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How to use focussen method in SeleniumBase

Best Python code snippet using SeleniumBase

streamlit_labeler.py

Source:streamlit_labeler.py

1import streamlit as st2import pandas as pd3import numpy as np4import zipfile5from PIL import Image6import os7from random import randrange8import random9import math10st.title('Label tool')11processed_path = "./processed.pkl"12zips_path = "./zips"13print("start refresh")14if not os.path.exists(zips_path):15    raise FileNotFoundError("folder containing zip files not found!")16if not os.path.exists(processed_path):17    # csv's in dir18    csv_list = ["Selecteer"] + [i for i in os.listdir() if i.endswith(".csv")]19    if len(csv_list) == 1:20        st.write("Place the .csv file in the same directory as the python script")21    else:22        option = st.selectbox('Select .csv file to start', csv_list)23        if option != "Selecteer":24            st.write('Processing ', option, "this can take a while ...")25            df = pd.read_csv(option, sep=";", skiprows=8, nrows=28901, engine='python')  # python engine is nodig omdat de C engine de encoding van de file niet kan lezen26            df = df[df.columns[:-50]]  # Er worden 50 columns teveel ingelezen27            df.dropna(how="all", axis=1, inplace=True)  # Lege collumns verwijderen28            file_names = df["Camera4 : Hyperlink original picture filename"]29            # columns verwijderen30            df.drop("Cause sequence interrupt", axis=1, inplace=True)  # voorlopig verwijderen, later misschien one hot encoden31            for col in df.columns:32                # Columns met 1 unieke value verwijderen33                counts = pd.value_counts(df[col])34                if len(counts) == 1:35                    df.drop(col, axis=1, inplace=True)36                    continue37                # object columns (strings) omzetten naar float38                if df[col].dtype == "object":39                    # als er een komma is dit vervangen met een . en omzetten40                    if df[col].str.contains(',').all():41                        df[col] = df[col].str.replace(',', '.').astype("float32")42                    # Er is geen komma dus het is geen getal43                    else:44                        df.drop(col, axis=1, inplace=True)45                # integer omzetten naar float3246                elif df[col].dtype == "int64":47                    df[col] = df[col].astype("float32")48            df = df.fillna(0)  # Nan's vervangen met 049            df["file_names"] = file_names50            # voorlopig enkel focussen op camera 4, deze hoort het meest accuraat te zijn51            verkeerde_cameras = ["Camera1", "Camera2", "Camera3", "Camera5"]52            for col in df.columns:53                for camera in verkeerde_cameras:54                    if camera in col:55                        df.drop(col, axis=1, inplace=True)56                        continue57            df.drop("Sequence #", axis=1, inplace=True)58            df = df.set_index("file_names")59            df["Quality_label"] = np.nan60            df.to_pickle("processed.pkl")61            st.write("Processing done")62            st.button("Start labeling")63            print(df.shape)64else:65    def load_data(file_path):66        # data = pd.read_csv(file_path)67        data = pd.read_pickle(file_path)68        # data = data.set_index(["file_names"])69        return data70    @st.cache71    def get_available_images():72        available_zips = []73        for zip in os.listdir(zips_path):74            with zipfile.ZipFile(os.path.join(zips_path, zip), "r") as zip_data:75                available_zips += [i[:-12] for i in zip_data.namelist()]76        return available_zips77    # @st.cache78    def get_unlabled_row(data):79        # random beschikbare image selecteren80        # random.seed(0)81        # random_index = randrange(len(available_images))82        # index = available_images[random_index]83        # row = data.loc[index]84        # if not math.isnan(row["Quality_label"]):85        #     get_unlabled_row(data)86        selection = data.loc[available_images]87        non_labled = selection[selection["Quality_label"].isna()]88        random_index = randrange(len(non_labled))89        row = selection[selection["Quality_label"].isna()].iloc[random_index]90        return row91    @st.cache92    def get_zipped_image(file_name):93        full_name = file_name + "_AIimage.png"94        # find image in zips95        for zip in os.listdir(zips_path):96            with zipfile.ZipFile(os.path.join(zips_path, zip), "r") as zip_data:97                if full_name in zip_data.namelist():98                    imagePIL = Image.open(zip_data.open(full_name))  # Image data lezen uit de zip file99                    imagePIL = np.flip(imagePIL, 0)  # Image horizontaal spiegelen omdat Pil de image omgekeerd inleest100                    imagePIL = imagePIL[348:482]101                    return imagePIL102        raise Exception("Image is niet gevonden")103    data = load_data(processed_path)104    available_images = get_available_images()105    app_state = st.experimental_get_query_params()106    if "file_name" in app_state:107        row = data.loc[app_state["file_name"][0]]108    else:109        row = get_unlabled_row(data)110        st.experimental_set_query_params(file_name=row.name)111    image_filename = row.name112    if st.checkbox('Show raw data'):113        st.subheader('Raw data')114        # st.dataframe(data)115        aantal_gelabelde = len(data) - len(data[data["Quality_label"].isna()])116        st.write("Aantal gelabelde:", aantal_gelabelde)117        # st.dataframe(data[["MV Pressure (bar)", "RV4 off time (ms)", "file_names", "Quality_label"]])118        st.dataframe(row)119    st.write("file name:", image_filename)120    image = get_zipped_image(image_filename)121    st.image(image)122    start_pos = row["Quality_label"] if type(row["Quality_label"]) is np.nan else 5123    qual = st.slider("What's the quality score:", 0, 10, start_pos)124    if st.button('Confirm label'):125        row["Quality_label"] = qual126        # data.loc[image_filename, "Quality_label"] = qual127        data.loc[image_filename] = row128        print(data.loc[image_filename])129        print(data.loc[image_filename]["Quality_label"])130        data.to_pickle("./processed.pkl")131        st.write("Label:", qual, "confirmed")132        st.write('On to the next ...')133        st.experimental_set_query_params()134        st.experimental_rerun()135    if st.button("Export to .csv"):136        data.to_csv("./processed_labels.csv", index=True)...

