├── src ├── __init__.py ├── utils │ ├── __init__.py │ └── utils.py ├── data_ingestion.py ├── data_prep.py ├── model_evaluation.py ├── anomaly_detection.py └── model_training.py ├── .gitignore ├── data ├── models │ └── lstm_model.keras ├── raw │ └── google_trends_data.csv └── processed │ └── processed_trends_data.csv ├── scripts ├── pipeline.sh └── flv_setup.sh ├── requirements.txt ├── app.py └── README.md /src/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /src/utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | venv/ 2 | .venv/ 3 | __pycache__ 4 | .env 5 | *.db 6 | -------------------------------------------------------------------------------- /data/models/lstm_model.keras: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/FaycalZM/Viro-Scope/HEAD/data/models/lstm_model.keras -------------------------------------------------------------------------------- /scripts/pipeline.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | FLASK_SERVER_SCRIPT="app.py" 4 | DATA_PREP="src/data_prep.py" 5 | MODEL_TRAINING="src/model_training.py" 6 | ANOMALY_DETECTION="src/anomaly_detection.py" 7 | 8 | # start flask server in the background 9 | nohup python3 $FLASK_SERVER_SCRIPT & 10 | 11 | # wait for flask server to start 12 | sleep 5 13 | 14 | # run data preparation script in the backgrround 15 | nohup python3 $DATA_PREP & 16 | 17 | # run model training script in the background 18 | nohup python3 $MODEL_TRAINING & 19 | 20 | # run anomaly detection script 21 | python3 $ANOMALY_DETECTION -------------------------------------------------------------------------------- /src/utils/utils.py: -------------------------------------------------------------------------------- 1 | from keras._tf_keras.keras.models import load_model 2 | import os 3 | 4 | 5 | BUFFER_MAX_SIZE = 16304 6 | 7 | 8 | def data_chunking(data_json: str) -> list[str]: 9 | # the buffer max_size is 16384 bytes, so it's not possible to send the data directly -> divide and conquer 10 | # convert string data to bytes 11 | byte_data = data_json.encode('utf-8') 12 | 13 | # split the bytes data into chunks of chunk_size bytes 14 | chunk_size = BUFFER_MAX_SIZE 15 | byte_chunks = [byte_data[i:i + chunk_size] 16 | for i in range(0, len(byte_data), chunk_size)] 17 | 18 | # convert each chunk back to string 19 | string_chunks = [chunk.decode('utf-8', errors='ignore') 20 | for chunk in byte_chunks] 21 | 22 | return string_chunks 23 | 24 | # Split the dataset into training and testing sets 25 | 26 | 27 | def split_data(data, train_size=0.8): 28 | train_len = int(len(data) * train_size) 29 | train_data = data[:train_len] 30 | test_data = data[train_len:] 31 | return train_data, test_data 32 | 33 | 34 | def load_lstm_model(model_name='lstm_model.keras'): 35 | model_path = os.path.join('data', 'models', model_name) 36 | model = load_model(model_path) 37 | return model 38 | -------------------------------------------------------------------------------- /src/data_ingestion.py: -------------------------------------------------------------------------------- 1 | from pytrends.request import TrendReq 2 | import pandas as pd 3 | from dateutil import parser 4 | import datetime 5 | import os 6 | 7 | 8 | def fetch_google_trends_data(keywords, timeframe='today 5-y', geo='', category=0) -> pd.DataFrame: 9 | pytrends = TrendReq(hl='en-US') 10 | pytrends.build_payload(keywords, cat=category, 11 | timeframe=timeframe, geo=geo, gprop='') 12 | 13 | # Get interest overtime 14 | data = pytrends.interest_over_time() 15 | if 'isPartial' in data.columns: 16 | data = data.drop(columns=['isPartial']) 17 | 18 | daily_data = data.resample('D').interpolate(method='linear') 19 | 20 | return daily_data 21 | 22 | 23 | def save_raw_data(data: pd.DataFrame, file_name='google_trends_data.csv'): 24 | raw_data_dir = os.path.join('data', 'raw') 25 | os.makedirs(raw_data_dir, exist_ok=True) 26 | file_path = os.path.join(raw_data_dir, file_name) 27 | data.to_csv(file_path) 28 | print(f"Data saved to {file_path}") 29 | 30 | 31 | def load_raw_data(file_name='google_trends_data.csv'): 32 | file_path = os.path.join('data', 'raw', file_name) 33 | return pd.read_csv(file_path, parse_dates=True, index_col='date') 34 | 35 | 36 | if __name__ == "__main__": 37 | keywords = ["flu"] 38 | data = fetch_google_trends_data(keywords) 39 | save_raw_data(data) 40 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==2.1.0 2 | APScheduler==3.10.4 3 | astunparse==1.6.3 4 | blinker==1.8.2 5 | certifi==2024.7.4 6 | charset-normalizer==3.3.2 7 | click==8.1.7 8 | contourpy==1.2.1 9 | cycler==0.12.1 10 | Flask==3.0.3 11 | flatbuffers==24.3.25 12 | fluvio==0.16.3 13 | fonttools==4.53.1 14 | gast==0.6.0 15 | google-pasta==0.2.0 16 | grpcio==1.66.0 17 | h5py==3.11.0 18 | idna==3.7 19 | itsdangerous==2.2.0 20 | Jinja2==3.1.4 21 | joblib==1.4.2 22 | keras==3.5.0 23 | kiwisolver==1.4.5 24 | libclang==18.1.1 25 | lxml==5.3.0 26 | Markdown==3.7 27 | markdown-it-py==3.0.0 28 | MarkupSafe==2.1.5 29 | matplotlib==3.9.2 30 | mdurl==0.1.2 31 | ml-dtypes==0.4.0 32 | namex==0.0.8 33 | numpy==1.26.4 34 | opt-einsum==3.3.0 35 | optree==0.12.1 36 | packaging==24.1 37 | pandas==2.2.2 38 | pillow==10.4.0 39 | protobuf==4.25.4 40 | Pygments==2.18.0 41 | pyparsing==3.1.2 42 | PyQt5==5.15.11 43 | PyQt5-Qt5==5.15.14 44 | PyQt5_sip==12.15.0 45 | python-dateutil==2.9.0.post0 46 | pytrends==4.9.2 47 | pytz==2024.1 48 | requests==2.32.3 49 | rich==13.7.1 50 | scikit-learn==1.5.1 51 | scipy==1.14.1 52 | setuptools==73.0.1 53 | six==1.16.0 54 | tensorboard==2.17.1 55 | tensorboard-data-server==0.7.2 56 | tensorflow==2.17.0 57 | termcolor==2.4.0 58 | threadpoolctl==3.5.0 59 | typing_extensions==4.12.2 60 | tzdata==2024.1 61 | tzlocal==5.2 62 | urllib3==2.2.2 63 | Werkzeug==3.0.4 64 | wheel==0.44.0 65 | wrapt==1.16.0 66 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | from flask import Flask 2 | from apscheduler.schedulers.background import BackgroundScheduler 3 | from fluvio import Fluvio 4 | from src.data_ingestion import fetch_google_trends_data, save_raw_data 5 | from src.utils.utils import data_chunking 6 | import os 7 | 8 | 9 | ''' 10 | This is a custom connector that fetches 11 | google trends data and integrates it 12 | into our Fluvio data pipeline 13 | ''' 14 | 15 | 16 | TOPIC_NAME = "google-trends-data" 17 | 18 | 19 | app = Flask(__name__) 20 | 21 | # fluvio initialization 22 | fluvio = Fluvio.connect() 23 | # use CLI to create the topic (fluvio python sdk does not support topics creation yet) 24 | # os.popen('fluvio topic create google-trends-data') 25 | producer = fluvio.topic_producer(TOPIC_NAME) 26 | 27 | 28 | # fetch and push google trends data to the topic 29 | def fetch_and_push_google_trends_data(): 30 | keywords = ["flu"] 31 | data = fetch_google_trends_data(keywords) 32 | # save_raw_data(data) 33 | 34 | if not data.empty: 35 | # convert the trends data to json string 36 | data_json = data.reset_index().to_json(orient='records', date_format="iso") 37 | 38 | string_chunks = data_chunking(data_json) 39 | 40 | for chunk in string_chunks: 41 | # push data to the topic 42 | producer.send_string(chunk) 43 | # flush the last entry 44 | producer.flush() 45 | producer.send_string("done") 46 | print(f"Data pushed to {TOPIC_NAME} topic") 47 | 48 | 49 | # now we schedule the fetch_and_push_google_trends_data function to run every 24 hours 50 | scheduler = BackgroundScheduler() 51 | scheduler.add_job(func=fetch_and_push_google_trends_data, 52 | trigger="interval", hours=24) 53 | scheduler.start() 54 | 55 | 56 | # route for manual data ingestion 57 | @app.route("/fetch-trends", methods=["GET"]) 58 | def fetch_google_trends(): 59 | fetch_and_push_google_trends_data() 60 | return "Trends fetched and pushed to Fluvio successfully" 61 | 62 | 63 | if __name__ == "__main__": 64 | app.run(host='0.0.0.0', port=5000, debug=True) 65 | 66 | 67 | # shut the scheduler down when on app exit 68 | @app.teardown_appcontext 69 | def shutdown_scheduler(exception=None): 70 | if scheduler.running: 71 | scheduler.shutdown() 72 | -------------------------------------------------------------------------------- /scripts/flv_setup.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # exit immediately if a command exits with a non-zero status 4 | set -e 5 | 6 | # list of topics to create 7 | TOPICS=( 8 | "google-trends-data" 9 | "preprocessed-trends-data" 10 | "predictions-data" 11 | "anomalies-data" 12 | ) 13 | 14 | # topics configuration goes here 15 | # retention-time = 1 day means the topic data will be deleted after 1 day 16 | declare -A TOPICS_CONFIG 17 | TOPICS_CONFIG["google-trends-data"]="--retention-time 1d" 18 | TOPICS_CONFIG["preprocessed-trends-data"]="--retention-time 1d" 19 | TOPICS_CONFIG["predictions-data"]="--retention-time 1d" 20 | TOPICS_CONFIG["anomalies-data"]="--retention-time 1d" 21 | 22 | 23 | install_fluvio_cli() { 24 | echo "Checking for Fluvio CLI installation..." 