bobo | faceted search engine | Search Engine library

sudo git clone https://github.com/MycroftAI/mycroft-core.git
Cloning into 'mycroft-core'...
remote: Enumerating objects: 552097, done.
remote: Counting objects: 100% (90/90), done.
remote: Compressing objects: 100% (59/59), done.
remote: Total 552097 (delta 43), reused 64 (delta 31), pack-reused 552007
Receiving objects: 100% (552097/552097), 115.56
Resolving deltas: 100% (542625/542625), done.

window.addEventListener("load", function(event) {
   const data = {
      pepe:10,
      popo:11,
      bobo:12
    };

   const res = db.collection('Group').doc("I want to add timestamp here as document").set(data);
});

Mydict = {
'Red': {'Pesho': 1250000, 'Chicho': 101},
'Blue': {'Pesho': 10000, 'Gosho': 10000, 'Jecho': 260000},
'White': {'Bobo': 10, 'Bebo': 10, 'Bibo': 100, 'Bubo': 10}
}

(Red) Pesho <-> 1250000
(Blue) Jecho <-> 260000
(Blue) Pesho <-> 10000
(Blue) Gosho <-> 10000
(Red) Chicho <-> 101
(White) Bibo <-> 100
(White) Bobo <-> 10
(White) Bebo <-> 10
(White) Bubo <-> 10

sorted_Mydict = dict(sorted(Mydict.items(), key=lambda x: -len(x[1].values()))) #Making a new order by the count of the nested dictionarie values
pairs = [(color, name, power) for color, inner in sorted_Mydict.items() for name, power in inner.items()] #Then creating list of pairs, as suggested in the comments
[print(f"({color}) {name} <-> {power}") for color, name, power in sorted(pairs, key=lambda x: -x[2])] #Finally printing in the desired format, ordered by the int values

/Applications/Digify.app/Contents/Resources/app.asar/node_modules/execa/lib/error.js:59 Uncaught (in promise) Error: Command failed with exit code 1: /Applications/Digify.app/Contents/Resources/ffmpeg -i /Users/martinbarker/Downloads/Felix Lebarty - Bobo/01. Bobo.mp3 -y -filter_complex concat=n=1:v=0:a=1 -c:a libmp3lame -b:a 320k /Users/martinbarker/Downloads/Felix Lebarty - Bobo/output-064237.mp3
ffmpeg version N-101191-g51a9f487ae-ntd_20150128 Copyright (c) 2000-2021 the FFmpeg developers
  built with Apple LLVM version 10.0.1 (clang-1001.0.46.4)
  configuration: pkg_config='pkg-config --static' --pkg-config-flags=--static --libdir=/usr/local/lib --extra-version=ntd_20150128 --disable-shared --disable-lzma --enable-gpl --enable-pthreads --enable-nonfree --disable-libass --enable-libfdk-aac --enable-libmp3lame --enable-libx264 --enable-static --enable-filters --enable-runtime-cpudetect
  libavutil      56. 65.100 / 56. 65.100
  libavcodec     58.125.100 / 58.125.100
  libavformat    58. 68.100 / 58. 68.100
  libavdevice    58. 12.100 / 58. 12.100
  libavfilter     7.107.100 /  7.107.100
  libswscale      5.  8.100 /  5.  8.100
  libswresample   3.  8.100 /  3.  8.100
  libpostproc    55.  8.100 / 55.  8.100
[mp3 @ 0x7feeae809c00] Estimating duration from bitrate, this may be inaccurate
Input #0, mp3, from '/Users/martinbarker/Downloads/Felix Lebarty - Bobo/01. Bobo.mp3':
  Metadata:
    comment         : 
    album           : Bobo
    artist          : Felix Lebarty
    title           : Funkytown Eklablog
    genre           : Boogie
    date            : 1984
  Duration: 00:05:19.53, start: 0.000000, bitrate: 320 kb/s
  Stream #0:0: Audio: mp3, 44100 Hz, stereo, fltp, 320 kb/s
/Users/martinbarker/Downloads/Felix Lebarty - Bobo/output-064237.mp3: Operation not permitted
    at makeError (/Applications/Digify.app/Contents/Resources/app.asar/node_modules/execa/lib/error.js:59)
    at handlePromise (/Applications/Digify.app/Contents/Resources/app.asar/node_modules/execa/index.js:114)
    at async file:/Applications/Digify.app/Contents/Resources/app.asar/src/js/newindex.js:1162

