README.md

@@ -5,90 +5,58 @@ This is the code implementation of my Bsc Thesis, which has topic of "Unsupervis
 
 ## Getting started
 
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+## Installation Prerequisites
+Before you get started,
 
+```bash
+1. System should have IDE or text editor
+2. Python version 3.9.0
+3. PIP
 ```
-cd existing_repo
-git remote add origin https://mygit.th-deg.de/ko11920/thesiscodeimplementation.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://mygit.th-deg.de/ko11920/thesiscodeimplementation/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
+# Set-up an Environment:
 
-***
+Clustering-Tool is built with Python and Streamlit. 
+For the installation of a local development is described below.
 
-# Editing this README
+### For a virtual environment, 
+Independent of which package management tool you use, it is recommended that commands on this page are 
+run in a virtual environment. By doing so, it ensures that the dependencies added for Streamlit will not 
+impact other Python projects you are working on.
 
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
+Tools use for environment management:
+ - [conda](https://www.anaconda.com/products/distribution)
 
-## Suggestions for a good README
-
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
+## 1. Clone repository to a destination folder
+```bash
+git clone https://mygit.th-deg.de/kv14423/clustering-tool.git
+```
 
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
+## Set-up
 
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
+```bash
+conda create --name <VenvEnvName> python==3.9.0 -y
+```
+```bash
+conda activate <VenvEnvName>
+```
 
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
+```bash
+pip install -r requirements.txt
+```
 
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
+## Code Structure
+For the replication of the results, you need to run those python files.
 
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
+Firstly to run those algorithms defined in the thesis and plots the CVI metrics, run the following pythn files as follow:
 
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+## 1. Run fine_tune_hyperparameter.py
+After the execution of the script, a new folder is created where CVI metrics of those datasets is created named as <b>cvi_results_of_clustering
 
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
+## 2. Run select_optimal_parameters.py
+Using the cvi score of clustering algorithms for each dataset of <b>cvi_results_of_clustering folder, run this script and new folder for composite score is created called <b>composite_score_results which takes the best parameters from the cvi_results.
 
-## License
-For open source projects, say how it is licensed.
+## 3. Run optimal_hyperparameter_plot.py
+This script plots the no of cluster for those cvi indices based on the composite score.
 
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+## 4. Run plot_for_all_cvi_from_all_data.py
+This script is used to plot the five CVI indices for the clusterd of whole dataset that shows the cvi-score for all the algorithms used, which represent the best algorithms according to cvi.
\ No newline at end of file

better-implem.py

@@ -706,7 +706,7 @@ def visualize_features_heatmap(daily_aggregates):
 # Example Usage
 if __name__ == "__main__":
     # Step 1: Load the dataset
-    data = pd.read_csv("/home/jvaldes/Desktop/krishna-thesis/thesiscodeimplementation/data/EFH (1).csv")  # Replace with your dataset file
+    data = pd.read_csv("/home/jvaldes/Desktop/krishna-thesis/thesiscodeimplementation/data/eth_data/EFH (8).csv")  # Replace with your dataset file
 
     # Process the data to get daily mean values
     daily_mean_df = process_daily_mean_power(data)

clustering_cvi.csv

 Silhouette Score,Davies-Bouldin Score,Calinski-Harabasz Score,Dunn Index,COP Index,Score Function
-0.23010091440535346,0.8125262754129975,75.67886360829836,0.00191514031834882,0.010523960350357313,0.08340997077066392
+0.3104714677604624,0.852058991814046,233.13824672633373,0.012709898306111338,0.007268193467649197,0.04207614520369935

dashboard.py

+                                    ###########################################
+                                    ###########       Imports       ###########
+                                    ###########################################
+
+from re import S
+import math
+import streamlit as st
+import matplotlib as plt
+import pandas as pd
+import textwrap
+import statsmodels.formula.api as smf
+import statsmodels.tsa.api as smt
+import statsmodels.api as sm
+import scipy.stats as scs
+import numba
+import numpy as np
+
+import matplotlib.cm as cm
+
+##### Plotly ######
+import plotly.express as px
+import plotly.graph_objects as go
+import plotly.figure_factory as ff
+import plotly.graph_objects as go
+import chart_studio
+from plotly import tools
+from plotly.subplots import make_subplots
+
+import time #from datetime import datetime
+import datetime
+
+
+from scipy.fftpack import rfft
+from scipy.stats import boxcox
+from sklearn.cluster import AgglomerativeClustering, KMeans
+from sklearn.metrics import davies_bouldin_score
+from sklearn_extra.cluster import KMedoids
+from sklearn.metrics import calinski_harabasz_score
+from scipy.cluster.hierarchy import single, complete, average, ward, dendrogram, linkage
