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How to use save_chart method in SeleniumBase

Best Python code snippet using SeleniumBase

chart.py

Source:chart.py

1import harvester2import matplotlib.pyplot as plt3import os4import shutil5import charts6DATABASE_FILE = "C:\\Users\\Has112\\Documents\\db_history\\28-05-2019\\ld-database.db"7DATABASE_VERIFICATION_TEMPLATE = 'database/create_database.sql'8SAVE_CHART = True9SAVE_CHART_DIRECTORY = 'C:\\Users\\Has112\\Documents\\db_history\\28-05-2019\\figures\\'  # Should end with '/'10SHOW_CHART = False11TOTAL_DOMAINS = 746091912TRANSPARENT = False13# Connect to Database14dbconnector, crawl_id = harvester.connect(DATABASE_FILE, crawl=False)15if harvester.verify_database(dbconnector, DATABASE_VERIFICATION_TEMPLATE):16    print("Database schema integrity has been verified.")17else:18    print("Error, database schema does not match the provided template.")19    exit(1)20# Override existing output folder if it exists21if not os.path.exists(SAVE_CHART_DIRECTORY):22    os.mkdir(SAVE_CHART_DIRECTORY)23else:24    shutil.rmtree(SAVE_CHART_DIRECTORY)25    os.mkdir(SAVE_CHART_DIRECTORY)26# Plot Seed Count-Time Scatter (with third dimension)27dbconnector.cursor.execute("""28        SELECT Crawl.crawlId, endDate - startDate as elapsed, count(distinct originSeedURI), count(address)29        FROM Crawl, Link30        WHERE Crawl.crawlId = Link.crawlId31        GROUP BY Crawl.crawlId;32    """)33seed_count_time_data = dbconnector.cursor.fetchall()34charts.request_time_scatter.seed_count_time_scatter_3d(seed_count_time_data, 'Seeds and Links Visited vs Time Requirements')35if SHOW_CHART:36    plt.show()37if SAVE_CHART:38    plt.savefig(SAVE_CHART_DIRECTORY + 'seeds_requests_crawl_size_time.png', bbox_inches="tight", dpi=300, transparent = TRANSPARENT)39plt.close()40# Plot Request Count-Time Scatter41dbconnector.cursor.execute("""42        SELECT Crawl.crawlId, endDate - startDate as elapsed, count(address)43        FROM Crawl, Link44        WHERE Crawl.crawlId = Link.crawlId45        GROUP BY Crawl.crawlId;46    """)47seed_count_time_data = dbconnector.cursor.fetchall()48charts.request_time_scatter.request_count_time_scatter(seed_count_time_data, 'Requests Made vs Time Requirements')49if SHOW_CHART:50    plt.show()51if SAVE_CHART:52    plt.savefig(SAVE_CHART_DIRECTORY + 'requests_crawl_size_time.png', bbox_inches="tight", dpi=300, transparent = TRANSPARENT)53plt.close()54# Plot Seed Count-Time Scatter55dbconnector.cursor.execute("""56            SELECT Crawl.crawlId, endDate - startDate as elapsed, count(distinct originSeedURI)57            FROM Crawl, Link58            WHERE Crawl.crawlId = Link.crawlId59            GROUP BY Crawl.crawlId;60        """)61seed_count_time_data = dbconnector.cursor.fetchall()62charts.request_time_scatter.seed_count_time_scatter(seed_count_time_data, 'Crawl Size vs Time Requirements')63if SHOW_CHART:64    plt.show()65if SAVE_CHART:66    plt.savefig(SAVE_CHART_DIRECTORY + 'seeds_crawl_size_time.png', bbox_inches="tight", dpi=300, transparent = TRANSPARENT)67plt.close()68# Plot Progress Pie Chart69dbconnector.cursor.execute("""70        SELECT COUNT(DISTINCT originSeedUri) 71        FROM LINK72    """)73VISITED_DOMAINS = dbconnector.cursor.fetchone()[0]74charts.progress_chart.progress_chart_pie(VISITED_DOMAINS, TOTAL_DOMAINS, "Project Progress")75if SHOW_CHART:76    plt.show()77if SAVE_CHART:78    plt.savefig(SAVE_CHART_DIRECTORY + 'project_progress.png', bbox_inches="tight", dpi=300, transparent = TRANSPARENT)79plt.close()80# Plot Response Format Pie Chart81dbconnector.cursor.execute("""82        SELECT contentFormat, COUNT(contentFormat) 83        FROM LINK84        GROUP BY contentFormat;85    """)86content_format_dict = dict(dbconnector.cursor.fetchall())87charts.file_format_chart.clean_formats(content_format_dict)88charts.file_format_chart.file_format_pie(content_format_dict, 'Response Format Breakdown')89if SHOW_CHART:90    plt.show()91if SAVE_CHART:92    plt.savefig(SAVE_CHART_DIRECTORY + 'content_format_pie.png', bbox_inches="tight", dpi=300, transparent = TRANSPARENT)93plt.close()94# Plot Response Format Bar Chart95charts.file_format_chart.file_format_bar(content_format_dict, 'Response Format Breakdown')96if SHOW_CHART:97    plt.show()98if SAVE_CHART:99    plt.savefig(SAVE_CHART_DIRECTORY + 'content_format_bar.png', bbox_inches="tight", dpi=300, transparent = TRANSPARENT)100plt.close()101# Plot RDF Response Format Pie Chart102dbconnector.cursor.execute("""103            SELECT contentFormat, COUNT(contentFormat) 104            FROM RdfURI105            GROUP BY contentFormat;106        """)107content_format_dict = dict(dbconnector.cursor.fetchall())108charts.file_format_chart.clean_formats(content_format_dict)109charts.file_format_chart.file_format_pie(content_format_dict, 'RDF Format Breakdown')110if SHOW_CHART:111    plt.show()112if SAVE_CHART:113    plt.savefig(SAVE_CHART_DIRECTORY + 'rdf_content_format_pie.png', bbox_inches="tight", dpi=300, transparent = TRANSPARENT)114plt.close()115# Plot RDF Response Format Bar Chart116charts.file_format_chart.file_format_bar(content_format_dict, 'RDF Format Breakdown')117if SHOW_CHART:118    plt.show()119if SAVE_CHART:120    plt.savefig(SAVE_CHART_DIRECTORY + 'rdf_content_format_bar.png', bbox_inches="tight", dpi=300, transparent = TRANSPARENT)121plt.close()122# Plot Site Size Histogram123dbconnector.cursor.execute("""124        SELECT originSeedURI, COUNT(DISTINCT address)125        FROM Link126        GROUP BY originSeedURI127        HAVING COUNT(DISTINCT address) > 1;128    """)129seed_size_data = dbconnector.cursor.fetchall()130charts.size_histogram.plot_size_histogram(seed_size_data, 100, "Domain Size Distribution")131if SHOW_CHART:132    plt.show()133if SAVE_CHART:134    plt.savefig(SAVE_CHART_DIRECTORY + 'domain_size_histogram.png', bbox_inches="tight", dpi=300, transparent = TRANSPARENT)135plt.close()136# Plot RDF Per Site Histogram137dbconnector.cursor.execute("""138        SELECT originSeedURI, COUNT(DISTINCT rdfSeedURI)139        FROM RdfURI140        GROUP BY originSeedURI141        HAVING COUNT(DISTINCT rdfSeedURI) > 1;142    """)143seed_size_data = dbconnector.cursor.fetchall()144charts.size_histogram.plot_size_histogram(seed_size_data, 100, "RDF Domain Size Distribution")145if SHOW_CHART:146    plt.show()147if SAVE_CHART:148    plt.savefig(SAVE_CHART_DIRECTORY + 'rdf_domain_size_histogram.png', bbox_inches="tight", dpi=300, transparent = TRANSPARENT)...