interface exerciseCategory {
  [exerciseCategories: string]: string[];
}

interface muscleGroup {
  [muscleGroups: string]: exerciseCategory;
}

export default interface Program {
  [days: string]: muscleGroup;
}

const program: Program = {
  "Day 1": {
    Chest: {
      warmUp: ["parallel bar dips"],
      main: ["Bench Press", "Inclined Bench press", "Decline Bench press"],
      secondary: ["Dumbbell Flys", "Cable Crossover Flys", "Pec-deck Fly"],
    },
    Biceps: {
      Lola: ["Barbell Curl", "Preacher Curl"],
      bobo: ["Hammer Curls", "Cable Curl", "Dumbbell Curl"],
    },
  },
  "Day 2": {
    Shoulders: {
      coso: ["parallel bar dips"],
      coque: ["Bench Press", "Inclined Bench press", "Decline Bench press"],
      sdary: ["Dumbbell Flys", "Cable Crossover Flys", "Pec-deck Fly"],
    },
    Abs: {
      main: ["Crunch", "Leg raises"],
    },
  },
  "Day 3": {
    Back: {
      warmUp: ["parallel bar dips"],
      main: ["Bench Press", "Inclined Bench press", "Decline Bench press"],
      secondary: ["Dumbbell Flys", "Cable Crossover Flys", "Pec-deck Fly"],
    },
    Triceps: {
      main: ["Barbell Curl", "Preacher Curl"],
      secondary: ["Hammer Curls", "Cable Curl", "Dumbbell Curl"],
    },
  },
};



const days: Array<[string, object]> = Object.entries(program);

days[0][0] = 'New day name'

const newProgram:Program = Object.fromEntries(days)

{
  "Day 1" :{
    "Chest": {
        "warmUp": ["parallel bar dips"],
        "main": ["Bench Press", "Inclined Bench press", "Decline Bench press"],
        "secondary": ["Dumbbell Flys", "Cable Crossover Flys", "Pec-deck Fly"]
      },
      "Biceps" : {
      "Lola": ["Barbell Curl", "Preacher Curl"],
      "bobo": ["Hammer Curls", "Cable Curl", "Dumbbell Curl"]
    }
  }

  <h2>Day </h2>
    <h4>Chest</h4>
        <h5>warmUp</h5>
         <ul>
          <li>Bench Press</li>
          <li>Inclined Bench press</li>
          <li>Decline Bench press</li>
         </ul>
        <h5>secondary</h5>
          <ul>
            <li>Dumbbell Flys</li>
            <li>Cable Crossover Flys</li>
            <li>Pec-deck Fly</li>
           </ul>

Object.entries(program).map((arrays) => {
    return arrays.map((renderArr, render_idx) => {
      if (render_idx === 0) {
        return render.push(<h2>{renderArr}</h2>);
      } else {
        for (const [muscleGroup, exerciseGroups] of Object.entries(renderArr)) {
          render.push(<h4>{muscleGroup}</h4>);
          for (const exerciseCategory in exerciseGroups) {
            render.push(<h5>{exerciseCategory}</h5>);
            exerciseGroups[exerciseCategory].map(
              (exercise, exercise_idx) => {
                return render.push(<li>{exercise}</li>);
              }
            );
           ;
          }
        }
      }
    });
  });

 public class PojoToCsv {
    private String label;
    private Set<String> names;
    @JsonUnwrapped
    private List<Address> addresses;

    public PojoToCsv() {
    }

    
    public PojoToCsv(String label, Set<String> names, List<Address> addresses) {
        this.label = label;
        this.names = names;
        this.addresses = addresses;
    }
    //Setters & Getters

public class Address {
    private String zipCode;
    private String mail;
    private int vatNumber;

Address address1 = new Address("label1","name1",1);
        Address address2 = new Address("label2","name2",2);
        Address address3 = new Address("label3","name3",3);
        Address address4 = new Address("label4","name4",4);