25 | if ! command -v fluvio &> /dev/null; then 26 | echo "Fluvio not found. Installing Fluvio CLI..." 27 | curl -fsS https://hub.infinyon.cloud/install/install.sh | bash 28 | # add fluvio CLI to PATH 29 | echo 'export PATH="${HOME}/.fvm/bin:${HOME}/.fluvio/bin:${PATH}"' >> ~/.bashrc 30 | echo 'source "${HOME}/.fvm/env"' >> ~/.bashrc 31 | # Source the profile to update PATH for the current session 32 | source ~/.profile || source ~/.bashrc || source ~/.zshrc 33 | echo "Fluvio CLI installed successfully." 34 | else 35 | echo "FLuvio CLI already installed." 36 | fi 37 | } 38 | 39 | start_fluvio_cluster(){ 40 | echo "Starting Fluvio cluster..." 41 | fluvio cluster start 42 | echo "Fluvio cluster started successfully." 43 | echo "Checking cluster status..." 44 | fluvio cluster status 45 | } 46 | 47 | 48 | create_fluvio_topic(){ 49 | local topic=$1 50 | local config=${TOPICS_CONFIG[$topic]} 51 | 52 | echo "Creating topic: $topic..." 53 | if fluvio topic list | grep -q "^$topic$"; then 54 | echo "Topic '$topic' already exists. Skipping creation." 55 | else 56 | if [ -z "$config" ]; then 57 | fluvio topic create "$topic" 58 | else 59 | fluvio topic create "$topic" $config 60 | fi 61 | fi 62 | } 63 | 64 | create_all_topics() { 65 | echo "Creating all topics..." 66 | for topic in "${TOPICS[@]}"; do 67 | create_fluvio_topic "$topic" 68 | done 69 | echo "All topics created successfully." 70 | } 71 | 72 | list_all_topics(){ 73 | echo "Listing all FLuvio topics..." 74 | fluvio topic list 75 | } 76 | 77 | main() { 78 | install_fluvio_cli 79 | start_fluvio_cluster 80 | create_all_topics 81 | list_all_topics 82 | echo "FLuvio setup completed successfully." 83 | } 84 | 85 | 86 | main 87 | 88 | -------------------------------------------------------------------------------- /src/data_prep.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from sklearn.preprocessing import MinMaxScaler 3 | import os 4 | from io import StringIO 5 | from fluvio import Fluvio, Offset 6 | from data_ingestion import load_raw_data 7 | from utils.utils import data_chunking 8 | 9 | 10 | CONSUMER_TOPIC = "google-trends-data" 11 | PRODUCER_TOPIC = "preprocessed-trends-data" 12 | 13 | 14 | def preprocess_data(data: pd.DataFrame): 15 | # Fill missing values 16 | data = data.fillna(method='ffill').fillna(method='bfill') 17 | 18 | # Scale data (Min-Max scaling) 19 | scaled_data = (data - data.min()) / (data.max() - data.min()) 20 | 21 | return scaled_data 22 | 23 | 24 | def save_preprocessed_data(data: pd.DataFrame, file_name='processed_trends_data.csv'): 25 | processed_data_dir = os.path.join('data', 'processed') 26 | os.makedirs(processed_data_dir, exist_ok=True) 27 | file_path = os.path.join(processed_data_dir, file_name) 28 | data['flu'].to_csv(file_path) 29 | print(f"Processed data saved to {file_path}") 30 | 31 | 32 | def load_processed_data(file_name='processed_trends_data.csv'): 33 | file_path = os.path.join('data', 'processed', file_name) 34 | return pd.read_csv(file_path, parse_dates=True, index_col='date') 35 | 36 | 37 | if __name__ == "__main__": 38 | fluvio = Fluvio.connect() 39 | consumer = fluvio.partition_consumer(CONSUMER_TOPIC, 0) 40 | producer = fluvio.topic_producer(PRODUCER_TOPIC) 41 | 42 | raw_data_json = "" 43 | stream = consumer.stream(Offset.from_beginning(0)) 44 | # continuous streaming 45 | for record in stream: 46 | if record.value_string() == "done": 47 | # convert json back to DataFrame 48 | data = pd.read_json(StringIO(raw_data_json), orient='records') 49 | # preprocess raw data 50 | data.set_index('date', inplace=True) 51 | processed_data = preprocess_data(data) 52 | save_preprocessed_data(processed_data) 53 | # send it to the corresponding topic 54 | processed_data_json = processed_data.reset_index().to_json( 55 | orient='records', date_format="iso") 56 | 57 | string_chunks = data_chunking(processed_data_json) 58 | for chunk in string_chunks: 59 | # push data to the topic 60 | producer.send_string(chunk) 61 | # flush the last entry 62 | producer.flush() 63 | producer.send_string("done") 64 | print(f"Preprocessed data pushed to {PRODUCER_TOPIC} topic") 65 | 66 | # reset raw_data_json 67 | raw_data_json = "" 68 | else: 69 | raw_data_json += record.value_string() 70 | -------------------------------------------------------------------------------- /src/model_evaluation.py: -------------------------------------------------------------------------------- 1 | from utils.utils import split_data, load_lstm_model 2 | from data_prep import load_processed_data 3 | from sklearn.metrics import mean_absolute_error, mean_squared_error 4 | import pandas as pd 5 | import numpy as np 6 | import matplotlib 7 | import matplotlib.pyplot as plt 8 | matplotlib.use('Qt5Agg') 9 | 10 | 11 | def evaluate_model_performance(model, test_data, window_size): 12 | """ 13 | Evaluate the model's performance on the test set. 14 | 15 | Parameters: 16 | - model: Trained LSTM model. 17 | - test_data: Test data for evaluation. 18 | - window_size: The number of previous time steps used as input for prediction. 19 | 20 | Returns: 21 | - predictions: Predicted values for the test set. 22 | - Y_test: Actual values for the test set. 23 | - mse: Mean Squared Error of the predictions. 24 | - mae: Mean Absolute Error of the predictions. 25 | """ 26 | 27 | test_data = test_data.values if hasattr( 28 | test_data, 'values') else np.array(test_data) 29 | 30 | # Prepare test data for prediction 31 | X_test, Y_test = [], [] 32 | for i in range(window_size, len(test_data)): 33 | X_test.append(test_data[i-window_size:i, 0]) 34 | Y_test.append(test_data[i, 0]) 35 | 36 | X_test = np.array(X_test).reshape(-1, window_size, 1) 37 | Y_test = np.array(Y_test) 38 | 39 | predictions = model.predict(X_test) 40 | 41 | # Calculate evaluation metrics 42 | mse = mean_squared_error(Y_test, predictions) 43 | mae = mean_absolute_error(Y_test, predictions) 44 | 45 | return predictions, Y_test, mse, mae 46 | 47 | 48 | def plot_predictions(dates, predictions, actual): 49 | """ 50 | Plot the model's predictions against the actual values. 51 | 52 | Parameters: 53 | - dates: The corresponding dates for the test set. 54 | - predictions: Predicted values for the test set. 55 | - actual: Actual values for the test set. 56 | """ 57 | # Adjust the predictions and actual data to align with the original dataset 58 | plt.figure(figsize=(14, 7)) 59 | 60 | plt.plot(dates, actual, label='Actual Data', color='blue') 61 | plt.plot(dates, predictions, label='Predictions', 62 | color='red', linestyle='dashed') 63 | 64 | plt.title('Actual vs Predicted Search Volume') 65 | plt.xlabel('Date') 66 | plt.ylabel('Search Volume') 67 | plt.legend() 68 | plt.grid(True) 69 | plt.show() 70 | 71 | 72 | if __name__ == "__main__": 73 | data = load_processed_data() 74 | train_data, test_data = split_data(data) 75 | window_size = 60 76 | dates = test_data.index[window_size:] 77 | 78 | model = load_lstm_model(model_name='lstm_model.keras') 79 | predictions, actual, mse, mae = evaluate_model_performance( 80 | model, test_data, window_size) 81 | 82 | plot_predictions(dates, predictions, actual) 83 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ViroScope - Global Disease Outbreak Monitoring System 🧑‍⚕️📈 2 | ## Overview 3 | ViroScope is a cutting-edge, real-time data pipeline designed to track and analyze global health data, providing early warnings of potential disease outbreaks. By leveraging public health-related data, AI-powered data processing, and [**Fluvio**](https://www.fluvio.io/)'s real-time data streaming capabilities, this system offers a comprehensive solution for monitoring, detecting, and responding to emerging health threats on a global scale. 4 | 5 | [![Quira Vote](https://img.shields.io/badge/Quira-View%20Repo-blue)](https://quira.sh/repo/FaycalZM-Viro-Scope-846580250?utm_source=copy&utm_share_context=quests_creators) 6 | 7 | ## Table of Contents 📑 8 | - [Features](#features) 9 | - [Technology Stack](#technology_stack) 10 | - [YouTube Demonstration](#youtube-demonstration) 11 | - [Requirements](#requirements) 12 | - [Installation](#installation) 13 | - [Usage](#usage) 14 | - [Contribution](#contribution) 15 | - [Support](#support) 16 | ## Features 🌟 17 | - Real-time data processing pipeline using Fluvio CLI and FLuvio' python SDK, and Time Series Forecasting using LSTM model 18 | - Predictions analysis & Anomaly detection: identify potential health outbreaks by understanding future trends of disease-related searches 19 | - Model evaluation & visualization 20 | ## Technology Stack ⚙️ 21 | - python with flask framework 22 | - fluvio CLI & fluvio python SDK 23 | - pandas, numpy for data preprocessing 24 | - scikit-learn, keras for model creation and training 25 | - matplotlib for visualization 26 | ## YouTube Demonstration 📹 27 | [Watch the video](https://www.youtube.com/watch?v=w4fCBKc0mjA) 28 | ## Requirements 🧰 29 | - Python 3.7 or higher 30 | - Rust compiler (for fluvio) 31 | ## Installation ⏳ 32 | 1. **Clone the repository**: 33 | ```bash 34 | git clone https://github.com/FaycalZM/Viro-Scope.git 35 | cd Viro-Scope 36 | ``` 37 | 38 | 2. **Create virtual env & activate it & Install required packages**: 39 | ```bash 40 | python3 -m venv venv 41 | source venv/bin/activate 42 | pip install -r requirements.txt 43 | ``` 44 | 45 | 3. **Set up Fluvio**: 46 | - run the *flv_setup.sh* in the scripts directory: 47 | ```bash 48 | ./scripts/flv_setup.sh 49 | ``` 50 | 51 | ## Usage 🌎 52 | 1. Run the *pipeline.sh* script in the scripts directory to start the pipeline execution, and wait for magic to happen: 53 | ```bash 54 | ./scripts/pipeline.sh 55 | ``` 56 | 57 | 2. Data is fetched every 24 hours, but you can trigger the pipeline execution manually by sending a GET request to http://localhost:5000/fetch-trends : 58 | ```bash 59 | curl http://localhost:5000/fetch-trends 60 | ``` 61 | 62 | 3. Run the *model_evaluation.py* script separately for additional informations: 63 | ```bash 64 | python src/model_evaluation.py 65 | ``` 66 | ## Contributing 🏗️ 67 | 1. **Fork the Repository:** 68 | - Create a fork of the repository. 