# Dash app to visualize scRNA-seq data quality control metrics from scanpy objects
# Shoutout to Coding-with-Adam for the initial template of the project: 
# https://github.com/Coding-with-Adam/Dash-by-Plotly/blob/master/Dash%20Components/Graph/dash-graph.py

import dash
from dash import dcc, html, Output, Input
import plotly.express as px
import dash_callback_chain
import yaml
import polars as pl
import os
pl.enable_string_cache(False)

# Set custom resolution for plots:
config_fig = {
  'toImageButtonOptions': {
    'format': 'svg',
    'filename': 'custom_image',
    'height': 600,
    'width': 700,
    'scale': 1,
  }
}
from adlfs import AzureBlobFileSystem
mountpount=os.environ['AZURE_MOUNT_POINT'],
AZURE_STORAGE_ACCESS_KEY=os.getenv('AZURE_STORAGE_ACCESS_KEY')
AZURE_STORAGE_ACCOUNT=os.getenv('AZURE_STORAGE_ACCOUNT')

# Load in config file
config_path = "./data/config.yaml"

# Add the read-in data from the yaml file
def read_config(filename):
    with open(filename, 'r') as yaml_file:
        config = yaml.safe_load(yaml_file)
    return config

config = read_config(config_path)
path_parquet = config.get("path_parquet")
col_batch = config.get("col_batch")
col_features = config.get("col_features")
col_counts = config.get("col_counts")
col_mt = config.get("col_mt")

filepath = f"az://{path_parquet}"

storage_options={'account_name': AZURE_STORAGE_ACCOUNT, 'account_key': AZURE_STORAGE_ACCESS_KEY,'anon': False}
#azfs = AzureBlobFileSystem(**storage_options )

# Load in multiple dataframes
df = pl.read_parquet(filepath, storage_options=storage_options)

# Setup the app
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets) #, requests_pathname_prefix='/dashboard1/'

#df = pl.read_parquet(filepath,storage_options=storage_options)
#df = pl.DataFrame()
#abfs = AzureBlobFileSystem(account_name=accountname,account_key=accountkey)
#df = df.rename({"__index_level_0__": "Unnamed: 0"})

#df1 = pl.read_parquet(filepath, storage_options=storage_options)

#df2 = pl.read_parquet(f"az://data10xflex/{dataset_chosen}.parquet", storage_options=storage_options)

tab0_content = html.Div([
    html.Label("Dataset chosen"),    
    dcc.Dropdown(id='dpdn1', value="corg/10xflexcorg_umap_clusres", multi=False,
                 options=["corg/10xflexcorg_umap_clusres","d1011/10xflexd1011_umap_clusres"])
])

@app.callback(
    Input(component_id='dpdn1', component_property='value')
)

def update_filepath(dpdn1):
    global df
    if str(f"az://data10xflex/{dpdn1}.parquet") != str(filepath):
        print("not identical filepath, chosing other")
        df2 = pl.read_parquet(f"az://data10xflex/{dpdn1}.parquet", storage_options=storage_options)
        df = df2
    return

#df = pl.read_parquet(filepath, storage_options=storage_options)
min_value = df[col_features].min()
max_value = df[col_features].max()

min_value_2 = df[col_counts].min()
min_value_2 = round(min_value_2)
max_value_2 = df[col_counts].max()
max_value_2 = round(max_value_2)

min_value_3 = df[col_mt].min()
min_value_3 = round(min_value_3, 1)
max_value_3 = df[col_mt].max()
max_value_3 = round(max_value_3, 1)

# Loads in the conditions specified in the yaml file

# Note: Future version perhaps all values from a column in the dataframe of the parquet file
# Note 2: This could also be a tsv of the categories and own specified colors
#conditions = df[col_batch].unique().to_list()
# Create the first tab content
# Add Sliders for three QC params: N genes by counts, total amount of reads and pct MT reads

