Minor bug fixes.

src/submissions/frontend/visualizations/control_charts.py

@@ -194,73 +194,6 @@ def construct_chart(df:pd.DataFrame, modes:list, ytitle:str|None=None) -> Figure
         fig.add_traces(bar.data)
     return generic_figure_markers(fig=fig, modes=modes, ytitle=ytitle)
 
-# Below are the individual construction functions. They must be named "construct_{mode}_chart" and 
-# take only json_in and mode to hook into the main processor.
-
-# def construct_refseq_chart(df:pd.DataFrame, group_name:str, mode:str) -> Figure:
-#     """
-#     Constructs intial refseq chart for both contains and matches (depreciated).
-
-#     Args:
-#         df (pd.DataFrame): dataframe containing all sample data for the group.
-#         group_name (str): name of the group being processed.
-#         mode (str): contains or matches, overwritten by hardcoding, so don't think about it too hard.
-
-#     Returns:
-#         Figure: initial figure with contains and matches traces.
-#     """    
-#     # This overwrites the mode from the signature, might get confusing.
-#     fig = Figure()
-#     modes = ['contains', 'matches']
-#     for ii, mode in enumerate(modes): 
-#         bar = px.bar(df, x="submitted_date", 
-#             y=f"{mode}_ratio", 
-#             color="target", 
-#             title=f"{group_name}_{mode}", 
-#             barmode='stack', 
-#             hover_data=["genus", "name", f"{mode}_hashes"], 
-#             text="genera"
-#         )
-#         bar.update_traces(visible = ii == 0)
-#         # Plotly express returns a full figure, so we have to use the data from that figure only.
-#         fig.add_traces(bar.data)
-#     # sys.exit(f"number of traces={len(fig.data)}")
-#     return generic_figure_markers(fig=fig, modes=modes)
-
-# def construct_kraken_chart(settings:dict, df:pd.DataFrame, group_name:str, mode:str) -> Figure:
-#     """
-#     Constructs intial refseq chart for each mode in the kraken config settings. (depreciated)
-
-#     Args:
-#         settings (dict): settings passed down from click.
-#         df (pd.DataFrame): dataframe containing all sample data for the group.
-#         group_name (str): name of the group being processed.
-#         mode (str): kraken modes retrieved from config file by setup.
-
-#     Returns:
-#         Figure: initial figure with traces for modes
-#     """    
-#     df[f'{mode}_count'] = pd.to_numeric(df[f'{mode}_count'],errors='coerce')
-#     df = df.groupby('submitted_date')[f'{mode}_count'].nlargest(2)
-    
-#     # The actual percentage from kraken was off due to exclusion of NaN, recalculating.
-#     df[f'{mode}_percent'] = 100 * df[f'{mode}_count'] / df.groupby('submitted_date')[f'{mode}_count'].transform('sum')
-#     modes = settings['modes'][mode]
-#     # This overwrites the mode from the signature, might get confusing.
-#     fig = Figure()
-#     for ii, entry in enumerate(modes):         
-#         bar = px.bar(df, x="submitted_date", 
-#             y=entry,
-#             color="genus",
-#             title=f"{group_name}_{entry}", 
-#             barmode="stack", 
-#             hover_data=["genus", "name", "target"],
-#             text="genera",
-#         )
-#         bar.update_traces(visible = ii == 0)
-#         fig.add_traces(bar.data)
-#     return generic_figure_markers(fig=fig, modes=modes)
-
 def divide_chunks(input_list:list, chunk_count:int):
     """
     Divides a list into {chunk_count} equal parts

src/submissions/frontend/visualizations/plate_map.py

@@ -1,9 +1,8 @@
 from pathlib import Path
-import sys
 from PIL import Image, ImageDraw, ImageFont
 import numpy as np
 from tools import check_if_app, jinja_template_loading
-import logging
+import logging, sys
 
 logger = logging.getLogger(f"submissions.{__name__}")