+from sklearn.metrics.cluster import contingency_matrix
+from sklearn.manifold import TSNE
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.cluster import KMeans
+from sklearn.impute import SimpleImputer
+
+from datetime import timedelta #from datetime import datetime
+
+
+# Algorithms
+from tslearn.barycenters import dtw_barycenter_averaging
+from tslearn.clustering import TimeSeriesKMeans
+from sktime.distances import dtw_distance
+from dtaidistance import clustering, dtw
+#from fcmeans import FCM
+
+
+# IMplementation for pyclustering kmeans
+from pyclustering.cluster.kmeans import kmeans
+from pyclustering.cluster.center_initializer import random_center_initializer
+from pyclustering.cluster.encoder import type_encoding
+from pyclustering.cluster.encoder import cluster_encoder
+from pyclustering.utils.metric import distance_metric
+from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
+from pyclustering.cluster.fcm import fcm
+from sklearn.metrics import pairwise_distances
+from validclust import cop, dunn
+
+
+# Preprocessing
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics.cluster import contingency_matrix
+from tslearn.clustering import TimeSeriesKMeans
+from tslearn.preprocessing import TimeSeriesScalerMeanVariance
+from netdata_pandas.data import get_data, get_chart_list
+from am4894plots.plots import plot_lines, plot_lines_grid
+from matplotlib.patches import Ellipse
+from sklearn import preprocessing
+
+#from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score, silhouette_samples
+from yellowbrick.cluster import SilhouetteVisualizer, KElbowVisualizer
+from sklearn.model_selection import train_test_split
+#from statsmodels.tsa.arima_model import ARIMA
+
+import warnings  # `do not disturbe` mode
+
+warnings.filterwarnings('ignore')
+
+
+
+                                        ###########################################
+                                        #  pre-Input for the Dynamic clustering   #
+
+
+diff = False # take diffs of the data or not
+preprocessing_meanvar = False # True to use TimeSeriesScalerMeanVariance preprocessing
+preprocessing_fft = False  # True if you want to do the clustering based on fft transformation of X
+preprocessing_sqrt = True
+preprocessing_log = False
+norm = True # normalize the data to 0-1 range
+
+
+                                        ###########################################
+
+####### DEF for null values #####
+
+def null_values(df):
+    null_test = (df.isnull().sum(axis=0) / len(df)).sort_values(ascending=False).index
+    null_data_test = pd.concat([
+        df.isnull().sum(axis=0),
+        (df.isnull().sum(axis=0) / len(df)).sort_values(ascending=False),
+        df.loc[:, df.columns.isin(list(null_test))].dtypes], axis=1)
+    null_data_test = null_data_test.rename(columns={0: '# null',
+                                                    1: '% null',
+                                                    2: 'type'}).sort_values(ascending=False, by='% null')
+    null_data_test = null_data_test[null_data_test["# null"] != 0]
+
+    return null_data_test
+
+
+
+def type(df):
+    return pd.DataFrame(df.dtypes, columns=['Type'])
+
+def preprocessing_meanvar(df):
+    X = TimeSeriesScalerMeanVariance().fit_transform(X)
+    df = pd.DataFrame(X.reshape(df.shape), columns=df.columns, index=df.index)
+    return df
+
+
+def purity_score(y_true, y_pred):
+    # compute contingency matrix (also called confusion matrix)
+    confusion_matrix = contingency_matrix(y_true, y_pred)
+    # return purity
+    return np.sum(np.amax(confusion_matrix, axis=0)) / np.sum(confusion_matrix)
+
+
+def read_csv_and_auto_update_time_interval(uploaded_file):
+    if uploaded_file is not None:
+        data = pd.read_csv(uploaded_file)
+        df = data.copy()
+
+        # Convert the 'Time' column to datetime
+        if 'Time' in df:
+            df['Time'] = pd.to_datetime(df['Time'])
+
+        # If 'Date' column exists, use it; otherwise, create it
+        if 'Date' not in df:
+            df['Date'] = df['Time'].dt.date if 'Time' in df else pd.to_datetime("now").date()
+
+        # Infer the time interval from the 'Time' or 'Date' column
+        if 'Time' in df:
+            time_interval = (df['Time'].diff().mean()).seconds
+        else:
+            # If only 'Date' is present, assume a 1-day interval
+            time_interval = 86400  # 1 day in seconds
+
+        time_interval_str = f"{time_interval // 60}T"
+
+        # Calculate the start date and end date based on the inferred time interval
+        start_date = df['Time'].min() if 'Time' in df else df['Date'].min()
+        end_date = df['Time'].max() if 'Time' in df else df['Date'].max()
+
+        # Use the inferred time interval to generate the date range
+        date_range = pd.date_range(start=start_date, end=end_date, freq=time_interval_str)
+
+        # Update the 'Date' column with the new date range
+        if 'Date' in df:
+            df['Date'] = date_range
+
+        # Extract time-related features
+        df['Hour'] = df['Date'].dt.hour
+        df['Days'] = df['Date'].dt.dayofyear
+        df['Weekday'] = df['Date'].dt.weekday
+        df['Month'] = pd.DatetimeIndex(df['Date']).month
+
+        return df, time_interval_str
+
+
+st.subheader('Please upload your .csv file to continue..!')