tab1_content = html.Div([
    html.Label("Column chosen"),    
    dcc.Dropdown(id='dpdn2', value="batch", multi=False,
                 options=df.columns),
    html.Label("N Genes by Counts"),
    dcc.RangeSlider(
        id='range-slider-1',
        step=250,
        value=[min_value, max_value],
        marks={i: str(i) for i in range(min_value, max_value + 1, 250)},
    ),
    dcc.Input(id='min-slider-1', type='number', value=min_value, debounce=True),
    dcc.Input(id='max-slider-1', type='number', value=max_value, debounce=True),
    html.Label("Total Counts"),
    dcc.RangeSlider(
        id='range-slider-2',
        step=7500,
        value=[min_value_2, max_value_2],
        marks={i: str(i) for i in range(min_value_2, max_value_2 + 1, 7500)},
    ),
    dcc.Input(id='min-slider-2', type='number', value=min_value_2, debounce=True),
    dcc.Input(id='max-slider-2', type='number', value=max_value_2, debounce=True),
    html.Label("Percent Mitochondrial Genes"),
    dcc.RangeSlider(
        id='range-slider-3',
        step=5,
        min=0,
        max=100,
        value=[min_value_3, max_value_3],
    ),
    dcc.Input(id='min-slider-3', type='number', value=min_value_3, debounce=True),
    dcc.Input(id='max-slider-3', type='number', value=max_value_3, debounce=True),
    html.Div([
        dcc.Graph(id='pie-graph', figure={}, className='four columns',config=config_fig),
        dcc.Graph(id='my-graph', figure={}, clickData=None, hoverData=None,
                  className='four columns',config=config_fig
                  ),
        dcc.Graph(id='scatter-plot', figure={}, className='four columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot-2', figure={}, className='four columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot-3', figure={}, className='four columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot-4', figure={}, className='four columns',config=config_fig)
    ]),
])

# Create the second tab content with scatter-plot-5 and scatter-plot-6
tab2_content = html.Div([
    html.Div([
            html.Label("S-cycle genes"),
            dcc.Dropdown(id='dpdn3', value="MCM5", multi=False,
                 options=[
    "MCM5",
    "PCNA",
    "TYMS",
    "FEN1",
    "MCM2",
    "MCM4",
    "RRM1",
    "UNG",
    "GINS2",
    "MCM6",
    "CDCA7",
    "DTL",
    "PRIM1",
    "UHRF1",
    "MLF1IP",
    "HELLS",
    "RFC2",
    "RPA2",
    "NASP",
    "RAD51AP1",
    "GMNN",
    "WDR76",
    "SLBP",
    "CCNE2",
    "UBR7",
    "POLD3",
    "MSH2",
    "ATAD2",
    "RAD51",
    "RRM2",
    "CDC45",
    "CDC6",
    "EXO1",
    "TIPIN",
    "DSCC1",
    "BLM",
    "CASP8AP2",
    "USP1",
    "CLSPN",
    "POLA1",
    "CHAF1B",
    "BRIP1",
    "E2F8"
]),
            html.Label("G2M-cycle genes"),
            dcc.Dropdown(id='dpdn4', value="TOP2A", multi=False,
                 options=[
    'HMGB2', 'CDK1', 'NUSAP1', 'UBE2C', 'BIRC5', 'TPX2', 'TOP2A', 'NDC80', 'CKS2', 'NUF2', 'CKS1B', 'MKI67', 'TMPO', 'CENPF', 'TACC3', 'FAM64A', 'SMC4', 'CCNB2', 'CKAP2L', 'CKAP2', 'AURKB', 'BUB1', 'KIF11', 'ANP32E', 'TUBB4B', 'GTSE1', 'KIF20B', 'HJURP', 'CDCA3', 'HN1', 'CDC20', 'TTK', 'CDC25C', 'KIF2C', 'RANGAP1', 'NCAPD2', 'DLGAP5', 'CDCA2', 'CDCA8', 'ECT2', 'KIF23', 'HMMR', 'AURKA', 'PSRC1', 'ANLN', 'LBR', 'CKAP5', 
                     'CENPE', 'CTCF', 'NEK2', 'G2E3', 'GAS2L3', 'CBX5', 'CENPA'
]),
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot-5', figure={}, className='three columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot-6', figure={}, className='three columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot-7', figure={}, className='three columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot-8', figure={}, className='three columns',config=config_fig)
    ]),
])

# Create the second tab content with scatter-plot-5 and scatter-plot-6
tab3_content = html.Div([
    html.Div([
            html.Label("UMAP condition 1"),
            dcc.Dropdown(id='dpdn5', value="batch", multi=False,
                 options=df.columns),
            html.Label("UMAP condition 2"),
            dcc.Dropdown(id='dpdn6', value="n_genes_by_counts", multi=False,
                 options=df.columns),
    html.Div([
        dcc.Graph(id='scatter-plot-9', figure={}, className='four columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot-10', figure={}, className='four columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot-11', figure={}, className='four columns',config=config_fig)
    ]),
    html.Div([
            dcc.Graph(id='my-graph2', figure={}, clickData=None, hoverData=None,
                  className='four columns',config=config_fig
                  )
    ]),
    ]),
])
#    html.Div([
#        dcc.Graph(id='scatter-plot-12', figure={}, className='four columns',config=config_fig)
#    ]),


tab4_content = html.Div([
    html.Div([
            html.Label("Multi gene"),
            dcc.Dropdown(id='dpdn7', value=["PAX6","TP63","S100A9"], multi=True,
                 options=df.columns),
]),
    html.Div([
        dcc.Graph(id='scatter-plot-12', figure={}, className='four columns',config=config_fig)
    ]),
])

