Conversion of some functions to generators.
This commit is contained in:
lwark
@@ -1,16 +1,12 @@
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"""
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Functions for constructing controls graphs using plotly.
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TODO: Move these functions to widgets.controls_charts
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"""
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import re
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import plotly
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import plotly.express as px
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import pandas as pd
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from pandas import DataFrame
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from plotly.graph_objects import Figure
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import logging
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# from backend.excel import get_unique_values_in_df_column
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from tools import Settings, get_unique_values_in_df_column, divide_chunks
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from tools import get_unique_values_in_df_column, divide_chunks
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from frontend.widgets.functions import select_save_file
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logger = logging.getLogger(f"submissions.{__name__}")
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@@ -18,232 +14,164 @@ logger = logging.getLogger(f"submissions.{__name__}")
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class CustomFigure(Figure):
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def __init__(self, ctx: Settings, df: pd.DataFrame, ytitle: str | None = None):
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def __init__(self, df: pd.DataFrame, modes: list, ytitle: str | None = None):
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super().__init__()
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self.construct_chart(df=df, modes=modes)
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self.generic_figure_markers(modes=modes, ytitle=ytitle)
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def construct_chart(self, df: pd.DataFrame, modes: list):
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"""
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Creates a plotly chart for controls from a pandas dataframe
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# NOTE: Start here.
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def create_charts(ctx: Settings, df: pd.DataFrame, ytitle: str | None = None) -> Figure:
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"""
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Constructs figures based on parsed pandas dataframe.
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Args:
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df (pd.DataFrame): input dataframe of controls
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modes (list): analysis modes to construct charts for
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ytitle (str | None, optional): title on the y-axis. Defaults to None.
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Args:
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ctx (Settings): settings passed down from gui
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df (pd.DataFrame): input dataframe
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ytitle (str | None, optional): title for the y-axis. Defaults to None.
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Returns:
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Figure: Plotly figure
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"""
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# from backend.excel import drop_reruns_from_df
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# converts starred genera to normal and splits off list of starred
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genera = []
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if df.empty:
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return None
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for item in df['genus'].to_list():
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try:
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if item[-1] == "*":
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genera.append(item[-1])
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else:
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genera.append("")
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except IndexError:
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genera.append("")
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df['genus'] = df['genus'].replace({'\*': ''}, regex=True).replace({"NaN": "Unknown"})
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df['genera'] = genera
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# NOTE: remove original runs, using reruns if applicable
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df = drop_reruns_from_df(ctx=ctx, df=df)
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# NOTE: sort by and exclude from
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sorts = ['submitted_date', "target", "genus"]
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exclude = ['name', 'genera']
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modes = [item for item in df.columns if item not in sorts and item not in exclude] # and "_hashes" not in item]
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# NOTE: Set descending for any columns that have "{mode}" in the header.
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ascending = [False if item == "target" else True for item in sorts]
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df = df.sort_values(by=sorts, ascending=ascending)
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# logger.debug(df[df.isna().any(axis=1)])
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# NOTE: actual chart construction is done by
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fig = construct_chart(df=df, modes=modes, ytitle=ytitle)
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return fig
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def drop_reruns_from_df(ctx: Settings, df: DataFrame) -> DataFrame:
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"""
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Removes semi-duplicates from dataframe after finding sequencing repeats.
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Args:
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settings (dict): settings passed from gui
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df (DataFrame): initial dataframe
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Returns:
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DataFrame: dataframe with originals removed in favour of repeats.
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"""
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if 'rerun_regex' in ctx:
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sample_names = get_unique_values_in_df_column(df, column_name="name")
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rerun_regex = re.compile(fr"{ctx.rerun_regex}")
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for sample in sample_names:
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if rerun_regex.search(sample):
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first_run = re.sub(rerun_regex, "", sample)
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df = df.drop(df[df.name == first_run].index)
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return df
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def generic_figure_markers(fig: Figure, modes: list = [], ytitle: str | None = None) -> Figure:
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"""
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Adds standard layout to figure.
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Args:
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fig (Figure): Input figure.
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modes (list, optional): List of modes included in figure. Defaults to [].
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ytitle (str, optional): Title for the y-axis. Defaults to None.
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Returns:
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Figure: Output figure with updated titles, rangeslider, buttons.
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"""
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if modes != []:
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ytitle = modes[0]
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# Creating visibles list for each mode.