69 | 2. **Open a Pull Request:** 70 | - Open a pull request from your forked repository to the main repository. 71 | ## Support 👍 72 | If you like this project, please support it by upvoting on Quira and starring the GitHub repository! 73 | 74 | [![Quira Repo](https://img.shields.io/badge/Quira-View%20Repo-blue)](https://quira.sh/repo/FaycalZM-Viro-Scope-846580250?utm_source=copy&utm_share_context=quests_creators) 75 | 76 | Thank you for your support! -------------------------------------------------------------------------------- /src/anomaly_detection.py: -------------------------------------------------------------------------------- 1 | from io import StringIO 2 | import json 3 | from model_training import make_predictions, split_data, WINDOW_SIZE 4 | # from model_evaluation import plot_predictions 5 | from data_prep import load_processed_data 6 | from utils.utils import load_lstm_model 7 | from fluvio import Fluvio, Offset 8 | import numpy as np 9 | import pandas as pd 10 | import matplotlib 11 | import matplotlib.pyplot as plt 12 | matplotlib.use('Qt5Agg') 13 | 14 | 15 | CONSUMER_TOPIC = "predictions-data" 16 | PRODUCER_TOPIC = "anomalies-data" 17 | 18 | 19 | def detect_anomalies(actual, predictions, threshold=2): 20 | """ 21 | Detect anomalies in a time series by comparing the residuals of predicted values with the actual values. 22 | 23 | Parameters: 24 | - actual: The actual values from the test set. 25 | - predictions: The predicted values by the model. 26 | - threshold: The number of standard deviations above the mean above which a value is considered an anomaly. Defaults to 2. 27 | 28 | Returns: 29 | - anomalies: A boolean array indicating whether each value is an anomaly. 30 | - residuals: The absolute difference between each actual value and its predicted value. 31 | - threshold: The number of standard deviations above the mean used to detect anomalies. 32 | 33 | """ 34 | residuals = np.abs(actual - predictions) 35 | mean_residual = np.mean(residuals) 36 | std_residual = np.std(residuals) 37 | 38 | anomalies = (residuals > mean_residual + std_residual * threshold) 39 | 40 | return anomalies, residuals, threshold 41 | 42 | 43 | def plot_anomalies(dates, actual, predictions, anomalies): 44 | """ 45 | Plot the actual data, predictions, and highlight anomalies. 46 | 47 | Parameters: 48 | - dates: The corresponding dates for the actual data. 49 | - actual: The actual values from the test set. 50 | - predictions: The predicted values by the model. 51 | - anomalies: A DataFrame of detected anomalies. 52 | """ 53 | plt.figure(figsize=(15, 8)) 54 | 55 | # Plot actual data and predictions 56 | plt.plot(dates, actual, label='Actual Data', color='blue') 57 | plt.plot(dates, predictions, label='Predictions', 58 | color='red', linestyle='dashed') 59 | 60 | # Highlight anomalies 61 | # Convert dates and actual to numpy arrays if they aren't already 62 | dates = np.array(dates) 63 | actual = np.array(actual) 64 | 65 | # Extract the anomalies indices from the anomalies dataframe 66 | anomalies_indices = np.where(anomalies)[0] 67 | 68 | # Plot the anomalies 69 | plt.scatter(dates[anomalies_indices], actual[anomalies_indices], 70 | color='orange', label='Anomalies', s=75, edgecolor='black') 71 | 72 | plt.title('Actual Data vs Predictions with Anomalies') 73 | plt.xlabel('Date') 74 | plt.ylabel('Search Volume') 75 | plt.legend() 76 | plt.grid(True) 77 | plt.show() 78 | 79 | 80 | if __name__ == "__main__": 81 | 82 | # data = load_processed_data() 83 | # train_data, test_data = split_data(data) 84 | # window_size = WINDOW_SIZE 85 | # model = load_lstm_model(model_name='lstm_model.keras') 86 | # predictions = make_predictions(model, test_data, window_size) 87 | # print(predictions) 88 | # dates = test_data.index[window_size:] 89 | # actual = test_data[window_size:] 90 | # anomalies, residuals, threshold = detect_anomalies( 91 | # actual, predictions, threshold=2.0) 92 | # plot_anomalies(dates, actual, predictions, anomalies) 93 | 94 | fluvio = Fluvio.connect() 95 | consumer = fluvio.partition_consumer(CONSUMER_TOPIC, 0) 96 | producer = fluvio.topic_producer(PRODUCER_TOPIC) 97 | 98 | evaluation_data_json = "" 99 | stream = consumer.stream(Offset.from_beginning(0)) 100 | # continuous streaming 101 | for record in stream: 102 | if record.value_string() == "done": 103 | # load the model 104 | model = load_lstm_model(model_name='lstm_model.keras') 105 | window_size = WINDOW_SIZE 106 | # convert string to json object 107 | evaluation_data_obj = json.loads(evaluation_data_json) 108 | # get predictions and actual data 109 | predictions = json.loads(evaluation_data_obj['predictions']) 110 | predictions = np.array(predictions) 111 | 112 | actual = evaluation_data_obj['actual'] 113 | actual_df = pd.read_json(StringIO(actual), orient='records') 114 | actual_df.set_index('date', inplace=True) 115 | if 'index' in actual_df.columns: 116 | actual_df = actual_df.drop(columns='index') 117 | actual = actual_df[window_size:] 118 | 119 | # detect anomalies 120 | anomalies, residuals, threshold = detect_anomalies( 121 | actual, predictions, threshold=2.0) 122 | 123 | # # plot anomalies 124 | dates = actual_df.index[window_size:] 125 | plot_anomalies(dates, actual, predictions, anomalies) 126 | 127 | else: 128 | evaluation_data_json += record.value_string() 129 | -------------------------------------------------------------------------------- /src/model_training.py: -------------------------------------------------------------------------------- 1 | from data_prep import load_processed_data 2 | from model_evaluation import evaluate_model_performance, plot_predictions 3 | import os 4 | import json 5 | from keras.src.layers import Dense, LSTM, Dropout 6 | from keras.src.models.sequential import Sequential 7 | from keras.src.optimizers import Adam 8 | from keras._tf_keras.keras.models import load_model 9 | from fluvio import Fluvio, Offset 10 | import numpy as np 11 | import pandas as pd 12 | import tensorflow as tf 13 | import matplotlib 14 | import matplotlib.pyplot as plt 15 | from io import StringIO 16 | from utils.utils import data_chunking, split_data, load_lstm_model 17 | matplotlib.use('Qt5Agg') 18 | 19 | 20 | WINDOW_SIZE = 60 21 | CONSUMER_TOPIC = "preprocessed-trends-data" 22 | PRODUCER_TOPIC = "predictions-data" 23 | 24 | 25 | # Build and compile the LSTM model 26 | def create_lstm_model(input_shape): 27 | model = Sequential() 28 | model.add(LSTM(100, return_sequences=True, input_shape=input_shape)) 29 | model.add(LSTM(50, return_sequences=False)) 30 | model.add(Dense(25)) 31 | model.add(Dense(1)) 32 | model.add(Dropout(0.2)) 33 | optimizer = Adam(learning_rate=0.001) 34 | model.compile(optimizer=optimizer, loss='mean_squared_error') 35 | return model 36 | 37 | 38 | # Prepare the data for LSTM input by creating sequences 39 | def prepare_data_for_lstm(data, window_size): 40 | data = data.values if hasattr(data, 'values') else np.array(data) 41 | X, Y = [], [] 42 | # enough data points to form a single input sequence for the model to predict the next time step 43 | if len(data) <= window_size: 44 | X.append(data) 45 | # no next step => no Y values 46 | # multiple input sequences 47 | else: 48 | for i in range(window_size, len(data)): 49 | X.append(data[i-window_size:i, 0]) 50 | Y.append(data[i, 0]) 51 | 52 | X = np.array(X) 53 | 54 | if X.ndim == 2: 55 | X = X.reshape(X.shape[0], X.shape[1], 1) 56 | 57 | Y = np.array(Y) if Y else None 58 | return X, Y 59 | 60 | 61 | def train_model(data, window_size=WINDOW_SIZE, epochs=60, batch_size=32): 62 | X, Y = prepare_data_for_lstm(data, window_size) 63 | model = create_lstm_model((X.shape[1], 1)) 64 | model.fit(X, Y, epochs=epochs, batch_size=batch_size) 65 | print(f"Model