+#@st.cache_resource
+
+
+def app():
+    #st.sidebar.title('Clustering-Tool')
+
+    activities = ["1. Exploratory Data Analysis", "2. Time Series Analysis", "3. Clustering", "4. Plotting and Visualization"]
+    choice = st.sidebar.selectbox("Select Activity", activities)
+
+
+
+    # Upload file way
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    global data, df, df1 # global declariation
+
+                                        ###########################################
+                                        #       Exploratory Data Analysis EDA     #
+                                        ###########################################
+
+    
+    #defining all_columns as global
+    if uploaded_file is not None and choice == "1. Exploratory Data Analysis":
+        df, time_interval = read_csv_and_auto_update_time_interval(uploaded_file)
+        #st.write(f"Data with inferred time interval ({time_interval}):")
+        #st.dataframe(df)
+
+        #print(data)
+        st.subheader(choice)
+
+        # Show row dataset
+        if st.checkbox("Show Dataset"):
+            rows = st.number_input("Number of rows", 10, len(data))
+            st.dataframe(data.head(rows))
+
+        # New data set
+        if st.checkbox("Show New Dataset"):
+            n_rows = st.number_input("Number of new rows", 10, len(df))
+            st.dataframe(df.head(n_rows))
+
+        # Show columns
+        if st.checkbox("Columns"):
+            st.write(df.columns)
+
+        # Shows Data types
+        if st.checkbox("Column types"):
+            st.write(df.dtypes.astype(str))
+
+        # Show Shape
+        if st.checkbox("Shape of Dataset"):
+            data_dim = st.radio("Show by", ("Rows", "Columns", "Shape"))
+            if data_dim == "Columns":
+                st.text("Number of Columns: ")
+                st.write(df.shape[1])
+            elif data_dim == "Rows":
+                st.text("Number of Rows: ")
+                st.write(df.shape[0])
+            else:
+                st.write(df.shape)
+
+        # Check null values in dataset
+        if st.checkbox("Check null values"):
+            nvalues = null_values(df)
+            st.write(nvalues)
+
+        # Show Data summary
+        if st.checkbox("Show Data Summary"):
+            st.text("Datatypes Summary")
+            st.write(df.describe())
+
+
+                                        ###########################################
+                                        #         Time Series Analysis            #
+                                        ###########################################2014 
+        st.subheader('2. Time Series Analysis')
+
+        file_container = st.expander("Timeseries Data")
+        file_container.write(df)
+
+        all_cols = df.columns.tolist()
+        nums = df.select_dtypes(include=np.number).columns.tolist()
+        dates = list(filter(lambda x: 'date' in x.lower(), df.columns))
+        no_dates = list(filter(lambda x: 'date' not in x.lower(), df.columns))
+
+        valu_col = st.selectbox(
+            '1. Pick the VALUE column for PLOTS in your data?',(nums),1)
+            #df.index
+            #)
+
+        dat_col = st.selectbox(
+            '2. Pick the date column in your data?',
+            (dates), 0)
+
+        piv_col = st.selectbox(
+            '3. Pick the PIVOT column in your data?',
+            (df.columns), df.columns.get_loc('Hour'))  # Set default to 'Hour'
+
+        dat_plt = st.selectbox(
+            '4. Pick the TIME for PLOTS in your data?',
+            ('YEAR', 'YEAR-MONTH', 'Days', 'Hour', 'Month', 'Weekday'), 3)  # Set default to 'Month'
+
+
+        filt_col = st.selectbox(
+            '5. Pick the filter column in your data?',
+            (no_dates), no_dates.index('Weekday'))  # Set default to 'Weekday'
+
+
+        piks = df[filt_col].unique()
+
+        filtrs = st.multiselect('6. Select values to be included',piks,piks)
+
+        df = df[df[filt_col].isin(filtrs)]
+
+        df['YEAR'] = pd.to_datetime(df[dat_col]).dt.year
+        df['YEAR-MONTH'] = pd.to_datetime(df[dat_col]).dt.to_period('M')
+
+        df1 = pd.pivot_table(df,
+                             values=valu_col,
+                             #valu_col,
+                             index=dat_plt,
+                           columns=piv_col)
+        df1.columns.name = None
+        df1 = df1.reset_index()
+
+        df1.set_index(dat_plt, inplace=True)
+        df1.index = df1.index.to_series().astype(str)
+
+
+        se = df1.sum()
+
+        star_date, en_date = st.select_slider(
+            'Select the date range',
+            options=df1.index, value=(df1.index[0], df1.index[-1])
+        )
+
+        st.write('Selected values are', star_date, ' and ', en_date)
+
+        if st.button('Series PLot from selection'):
+            fig = px.line(df1[star_date:en_date]
+
+            )
+            st.plotly_chart(fig)
+
+        if st.button('Get the pivot table from your selction and continue with Clustering'):
+
+
+            st.write('Pivot Table from your selection')
+
+            st.write(df1[star_date:en_date])# genertaes the pivot table from the selection
+
+            # writing data frame to a CSV file
+            c_u = df1.to_csv()
+
+            col1, col2, col3 = st.columns(3)
+
+            with col2:
+                pass
+            with col3:
+                pass
+            with col1:
+                center_button = st.download_button(
+                    label="Save PIVOT",
+                    data=c_u,
+                    file_name='pivot.csv',
+                    mime='text/csv',
+                )
+
+                                        ###########################################
+                                        #     Plotting and Visualization     #
+                                        ###########################################
+
+    elif uploaded_file is not None and choice == "4. Plotting and Visualization":
+        st.subheader(choice)
+
+
+        all_columns = df.columns.tolist()
+        #all_cols = df.columns.tolist()
+        num = df.select_dtypes(include=np.number).columns.tolist()
+        no_date = list(filter(lambda x: 'date' not in x.lower(), df.columns))
+
+        type_of_plot = st.selectbox("Select Type of Plot",
+                                    ["line", "scatter", "bar", "correlation", "distribution"])
