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Pre-sample/control connect

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This commit is contained in:
Landon Wark
2023-12-05 10:20:46 -06:00
parent 283e77fee5
commit cddb947ec8
29 changed files with 1357 additions and 1042 deletions
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11
src/submissions/frontend/visualizations/barcode.py
View File

@@ -4,5 +4,16 @@ from reportlab.lib.units import mm
def make_plate_barcode(text:str, width:int=100, height:int=25) -> Drawing:
"""
Creates a barcode image for a given str.
Args:
text (str): Input string
width (int, optional): Width (pixels) of image. Defaults to 100.
height (int, optional): Height (pixels) of image. Defaults to 25.
Returns:
Drawing: image object
"""
# return createBarcodeDrawing('Code128', value=text, width=200, height=50, humanReadable=True)
return createBarcodeImageInMemory('Code128', value=text, width=width*mm, height=height*mm, humanReadable=True, format="png")

141
src/submissions/frontend/visualizations/control_charts.py
View File

@@ -4,25 +4,26 @@ Functions for constructing controls graphs using plotly.
import plotly
import plotly.express as px
import pandas as pd
from pathlib import Path
from plotly.graph_objects import Figure
import logging
from backend.excel import get_unique_values_in_df_column
from tools import Settings
from frontend.widgets.functions import select_save_file
logger = logging.getLogger(f"submissions.{__name__}")
def create_charts(ctx:dict, df:pd.DataFrame, ytitle:str|None=None) -> Figure:
def create_charts(ctx:Settings, df:pd.DataFrame, ytitle:str|None=None) -> Figure:
"""
Constructs figures based on parsed pandas dataframe.
Args:
settings (dict): settings passed down from gui
ctx (Settings): settings passed down from gui
df (pd.DataFrame): input dataframe
group_name (str): controltype
ytitle (str | None, optional): title for the y-axis. Defaults to None.
Returns:
Figure: plotly figure
Figure: Plotly figure
"""
from backend.excel import drop_reruns_from_df
# converts starred genera to normal and splits off list of starred
@@ -54,8 +55,6 @@ def create_charts(ctx:dict, df:pd.DataFrame, ytitle:str|None=None) -> Figure:
fig = construct_chart(df=df, modes=modes, ytitle=ytitle)
return fig
def generic_figure_markers(fig:Figure, modes:list=[], ytitle:str|None=None) -> Figure:
"""
Adds standard layout to figure.
@@ -63,6 +62,7 @@ def generic_figure_markers(fig:Figure, modes:list=[], ytitle:str|None=None) -> F
Args:
fig (Figure): Input figure.
modes (list, optional): List of modes included in figure. Defaults to [].
ytitle (str, optional): Title for the y-axis. Defaults to None.
Returns:
Figure: Output figure with updated titles, rangeslider, buttons.
@@ -102,7 +102,6 @@ def generic_figure_markers(fig:Figure, modes:list=[], ytitle:str|None=None) -> F
assert type(fig) == Figure
return fig
def make_buttons(modes:list, fig_len:int) -> list:
"""
Creates list of buttons with one for each mode to be used in showing/hiding mode traces.
@@ -135,7 +134,7 @@ def make_buttons(modes:list, fig_len:int) -> list:
))
return buttons
def output_figures(settings:dict, figs:list, group_name:str):
def output_figures(figs:list, group_name:str):
"""
Writes plotly figure to html file.
@@ -144,21 +143,19 @@ def output_figures(settings:dict, figs:list, group_name:str):
fig (Figure): input figure object
group_name (str): controltype
"""
with open(Path(settings['folder']['output']).joinpath(f'{group_name}.html'), "w") as f:
output = select_save_file(None, default_name=group_name, extension="html")
with open(output, "w") as f:
for fig in figs:
try:
f.write(fig.to_html(full_html=False, include_plotlyjs='cdn'))
except AttributeError:
logger.error(f"The following figure was a string: {fig}")
def construct_chart(df:pd.DataFrame, modes:list, ytitle:str|None=None) -> Figure:
"""
Creates a plotly chart for controls from a pandas dataframe
Args:
ctx (dict): settings passed down from gui
df (pd.DataFrame): input dataframe of controls
modes (list): analysis modes to construct charts for
ytitle (str | None, optional): title on the y-axis. Defaults to None.
@@ -200,72 +197,69 @@ def construct_chart(df:pd.DataFrame, modes:list, ytitle:str|None=None) -> Figure
# 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(settings:dict, df:pd.DataFrame, group_name:str, mode:str) -> Figure:
"""
Constructs intial refseq chart for both contains and matches (depreciated).
# def construct_refseq_chart(df:pd.DataFrame, group_name:str, mode:str) -> Figure:
# """
# Constructs intial refseq chart for both contains and matches (depreciated).
Args:
settings (dict): settings passed down from gui.
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.
# 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)
# 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)
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.
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)
# 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)
# # 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):
"""
@@ -281,7 +275,6 @@ def divide_chunks(input_list:list, chunk_count:int):
k, m = divmod(len(input_list), chunk_count)
return (input_list[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(chunk_count))
def construct_html(figure:Figure) -> str:
"""
Creates final html code from plotly

22
src/submissions/frontend/visualizations/plate_map.py
View File

@@ -84,14 +84,17 @@ def make_plate_map(sample_list:list) -> Image:
return new_img
def make_plate_map_html(sample_list:list, plate_rows:int=8, plate_columns=12) -> str:
try:
plate_num = sample_list[0]['plate_name']
except IndexError as e:
logger.error(f"Couldn't get a plate number. Will not make plate.")
return None
except TypeError as e:
logger.error(f"No samples for this plate. Nothing to do.")
return None
"""
Constructs an html based plate map.
Args:
sample_list (list): List of submission samples
plate_rows (int, optional): Number of rows in the plate. Defaults to 8.
plate_columns (int, optional): Number of columns in the plate. Defaults to 12.
Returns:
str: html output string.
"""
for sample in sample_list:
if sample['positive']:
sample['background_color'] = "#f10f07"
@@ -108,4 +111,5 @@ def make_plate_map_html(sample_list:list, plate_rows:int=8, plate_columns=12) ->
env = jinja_template_loading()
template = env.get_template("plate_map.html")
html = template.render(samples=output_samples, PLATE_ROWS=plate_rows, PLATE_COLUMNS=plate_columns)
return html
return html