trained with {epochs} epochs and {batch_size} batch size") 66 | return model 67 | 68 | 69 | def save_model(model, filename='lstm_model.keras'): 70 | model_dir = os.path.join('data', 'models') 71 | os.makedirs(model_dir, exist_ok=True) 72 | model_path = os.path.join(model_dir, filename) 73 | model.save(model_path) 74 | print(f"Model saved to {model_path}") 75 | 76 | 77 | def make_predictions(model, data, window_size=WINDOW_SIZE): 78 | if window_size > len(data): 79 | raise ValueError( 80 | "Window size cannot be larger than the length of the data") 81 | X, _ = prepare_data_for_lstm(data, window_size) 82 | return model.predict(X) 83 | 84 | 85 | if __name__ == "__main__": 86 | # data = load_processed_data() 87 | 88 | # # split data 89 | # train_data, test_data = split_data(data) 90 | 91 | # window_size = WINDOW_SIZE 92 | # model = train_model(data=train_data, window_size=window_size) 93 | # save_model(model) 94 | 95 | fluvio = Fluvio.connect() 96 | consumer = fluvio.partition_consumer(CONSUMER_TOPIC, 0) 97 | producer = fluvio.topic_producer(PRODUCER_TOPIC) 98 | 99 | processed_data_json = "" 100 | stream = consumer.stream(Offset.from_beginning(0)) 101 | # continuous streaming 102 | for record in stream: 103 | if record.value_string() == "done": 104 | # convert json back to DataFrame 105 | data = pd.read_json( 106 | StringIO(processed_data_json), orient='records') 107 | data.set_index('date', inplace=True) 108 | if 'index' in data.columns: 109 | data = data.drop(columns='index') 110 | train_data, test_data = split_data(data) 111 | # train the model 112 | window_size = WINDOW_SIZE 113 | 114 | # model = train_model(data=train_data, window_size=window_size) 115 | # save_model(model) 116 | model = load_lstm_model('lstm_model.keras') 117 | # evaluate model performance 118 | predictions, actual, mse, mae = evaluate_model_performance( 119 | model, test_data, window_size) 120 | # convert predictions, actual data to json string and send it to the corresponding topic 121 | predictions_json = json.dumps(np.array(predictions).tolist()) 122 | # print(test_data) 123 | actual_json = test_data.reset_index().to_json( 124 | orient='records', date_format="iso") 125 | output = json.loads( 126 | '{"predictions" : [],"actual":[],"mse":0,"mae":0}') 127 | output['predictions'] = predictions_json 128 | output['actual'] = actual_json 129 | output['mse'] = mse 130 | output['mae'] = mae 131 | output = json.dumps(output) 132 | string_chunks = data_chunking(output) 133 | for chunk in string_chunks: 134 | # push data to the topic 135 | producer.send_string(chunk) 136 | # flush the last entry 137 | producer.flush() 138 | producer.send_string("done") 139 | print(f"model evaluation results pushed to {PRODUCER_TOPIC} topic") 140 | 141 | # reset raw_data_json 142 | processed_data_json = "" 143 | else: 144 | processed_data_json += record.value_string() 145 | -------------------------------------------------------------------------------- /data/raw/google_trends_data.csv: -------------------------------------------------------------------------------- 1 | date,flu 2 | 2019-08-25,8.0 3 | 2019-08-26,8.0 4 | 2019-08-27,8.0 5 | 2019-08-28,8.0 6 | 2019-08-29,8.0 7 | 2019-08-30,8.0 8 | 2019-08-31,8.0 9 | 2019-09-01,8.0 10 | 2019-09-02,8.285714285714286 11 | 2019-09-03,8.571428571428571 12 | 2019-09-04,8.857142857142858 13 | 2019-09-05,9.142857142857142 14 | 2019-09-06,9.428571428571429 15 | 2019-09-07,9.714285714285714 16 | 2019-09-08,10.0 17 | 2019-09-09,10.428571428571429 18 | 2019-09-10,10.857142857142858 19 | 2019-09-11,11.285714285714285 20 | 2019-09-12,11.714285714285714 21 | 2019-09-13,12.142857142857142 22 | 2019-09-14,12.571428571428571 23 | 2019-09-15,13.0 24 | 2019-09-16,13.285714285714286 25 | 2019-09-17,13.571428571428571 26 | 2019-09-18,13.857142857142858 27 | 2019-09-19,14.142857142857142 28 | 2019-09-20,14.428571428571429 29 | 2019-09-21,14.714285714285714 30 | 2019-09-22,15.0 31 | 2019-09-23,15.285714285714286 32 | 2019-09-24,15.571428571428571 33 | 2019-09-25,15.857142857142858 34 | 2019-09-26,16.142857142857142 35 | 2019-09-27,16.428571428571427 36 | 2019-09-28,16.714285714285715 37 | 2019-09-29,17.0 38 | 2019-09-30,17.285714285714285 39 | 2019-10-01,17.571428571428573 40 | 2019-10-02,17.857142857142858 41 | 2019-10-03,18.142857142857142 42 | 2019-10-04,18.428571428571427 43 | 2019-10-05,18.714285714285715 44 | 2019-10-06,19.0 45 | 2019-10-07,19.0 46 | 2019-10-08,19.0 47 | 2019-10-09,19.0 48 | 2019-10-10,19.0 49 | 2019-10-11,19.0 50 | 2019-10-12,19.0 51 | 2019-10-13,19.0 52 | 2019-10-14,19.142857142857142 53 | 2019-10-15,19.285714285714285 54 | 2019-10-16,19.428571428571427 55 | 2019-10-17,19.571428571428573 56 | 2019-10-18,19.714285714285715 57 | 2019-10-19,19.857142857142858 58 | 2019-10-20,20.0 59 | 2019-10-21,19.571428571428573 60 | 2019-10-22,19.142857142857142 61 | 2019-10-23,18.714285714285715 62 | 2019-10-24,18.285714285714285 63 | 2019-10-25,17.857142857142858 64 | 2019-10-26,17.42857142857143 65 | 2019-10-27,17.0 66 | 2019-10-28,17.0 67 | 2019-10-29,17.0 68 | 2019-10-30,17.0 69 | 2019-10-31,17.0 70 | 2019-11-01,17.0 71 | 2019-11-02,17.0 72 | 2019-11-03,17.0 73 | 2019-11-04,16.857142857142858 74 | 2019-11-05,16.714285714285715 75 | 2019-11-06,16.571428571428573 76 | 2019-11-07,16.428571428571427 77 | 2019-11-08,16.285714285714285 78 | 2019-11-09,16.142857142857142 79 | 2019-11-10,16.0 80 | 2019-11-11,16.0 81 | 2019-11-12,16.0 82 | 2019-11-13,16.0 83 | 2019-11-14,16.0 84 | 2019-11-15,16.0 85 | 2019-11-16,16.0 86 | 2019-11-17,16.0 87 | 2019-11-18,15.714285714285714 88 | 2019-11-19,15.428571428571429 89 | 2019-11-20,15.142857142857142 90 | 2019-11-21,14.857142857142858 91 | 2019-11-22,14.571428571428571 92 | 2019-11-23,14.285714285714286 93 | 2019-11-24,14.0 94 | 2019-11-25,14.285714285714286 95 | 2019-11-26,14.571428571428571 96 | 2019-11-27,14.857142857142858 97 | 2019-11-28,15.142857142857142 98 | 2019-11-29,15.428571428571429 99 | 2019-11-30,15.714285714285714 100 | 2019-12-01,16.0 101 | 2019-12-02,16.285714285714285 102 | 2019-12-03,16.571428571428573 103 | 2019-12-04,16.857142857142858 104 | 2019-12-05,17.142857142857142 105 | 2019-12-06,17.428571428571427 106 | 2019-12-07,17.714285714285715 107 | 2019-12-08,18.0 108 | 2019-12-09,18.428571428571427 109 | 2019-12-10,18.857142857142858 110 | 2019-12-11,19.285714285714285 111 | 2019-12-12,19.714285714285715 112 | 2019-12-13,20.142857142857142 113 | 2019-12-14,20.57142857142857 114 | 2019-12-15,21.0 115 | 2019-12-16,21.428571428571427 116 | 2019-12-17,21.857142857142858 117 | 2019-12-18,22.285714285714285 118 | 2019-12-19,22.714285714285715 119 | 2019-12-20,23.142857142857142 120 | 2019-12-21,23.57142857142857 121 | 2019-12-22,24.0 122 | 2019-12-23,24.0 123 | 2019-12-24,24.0 124 | 2019-12-25,24.0 125 | 2019-12-26,24.0 126 | 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2020-05-03,21.0 255 | 2020-05-04,20.714285714285715 256 | 2020-05-05,20.428571428571427 257 | 2020-05-06,20.142857142857142 258 | 2020-05-07,19.857142857142858 259 | 2020-05-08,19.571428571428573 260 | 2020-05-09,19.285714285714285 261 | 2020-05-10,19.0 262 | 2020-05-11,18.857142857142858 263 | 2020-05-12,18.714285714285715 264 | 2020-05-13,18.571428571428573 265 | 2020-05-14,18.428571428571427 266 | 2020-05-15,18.285714285714285 267 | 2020-05-16,18.142857142857142 268 | 2020-05-17,18.0 269 | 2020-05-18,17.714285714285715 270 | 2020-05-19,17.428571428571427 271 | 2020-05-20,17.142857142857142 272 | 2020-05-21,16.857142857142858 273 | 2020-05-22,16.571428571428573 274 | 2020-05-23,16.285714285714285 275 | 2020-05-24,16.0 276 | 2020-05-25,15.142857142857142 277 | 2020-05-26,14.285714285714286 278 | 2020-05-27,13.428571428571429 279 | 2020-05-28,12.571428571428571 280 | 2020-05-29,11.714285714285715 281 | 2020-05-30,10.857142857142858 282 | 2020-05-31,10.0 283 | 2020-06-01,10.0 284 | 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2020-07-08,18.857142857142858 321 | 2020-07-09,19.142857142857142 322 | 2020-07-10,19.428571428571427 323 | 2020-07-11,19.714285714285715 324 | 2020-07-12,20.0 325 | 2020-07-13,19.285714285714285 326 | 2020-07-14,18.571428571428573 327 | 2020-07-15,17.857142857142858 328 | 2020-07-16,17.142857142857142 329 | 2020-07-17,16.428571428571427 330 | 2020-07-18,15.714285714285715 331 | 2020-07-19,15.0 332 | 2020-07-20,14.714285714285714 333 | 2020-07-21,14.428571428571429 334 | 2020-07-22,14.142857142857142 335 | 2020-07-23,13.857142857142858 336 | 2020-07-24,13.571428571428571 337 | 2020-07-25,13.285714285714286 338 | 2020-07-26,13.0 339 | 2020-07-27,13.0 340 | 2020-07-28,13.0 341 | 2020-07-29,13.0 342 | 2020-07-30,13.0 343 | 2020-07-31,13.0 344 | 2020-08-01,13.0 345 | 2020-08-02,13.0 346 | 2020-08-03,13.142857142857142 347 | 2020-08-04,13.285714285714286 348 | 2020-08-05,13.428571428571429 349 | 2020-08-06,13.571428571428571 350 | 2020-08-07,13.714285714285714 351 | 2020-08-08,13.857142857142858 352 | 2020-08-09,14.0 