+
+
+        if type_of_plot == "line":
+
+            line_y = st.selectbox("Select the value col", (num), 0)
+            line_x = st.selectbox("Select a column for X Axis", all_columns)
+
+            fil_coll = st.selectbox("Pick the filter feature in data?",
+                                   (no_date), 0)
+            if st.button('Line Chart'):
+
+                fig = px.line(df, x=line_x, y=line_y, color=fil_coll, markers=True)
+
+                st.plotly_chart(fig)
+
+
+        elif type_of_plot == "bar":
+
+            bar_y = st.selectbox("Select the value col", (num), 0)
+            bar_x = st.selectbox("Select a column for X Axis", all_columns)
+
+            fils_coll = st.selectbox("Pick the filter feature for Bar",
+                                    (no_date), 0)
+
+            if st.button('Bar Chart'):
+
+                fig = px.bar(df, x=bar_x, y=bar_y, color=fils_coll)
+                st.plotly_chart(fig)
+
+        elif type_of_plot == "correlation":
+
+            cor_coll = st.selectbox("Pick the filter feature for Corelation",
+                                     (no_date), 0)
+
+            if st.button('Correlation plot'):
+                fig = px.scatter_matrix(df,
+
+                                        color=cor_coll,
+
+                                         )
+                st.plotly_chart(fig)
+
+        elif type_of_plot == "scatter":
+            st.write("Scatter Plot")
+
+            scatter_y = st.selectbox("Select the value col", (num), 0)
+            scatter_x = st.selectbox("Select a column for X Axis", all_columns)
+
+            fil_col = st.selectbox("Pick the filter feature in your data?",
+                (no_date), 0)
+
+            if st.button('Scatter plot'):
+
+                fig = px.scatter(df,
+                    x= scatter_x,
+                    y= scatter_y,
+                    color= fil_col,
+                )
+                st.plotly_chart(fig)
+
+
+        elif type_of_plot == "distribution":
+
+            hist_y = st.selectbox("Select the value col", (num), 0)
+            hist_x = st.selectbox("Select a column for X Axis", all_columns)
+
+            hist_col = st.selectbox("Pick the filter feature in your data?",
+                                   (no_date), 0)
+
+            if st.button('Distribution Chart'):
+
+                fig = px.histogram(df, x=hist_x, y=hist_y, color=hist_col, marginal="box",
+                                   hover_data=df[all_columns])
+                st.plotly_chart(fig)
+
+                                        ###########################################
+                                        #               Clustering                #
+                                        ###########################################
+                                        
+    elif uploaded_file is not None and choice == "3. Clustering":
+        st.subheader(choice)
+        st.sidebar.subheader('Clustering Parameters')
+
+        # New data set
+        dataf = df1.copy()
+        file_container = st.expander("Data Set")
+        file_container.write(dataf)
+
+        all_column = df.columns.tolist()
+
+        nums = df.select_dtypes(include=np.number).columns.tolist()
+        non_date = list(filter(lambda x: 'date' not in x.lower(), df.columns))
+
+        if st.checkbox("Plot the Pre-Cluster data set"):
+
+            fig = px.scatter(df1,
+                             )
+            st.plotly_chart(fig)
+
+        Xx = df1.copy()
+        # Check for and handle missing values
+        if Xx.isna().sum().sum() > 0:
+            imputer = SimpleImputer(strategy='mean')
+            Xx = imputer.fit_transform(Xx)
+
+        # Scale the data
+        scaler = MinMaxScaler()
+        Xx = scaler.fit_transform(Xx)
+        n_clusters = st.sidebar.number_input("Choose the number of Clusters", 2, 50, step=2, key='no_of_clusters')
+        number = st.sidebar.number_input('Random Seed', min_value=10, step=15)
+        kmeans= KMeans(n_clusters)
+        kmeans.fit(Xx)
+
+        clustered_data = df1.copy()  # Create a new DataFrame
+        clustered_data['cluster_pred'] = kmeans.fit_predict(Xx)  # Add the 'cluster_pred' column
+
+        x_array = np.array(df1)
+
+        scaler = MinMaxScaler()
+        x_scaled = scaler.fit_transform(x_array)
+
+                                        ###########################################
+                                        ###           Silhouette Plot           ###
+                                        ###########################################
+        if st.checkbox("Plot Silhouette score"):
+            st.write("SIl plot ")
+
+            # Now check silhouette coefficient
+
+            figures = []
+            r_n_clus=range(2, 12)
+
+            for n_clusters in r_n_clus:
+                # Create a subplot with 1 row and 2 columns
+                fig = tools.make_subplots(rows=1, cols=2,
+                                          print_grid=False,
+                                          subplot_titles=('The silhouette plot for the different clusters.',
+                                                          'With visualization of the clustered data.'))
+
+
+                # SIl range among [-0.1, 1]
+                fig['layout']['xaxis1'].update(title='The SIL coefficient values',
+                                               range=[-0.1, 1])
+
+
+                # Let's print every clusters, to delimit it clearly.
+                fig['layout']['yaxis1'].update(title='Cluster label',
+                                               showticklabels=False,
+                                               range=[0, len(x_array) + (n_clusters + 1) * 10])
+
+                # Initialize the clusterer with n_clusters value and a random generator
+
+                cl_er = KMeans(n_clusters=n_clusters, random_state=number)
+                # Impute missing values
+                imputer = SimpleImputer(strategy='mean')
+                x_array = imputer.fit_transform(x_array)
+
+                # Scale the data
+                scaler = MinMaxScaler()
+                x_array = scaler.fit_transform(x_array)
+                cl_lab = cl_er.fit_predict(x_array)
+
+                # The silhouette_score gives the average value for all the samples.