# Define the tabs layout
app.layout = html.Div([
    dcc.Tabs(id='tabs', style= {'width': 600,
        'font-size': '100%',
        'height': 50}, value='tab1',children=[
        dcc.Tab(label='Dataset', value='tab0', children=tab0_content),
        dcc.Tab(label='QC', value='tab1', children=tab1_content),
        dcc.Tab(label='Cell cycle', value='tab2', children=tab2_content),
        dcc.Tab(label='Custom', value='tab3', children=tab3_content),
        dcc.Tab(label='Multi dot', value='tab4', children=tab4_content),
    ]),
])

# Define the circular callback
@app.callback(
    Output("min-slider-1", "value"),
    Output("max-slider-1", "value"),
    Output("min-slider-2", "value"),
    Output("max-slider-2", "value"),
    Output("min-slider-3", "value"),
    Output("max-slider-3", "value"),
    Input("min-slider-1", "value"),
    Input("max-slider-1", "value"),
    Input("min-slider-2", "value"),
    Input("max-slider-2", "value"),
    Input("min-slider-3", "value"),
    Input("max-slider-3", "value"),
)
def circular_callback(min_1, max_1, min_2, max_2, min_3, max_3):
    return min_1, max_1, min_2, max_2, min_3, max_3

@app.callback(
    Output('range-slider-1', 'value'),
    Output('range-slider-2', 'value'),
    Output('range-slider-3', 'value'),
    Input('min-slider-1', 'value'),
    Input('max-slider-1', 'value'),
    Input('min-slider-2', 'value'),
    Input('max-slider-2', 'value'),
    Input('min-slider-3', 'value'),
    Input('max-slider-3', 'value'),
)
def update_slider_values(min_1, max_1, min_2, max_2, min_3, max_3):
    return [min_1, max_1], [min_2, max_2], [min_3, max_3]

@app.callback(
    Output(component_id='my-graph', component_property='figure'),
    Output(component_id='pie-graph', component_property='figure'),
    Output(component_id='scatter-plot', component_property='figure'),
    Output(component_id='scatter-plot-2', component_property='figure'),
    Output(component_id='scatter-plot-3', component_property='figure'),
    Output(component_id='scatter-plot-4', component_property='figure'),  # Add this new scatter plot
    Output(component_id='scatter-plot-5', component_property='figure'),
    Output(component_id='scatter-plot-6', component_property='figure'),
    Output(component_id='scatter-plot-7', component_property='figure'),
    Output(component_id='scatter-plot-8', component_property='figure'),
    Output(component_id='scatter-plot-9', component_property='figure'),
    Output(component_id='scatter-plot-10', component_property='figure'),
    Output(component_id='scatter-plot-11', component_property='figure'),
    Output(component_id='scatter-plot-12', component_property='figure'),
    Output(component_id='my-graph2', component_property='figure'),
    Input(component_id='dpdn2', component_property='value'),
    Input(component_id='dpdn3', component_property='value'),
    Input(component_id='dpdn4', component_property='value'),
    Input(component_id='dpdn5', component_property='value'),
    Input(component_id='dpdn6', component_property='value'),
    Input(component_id='dpdn7', component_property='value'),
    Input(component_id='range-slider-1', component_property='value'),
    Input(component_id='range-slider-2', component_property='value'),
    Input(component_id='range-slider-3', component_property='value')
)

def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chosen, condition2_chosen, condition3_chosen, range_value_1, range_value_2, range_value_3): #batch_chosen, 
    batch_chosen = df[col_chosen].unique().to_list()
    dff = df.filter(
        (pl.col(col_chosen).cast(str).is_in(batch_chosen)) &
        (pl.col(col_features) >= range_value_1[0]) &
        (pl.col(col_features) <= range_value_1[1]) &
        (pl.col(col_counts) >= range_value_2[0]) &
        (pl.col(col_counts) <= range_value_2[1]) &
        (pl.col(col_mt) >= range_value_3[0]) &
        (pl.col(col_mt) <= range_value_3[1])
)
    
    #Drop categories that are not in the filtered data
    dff = dff.with_columns(dff[col_chosen].cast(pl.Categorical))

    dff = dff.sort(col_chosen)
    
    # Plot figures
    fig_violin = px.violin(data_frame=dff, x=col_chosen, y=col_features, box=True, points="all",
                            color=col_chosen, hover_name=col_chosen,template="seaborn")

    # Cache commonly used subexpressions
    total_count = pl.lit(len(dff))
    category_counts = dff.group_by(col_chosen).agg(pl.col(col_chosen).count().alias("count"))
    category_counts = category_counts.with_columns(((pl.col("count") / total_count * 100).round(decimals=2)).alias("normalized_count"))

    # Sort the dataframe
    #category_counts = category_counts.sort(col_chosen) does not work check if the names are different ...
    