353 | 2020-08-10,14.142857142857142 354 | 2020-08-11,14.285714285714286 355 | 2020-08-12,14.428571428571429 356 | 2020-08-13,14.571428571428571 357 | 2020-08-14,14.714285714285714 358 | 2020-08-15,14.857142857142858 359 | 2020-08-16,15.0 360 | 2020-08-17,14.857142857142858 361 | 2020-08-18,14.714285714285714 362 | 2020-08-19,14.571428571428571 363 | 2020-08-20,14.428571428571429 364 | 2020-08-21,14.285714285714286 365 | 2020-08-22,14.142857142857142 366 | 2020-08-23,14.0 367 | 2020-08-24,14.142857142857142 368 | 2020-08-25,14.285714285714286 369 | 2020-08-26,14.428571428571429 370 | 2020-08-27,14.571428571428571 371 | 2020-08-28,14.714285714285714 372 | 2020-08-29,14.857142857142858 373 | 2020-08-30,15.0 374 | 2020-08-31,15.428571428571429 375 | 2020-09-01,15.857142857142858 376 | 2020-09-02,16.285714285714285 377 | 2020-09-03,16.714285714285715 378 | 2020-09-04,17.142857142857142 379 | 2020-09-05,17.57142857142857 380 | 2020-09-06,18.0 381 | 2020-09-07,18.571428571428573 382 | 2020-09-08,19.142857142857142 383 | 2020-09-09,19.714285714285715 384 | 2020-09-10,20.285714285714285 385 | 2020-09-11,20.857142857142858 386 | 2020-09-12,21.428571428571427 387 | 2020-09-13,22.0 388 | 2020-09-14,22.714285714285715 389 | 2020-09-15,23.428571428571427 390 | 2020-09-16,24.142857142857142 391 | 2020-09-17,24.857142857142858 392 | 2020-09-18,25.571428571428573 393 | 2020-09-19,26.285714285714285 394 | 2020-09-20,27.0 395 | 2020-09-21,26.857142857142858 396 | 2020-09-22,26.714285714285715 397 | 2020-09-23,26.571428571428573 398 | 2020-09-24,26.428571428571427 399 | 2020-09-25,26.285714285714285 400 | 2020-09-26,26.142857142857142 401 | 2020-09-27,26.0 402 | 2020-09-28,26.571428571428573 403 | 2020-09-29,27.142857142857142 404 | 2020-09-30,27.714285714285715 405 | 2020-10-01,28.285714285714285 406 | 2020-10-02,28.857142857142858 407 | 2020-10-03,29.428571428571427 408 | 2020-10-04,30.0 409 | 2020-10-05,29.428571428571427 410 | 2020-10-06,28.857142857142858 411 | 2020-10-07,28.285714285714285 412 | 2020-10-08,27.714285714285715 413 | 2020-10-09,27.142857142857142 414 | 2020-10-10,26.571428571428573 415 | 2020-10-11,26.0 416 | 2020-10-12,25.857142857142858 417 | 2020-10-13,25.714285714285715 418 | 2020-10-14,25.571428571428573 419 | 2020-10-15,25.428571428571427 420 | 2020-10-16,25.285714285714285 421 | 2020-10-17,25.142857142857142 422 | 2020-10-18,25.0 423 | 2020-10-19,24.714285714285715 424 | 2020-10-20,24.428571428571427 425 | 2020-10-21,24.142857142857142 426 | 2020-10-22,23.857142857142858 427 | 2020-10-23,23.571428571428573 428 | 2020-10-24,23.285714285714285 429 | 2020-10-25,23.0 430 | 2020-10-26,22.285714285714285 431 | 2020-10-27,21.571428571428573 432 | 2020-10-28,20.857142857142858 433 | 2020-10-29,20.142857142857142 434 | 2020-10-30,19.428571428571427 435 | 2020-10-31,18.714285714285715 436 | 2020-11-01,18.0 437 | 2020-11-02,18.0 438 | 2020-11-03,18.0 439 | 2020-11-04,18.0 440 | 2020-11-05,18.0 441 | 2020-11-06,18.0 442 | 2020-11-07,18.0 443 | 2020-11-08,18.0 444 | 2020-11-09,18.142857142857142 445 | 2020-11-10,18.285714285714285 446 | 2020-11-11,18.428571428571427 447 | 2020-11-12,18.571428571428573 448 | 2020-11-13,18.714285714285715 449 | 2020-11-14,18.857142857142858 450 | 2020-11-15,19.0 451 | 2020-11-16,18.571428571428573 452 | 2020-11-17,18.142857142857142 453 | 2020-11-18,17.714285714285715 454 | 2020-11-19,17.285714285714285 455 | 2020-11-20,16.857142857142858 456 | 2020-11-21,16.42857142857143 457 | 2020-11-22,16.0 458 | 2020-11-23,16.0 459 | 2020-11-24,16.0 460 | 2020-11-25,16.0 461 | 2020-11-26,16.0 462 | 2020-11-27,16.0 463 | 2020-11-28,16.0 464 | 2020-11-29,16.0 465 | 2020-11-30,16.142857142857142 466 | 2020-12-01,16.285714285714285 467 | 2020-12-02,16.428571428571427 468 | 2020-12-03,16.571428571428573 469 | 2020-12-04,16.714285714285715 470 | 2020-12-05,16.857142857142858 471 | 2020-12-06,17.0 472 | 2020-12-07,16.857142857142858 473 | 2020-12-08,16.714285714285715 474 | 2020-12-09,16.571428571428573 475 | 2020-12-10,16.428571428571427 476 | 2020-12-11,16.285714285714285 477 | 2020-12-12,16.142857142857142 478 | 2020-12-13,16.0 479 | 2020-12-14,15.714285714285714 480 | 2020-12-15,15.428571428571429 481 | 2020-12-16,15.142857142857142 482 | 2020-12-17,14.857142857142858 483 | 2020-12-18,14.571428571428571 484 | 2020-12-19,14.285714285714286 485 | 2020-12-20,14.0 486 | 2020-12-21,14.142857142857142 487 | 2020-12-22,14.285714285714286 488 | 2020-12-23,14.428571428571429 489 | 2020-12-24,14.571428571428571 490 | 2020-12-25,14.714285714285714 491 | 2020-12-26,14.857142857142858 492 | 2020-12-27,15.0 493 | 2020-12-28,15.571428571428571 494 | 2020-12-29,16.142857142857142 495 | 2020-12-30,16.714285714285715 496 | 2020-12-31,17.285714285714285 497 | 2021-01-01,17.857142857142858 498 | 2021-01-02,18.428571428571427 499 | 2021-01-03,19.0 500 | 2021-01-04,18.857142857142858 501 | 2021-01-05,18.714285714285715 502 | 2021-01-06,18.571428571428573 503 | 2021-01-07,18.428571428571427 504 | 2021-01-08,18.285714285714285 505 | 2021-01-09,18.142857142857142 506 | 2021-01-10,18.0 507 | 2021-01-11,17.428571428571427 508 | 2021-01-12,16.857142857142858 509 | 2021-01-13,16.285714285714285 510 | 2021-01-14,15.714285714285715 511 | 2021-01-15,15.142857142857142 512 | 2021-01-16,14.571428571428571 513 | 2021-01-17,14.0 514 | 2021-01-18,13.714285714285714 515 | 2021-01-19,13.428571428571429 516 | 2021-01-20,13.142857142857142 517 | 2021-01-21,12.857142857142858 518 | 2021-01-22,12.571428571428571 519 | 2021-01-23,12.285714285714286 520 | 2021-01-24,12.0 521 | 2021-01-25,11.857142857142858 522 | 2021-01-26,11.714285714285714 523 | 2021-01-27,11.571428571428571 524 | 2021-01-28,11.428571428571429 525 | 2021-01-29,11.285714285714286 526 | 2021-01-30,11.142857142857142 527 | 2021-01-31,11.0 528 | 2021-02-01,10.714285714285714 529 | 2021-02-02,10.428571428571429 530 | 2021-02-03,10.142857142857142 531 | 2021-02-04,9.857142857142858 532 | 2021-02-05,9.571428571428571 533 | 2021-02-06,9.285714285714286 534 | 2021-02-07,9.0 535 | 2021-02-08,9.0 536 | 2021-02-09,9.0 537 | 2021-02-10,9.0 538 | 2021-02-11,9.0 539 | 2021-02-12,9.0 540 | 2021-02-13,9.0 541 | 2021-02-14,9.0 542 | 2021-02-15,9.285714285714286 543 | 2021-02-16,9.571428571428571 544 | 2021-02-17,9.857142857142858 545 | 2021-02-18,10.142857142857142 546 | 2021-02-19,10.428571428571429 547 | 2021-02-20,10.714285714285714 548 | 2021-02-21,11.0 549 | 2021-02-22,10.714285714285714 550 | 2021-02-23,10.428571428571429 551 | 2021-02-24,10.142857142857142 552 | 2021-02-25,9.857142857142858 553 | 2021-02-26,9.571428571428571 554 | 2021-02-27,9.285714285714286 555 | 2021-02-28,9.0 556 | 2021-03-01,8.857142857142858 557 | 2021-03-02,8.714285714285714 558 | 2021-03-03,8.571428571428571 559 | 2021-03-04,8.428571428571429 560 | 2021-03-05,8.285714285714286 561 | 2021-03-06,8.142857142857142 562 | 2021-03-07,8.0 563 | 2021-03-08,8.142857142857142 564 | 2021-03-09,8.285714285714286 565 | 2021-03-10,8.428571428571429 566 | 2021-03-11,8.571428571428571 567 | 2021-03-12,8.714285714285714 568 | 2021-03-13,8.857142857142858 569 | 2021-03-14,9.0 570 | 2021-03-15,9.0 571 | 2021-03-16,9.0 572 | 2021-03-17,9.0 573 | 2021-03-18,9.0 574 | 2021-03-19,9.0 575 | 2021-03-20,9.0 576 | 2021-03-21,9.0 577 | 2021-03-22,9.0 578 | 2021-03-23,9.0 579 | 2021-03-24,9.0 580 | 2021-03-25,9.0 581 | 2021-03-26,9.0 582 | 2021-03-27,9.0 583 | 2021-03-28,9.0 584 | 2021-03-29,9.0 585 | 2021-03-30,9.0 586 | 2021-03-31,9.0 587 | 2021-04-01,9.0 588 | 2021-04-02,9.0 589 | 2021-04-03,9.0 590 | 2021-04-04,9.0 591 | 2021-04-05,9.142857142857142 592 | 2021-04-06,9.285714285714286 593 | 2021-04-07,9.428571428571429 594 | 2021-04-08,9.571428571428571 595 | 2021-04-09,9.714285714285714 596 | 2021-04-10,9.857142857142858 597 | 2021-04-11,10.0 598 | 2021-04-12,10.0 599 | 2021-04-13,10.0 600 | 2021-04-14,10.0 601 | 2021-04-15,10.0 602 | 2021-04-16,10.0 603 | 2021-04-17,10.0 604 | 2021-04-18,10.0 605 | 2021-04-19,9.857142857142858 606 | 2021-04-20,9.714285714285714 607 | 2021-04-21,9.571428571428571 608 | 2021-04-22,9.428571428571429 609 | 2021-04-23,9.285714285714286 610 | 2021-04-24,9.142857142857142 611 | 2021-04-25,9.0 612 | 2021-04-26,9.0 613 | 2021-04-27,9.0 614 | 2021-04-28,9.0 615 | 2021-04-29,9.0 616 | 2021-04-30,9.0 617 | 2021-05-01,9.0 618 | 2021-05-02,9.0 619 | 2021-05-03,9.0 620 | 2021-05-04,9.0 621 | 2021-05-05,9.0 622 | 2021-05-06,9.0 623 | 2021-05-07,9.0 624 | 2021-05-08,9.0 625 | 2021-05-09,9.0 626 | 2021-05-10,9.0 627 | 2021-05-11,9.0 628 | 2021-05-12,9.0 629 | 2021-05-13,9.0 630 | 2021-05-14,9.0 631 | 2021-05-15,9.0 632 | 2021-05-16,9.0 633 | 2021-05-17,9.0 634 | 2021-05-18,9.0 635 | 2021-05-19,9.0 636 | 2021-05-20,9.0 637 | 2021-05-21,9.0 638 | 2021-05-22,9.0 639 | 2021-05-23,9.0 640 | 2021-05-24,9.571428571428571 641 | 2021-05-25,10.142857142857142 642 | 2021-05-26,10.714285714285714 643 | 2021-05-27,11.285714285714285 644 | 2021-05-28,11.857142857142858 