+                # This gives a perspective into the density and separation of the formed
+                # clusters
+                silhouette_avg = silhouette_score(x_array, cl_lab)
+                #print("For n_clusters =", n_clusters,
+                #      "The average silhouette_score is :", silhouette_avg)
+
+
+                # Compute the silhouette scores for each sample
+                sam_sil = silhouette_samples(x_array, cl_lab)
+                y_low = 10
+
+                for i in range(n_clusters):
+                    # Aggregate the silhouette scores for samples belonging to
+                    # cluster i, and sort them
+                    n_sil_values = sam_sil[cl_lab == i]
+
+                    n_sil_values.sort()
+
+                    dm_cl_i = n_sil_values.shape[0]
+                    y_up = y_low + dm_cl_i
+
+
+                    d_area = go.Line( y=np.arange(y_low, y_up),
+                                             x=n_sil_values,
+                                             mode='lines',
+                                             showlegend=True,
+                                            #color='dodgerblue',
+                                             line=dict(width=0.5, color='dodgerblue'),
+                                             fill='tozerox')
+                    fig.append_trace(d_area, 1, 1)
+
+                    # Compute the new y_lower for next plot
+                    y_low = y_up + 10  # 10 for the 0 samples
+
+                    # The vertical line for average silhouette score of all the values
+                    a_line = go.Line(x=[silhouette_avg],
+                                        #y=[0, 10],
+                                        showlegend=True,
+                                        mode='lines',
+                                        line=dict(color="red", dash='dash',
+                                        width=1))
+
+                    fig.append_trace(a_line, 1, 1)
+
+                    # 2nd Plot showing the actual clusters formed
+                    clr = cm.nipy_spectral(cl_lab.astype(float) / n_clusters)
+
+
+                    cfig = go.Line(
+                                          x=x_array[:, 0],
+                                          y=x_array[:, 1],
+                                          showlegend=True,
+                                          mode='markers',
+                                          marker=dict(color=clr,
+                                                      size=4)
+                                          )
+                    fig.append_trace(cfig, 1, 2)
+
+                    # Labeling the clusters
+                    cen = cl_er.cluster_centers_
+                    # circles at cluster centers
+                    cen = go.Line(x=cen[:, 0],
+                                         y=cen[:, 1],
+                                         showlegend=True,
+                                         mode='markers',
+                                         marker=dict(color='green', size=10,
+                                                     line=dict(color='black',
+                                                               width=1))
+                                         )
+
+                    fig.append_trace(cen, 1, 2)
+
+                fig['layout']['xaxis2'].update(title='Feature space for the 1st feature',
+                                              zeroline=False)
+                fig['layout']['yaxis2'].update(title='Feature space for the 2nd feature',
+                                               zeroline=False)
+
+                fig['layout'].update(title="Silhouette analysis for KMeans clustering "
+                                           "with n_clusters = %d" % n_clusters)
+
+                figures.append(fig)
+                st.plotly_chart(fig)
+
+                #st.checkbox("Number of cluster with average of Silhouette Score")
+                sil_con = st.expander("For no_cluster with avg. of Sil Score")
+                st.write("For n_clusters =", n_clusters,
+                          "The average silhouette_score is :", silhouette_avg)
+
+                                    ###########################################
+                                    ###         Dynamic Clustering          ###
+                                    ###########################################
+
+        if st.sidebar.checkbox("Dynamic Clustering"):
+            st.subheader("Dynamic Clusterng ")
+
+            mod_l = ['Time Series K-Means', 'hierarchical', 'DBA', 'KMedoids']
+            mod_choice = st.sidebar.selectbox("Select model", mod_l)
+
+
+
+            """centr = ['mean','min','max','sum', 'median']
+            # "mean", "median", "shape", "dba", "pam", "sdtw_cent"]
+            centr_choice = st.sidebar.selectbox("Select cenroid", centr)"""
+
+
+
+
+            ## Dynamic Clustering
+            norm = True  #  data to 0-1 range normalizing
+            preprocessing_meanvar = False  # Set to True if use TimeSeriesScalerMeanVariance
+
+            # Fast Fourier Transform (fft) and Clustering of Time Series
+            # Set to True if to do the clustering based on fft transformation of X : can use in future development
+            preprocessing_fft = False
+            preprocessing_sqrt = True
+            preprocessing_log = False
+            model = mod_choice  # ['Partitional', 'hierarchical', 'DBA', 'KMedoids'] # you can add DTW, soft-DTW, kshape too
+            min_n = 0  # only interested in clusters with min_n or more members
+
+            # Perform smoothing as specified
+            """if smooth_n > 0:
+                if smooth_func == 'mean':
+                    df2 = df2.rolling(smooth_n).mean().dropna(how='all')
+                elif smooth_func == 'min':
+                    df2 = df2.rolling(smooth_n).min().dropna(how='all')
+                elif smooth_func == 'max':
+                    df2 = df2.rolling(smooth_n).max().dropna(how='all')
+                elif smooth_func == 'sum':
+                    df2 = df2.rolling(smooth_n).sum().dropna(how='all')
+                elif smooth_func == 'median':
+                    df2 = df2.rolling(smooth_n).median().dropna(how='all')
+                else:
+                    df2 = df2.rolling(smooth_n).mean().dropna(how='all')""" # throwing some errors
+
+            df2= df1.copy()
+
+            df2 = df2.loc[:, ~df2.columns.duplicated()]
+
+            # To drop any empty columns
+            df2 = df2.dropna(axis=1, how='all')
+
+            # use if necessary to try remove any N/A values and use forward fill and backward fill to
+            df2 = df2.ffill().bfill()
+            
+            df2 = df2.fillna(df2.mean())
+
+
+            # do sqrt
+            if preprocessing_sqrt:
+                df2 = df2.apply(lambda col: np.sqrt(col))
+
+
+            # do log if set True
+            if preprocessing_log:
+                df2 = df2.apply(lambda col: np.log1p(col))
+
+            # take differences if specified
+            if diff:
+                df2 = df2.diff()
+
+            start_time = time.perf_counter()
+
+            # normalizing data once
+            if norm:
+                df2 = (df2 - df2.min()) / (df2.max() - df2.min())
+
+            # drop any empty columns that may remain
+            df2 = df2.dropna(axis=1, how='all')
+
+
+            # look at our data
+            #print("DF2 ",df2.shape, " shape is")
+            #print(df2.index.min(), df2.index.max())
+            df2.head()
+            #print("latest DF2 ", df2, " is.")