main.py

Source:main.py

...19    dev = io.open(path_dev, "r", encoding="utf-8")20    train = io.open(path_train, "r", encoding="utf-8")21    test = io.open(path_test, "r", encoding="utf-8")22    return [*file_to_trees(dev), *file_to_trees(train), *file_to_trees(test)]23def save_chart(chart, file_name: str = "figure.pdf"):24    chart.figure.savefig(os.path.join(DIST_IMAGES_PATH, file_name))25def freq(items: List[str]) -> pd.DataFrame:26    frequencies = {}27    for i in items:28        count = frequencies.get(i, 0)29        frequencies[i] = count + 130    for i in frequencies:31        frequencies[i] = (frequencies[i], frequencies[i] / len(items))32    values = np.array([v[0] for v in frequencies.values()])33    names = np.array([k for k in frequencies.keys()])34    df = pd.DataFrame(values, index=names).sort_values(by=0, ascending=False)35    df[1] = [df[0][0] / ind for ind in np.arange(1, len(frequencies) + 1)]36    df.columns = ["ÐÑÑÐ¸Ð¼Ð°Ð½Ð¸Ð¹ ÑÐ¾Ð·Ð¿Ð¾Ð´ÑÐ»", "Ð Ð¾Ð·Ð¿Ð¾Ð´ÑÐ» ÐÑÐ¿ÑÐ°"]37    return df38def sig_sentence(sentence: List[Signature]):39    types = {}40    for sig in sentence:41        amount = types.get(sig.type, 0)42        types[sig.type] = amount + 143    return f"w{types.get(SigType.W, 0)},d{types.get(SigType.D, 0)},n{types.get(SigType.N)}"44def process(treebank: List[ParseTree], lang: str):45    sentences = [[tree.signature for tree in sentence_tree.to_list()] for sentence_tree in treebank]46    signatures = [signature for signatures in sentences for signature in signatures]47    deprel = [sig.node[0] for sig in signatures]48    upos = [sig.node[1] for sig in signatures]49    deprel_upos = [f"{sig.node[0]}+{sig.node[1]}" for sig in signatures]50    type_w = [f"{sig.type},{sig.sentence_id}" for sig in signatures if sig.type == SigType.W]51    type_d = [f"{sig.type},{sig.height},{sig.subtree_type}" for sig in signatures if sig.type == SigType.D]52    type_upos = [f"{sig.type}+{sig.node[1]}" for sig in signatures]53    sent_d_w_n = [sig for sig in map(sig_sentence, sentences)]54    freq_deprel = freq(deprel)55    freq_upos = freq(upos)56    freq_deprel_upos = freq(deprel_upos)57    freq_type_w = freq(type_w)58    freq_type_d = freq(type_d)59    freq_type_upos = freq(type_upos)60    freq_sent_d_w_n = freq(sent_d_w_n)61    chart_deprel = freq_deprel.plot(kind="bar")62    chart_upos = freq_upos.plot(kind="bar")63    chart_deprel_upos = freq_deprel_upos.plot(xticks=[],64        xlabel=f"Ð Ð¾Ð·Ð¿Ð¾Ð´ÑÐ» deprel+upos. ÐÑÑÐ¾Ð³Ð¾: {len(deprel_upos)}. Ð£Ð½ÑÐºÐ°Ð»ÑÐ½Ð¸Ñ: {len(freq_deprel_upos)}")65    chart_type_w = freq_type_w.plot(xticks=[],66        xlabel=f"Ð Ð¾Ð·Ð¿Ð¾Ð´ÑÐ» ÑÐ¸Ð¿Ñ w. ÐÑÑÐ¾Ð³Ð¾: {len(type_w)}. Ð£Ð½ÑÐºÐ°Ð»ÑÐ½Ð¸Ñ: {len(freq_type_w)}")67    chart_type_d = freq_type_d.plot(xticks=[],68        xlabel=f"Ð Ð¾Ð·Ð¿Ð¾Ð´ÑÐ» ÑÐ¸Ð¿Ñ d. ÐÑÑÐ¾Ð³Ð¾: {len(type_d)}. Ð£Ð½ÑÐºÐ°Ð»ÑÐ½Ð¸Ñ: {len(freq_type_d)}")69    chart_type_upos = freq_type_upos.plot(xticks=[], kind="bar",70        xlabel=f"Ð Ð¾Ð·Ð¿Ð¾Ð´ÑÐ» Ð¢Ð¸Ð¿ + Ð§Ð°ÑÑÐ¸Ð½Ð° Ð¼Ð¾Ð²Ð¸. ÐÑÑÐ¾Ð³Ð¾: {len(type_upos)}. Ð£Ð½ÑÐºÐ°Ð»ÑÐ½Ð¸Ñ: {len(freq_type_upos)}")71    chart_sent_d_w_n = freq_sent_d_w_n.plot(xticks=[],72        xlabel=f"Ð Ð¾Ð·Ð¿Ð¾Ð´ÑÐ» ÑÐ¸Ð³Ð½Ð°ÑÑÑ ÑÐµÑÐµÐ½Ð½Ñ. ÐÑÑÐ¾Ð³Ð¾: {len(sent_d_w_n)}. Ð£Ð½ÑÐºÐ°Ð»ÑÐ½Ð¸Ñ: {len(freq_sent_d_w_n)}")73    save_chart(chart_deprel, f"chart_{lang}_deprel.pdf")74    save_chart(chart_upos, f"chart_{lang}_upos.pdf")75    save_chart(chart_deprel_upos, f"chart_{lang}_deprel_upos.pdf")76    save_chart(chart_type_w, f"chart_{lang}_type_w.pdf")77    save_chart(chart_type_d, f"chart_{lang}_type_d.pdf")78    save_chart(chart_type_upos, f"chart_{lang}_type_upos.pdf")79    save_chart(chart_sent_d_w_n, f"chart_{lang}_sent_d_w_n.pdf")80def parse_cli_args():81    args = filter(lambda txt: re.search(r"--\w+=.+", txt), sys.argv[1:])82    keys = lambda txt: txt[2:txt.index('=')]83    vals = lambda txt: txt[txt.index('=') + 1:]84    return dict((keys(v), vals(v)) for v in args)85def print_tree(tree):86    print(tree.metadata.get("text"), "\n")87    for node in tree.to_list():88        print("{:25}{}".format(node.token.get("form"), node.signature))89    print("-" * 100, "\n")90def main():91    uk_trees = get_token_trees(UK_DEV_PATH, UK_TRAIN_PATH, UK_TEST_PATH)92    # es_trees = get_token_trees(ES_DEV_PATH, ES_TRAIN_PATH, ES_TEST_PATH)93    args = parse_cli_args()...