645 | 2021-05-29,12.428571428571429 646 | 2021-05-30,13.0 647 | 2021-05-31,12.285714285714286 648 | 2021-06-01,11.571428571428571 649 | 2021-06-02,10.857142857142858 650 | 2021-06-03,10.142857142857142 651 | 2021-06-04,9.428571428571429 652 | 2021-06-05,8.714285714285715 653 | 2021-06-06,8.0 654 | 2021-06-07,8.0 655 | 2021-06-08,8.0 656 | 2021-06-09,8.0 657 | 2021-06-10,8.0 658 | 2021-06-11,8.0 659 | 2021-06-12,8.0 660 | 2021-06-13,8.0 661 | 2021-06-14,8.0 662 | 2021-06-15,8.0 663 | 2021-06-16,8.0 664 | 2021-06-17,8.0 665 | 2021-06-18,8.0 666 | 2021-06-19,8.0 667 | 2021-06-20,8.0 668 | 2021-06-21,8.142857142857142 669 | 2021-06-22,8.285714285714286 670 | 2021-06-23,8.428571428571429 671 | 2021-06-24,8.571428571428571 672 | 2021-06-25,8.714285714285714 673 | 2021-06-26,8.857142857142858 674 | 2021-06-27,9.0 675 | 2021-06-28,9.142857142857142 676 | 2021-06-29,9.285714285714286 677 | 2021-06-30,9.428571428571429 678 | 2021-07-01,9.571428571428571 679 | 2021-07-02,9.714285714285714 680 | 2021-07-03,9.857142857142858 681 | 2021-07-04,10.0 682 | 2021-07-05,9.857142857142858 683 | 2021-07-06,9.714285714285714 684 | 2021-07-07,9.571428571428571 685 | 2021-07-08,9.428571428571429 686 | 2021-07-09,9.285714285714286 687 | 2021-07-10,9.142857142857142 688 | 2021-07-11,9.0 689 | 2021-07-12,9.142857142857142 690 | 2021-07-13,9.285714285714286 691 | 2021-07-14,9.428571428571429 692 | 2021-07-15,9.571428571428571 693 | 2021-07-16,9.714285714285714 694 | 2021-07-17,9.857142857142858 695 | 2021-07-18,10.0 696 | 2021-07-19,10.0 697 | 2021-07-20,10.0 698 | 2021-07-21,10.0 699 | 2021-07-22,10.0 700 | 2021-07-23,10.0 701 | 2021-07-24,10.0 702 | 2021-07-25,10.0 703 | 2021-07-26,10.0 704 | 2021-07-27,10.0 705 | 2021-07-28,10.0 706 | 2021-07-29,10.0 707 | 2021-07-30,10.0 708 | 2021-07-31,10.0 709 | 2021-08-01,10.0 710 | 2021-08-02,10.142857142857142 711 | 2021-08-03,10.285714285714286 712 | 2021-08-04,10.428571428571429 713 | 2021-08-05,10.571428571428571 714 | 2021-08-06,10.714285714285714 715 | 2021-08-07,10.857142857142858 716 | 2021-08-08,11.0 717 | 2021-08-09,11.0 718 | 2021-08-10,11.0 719 | 2021-08-11,11.0 720 | 2021-08-12,11.0 721 | 2021-08-13,11.0 722 | 2021-08-14,11.0 723 | 2021-08-15,11.0 724 | 2021-08-16,11.0 725 | 2021-08-17,11.0 726 | 2021-08-18,11.0 727 | 2021-08-19,11.0 728 | 2021-08-20,11.0 729 | 2021-08-21,11.0 730 | 2021-08-22,11.0 731 | 2021-08-23,11.142857142857142 732 | 2021-08-24,11.285714285714286 733 | 2021-08-25,11.428571428571429 734 | 2021-08-26,11.571428571428571 735 | 2021-08-27,11.714285714285714 736 | 2021-08-28,11.857142857142858 737 | 2021-08-29,12.0 738 | 2021-08-30,12.142857142857142 739 | 2021-08-31,12.285714285714286 740 | 2021-09-01,12.428571428571429 741 | 2021-09-02,12.571428571428571 742 | 2021-09-03,12.714285714285714 743 | 2021-09-04,12.857142857142858 744 | 2021-09-05,13.0 745 | 2021-09-06,13.428571428571429 746 | 2021-09-07,13.857142857142858 747 | 2021-09-08,14.285714285714285 748 | 2021-09-09,14.714285714285714 749 | 2021-09-10,15.142857142857142 750 | 2021-09-11,15.571428571428571 751 | 2021-09-12,16.0 752 | 2021-09-13,16.142857142857142 753 | 2021-09-14,16.285714285714285 754 | 2021-09-15,16.428571428571427 755 | 2021-09-16,16.571428571428573 756 | 2021-09-17,16.714285714285715 757 | 2021-09-18,16.857142857142858 758 | 2021-09-19,17.0 759 | 2021-09-20,17.142857142857142 760 | 2021-09-21,17.285714285714285 761 | 2021-09-22,17.428571428571427 762 | 2021-09-23,17.571428571428573 763 | 2021-09-24,17.714285714285715 764 | 2021-09-25,17.857142857142858 765 | 2021-09-26,18.0 766 | 2021-09-27,18.285714285714285 767 | 2021-09-28,18.571428571428573 768 | 2021-09-29,18.857142857142858 769 | 2021-09-30,19.142857142857142 770 | 2021-10-01,19.428571428571427 771 | 2021-10-02,19.714285714285715 772 | 2021-10-03,20.0 773 | 2021-10-04,20.142857142857142 774 | 2021-10-05,20.285714285714285 775 | 2021-10-06,20.428571428571427 776 | 2021-10-07,20.571428571428573 777 | 2021-10-08,20.714285714285715 778 | 2021-10-09,20.857142857142858 779 | 2021-10-10,21.0 780 | 2021-10-11,21.0 781 | 2021-10-12,21.0 782 | 2021-10-13,21.0 783 | 2021-10-14,21.0 784 | 2021-10-15,21.0 785 | 2021-10-16,21.0 786 | 2021-10-17,21.0 787 | 2021-10-18,20.714285714285715 788 | 2021-10-19,20.428571428571427 789 | 2021-10-20,20.142857142857142 790 | 2021-10-21,19.857142857142858 791 | 2021-10-22,19.571428571428573 792 | 2021-10-23,19.285714285714285 793 | 2021-10-24,19.0 794 | 2021-10-25,18.857142857142858 795 | 2021-10-26,18.714285714285715 796 | 2021-10-27,18.571428571428573 797 | 2021-10-28,18.428571428571427 798 | 2021-10-29,18.285714285714285 799 | 2021-10-30,18.142857142857142 800 | 2021-10-31,18.0 801 | 2021-11-01,17.857142857142858 802 | 2021-11-02,17.714285714285715 803 | 2021-11-03,17.571428571428573 804 | 2021-11-04,17.428571428571427 805 | 2021-11-05,17.285714285714285 806 | 2021-11-06,17.142857142857142 807 | 2021-11-07,17.0 808 | 2021-11-08,16.857142857142858 809 | 2021-11-09,16.714285714285715 810 | 2021-11-10,16.571428571428573 811 | 2021-11-11,16.428571428571427 812 | 2021-11-12,16.285714285714285 813 | 2021-11-13,16.142857142857142 814 | 2021-11-14,16.0 815 | 2021-11-15,15.857142857142858 816 | 2021-11-16,15.714285714285714 817 | 2021-11-17,15.571428571428571 818 | 2021-11-18,15.428571428571429 819 | 2021-11-19,15.285714285714286 820 | 2021-11-20,15.142857142857142 821 | 2021-11-21,15.0 822 | 2021-11-22,15.142857142857142 823 | 2021-11-23,15.285714285714286 824 | 2021-11-24,15.428571428571429 825 | 2021-11-25,15.571428571428571 826 | 2021-11-26,15.714285714285714 827 | 2021-11-27,15.857142857142858 828 | 2021-11-28,16.0 829 | 2021-11-29,15.857142857142858 830 | 2021-11-30,15.714285714285714 831 | 2021-12-01,15.571428571428571 832 | 2021-12-02,15.428571428571429 833 | 2021-12-03,15.285714285714286 834 | 2021-12-04,15.142857142857142 835 | 2021-12-05,15.0 836 | 2021-12-06,15.142857142857142 837 | 2021-12-07,15.285714285714286 838 | 2021-12-08,15.428571428571429 839 | 2021-12-09,15.571428571428571 840 | 2021-12-10,15.714285714285714 841 | 2021-12-11,15.857142857142858 842 | 2021-12-12,16.0 843 | 2021-12-13,16.428571428571427 844 | 2021-12-14,16.857142857142858 845 | 2021-12-15,17.285714285714285 846 | 2021-12-16,17.714285714285715 847 | 2021-12-17,18.142857142857142 848 | 2021-12-18,18.57142857142857 849 | 2021-12-19,19.0 850 | 2021-12-20,19.285714285714285 851 | 2021-12-21,19.571428571428573 852 | 2021-12-22,19.857142857142858 853 | 2021-12-23,20.142857142857142 854 | 2021-12-24,20.428571428571427 855 | 2021-12-25,20.714285714285715 856 | 2021-12-26,21.0 857 | 2021-12-27,21.142857142857142 858 | 2021-12-28,21.285714285714285 859 | 2021-12-29,21.428571428571427 860 | 2021-12-30,21.571428571428573 861 | 2021-12-31,21.714285714285715 862 | 2022-01-01,21.857142857142858 863 | 2022-01-02,22.0 864 | 2022-01-03,21.142857142857142 865 | 2022-01-04,20.285714285714285 866 | 2022-01-05,19.42857142857143 867 | 2022-01-06,18.571428571428573 868 | 2022-01-07,17.714285714285715 869 | 2022-01-08,16.857142857142858 870 | 2022-01-09,16.0 871 | 2022-01-10,15.285714285714286 872 | 2022-01-11,14.571428571428571 873 | 2022-01-12,13.857142857142858 874 | 2022-01-13,13.142857142857142 875 | 2022-01-14,12.428571428571429 876 | 2022-01-15,11.714285714285715 877 | 2022-01-16,11.0 878 | 2022-01-17,10.857142857142858 879 | 2022-01-18,10.714285714285714 880 | 2022-01-19,10.571428571428571 881 | 2022-01-20,10.428571428571429 882 | 2022-01-21,10.285714285714286 883 | 2022-01-22,10.142857142857142 884 | 2022-01-23,10.0 885 | 2022-01-24,10.0 886 | 2022-01-25,10.0 887 | 2022-01-26,10.0 888 | 2022-01-27,10.0 889 | 2022-01-28,10.0 890 | 2022-01-29,10.0 891 | 2022-01-30,10.0 892 | 2022-01-31,10.0 893 | 2022-02-01,10.0 894 | 2022-02-02,10.0 895 | 2022-02-03,10.0 896 | 2022-02-04,10.0 897 | 2022-02-05,10.0 898 | 2022-02-06,10.0 899 | 2022-02-07,10.0 900 | 2022-02-08,10.0 901 | 2022-02-09,10.0 902 | 2022-02-10,10.0 903 | 2022-02-11,10.0 904 | 2022-02-12,10.0 905 | 2022-02-13,10.0 906 | 2022-02-14,10.0 907 | 2022-02-15,10.0 908 | 2022-02-16,10.0 909 | 2022-02-17,10.0 910 | 2022-02-18,10.0 911 | 2022-02-19,10.0 912 | 2022-02-20,10.0 913 | 2022-02-21,9.857142857142858 914 | 2022-02-22,9.714285714285714 915 | 2022-02-23,9.571428571428571 916 | 2022-02-24,9.428571428571429 917 | 2022-02-25,9.285714285714286 918 | 2022-02-26,9.142857142857142 919 | 2022-02-27,9.0 920 | 2022-02-28,9.0 921 | 2022-03-01,9.0 922 | 2022-03-02,9.0 923 | 2022-03-03,9.0 924 | 2022-03-04,9.0 925 | 2022-03-05,9.0 926 | 2022-03-06,9.0 927 | 2022-03-07,9.0 928 | 2022-03-08,9.0 929 | 2022-03-09,9.0 930 | 2022-03-10,9.0 931 | 2022-03-11,9.0 932 | 2022-03-12,9.0 933 | 2022-03-13,9.0 934 | 2022-03-14,9.142857142857142 935 | 2022-03-15,9.285714285714286 936 | 2022-03-16,9.428571428571429 937 | 2022-03-17,9.571428571428571 938 | 2022-03-18,9.714285714285714 939 | 2022-03-19,9.857142857142858 940 | 2022-03-20,10.0 941 | 