+            #st.write("New DF ")
+            #file_contai = st.expander("New DataSet")
+            #file_contai.write(df2)
+
+            # Handle missing values with SimpleImputer
+            imputer = SimpleImputer(strategy='mean')
+            df2 = imputer.fit_transform(df2)
+
+            # Get values to cluster on
+            if preprocessing_fft:
+                X = rfft(df2).transpose()
+            else:
+                X = df2.transpose()
+
+
+            iter = st.sidebar.number_input('Max Iteration', min_value=10, step=5)
+
+            n_init = st.sidebar.number_input('n init', min_value=2, step=2)
+
+                                    ###########################################
+                                    ###         Clustering Models           ###
+                                    ###########################################
+
+            if (mod_choice == 'Time Series K-Means'):
+                dis = ["euclidean", "dtw", "softdtw"]
+                dis_choice = st.sidebar.selectbox("Select Distance measure", dis)
+                mod_choice = TimeSeriesKMeans(n_clusters=n_clusters, metric=dis_choice, max_iter=iter, n_init=n_init, random_state=number).fit(X)
+
+
+            elif (mod_choice == 'DTW'):
+                dis = ["euclidean", "dtw", "softdtw"]
+                dis_choice = st.sidebar.selectbox("Select Distance measure", dis)
+                mod_choice = TimeSeriesKMeans(n_clusters=n_clusters, metric=dis_choice, max_iter=iter, n_init=n_init, random_state=number).fit(X)
+
+
+
+            elif (mod_choice == 'softdtw'):
+                dis = ["euclidean", "dtw", "softdtw"]
+                dis_choice = st.sidebar.selectbox("Select Distance measure", dis)
+                mod_choice = TimeSeriesKMeans(n_clusters=n_clusters, metric=dis_choice, max_iter=iter, n_init=n_init, random_state=number).fit(X)
+
+
+            elif (mod_choice == 'hierarchical'):
+                dis = ["euclidean", "manhattan", "cosine"]
+                dis_choice = st.sidebar.selectbox("Select Affinity measure", dis)
+                link = ['single', 'complete', 'average', 'ward']
+                link_choice = st.sidebar.selectbox("Select Linkage", link)
+                mod_choice = AgglomerativeClustering(distance_threshold=None, n_clusters=n_clusters, affinity=dis_choice,
+                                                     linkage=link_choice).fit(X)
+                if st.checkbox("Plot Dendrogram"):
+                    #plots_title = ( 'The Dendrogram for %d' % link_choice )
+
+                    if link_choice == 'single':
+                        Z = linkage(X, 'single')
+                    elif link_choice == 'complete':
+                        Z = linkage(X, 'complete')
+                    elif link_choice == 'average':
+                        Z = linkage(X, 'average')
+                    elif link_choice == 'ward':
+                        Z = linkage(X, 'ward')
+
+                    fig = ff.create_dendrogram(Z)
+                    #fig.update_layout(color_threshold=1.5)
+                    st.plotly_chart(fig)
+
+
+            elif (mod_choice == 'Fuzzy'): ## need some development
+
+                cent = ['fcm', 'fcmdd']
+                cent_choice = st.sidebar.selectbox("Select Centroid", cent)
+
+
+                #initial_centers = kmeans_plusplus_initializer(df2, 3,
+                #                                              kmeans_plusplus_initializer.FARTHEST_CENTER_CANDIDATE).initialize()
+                mod_choice = fcm( method="Cmeans", n_clusters = n_clusters, max_iter=iter,
+                                              random_state=number, matric= 'euclidean')
+
+                #create instance of Fuzzy C-Means algorithm
+                fcm_instance = fcm(df2, initial_centers)
+
+                # run cluster analysis and obtain results
+                fcm_instance.process()
+                df_cluster = fcm_instance.get_clusters()
+                #print("FCM labels ",FCM.labels_, " this.")
+                #fcm_fit = mod_choice.fit(tr_set)
+                #mod_choice = mod_choice.centers.T
+
+
+            elif (mod_choice == 'DBA'):
+                dis = ["euclidean", "dtw", "softdtw"]
+                dis_choice = st.sidebar.selectbox("Select Distance measure", dis)
+                mod_choice = TimeSeriesKMeans(n_clusters=n_clusters, n_init=n_init, metric=dis_choice, verbose=True,
+                                              max_iter_barycenter=iter,
+                                            random_state=number).fit(X)
+
+
+
+            elif (mod_choice == 'KMedoids'):
+                dis = ["euclidean", "manhattan", "chebyshev", "canberra", "minkowski", "cosine"]
+                dis_choice = st.sidebar.selectbox("Select Distance measure", dis)
+                mod_choice = KMedoids(n_clusters=n_clusters, metric=dis_choice, method='pam', random_state=number).fit(X)
+
+
+
+            #print(len(set(mod_choice.labels_)))
+            #print("Model labels are ",mod_choice.labels_, " is this.")
+
+
+
+            silhouette_avrg = silhouette_score(X, mod_choice.labels_)
+            #print("Latest Silhouette ",round(silhouette_avrg,2), " average is.")
+
+            df2 = pd.DataFrame(df2)
+            #  Generate a darafreame along with metrics and their cluster labels
+            cl_df = pd.DataFrame(list(zip(df2.columns, mod_choice.labels_)), columns=['metric', 'cluster'])
+
+            # Generating helper dictionaries and lists
+            cl_met = cl_df.groupby(['cluster'])['metric'].apply(lambda x: [x for x in x]).to_dict()
+            cl_count = cl_df['cluster'].value_counts().to_dict()
+            a_clust = [cluster for cluster in cl_count]
+            drp_clust = [cluster for cluster in cl_count if cl_count[cluster] < min_n]
+            list_clust = [cluster for cluster in cl_count if cl_count[cluster] >= min_n]
+            clst_final= np.array(list_clust)
+
+            if st.checkbox("Data frame WIth Metric and CLuster "):
+                #st.write("DTW plot ")
+
+                ff_contai = st.expander("DataFrame Metric and CLuster")
+                ff_contai.write(cl_df)
+
+                # writing data frame to a CSV file
+                cl_u = cl_df.to_csv()
+
+                col1, col2, col3 = st.columns(3)
+
+                with col1:
+                    pass
+                with col3:
+                    pass
+                with col2:
+                    center_button = st.download_button(
+                        label="Save DataFrame",
+                        data=cl_u,
+                        file_name='df_met_clust.csv',
+                        mime='text/csv',
+                    )
+
+
+            #print(cl_df)
+
+            data2 = np.array([mod_choice.labels_])
+            #print(data2)
+
+            d_cl_qua = {}
+            for clst_i in a_clust:
+                cr_x = df2[cl_met[clst_i]].corr().abs().values
+                cr_x_mean = round(cr_x[np.triu_indices(cr_x.shape[0], 1)].mean(), 2)
+                d_cl_qua[clst_i] = cr_x_mean
+
+            # get quality score for each cluster
+            #if st.checkbox("View CVI's"):
+
+                #clst_eqal_choice == 'sil'
+                silhouette_scores = silhouette_samples(X, mod_choice.labels_)
+                cl_df_sil = pd.DataFrame(
+                        list(zip(mod_choice.labels_, silhouette_scores)),
+                        columns=['cluster', 'silhouette_score'])
+                cl_df_sil = cl_df_sil.groupby(['cluster']).max()
+                # print("Cluster Sil ",cl_df_sil, " score.")