graph_examples.py

Source:graph_examples.py

...15chart_count = 016if save_charts and not os.path.exists(dir_save):17    os.mkdir(dir_save)18    19def save_chart(fig):20    if save_charts:21        global chart_count22        chart_count += 123        plotly.io.write_image(fig, dir_save + str(chart_count) + ".svg")24# Make some fake data25def shoe_to_height(x):26    return x*x27shoe_size = np.random.standard_normal(size=10000)28height = shoe_to_height(shoe_size) + (np.random.rand(shoe_size.shape[0]) - 0.5) * 0.229hat_size = np.random.randint(0,5, size=shoe_size.shape[0])30hair_colour = np.tile(['blonde', 'brunette'], int(shoe_size.shape[0]/2))31is_male = np.random.randint(0, 2, size=shoe_size.shape[0])32data = {33    'shoe_size' : shoe_size,34    'person_height': height,35    'hat_size': hat_size,36    'hair_colour': hair_colour,37    'is_male': is_male38}39# Convert it into a table using pandas40dataset = pandas.DataFrame(data)41# Make example graphs.42def line_1_eq(x):43    return 0.02 * x ** 2 + 0.1 * x + 144def line_2_eq(x):45    return -0.02 * (x - 2) ** 2 + 0.1 * x + 246def line_3_eq(x):47    return -0.04 * (x + 2) ** 2 + 0.2 * x - 248# Plot single function49fig = graphing.line_2D(("line number one", line_1_eq), 50                x_range=[-10,10], 51                label_x="x-axis", 52                label_y="Goodness (cm)",53                show=show_charts)54save_chart(fig)55# Plot multiple functions56fig = graphing.line_2D([  ("line number one", line_1_eq), 57                    ("line number two", line_2_eq), 58                    ("line number three", line_3_eq)], 59                x_range=[-10,10], 60                label_x="x-axis", 61                label_y="Goodness (cm)",62                legend_title="Line number",63                title="Line plot", show=show_charts)64save_chart(fig)65def surf_eq(x, y):66    return x * 8 + y ** 267fig = graphing.surface(np.array([0,1]), np.linspace(-2,2), surf_eq, axis_title_x="X values", axis_title_y="Y Values", axis_title_z="Z values", show=show_charts)68save_chart(fig)69for n in [10,50,200,1000]:70    fig = graphing.scatter_2D(dataset[:n], title=f"A 2D scatter with {n} points", show=show_charts, trendline=shoe_to_height)71    save_chart(fig)72fig = graphing.scatter_3D(dataset, title="A 3D scatter", show=show_charts)73save_chart(fig)74fig = graphing.box_and_whisker(dataset, title="A simple box and whisker", show=show_charts)75save_chart(fig)76fig = graphing.box_and_whisker(dataset, label_x='hat_size', title="A box and whisker, 1 group level", show=show_charts)77save_chart(fig)78fig = graphing.box_and_whisker(dataset, label_x='hat_size', label_x2='hair_colour', title="A box and whisker, 2 group levels", show=show_charts)79save_chart(fig)80fig = graphing.histogram(dataset, title="A histogram (one variable)", show=show_charts)81save_chart(fig)82fig = graphing.histogram(dataset, label_x="hat_size", label_y="shoe_size", title="A histogram (two variables)", show=show_charts)83save_chart(fig)84fig = graphing.histogram(dataset, label_colour="hat_size", title="A stacked histogram", show=show_charts)85save_chart(fig)86fig = graphing.histogram(dataset, title="A histogram (one variable + boxplot)", include_boxplot=True, show=show_charts)87save_chart(fig)88fig = graphing.histogram(dataset, label_colour="hat_size", title="A stacked histogram (+ boxplot)", include_boxplot=True, show=show_charts)89save_chart(fig)90fig = graphing.multiple_histogram(dataset, label_x="hat_size", label_y="is_male", histfunc='avg', label_group="hair_colour", title="A histogram (two variables)", show=show_charts)91save_chart(fig)92fig = graphing.multiple_histogram(dataset, label_x="hat_size", label_group="hair_colour", title="A histogram (two variables)", show=show_charts)...

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