acsadviesjargon.py

Source:acsadviesjargon.py

1import random2items1 = [3    "Human capital",4    "De factor mens",5    "Het management",6    "De communicatie",7    "De kerncompetentie",8    "De organisatie-ontwikkeling",9    "De missie",10    "Kennismanagement",11    "De eerste aanzet",12    "Human capital",13    "Globalisatie",14    "Knowledge management",15    "Management",16    "The mission",17    "New business development",18    "Process management",19    "The human factor",20    "ICT",21    "Verduurzaming",22    "Het klimaatbeleid",23]24items2 = [25    "moet meerwaarde leveren bij",26    "stelt eisen aan",27    "dient te faciliteren bij",28    "is uitgangspunt bij",29    "is onlosmakelijk verbonden met",30    "schept voorwaarden voor",31    "dient te focussen op",32    "stuurt",33    "hangt nauw samen met",34    "moet een opstap bieden voor",35    "moet een kader bieden voor",36    "zet doelstellingen voor",37    "geeft een nieuw momentum voor",38]39items3 = [40    "de implementatie van",41    "de terugkoppeling van",42    "het aftimmeren van",43    "het aansturen van",44    "de ontwikkeling van",45    "de flexibilisering van",46    "de integratie van",47    "de inventarisatie van",48    "de definitie van",49    "de insteek van",50    "de inbedding",51    "het design van",52    "de marketing van",53    "de herstructurering van",54    "de marktpenetratie van",55    "het groeiproces van",56]57items4 = [58    "optimale",59    "in elkaar grijpende",60    "eenduidige onderling afhankelijke",61    "structurele",62    "pro-actieve",63    "resultaatgerichte",64    "efficiÃ«nte",65    "self learning",66    "consistente",67    "individuele",68    "highly developped",69    "duurzame",70    "klimaatneutrale",71]72items5 = [73    "supply chain processen",74    "business architecture",75    "mijlpalen",76    "targets",77    "business units",78    "organisatie-onderdelen",79    "scenario's",80    "best practices",81    "business models",82    "conceptplannen",83    "organizational units",84    "milestones",85    "supply chain",86    "systems",87    "product concepts",88    "human resource",89]90items6 = [91    "waarbij het belang van",92    "waarbij de feedback van",93    "waarbij het kader voor",94    "waarbij afstemming met",95    "waarbij de structuur van",96    "waarbij de synergie met",97    "waarbij de interface met",98    "waarbij input van",99    "waarbij commitment van",100    "waarbij klankborden met",101    "waarbij inbedding van",102]103items7 = [104    "strategisch beleid",105    "de taskforce",106    "de communicatie",107    "de werkgroepen",108    "new business",109    "development",110    "de systeemintegratie",111    "de markt",112    "de stakeholders",113    "het management",114    "de projectorganisatie",115    "management",116    "core business",117    "task force",118    "feedback",119    "niche marketing",120    "strategic partnership",121    "cutting edge solutions",122    "de holding",123    "de aandeelhouders",124    "de commisarissen",125    "de directie",126    "duurzame ontwikkeling",127    "persoonljke ontwikkeling",128    "klimaatneutraal beleid",129    "de vermindering van CO2-uitstoot",130    "de vermindering van stiksofuitstoot",131    "eye-openers",132]133items8 = [134    "moet uitkristalliseren",135    "voorop staat",136    "centraal staat",137    "wordt aangestuurd",138    "leading is",139    "toegevoegde waarde levert",140    "win-win situaties creÃ«ert",141    "moet worden gemanaged",142    "voldoende draagvlak heeft",143    "doorslaggevend is",144    "essentieel is",145    "gewaarborgd is",146    "cruciaal is",147    "nieuwe mogelijkheden biedt",148    "een nieuwe standaard zet",149    "vanzelfsprekend is",150    "een blijvende voorsprong geeft",151    "in het bedrijf wordt ingebed",152]153def random_item(items):154    random.seed()155    return items[random.randint(0, len(items)-1)]156def report_adviesjargon(reply_to):157    reply = (158        "cc @" + reply_to + "\n\n" +159        random_item(items1) + " " +160        random_item(items2) + " " +161        random_item(items3) + " " +162        random_item(items4) + " " +163        random_item(items5) + " " +164        random_item(items6) + " " +165        random_item(items7) + " " +166        random_item(items8) + "." +167        " #adviesjargon"168    )...

oef4.py

Source:oef4.py

1import matplotlib.pyplot as plt2import pandas as pd3import seaborn as sns4df = pd.read_csv("student-mat.csv", header=0, delimiter=";")5print(df.head())6# Hoeveel studenten steken in de dataset?7print("Er zitten ", df.sex.count(), "studenten")8# Gebruik de describe-functie om een overzicht te bekomen van de variabelen in de dataset9print(df.describe())10# Bepaal het geslacht en de leeftijd van de vijf studenten met de beste score11highest_five = df.nlargest(5, "G3")12print(highest_five[["sex", "age", "G3"]])13# Gebruik een countplot om een staafdiagramma met de leeftijden weer te geven14# sns.countplot(x="age", data=df)15# We focussen ons nu op de leeftijd van de studenten.16ages = df['age']17# Bereken de volgende centrummaten en de spreidingsmaten18# -Het gemiddelde19print("Gemiddelde: ", ages.mean())20# -De mediaan21print("Median: ", ages.median())22# -Variantie23print("Variance: ", ages.var())24# -Standaardafwijking25print("Standard: ", ages.std())26# -De range27print("Range: ", ages.max() - ages.min())28# -Q1 en Q3 en bereken hieruit de interkwartielafstand29print("IQR: ", ages.quantile(0.75) - ages.quantile(0.25))30# -scheefheid31print("Skew: ", ages.skew())32# -De kurtosis33print("Skew: ", ages.kurt())34# Bepaal een boxplot van de leeftijd(voorbeeld zie onder)35# sns.boxplot(x=df["age"])36# Kan je ook een onderscheid maken tussen mannen en vrouwen?37sns.boxplot(x=df["sex"], y=df["age"])...

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