+                d_cl_qua = cl_df_sil.to_dict()['silhouette_score']
+               # print("1. SIlhouette index ", silhouette_avrg, " is.")
+                #print("2. Latest Silhouette ", round(silhouette_avrg, 2), " average is.")
+
+
+            # build cluster level df with some cluster metadata
+            cl_df_met = pd.DataFrame.from_dict(cl_count, orient='index', columns=['n'])
+            cl_df_met.index.names = ['cluster']
+            #d_cl_qua = silhouette_avrg.to_dict()['silhouette_score']
+            cl_df_met['quality_score'] = cl_df_met.index.map(d_cl_qua)
+            cl_df_met = cl_df_met.sort_values('quality_score', ascending=False)
+            #cl_df_met.head()
+            #print(cl_df_met)
+
+            if st.checkbox("CLuster with quality score"):
+                f_contai = st.expander("CLuster and its quality score")
+                f_contai.write(cl_df_met)
+                # writing data frame to a CSV file
+                cl_cu = cl_df_met.to_csv()
+
+                col1, col2, col3 = st.columns(3)
+
+                with col1:
+                    pass
+                with col3:
+                    pass
+                with col2:
+                    center_button = st.download_button(
+                        label="Save quality score",
+                        data=cl_cu,
+                        file_name='quality_score.csv',
+                        mime='text/csv',
+                    )
+
+
+            # Plotting every clusters
+            if st.checkbox("Plot each cluster"):
+                for clst_i, row in cl_df_met.iterrows():
+                    if clst_i in list_clust:
+                        plot_title = f"cluster {cl_met[clst_i]} (quality={row['quality_score']}, n={row['n']})"
+                        color_c = cl_met[clst_i]
+                        fig = px.line(df2.values,
+                                        y = list_clust,
+                                      labels={"variable": "Cluster"},
+                                      title=plot_title,
+
+                                      )
+                        st.plotly_chart(fig)
+
+            
+            # Create a figure to plot all clusters
+            all_clusters_fig = go.Figure()
+            
+            clustered_data = df2.groupby(mod_choice.labels_, axis=1)
+
+            # Filter out clusters that are not in list_clust
+            clustered_data = {k: v for k, v in clustered_data if k in list_clust}
+
+            # Create dictionaries to hold the mean, max, and min values for each cluster
+            mean_data = {}
+            upper_quartile = {}
+            lower_quartile = {}
+
+            # Calculate the mean, max, and min for each cluster and add traces to the 'all_clusters_fig'
+            for cluster_id in list_clust:
+                plot_title = f"Cluster {cluster_id} - Mean, Max, and Min Lines"
+                mean_data[cluster_id] = clustered_data[cluster_id].mean(axis=1)
+                upper_quartile[cluster_id] = clustered_data[cluster_id].max(axis=1)
+                lower_quartile[cluster_id] = clustered_data[cluster_id].min(axis=1)
+
+                # Add mean line
+                all_clusters_fig.add_trace(go.Scatter(x=df2.columns, y=mean_data[cluster_id], mode='lines', name=f'Mean (Cluster {cluster_id})'))
+
+                # Add max line
+                all_clusters_fig.add_trace(go.Scatter(x=df2.columns, y=upper_quartile[cluster_id], mode='lines', name=f'Max (Cluster {cluster_id})', line=dict(color='lightgray')))
+
+                # Add min line
+                all_clusters_fig.add_trace(go.Scatter(x=df2.columns, y=lower_quartile[cluster_id], mode='lines', name=f'Min (Cluster {cluster_id})', fill='tonexty', line=dict(color='lightgray')))
+
+                all_clusters_fig.update_layout(
+                    xaxis_title="Metrics",
+                    yaxis_title="Values",
+                    title=plot_title,
+                )
+
+            # Aggregate the mean, max, and min values across all clusters
+            mean_aggregated = np.mean([mean_data[cluster_id] for cluster_id in list_clust], axis=0)
+            max_aggregated = np.max([upper_quartile[cluster_id] for cluster_id in list_clust], axis=0)
+            min_aggregated = np.min([lower_quartile[cluster_id] for cluster_id in list_clust], axis=0)
+
+            # Create a 3-line plot with the original clusters and the aggregated lines
+            plot_title = f"All Clusters - Original and Aggregated Lines"
+            all_clusters_fig = go.Figure()
+
+            for cluster_id in list_clust:
+                # Add original cluster line
+                all_clusters_fig.add_trace(go.Scatter(x=df2.columns, y=mean_data[cluster_id], mode='lines', name=f'Cluster {cluster_id}'))
+
+            # Add aggregated mean line
+            all_clusters_fig.add_trace(go.Scatter(x=df2.columns, y=mean_aggregated, mode='lines', name='Aggregated Mean', line=dict(color='black')))
+
+            # Add aggregated max line
+            all_clusters_fig.add_trace(go.Scatter(x=df2.columns, y=max_aggregated, mode='lines', name='Aggregated Max', line=dict(color='lightgray')))
+
+            # Add aggregated min line
+            all_clusters_fig.add_trace(go.Scatter(x=df2.columns, y=min_aggregated, mode='lines', name='Aggregated Min', fill='tonexty', line=dict(color='lightgray')))
+
+            all_clusters_fig.update_layout(
+                xaxis_title="Metrics",
+                yaxis_title="Values",
+                title=plot_title,
+            )
+
+            # Plotting every clusters
+            if st.checkbox("Visualize all clusters"):
+                # Customize this part based on your preferences
+                all_clusters_fig.update_layout(
+                    showlegend=True,  # Show legend to distinguish clusters
+                    title="All Clusters with Original and Aggregated Lines"
+                )
+
+                st.plotly_chart(all_clusters_fig)
+
+
+
+
+
+            # Getting cluster centers