2022-03-21,10.428571428571429 942 | 2022-03-22,10.857142857142858 943 | 2022-03-23,11.285714285714285 944 | 2022-03-24,11.714285714285714 945 | 2022-03-25,12.142857142857142 946 | 2022-03-26,12.571428571428571 947 | 2022-03-27,13.0 948 | 2022-03-28,12.714285714285714 949 | 2022-03-29,12.428571428571429 950 | 2022-03-30,12.142857142857142 951 | 2022-03-31,11.857142857142858 952 | 2022-04-01,11.571428571428571 953 | 2022-04-02,11.285714285714286 954 | 2022-04-03,11.0 955 | 2022-04-04,10.857142857142858 956 | 2022-04-05,10.714285714285714 957 | 2022-04-06,10.571428571428571 958 | 2022-04-07,10.428571428571429 959 | 2022-04-08,10.285714285714286 960 | 2022-04-09,10.142857142857142 961 | 2022-04-10,10.0 962 | 2022-04-11,10.0 963 | 2022-04-12,10.0 964 | 2022-04-13,10.0 965 | 2022-04-14,10.0 966 | 2022-04-15,10.0 967 | 2022-04-16,10.0 968 | 2022-04-17,10.0 969 | 2022-04-18,10.142857142857142 970 | 2022-04-19,10.285714285714286 971 | 2022-04-20,10.428571428571429 972 | 2022-04-21,10.571428571428571 973 | 2022-04-22,10.714285714285714 974 | 2022-04-23,10.857142857142858 975 | 2022-04-24,11.0 976 | 2022-04-25,10.857142857142858 977 | 2022-04-26,10.714285714285714 978 | 2022-04-27,10.571428571428571 979 | 2022-04-28,10.428571428571429 980 | 2022-04-29,10.285714285714286 981 | 2022-04-30,10.142857142857142 982 | 2022-05-01,10.0 983 | 2022-05-02,10.285714285714286 984 | 2022-05-03,10.571428571428571 985 | 2022-05-04,10.857142857142858 986 | 2022-05-05,11.142857142857142 987 | 2022-05-06,11.428571428571429 988 | 2022-05-07,11.714285714285714 989 | 2022-05-08,12.0 990 | 2022-05-09,11.714285714285714 991 | 2022-05-10,11.428571428571429 992 | 2022-05-11,11.142857142857142 993 | 2022-05-12,10.857142857142858 994 | 2022-05-13,10.571428571428571 995 | 2022-05-14,10.285714285714286 996 | 2022-05-15,10.0 997 | 2022-05-16,10.142857142857142 998 | 2022-05-17,10.285714285714286 999 | 2022-05-18,10.428571428571429 1000 | 2022-05-19,10.571428571428571 1001 | 2022-05-20,10.714285714285714 1002 | 2022-05-21,10.857142857142858 1003 | 2022-05-22,11.0 1004 | 2022-05-23,11.0 1005 | 2022-05-24,11.0 1006 | 2022-05-25,11.0 1007 | 2022-05-26,11.0 1008 | 2022-05-27,11.0 1009 | 2022-05-28,11.0 1010 | 2022-05-29,11.0 1011 | 2022-05-30,10.857142857142858 1012 | 2022-05-31,10.714285714285714 1013 | 2022-06-01,10.571428571428571 1014 | 2022-06-02,10.428571428571429 1015 | 2022-06-03,10.285714285714286 1016 | 2022-06-04,10.142857142857142 1017 | 2022-06-05,10.0 1018 | 2022-06-06,9.857142857142858 1019 | 2022-06-07,9.714285714285714 1020 | 2022-06-08,9.571428571428571 1021 | 2022-06-09,9.428571428571429 1022 | 2022-06-10,9.285714285714286 1023 | 2022-06-11,9.142857142857142 1024 | 2022-06-12,9.0 1025 | 2022-06-13,9.0 1026 | 2022-06-14,9.0 1027 | 2022-06-15,9.0 1028 | 2022-06-16,9.0 1029 | 2022-06-17,9.0 1030 | 2022-06-18,9.0 1031 | 2022-06-19,9.0 1032 | 2022-06-20,9.0 1033 | 2022-06-21,9.0 1034 | 2022-06-22,9.0 1035 | 2022-06-23,9.0 1036 | 2022-06-24,9.0 1037 | 2022-06-25,9.0 1038 | 2022-06-26,9.0 1039 | 2022-06-27,9.0 1040 | 2022-06-28,9.0 1041 | 2022-06-29,9.0 1042 | 2022-06-30,9.0 1043 | 2022-07-01,9.0 1044 | 2022-07-02,9.0 1045 | 2022-07-03,9.0 1046 | 2022-07-04,8.857142857142858 1047 | 2022-07-05,8.714285714285714 1048 | 2022-07-06,8.571428571428571 1049 | 2022-07-07,8.428571428571429 1050 | 2022-07-08,8.285714285714286 1051 | 2022-07-09,8.142857142857142 1052 | 2022-07-10,8.0 1053 | 2022-07-11,8.0 1054 | 2022-07-12,8.0 1055 | 2022-07-13,8.0 1056 | 2022-07-14,8.0 1057 | 2022-07-15,8.0 1058 | 2022-07-16,8.0 1059 | 2022-07-17,8.0 1060 | 2022-07-18,8.0 1061 | 2022-07-19,8.0 1062 | 2022-07-20,8.0 1063 | 2022-07-21,8.0 1064 | 2022-07-22,8.0 1065 | 2022-07-23,8.0 1066 | 2022-07-24,8.0 1067 | 2022-07-25,8.0 1068 | 2022-07-26,8.0 1069 | 2022-07-27,8.0 1070 | 2022-07-28,8.0 1071 | 2022-07-29,8.0 1072 | 2022-07-30,8.0 1073 | 2022-07-31,8.0 1074 | 2022-08-01,7.857142857142857 1075 | 2022-08-02,7.714285714285714 1076 | 2022-08-03,7.571428571428571 1077 | 2022-08-04,7.428571428571429 1078 | 2022-08-05,7.285714285714286 1079 | 2022-08-06,7.142857142857143 1080 | 2022-08-07,7.0 1081 | 2022-08-08,7.142857142857143 1082 | 2022-08-09,7.285714285714286 1083 | 2022-08-10,7.428571428571429 1084 | 2022-08-11,7.571428571428571 1085 | 2022-08-12,7.714285714285714 1086 | 2022-08-13,7.857142857142857 1087 | 2022-08-14,8.0 1088 | 2022-08-15,8.428571428571429 1089 | 2022-08-16,8.857142857142858 1090 | 2022-08-17,9.285714285714285 1091 | 2022-08-18,9.714285714285714 1092 | 2022-08-19,10.142857142857142 1093 | 2022-08-20,10.571428571428571 1094 | 2022-08-21,11.0 1095 | 2022-08-22,10.714285714285714 1096 | 2022-08-23,10.428571428571429 1097 | 2022-08-24,10.142857142857142 1098 | 2022-08-25,9.857142857142858 1099 | 2022-08-26,9.571428571428571 1100 | 2022-08-27,9.285714285714286 1101 | 2022-08-28,9.0 1102 | 2022-08-29,9.285714285714286 1103 | 2022-08-30,9.571428571428571 1104 | 2022-08-31,9.857142857142858 1105 | 2022-09-01,10.142857142857142 1106 | 2022-09-02,10.428571428571429 1107 | 2022-09-03,10.714285714285714 1108 | 2022-09-04,11.0 1109 | 2022-09-05,11.285714285714286 1110 | 2022-09-06,11.571428571428571 1111 | 2022-09-07,11.857142857142858 1112 | 2022-09-08,12.142857142857142 1113 | 2022-09-09,12.428571428571429 1114 | 2022-09-10,12.714285714285714 1115 | 2022-09-11,13.0 1116 | 2022-09-12,13.285714285714286 1117 | 2022-09-13,13.571428571428571 1118 | 2022-09-14,13.857142857142858 1119 | 2022-09-15,14.142857142857142 1120 | 2022-09-16,14.428571428571429 1121 | 2022-09-17,14.714285714285714 1122 | 2022-09-18,15.0 1123 | 2022-09-19,15.142857142857142 1124 | 2022-09-20,15.285714285714286 1125 | 2022-09-21,15.428571428571429 1126 | 2022-09-22,15.571428571428571 1127 | 2022-09-23,15.714285714285714 1128 | 2022-09-24,15.857142857142858 1129 | 2022-09-25,16.0 1130 | 2022-09-26,16.142857142857142 1131 | 2022-09-27,16.285714285714285 1132 | 2022-09-28,16.428571428571427 1133 | 2022-09-29,16.571428571428573 1134 | 2022-09-30,16.714285714285715 1135 | 2022-10-01,16.857142857142858 1136 | 2022-10-02,17.0 1137 | 2022-10-03,17.428571428571427 1138 | 2022-10-04,17.857142857142858 1139 | 2022-10-05,18.285714285714285 1140 | 2022-10-06,18.714285714285715 1141 | 2022-10-07,19.142857142857142 1142 | 2022-10-08,19.57142857142857 1143 | 2022-10-09,20.0 1144 | 2022-10-10,20.142857142857142 1145 | 2022-10-11,20.285714285714285 1146 | 2022-10-12,20.428571428571427 1147 | 2022-10-13,20.571428571428573 1148 | 2022-10-14,20.714285714285715 1149 | 2022-10-15,20.857142857142858 1150 | 2022-10-16,21.0 1151 | 2022-10-17,21.285714285714285 1152 | 2022-10-18,21.571428571428573 1153 | 2022-10-19,21.857142857142858 1154 | 2022-10-20,22.142857142857142 1155 | 2022-10-21,22.428571428571427 1156 | 2022-10-22,22.714285714285715 1157 | 2022-10-23,23.0 1158 | 2022-10-24,23.0 1159 | 2022-10-25,23.0 1160 | 2022-10-26,23.0 1161 | 2022-10-27,23.0 1162 | 2022-10-28,23.0 1163 | 2022-10-29,23.0 1164 | 2022-10-30,23.0 1165 | 2022-10-31,22.857142857142858 1166 | 2022-11-01,22.714285714285715 1167 | 2022-11-02,22.571428571428573 1168 | 2022-11-03,22.428571428571427 1169 | 2022-11-04,22.285714285714285 1170 | 2022-11-05,22.142857142857142 1171 | 2022-11-06,22.0 1172 | 2022-11-07,22.0 1173 | 2022-11-08,22.0 1174 | 2022-11-09,22.0 1175 | 2022-11-10,22.0 1176 | 2022-11-11,22.0 1177 | 2022-11-12,22.0 1178 | 2022-11-13,22.0 1179 | 2022-11-14,22.0 1180 | 2022-11-15,22.0 1181 | 2022-11-16,22.0 1182 | 2022-11-17,22.0 1183 | 2022-11-18,22.0 1184 | 2022-11-19,22.0 1185 | 2022-11-20,22.0 1186 | 2022-11-21,22.285714285714285 1187 | 2022-11-22,22.571428571428573 1188 | 2022-11-23,22.857142857142858 1189 | 2022-11-24,23.142857142857142 1190 | 2022-11-25,23.428571428571427 1191 | 2022-11-26,23.714285714285715 1192 | 2022-11-27,24.0 1193 | 2022-11-28,24.0 1194 | 2022-11-29,24.0 1195 | 2022-11-30,24.0 1196 | 2022-12-01,24.0 1197 | 2022-12-02,24.0 1198 | 2022-12-03,24.0 1199 | 2022-12-04,24.0 1200 | 2022-12-05,24.0 1201 | 2022-12-06,24.0 1202 | 2022-12-07,24.0 1203 | 2022-12-08,24.0 1204 | 2022-12-09,24.0 1205 | 2022-12-10,24.0 1206 | 2022-12-11,24.0 1207 | 2022-12-12,23.857142857142858 1208 | 2022-12-13,23.714285714285715 1209 | 2022-12-14,23.571428571428573 1210 | 2022-12-15,23.428571428571427 1211 | 2022-12-16,23.285714285714285 1212 | 2022-12-17,23.142857142857142 1213 | 2022-12-18,23.0 1214 | 2022-12-19,22.571428571428573 1215 | 2022-12-20,22.142857142857142 1216 | 2022-12-21,21.714285714285715 1217 | 2022-12-22,21.285714285714285 1218 | 2022-12-23,20.857142857142858 1219 | 2022-12-24,20.42857142857143 1220 | 2022-12-25,20.0 1221 | 2022-12-26,19.285714285714285 1222 | 2022-12-27,18.571428571428573 1223 | 2022-12-28,17.857142857142858 1224 | 2022-12-29,17.142857142857142 1225 | 2022-12-30,16.428571428571427 1226 | 2022-12-31,15.714285714285715 1227 | 2023-01-01,15.0 1228 | 2023-01-02,14.571428571428571 1229 | 2023-01-03,14.142857142857142 1230 | 2023-01-04,13.714285714285715 