe thead th {
    858. 

  858.         text-align: right;
    859. 

  859.     }
    860. 

  860. </style>
    861. 

  861. <table border="1" class="dataframe">
    862. 

  862.   <thead>
    863. 

  863.     <tr style="text-align: right;">
    864. 

  864.       <th></th>
    865. 

  865.       <th>timestamp</th>
    866. 

  866.       <th>solar_curtailment</th>
    867. 

  867.       <th>solar</th>
    868. 

  868.       <th>installed_capacity</th>
    869. 

  869.     </tr>
    870. 

  870.   </thead>
    871. 

  871.   <tbody>
    872. 

  872.     <tr>
    873. 

  873.       <th>0</th>
    874. 

  874.       <td>2017-02-01 00:00:00-08:00</td>
    875. 

  875.       <td>706.721955</td>
    876. 

  876.       <td>53045.729532</td>
    877. 

  877.       <td>10192.63</td>
    878. 

  878.     </tr>
    879. 

  879.     <tr>
    880. 

  880.       <th>1</th>
    881. 

  881.       <td>2017-02-02 00:00:00-08:00</td>
    882. 

  882.       <td>24.079722</td>
    883. 

  883.       <td>41689.418186</td>
    884. 

  884.       <td>10192.63</td>
    885. 

  885.     </tr>
    886. 

  886.     <tr>
    887. 

  887.       <th>2</th>
    888. 

  888.       <td>2017-02-03 00:00:00-08:00</td>
    889. 

  889.       <td>72.433650</td>
    890. 

  890.       <td>44321.327353</td>
    891. 

  891.       <td>10192.63</td>
    892. 

  892.     </tr>
    893. 

  893.     <tr>
    894. 

  894.       <th>3</th>
    895. 

  895.       <td>2017-02-04 00:00:00-08:00</td>
    896. 

  896.       <td>5821.548055</td>
    897. 

  897.       <td>53032.304312</td>
    898. 

  898.       <td>10192.63</td>
    899. 

  899.     </tr>
    900. 

  900.     <tr>
    901. 

  901.       <th>4</th>
    902. 

  902.       <td>2017-02-05 00:00:00-08:00</td>
    903. 

  903.       <td>2.112333</td>
    904. 

  904.       <td>33350.929800</td>
    905. 

  905.       <td>10192.63</td>
    906. 

  906.     </tr>
    907. 

  907.     <tr>
    908. 

  908.       <th>...</th>
    909. 

  909.       <td>...</td>
    910. 

  910.       <td>...</td>
    911. 

  911.       <td>...</td>
    912. 

  912.       <td>...</td>
    913. 

  913.     </tr>
    914. 

  914.     <tr>
    915. 

  915.       <th>349</th>
    916. 

  916.       <td>2019-05-27 00:00:00-07:00</td>
    917. 

  917.       <td>37029.533524</td>
    918. 

  918.       <td>64339.707821</td>
    919. 

  919.       <td>12338.86</td>
    920. 

  920.     </tr>
    921. 

  921.     <tr>
    922. 

  922.       <th>350</th>
    923. 

  923.       <td>2019-05-28 00:00:00-07:00</td>
    924. 

  924.       <td>5296.214229</td>
    925. 

  925.       <td>99557.727339</td>
    926. 

  926.       <td>12338.86</td>
    927. 

  927.     </tr>
    928. 

  928.     <tr>
    929. 

  929.       <th>351</th>
    930. 

  930.       <td>2019-05-29 00:00:00-07:00</td>
    931. 

  931.       <td>6063.860798</td>
    932. 

  932.       <td>103321.775049</td>
    933. 

  933.       <td>12338.86</td>
    934. 

  934.     </tr>
    935. 

  935.     <tr>
    936. 

  936.       <th>352</th>
    937. 

  937.       <td>2019-05-30 00:00:00-07:00</td>
    938. 

  938.       <td>638.454621</td>
    939. 

  939.       <td>107952.109967</td>
    940. 

  940.       <td>12338.86</td>
    941. 

  941.     </tr>
    942. 

  942.     <tr>
    943. 

  943.       <th>353</th>
    944. 

  944.       <td>2019-05-31 00:00:00-07:00</td>
    945. 

  945.       <td>4035.895438</td>
    946. 

  946.       <td>103672.769141</td>
    947. 

  947.       <td>12338.86</td>
    948. 

  948.     </tr>
    949. 

  949.   </tbody>
    950. 

  950. </table>
    951. 

  951. <p>354 rows × 4 columns</p>
    952. 

  952. </div></div>
    953. 

  953. </div>
    954. 

  954. <div class="nbinput nblast docutils container">
    955. 

  955. <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
    956. 

  956. </pre></div>
    957. 

  957. </div>
    958. 

  958. <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
    959. 

  959. <span></span>
    960. 

  960. </pre></div>
    961. 

  961. </div>
    962. 

  962. </div>
    963. 

  963. 

  964. 

  965.       </div>
    966. 

  966.       <div class="bottomnav" role="navigation" aria-label="bottom navigation">
    967. 

  967.       
    968. 

  968.         <p>
    969. 

  969.         <a class="uplink" href="../index.html">Contents</a>
    970. 

  970.         </p>
    971. 

  971. 

  972.       </div>
    973. 

  973. 

  974.     <div class="footer" role="contentinfo">
    975. 

  975.         &#169; Copyright Thomas Tu, 2020.
    976. 

  976.       Created using <a href="http://sphinx-doc.org/">Sphinx</a> 2.4.4.
    977. 

  977.     </div>
    978. 

  978.   </body>
    979. 

  979. </html>
    980.
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