+            if st.checkbox("Print cluster centers"):
+                cen_df_cl = pd.DataFrame(mod_choice.cluster_centers_.reshape(mod_choice.cluster_centers_.shape[0],
+                                                                                 mod_choice.cluster_centers_.shape[
+                                                                                     1])).transpose()
+                cen_df_cl.index = df2.index
+                for cluster in cl_df_met.index:
+                    print('\n'.join(textwrap.wrap(
+                        f"Cluster {cluster} (n={cl_count[cluster]}, score = {d_cl_qua[cluster]})",
+                        width=175, replace_whitespace=False)))
+                    print(f"CLuster distribution = {(cl_met[cluster])}")
+
+                col1, col2, col3, col4 = st.columns(4)
+                with col1:
+                    col1.subheader("Cluster")
+                    col1.write(cluster)
+
+                with col2:
+                    col2.subheader("n=")
+                    col2.write(cl_count)
+
+                with col3:
+                    col3.subheader("score=")
+                    col3.write(d_cl_qua)
+
+                with col4:
+                    col4.subheader("Cluster distribution")
+                    col4.write(cl_met)
+
+
+                fig = px.line(cen_df_cl,
+                                  title=f"Cluster {cluster} Centers",
+                              labels={"variable": "Cluster"},
+                                  )
+                st.plotly_chart(fig)
+
+                                    ###########################################
+                                    ###         Evaluate Clustering         ###
+                                    ###########################################
+
+            if st.sidebar.checkbox("Evaluate Cluster"):
+                st.subheader("Evaluate Clustering")
+
+                s_c = st.expander("Summary")
+                if mod_choice == 'hierarchical':
+                    s_c.write(f"{mod_choice} clustering with {dis_choice} Affinity and  with "
+                             f"{link_choice} linkage method and {n_clusters} clusters were created."
+                             f"The measured execution time was {time.perf_counter() - start_time} seconds.")
+                else:
+                    s_c.write(f"{mod_choice} clustering with {dis_choice} distance."
+                              f"{n_clusters} clusters were created. "
+                              f"The measured execution time was {time.perf_counter() - start_time} seconds.")
+
+
+                                    ###########################################
+                                    ###                CVI's                ###
+                                    ###########################################
+
+
+
+                st.write('Select Cluster Validity Index')
+
+                indec_si = st.expander("Silhouette score (SI)")
+                indec_si.write(silhouette_avrg)
+
+
+                indec_ch = st.expander("Calinski Harabasz Index (CH)")
+                calinski_harabasz_index = calinski_harabasz_score(X, mod_choice.labels_)
+                indec_ch.write(calinski_harabasz_index)
+
+
+                indec_db = st.expander("Davies Bouldin Index (DB)")
+                davies_bouldin_index = davies_bouldin_score(X, mod_choice.labels_)
+                indec_db.write(davies_bouldin_index)
+
+                # creating a data frame
+                cvi = pd.DataFrame([['SI',silhouette_avrg ], ['CH', calinski_harabasz_index], ['DB', davies_bouldin_index]],
+                                   columns=['CVI', 'Index Score'])
+
+                # writing data frame to a CSV file
+                cl_cui = cvi.to_csv()
+                #csv = pd.DataFrame(matrix).to_csv()
+
+                col1, col2, col3 = st.columns(3)
+
+                with col1:
+                    pass
+                with col3:
+                    pass
+                with col2:
+                    center_button = st.download_button(
+                    label="Download CVI's",
+                    data=cl_cui,
+                    file_name='cvis.csv',
+                    mime='text/csv',
+                )
+
+
+                # squared distance to cluster center
+                ## Getting the Distance matrix
+                n_df = (df2.values)
+                #print("Last Data frame ", df2, " is.")
+
+
+                (df2.values).shape
+                matrix = np.zeros(((df2.values).shape[0], (df2.values).shape[0]))
+
+                for i in range((df2.values).shape[0]):
+                    for j in range((df2.values).shape[0]):
+                        matrix[i, j] = np.sqrt(np.sum((n_df[i] - n_df[j]) ** 2))
+
+
+                f_c = st.expander("Cross distance matrix")
+                f_c.write(matrix)
+
+                csv = pd.DataFrame(matrix).to_csv()
+
+
+
+                """ddf = pd.DataFrame(df, columns=['Month', 'Weekday', 'Hour']) ## I need this only for example comment
+
+                dd_cluster = pd.DataFrame(cl_df, columns=['cluster'])
+
+
+
+                df3 = [dd_cluster, ddf]
+                df_final = pd.concat(df3, axis=1)
+
+
+
+                df_final['cluster'] = df_final['cluster'].fillna(df_final['cluster'].median())
+
+                df_final['cluster'] = df_final['cluster'].astype(int)
+
+
+                file_con = st.expander("Data Set with Metric and Cluster")
+                file_con.write(df_final) # needs more evelopment"""
+
+                col1, col2, col3 = st.columns(3)
+
+                with col1:
+                    pass
+                with col3:
+                    pass
+                with col2:
+                    center_button = st.download_button(
+                        label="Download Distance matrix",
+                        data=csv,
+                        file_name='Distance_matrix.csv',
+                        mime='text/csv',
+                    )
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+