1231 | 2023-01-05,13.285714285714286 1232 | 2023-01-06,12.857142857142858 1233 | 2023-01-07,12.428571428571429 1234 | 2023-01-08,12.0 1235 | 2023-01-09,11.857142857142858 1236 | 2023-01-10,11.714285714285714 1237 | 2023-01-11,11.571428571428571 1238 | 2023-01-12,11.428571428571429 1239 | 2023-01-13,11.285714285714286 1240 | 2023-01-14,11.142857142857142 1241 | 2023-01-15,11.0 1242 | 2023-01-16,10.857142857142858 1243 | 2023-01-17,10.714285714285714 1244 | 2023-01-18,10.571428571428571 1245 | 2023-01-19,10.428571428571429 1246 | 2023-01-20,10.285714285714286 1247 | 2023-01-21,10.142857142857142 1248 | 2023-01-22,10.0 1249 | 2023-01-23,10.0 1250 | 2023-01-24,10.0 1251 | 2023-01-25,10.0 1252 | 2023-01-26,10.0 1253 | 2023-01-27,10.0 1254 | 2023-01-28,10.0 1255 | 2023-01-29,10.0 1256 | 2023-01-30,10.0 1257 | 2023-01-31,10.0 1258 | 2023-02-01,10.0 1259 | 2023-02-02,10.0 1260 | 2023-02-03,10.0 1261 | 2023-02-04,10.0 1262 | 2023-02-05,10.0 1263 | 2023-02-06,9.857142857142858 1264 | 2023-02-07,9.714285714285714 1265 | 2023-02-08,9.571428571428571 1266 | 2023-02-09,9.428571428571429 1267 | 2023-02-10,9.285714285714286 1268 | 2023-02-11,9.142857142857142 1269 | 2023-02-12,9.0 1270 | 2023-02-13,9.142857142857142 1271 | 2023-02-14,9.285714285714286 1272 | 2023-02-15,9.428571428571429 1273 | 2023-02-16,9.571428571428571 1274 | 2023-02-17,9.714285714285714 1275 | 2023-02-18,9.857142857142858 1276 | 2023-02-19,10.0 1277 | 2023-02-20,10.0 1278 | 2023-02-21,10.0 1279 | 2023-02-22,10.0 1280 | 2023-02-23,10.0 1281 | 2023-02-24,10.0 1282 | 2023-02-25,10.0 1283 | 2023-02-26,10.0 1284 | 2023-02-27,10.142857142857142 1285 | 2023-02-28,10.285714285714286 1286 | 2023-03-01,10.428571428571429 1287 | 2023-03-02,10.571428571428571 1288 | 2023-03-03,10.714285714285714 1289 | 2023-03-04,10.857142857142858 1290 | 2023-03-05,11.0 1291 | 2023-03-06,10.714285714285714 1292 | 2023-03-07,10.428571428571429 1293 | 2023-03-08,10.142857142857142 1294 | 2023-03-09,9.857142857142858 1295 | 2023-03-10,9.571428571428571 1296 | 2023-03-11,9.285714285714286 1297 | 2023-03-12,9.0 1298 | 2023-03-13,9.0 1299 | 2023-03-14,9.0 1300 | 2023-03-15,9.0 1301 | 2023-03-16,9.0 1302 | 2023-03-17,9.0 1303 | 2023-03-18,9.0 1304 | 2023-03-19,9.0 1305 | 2023-03-20,9.142857142857142 1306 | 2023-03-21,9.285714285714286 1307 | 2023-03-22,9.428571428571429 1308 | 2023-03-23,9.571428571428571 1309 | 2023-03-24,9.714285714285714 1310 | 2023-03-25,9.857142857142858 1311 | 2023-03-26,10.0 1312 | 2023-03-27,9.857142857142858 1313 | 2023-03-28,9.714285714285714 1314 | 2023-03-29,9.571428571428571 1315 | 2023-03-30,9.428571428571429 1316 | 2023-03-31,9.285714285714286 1317 | 2023-04-01,9.142857142857142 1318 | 2023-04-02,9.0 1319 | 2023-04-03,9.142857142857142 1320 | 2023-04-04,9.285714285714286 1321 | 2023-04-05,9.428571428571429 1322 | 2023-04-06,9.571428571428571 1323 | 2023-04-07,9.714285714285714 1324 | 2023-04-08,9.857142857142858 1325 | 2023-04-09,10.0 1326 | 2023-04-10,9.714285714285714 1327 | 2023-04-11,9.428571428571429 1328 | 2023-04-12,9.142857142857142 1329 | 2023-04-13,8.857142857142858 1330 | 2023-04-14,8.571428571428571 1331 | 2023-04-15,8.285714285714286 1332 | 2023-04-16,8.0 1333 | 2023-04-17,8.0 1334 | 2023-04-18,8.0 1335 | 2023-04-19,8.0 1336 | 2023-04-20,8.0 1337 | 2023-04-21,8.0 1338 | 2023-04-22,8.0 1339 | 2023-04-23,8.0 1340 | 2023-04-24,8.0 1341 | 2023-04-25,8.0 1342 | 2023-04-26,8.0 1343 | 2023-04-27,8.0 1344 | 2023-04-28,8.0 1345 | 2023-04-29,8.0 1346 | 2023-04-30,8.0 1347 | 2023-05-01,8.0 1348 | 2023-05-02,8.0 1349 | 2023-05-03,8.0 1350 | 2023-05-04,8.0 1351 | 2023-05-05,8.0 1352 | 2023-05-06,8.0 1353 | 2023-05-07,8.0 1354 | 2023-05-08,8.285714285714286 1355 | 2023-05-09,8.571428571428571 1356 | 2023-05-10,8.857142857142858 1357 | 2023-05-11,9.142857142857142 1358 | 2023-05-12,9.428571428571429 1359 | 2023-05-13,9.714285714285714 1360 | 2023-05-14,10.0 1361 | 2023-05-15,9.714285714285714 1362 | 2023-05-16,9.428571428571429 1363 | 2023-05-17,9.142857142857142 1364 | 2023-05-18,8.857142857142858 1365 | 2023-05-19,8.571428571428571 1366 | 2023-05-20,8.285714285714286 1367 | 2023-05-21,8.0 1368 | 2023-05-22,8.142857142857142 1369 | 2023-05-23,8.285714285714286 1370 | 2023-05-24,8.428571428571429 1371 | 2023-05-25,8.571428571428571 1372 | 2023-05-26,8.714285714285714 1373 | 2023-05-27,8.857142857142858 1374 | 2023-05-28,9.0 1375 | 2023-05-29,8.714285714285714 1376 | 2023-05-30,8.428571428571429 1377 | 2023-05-31,8.142857142857142 1378 | 2023-06-01,7.857142857142858 1379 | 2023-06-02,7.571428571428571 1380 | 2023-06-03,7.285714285714286 1381 | 2023-06-04,7.0 1382 | 2023-06-05,7.0 1383 | 2023-06-06,7.0 1384 | 2023-06-07,7.0 1385 | 2023-06-08,7.0 1386 | 2023-06-09,7.0 1387 | 2023-06-10,7.0 1388 | 2023-06-11,7.0 1389 | 2023-06-12,7.0 1390 | 2023-06-13,7.0 1391 | 2023-06-14,7.0 1392 | 2023-06-15,7.0 1393 | 2023-06-16,7.0 1394 | 2023-06-17,7.0 1395 | 2023-06-18,7.0 1396 | 2023-06-19,7.0 1397 | 2023-06-20,7.0 1398 | 2023-06-21,7.0 1399 | 2023-06-22,7.0 1400 | 2023-06-23,7.0 1401 | 2023-06-24,7.0 1402 | 2023-06-25,7.0 1403 | 2023-06-26,6.857142857142857 1404 | 2023-06-27,6.714285714285714 1405 | 2023-06-28,6.571428571428571 1406 | 2023-06-29,6.428571428571429 1407 | 2023-06-30,6.285714285714286 1408 | 2023-07-01,6.142857142857143 1409 | 2023-07-02,6.0 1410 | 2023-07-03,6.142857142857143 1411 | 2023-07-04,6.285714285714286 1412 | 2023-07-05,6.428571428571429 1413 | 2023-07-06,6.571428571428571 1414 | 2023-07-07,6.714285714285714 1415 | 2023-07-08,6.857142857142857 1416 | 2023-07-09,7.0 1417 | 2023-07-10,7.142857142857143 1418 | 2023-07-11,7.285714285714286 1419 | 2023-07-12,7.428571428571429 1420 | 2023-07-13,7.571428571428571 1421 | 2023-07-14,7.714285714285714 1422 | 2023-07-15,7.857142857142857 1423 | 2023-07-16,8.0 1424 | 2023-07-17,8.857142857142858 1425 | 2023-07-18,9.714285714285714 1426 | 2023-07-19,10.571428571428571 1427 | 2023-07-20,11.428571428571429 1428 | 2023-07-21,12.285714285714285 1429 | 2023-07-22,13.142857142857142 1430 | 2023-07-23,14.0 1431 | 2023-07-24,14.428571428571429 1432 | 2023-07-25,14.857142857142858 1433 | 2023-07-26,15.285714285714285 1434 | 2023-07-27,15.714285714285714 1435 | 2023-07-28,16.142857142857142 1436 | 2023-07-29,16.57142857142857 1437 | 2023-07-30,17.0 1438 | 2023-07-31,16.571428571428573 1439 | 2023-08-01,16.142857142857142 1440 | 2023-08-02,15.714285714285715 1441 | 2023-08-03,15.285714285714286 1442 | 2023-08-04,14.857142857142858 1443 | 2023-08-05,14.428571428571429 1444 | 2023-08-06,14.0 1445 | 2023-08-07,13.428571428571429 1446 | 2023-08-08,12.857142857142858 1447 | 2023-08-09,12.285714285714286 1448 | 2023-08-10,11.714285714285715 1449 | 2023-08-11,11.142857142857142 1450 | 2023-08-12,10.571428571428571 1451 | 2023-08-13,10.0 1452 | 2023-08-14,10.0 1453 | 2023-08-15,10.0 1454 | 2023-08-16,10.0 1455 | 2023-08-17,10.0 1456 | 2023-08-18,10.0 1457 | 2023-08-19,10.0 1458 | 2023-08-20,10.0 1459 | 2023-08-21,10.0 1460 | 2023-08-22,10.0 1461 | 2023-08-23,10.0 1462 | 2023-08-24,10.0 1463 | 2023-08-25,10.0 1464 | 2023-08-26,10.0 1465 | 2023-08-27,10.0 1466 | 2023-08-28,10.0 1467 | 2023-08-29,10.0 1468 | 2023-08-30,10.0 1469 | 2023-08-31,10.0 1470 | 2023-09-01,10.0 1471 | 2023-09-02,10.0 1472 | 2023-09-03,10.0 1473 | 2023-09-04,10.285714285714286 1474 | 2023-09-05,10.571428571428571 1475 | 2023-09-06,10.857142857142858 1476 | 2023-09-07,11.142857142857142 1477 | 2023-09-08,11.428571428571429 1478 | 2023-09-09,11.714285714285714 1479 | 2023-09-10,12.0 1480 | 2023-09-11,12.285714285714286 1481 | 2023-09-12,12.571428571428571 1482 | 2023-09-13,12.857142857142858 1483 | 2023-09-14,13.142857142857142 1484 | 2023-09-15,13.428571428571429 1485 | 2023-09-16,13.714285714285714 1486 | 2023-09-17,14.0 1487 | 2023-09-18,14.285714285714286 1488 | 2023-09-19,14.571428571428571 1489 | 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2023-11-20,13.285714285714286 1551 | 2023-11-21,13.571428571428571 1552 | 2023-11-22,13.857142857142858 1553 | 2023-11-23,14.142857142857142 1554 | 2023-11-24,14.428571428571429 1555 | 2023-11-25,14.714285714285714 1556 | 2023-11-26,15.0 1557 | 2023-11-27,15.0 1558 | 2023-11-28,15.0 1559 | 2023-11-29,15.0 1560 | 2023-11-30,15.0 1561 | 2023-12-01,15.0 1562 | 2023-12-02,15.0 1563 | 2023-12-03,15.0 1564 | 2023-12-04,15.142857142857142 1565 | 2023-12-05,15.285714285714286 1566 | 2023-12-06,15.428571428571429 1567 | 2023-12-07,15.571428571428571 1568 | 2023-12-08,15.714285714285714 1569 | 2023-12-09,15.857142857142858 1570 | 2023-12-10,16.0 1571 | 2023-12-11,16.571428571428573 1572 | 2023-12-12,17.142857142857142 1573 | 2023-12-13,17.714285714285715 1574 | 2023-12-14,18.285714285714285 1575 | 2023-12-15,18.857142857142858 1576 | 2023-12-16,19.428571428571427 1577 | 2023-12-17,20.0 1578 | 2023-12-18,19.857142857142858 1579 | 2023-12-19,19.714285714285715 1580 | 2023-12-20,19.571428571428573 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