    # Display the result
    total_cells = total_count  # Calculate total number of cells
    pie_title = f'Percentage of Total Cells: {total_cells}'  # Include total cells in the title

    # Calculate the mean expression
    
    # Melt wide format DataFrame into long format
    # Specify batch column as string type and gene columns as float type
    list_conds = condition3_chosen
    list_conds += [col_chosen]
    dff_pre = dff.select(list_conds)
    
    # Melt wide format DataFrame into long format
    dff_long = dff_pre.melt(id_vars=col_chosen, variable_name="Gene", value_name="Mean expression")

    # Calculate the mean expression levels for each gene in each region
    expression_means = dff_long.lazy().group_by([col_chosen, "Gene"]).agg(pl.mean("Mean expression")).collect()
    
    # Calculate the percentage total expressed
    dff_long1 = dff_pre.melt(id_vars=col_chosen, variable_name="Gene")#.group_by(pl.all()).agg(pl.len())
    count = 1
    dff_long2 = dff_long1.with_columns(pl.lit(count).alias("len"))
    dff_long3 = dff_long2.filter(pl.col("value") > 0).group_by([col_chosen, "Gene"]).agg(pl.sum("len").alias("len"))
    dff_long4 = dff_long2.group_by([col_chosen, "Gene"]).agg(pl.sum("len").alias("total"))
    dff_5 = dff_long4.join(dff_long3, on=[col_chosen,"Gene"], how="outer")
    result = dff_5.select([
        pl.when((pl.col('len').is_not_null()) & (pl.col('total').is_not_null()))
              .then(pl.col('len') / pl.col('total')*100)
              .otherwise(None).alias("%"),
    ])
    result = result.with_columns(pl.col("%").fill_null(100))
    dff_5[["percentage"]] = result[["%"]]
    dff_5 = dff_5.select(pl.col(col_chosen,"Gene","percentage"))

    # Final part to join the percentage expressed and mean expression levels
    # TO DO
    expression_means = expression_means.join(dff_5, on=[col_chosen,"Gene"], how="inner")

    # Order the dataframe on ascending categories
    expression_means = expression_means.sort(col_chosen, descending=True)
    
    #expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
    category_counts = category_counts.sort(col_chosen)
    
    fig_pie = px.pie(category_counts, values="normalized_count", names=col_chosen, labels=col_chosen, hole=.3, title=pie_title, template="seaborn")

    #labels = category_counts[col_chosen].to_list()
    #values = category_counts["normalized_count"].to_list()

    # Create the scatter plots
    fig_scatter = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_chosen,
                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                             hover_name='batch',template="seaborn")

    fig_scatter_2 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_mt,
                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                             hover_name='batch',template="seaborn")

    fig_scatter_3 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_features,
                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                             hover_name='batch',template="seaborn")


    fig_scatter_4 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_counts,
                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                             hover_name='batch',template="seaborn")
    
    fig_scatter_5 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=s_chosen,
                            labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                            hover_name='batch', title="S-cycle gene:",template="seaborn")

    fig_scatter_6 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=g2m_chosen,
                            labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                            hover_name='batch', title="G2M-cycle gene:",template="seaborn")
    
    fig_scatter_7 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="S_score",
                            labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                            hover_name='batch', title="S score:",template="seaborn")
    
    fig_scatter_8 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="G2M_score",
                            labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                            hover_name='batch', title="G2M score:",template="seaborn")

    # Sort values of custom in-between
    dff = dff.sort(condition1_chosen)
    
    fig_scatter_9 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition1_chosen,
                            labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                            hover_name='batch',template="seaborn")
    
    fig_scatter_10 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition2_chosen,
                            labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                            hover_name='batch',template="seaborn")
    
    fig_scatter_11 = px.scatter(data_frame=dff, x=condition1_chosen, y=condition2_chosen, color=condition1_chosen,
                            #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                            hover_name='batch',template="seaborn")
    
    fig_scatter_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression",
                                size="percentage", size_max = 20,
                            #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                            hover_name=col_chosen,template="seaborn")
    
    fig_violin2 = px.violin(data_frame=dff, x=condition1_chosen, y=condition2_chosen, box=True, points="all",
                            color=condition1_chosen, hover_name=condition1_chosen,template="seaborn")


    return fig_violin, fig_pie, fig_scatter, fig_scatter_2, fig_scatter_3, fig_scatter_4, fig_scatter_5, fig_scatter_6, fig_scatter_7, fig_scatter_8, fig_scatter_9, fig_scatter_10, fig_scatter_11, fig_scatter_12, fig_violin2

# Set http://localhost:5000/ in web browser
# Now create your regular FASTAPI application

if __name__ == '__main__':
    app.run_server(debug=False, use_reloader=False, host='0.0.0.0', port=5000) #