        PojoToCsv pojoToCsv1 = new PojoToCsv("value1", Stream.of("baba","bobo").collect(Collectors.toSet()), Arrays.asList(address1,address2));
        PojoToCsv pojoToCsv2 = new PojoToCsv("value2", Stream.of("tata","toto").collect(Collectors.toSet()), Arrays.asList(address3,address4));

label,names,addresses
"value1",["baba","bobo"],[{"zipCode":"label1","mail":"name1","vatNumber":1},{"zipCode":"label2","mail":"name2","vatNumber":2}]
"value2",["tata","toto"],[{"zipCode":"label3","mail":"name3","vatNumber":3},{"zipCode":"label4","mail":"name4","vatNumber":4}]

        ObjectMapper mapper = new ObjectMapper();
        JsonNode node = mapper.convertValue(generatePojoList(), JsonNode.class);

        CsvSchema.Builder csvSchemaBuilder = CsvSchema.builder();
        JsonNode firstNode = node.elements().next();
        firstNode.fieldNames().forEachRemaining(name -> {csvSchemaBuilder.addColumn(name);});
        CsvSchema csvSchema = csvSchemaBuilder.build().withHeader();

        CsvMapper csvMapper = new CsvMapper();
        csvMapper.enable(CsvParser.Feature.WRAP_AS_ARRAY);

        csvMapper.writerFor(JsonNode.class)
                .with(csvSchema)
                .writeValue(new File("src/main/resources/export.csv"), node);

{
    "726623241984278589": {
        "322366672625467392": {
            "Moderation": "Kick",
            "Target": "Bobo#1601",
            "Moderator": "Bobosky#3914",
            "Reason": "Testing",
            "ID": "#XLR6RV8M"
        }
    }
}

const TargetMemberID = TargetMember.id

data[message.guild.id] = {
    TargetMemberID: {
        Moderation: 'Kick',
        Target: `${TargetMember.user.tag}`,
        Moderator: `${message.author.tag}`,
        Reason: `${Reason}`,
        ID: `#${ID}`
    }
}

fs.writeFile("./data.json", JSON.stringify(data), (err) => {
    if (err) console.log(err);
});

{
    "726623241984278589": {
        "TargetMemberID": {
            "Moderation": "Kick",
            "Target": "Bobo#1601",
            "Moderator": "Bobosky#3914",
            "Reason": "Testing",
            "ID": "#XLR6RV8M"
        }
    }
}

data[message.guild.id[TargetMember.id]]

###imports
from selenium.webdriver.common.keys import Keys
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import time
import csv
import pandas as pd


#driver path
driver = webdriver.Firefox(executable_path="D:\Programy\geckoDriver\geckodriver.exe")

#init + search
driver.get("https://pc.bazos.sk/pc/")
time.sleep(1)
nazov = driver.find_element_by_name("hledat")
nazov.send_keys("xeon")
cenamin = driver.find_element_by_name("cenaod")
cenamin.send_keys("")
cenamax = driver.find_element_by_name("cenado")
cenamax.send_keys("300")
driver.find_element_by_name("Submit").click()

##cookie acceptor
driver.find_element_by_xpath("/html/body/div[1]/button").click()

##main
x = 3
for i in range(x):
    try:
        main = WebDriverWait(driver, 7).until(
            EC.presence_of_element_located((By.XPATH, "/html/body/div[1]/table/tbody/tr/td[2]"))
        )
        
        ##find listings in table
        inzeraty = main.find_elements_by_class_name("vypis")
        for vypis in inzeraty:
            nadpis = vypis.find_element_by_class_name("nadpis")    
            ##print listings to check correctness
            nadpist = nadpis.text
            print(nadpist)
        
        ##find the price and print 
        for vypis in inzeraty:
            cena = vypis.find_element_by_class_name("cena")
            cenat = cena.text
            print(cenat)
        
        ##export to csv - not working
        time.sleep(1)
        print("Writing to csv")
        d = {"Nazov": [nadpist]*20*x,"Cena": [cenat]*20*x}
        df = pd.DataFrame(data=d)
        df.to_csv("bobo.csv")
        time.sleep(1)
        print("Writing to csv done !")
        
        ##next page
        dalsia = driver.find_element_by_link_text("Ďalšia")
        dalsia.click()
    except:
        driver.quit()