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fig.update_layout(
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xaxis_title="Submitted Date (* - Date parsed from fastq file creation date)",
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yaxis_title=ytitle,
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showlegend=True,
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barmode='stack',
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updatemenus=[
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dict(
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type="buttons",
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direction="right",
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x=0.7,
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y=1.2,
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showactive=True,
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buttons=make_buttons(modes=modes, fig_len=len(fig.data)),
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)
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]
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)
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fig.update_xaxes(
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rangeslider_visible=True,
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rangeselector=dict(
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buttons=list([
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dict(count=1, label="1m", step="month", stepmode="backward"),
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dict(count=3, label="3m", step="month", stepmode="backward"),
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dict(count=6, label="6m", step="month", stepmode="backward"),
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dict(count=1, label="YTD", step="year", stepmode="todate"),
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dict(count=1, label="1y", step="year", stepmode="backward"),
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dict(step="all")
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])
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)
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)
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assert type(fig) == Figure
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return fig
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def make_buttons(modes: list, fig_len: int) -> list:
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"""
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Creates list of buttons with one for each mode to be used in showing/hiding mode traces.
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Args:
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modes (list): list of modes used by main parser.
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fig_len (int): number of traces in the figure
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Returns:
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list: list of buttons.
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"""
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buttons = []
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if len(modes) > 1:
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Returns:
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Figure: output stacked bar chart.
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"""
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# fig = Figure()
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for ii, mode in enumerate(modes):
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# What I need to do is create a list of bools with the same length as the fig.data
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mode_vis = [True] * fig_len
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# And break it into {len(modes)} chunks
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mode_vis = list(divide_chunks(mode_vis, len(modes)))
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# Then, for each chunk, if the chunk index isn't equal to the index of the current mode, set to false
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for jj, sublist in enumerate(mode_vis):
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if jj != ii:
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mode_vis[jj] = [not elem for elem in mode_vis[jj]]
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# Finally, flatten list.
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mode_vis = [item for sublist in mode_vis for item in sublist]
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# Now, make button to add to list
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buttons.append(dict(label=mode, method="update", args=[
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{"visible": mode_vis},
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{"yaxis.title.text": mode},
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if "count" in mode:
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df[mode] = pd.to_numeric(df[mode], errors='coerce')
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color = "genus"
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color_discrete_sequence = None
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elif 'percent' in mode:
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color = "genus"
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color_discrete_sequence = None
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else:
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color = "target"
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match get_unique_values_in_df_column(df, 'target'):
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case ['Target']:
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color_discrete_sequence = ["blue"]
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case ['Off-target']:
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color_discrete_sequence = ['red']
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case _:
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color_discrete_sequence = ['blue', 'red']
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bar = px.bar(df,
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x="submitted_date",
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y=mode,
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color=color,
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title=mode,
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barmode='stack',
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hover_data=["genus", "name", "target", mode],
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text="genera",
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color_discrete_sequence=color_discrete_sequence
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)
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bar.update_traces(visible=ii == 0)
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self.add_traces(bar.data)
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# return generic_figure_markers(modes=modes, ytitle=ytitle)
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def generic_figure_markers(self, modes: list = [], ytitle: str | None = None):
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"""
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Adds standard layout to figure.
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Args:
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fig (Figure): Input figure.
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modes (list, optional): List of modes included in figure. Defaults to [].
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ytitle (str, optional): Title for the y-axis. Defaults to None.
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Returns:
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Figure: Output figure with updated titles, rangeslider, buttons.
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"""
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if modes:
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ytitle = modes[0]
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# Creating visibles list for each mode.
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self.update_layout(
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xaxis_title="Submitted Date (* - Date parsed from fastq file creation date)",
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yaxis_title=ytitle,
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showlegend=True,
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barmode='stack',
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updatemenus=[
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dict(
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type="buttons",
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direction="right",
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x=0.7,
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y=1.2,
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showactive=True,
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buttons=[button for button in self.make_buttons(modes=modes)],
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)
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]
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))
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return buttons
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)
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self.update_xaxes(
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rangeslider_visible=True,
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rangeselector=dict(
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buttons=list([
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dict(count=1, label="1m", step="month", stepmode="backward"),
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dict(count=3, label="3m", step="month", stepmode="backward"),
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dict(count=6, label="6m", step="month", stepmode="backward"),
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dict(count=1, label="YTD", step="year", stepmode="todate"),
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dict(count=1, label="1y", step="year", stepmode="backward"),
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dict(step="all")
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])
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)
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)
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assert isinstance(self, Figure)
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# return fig
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def make_buttons(self, modes: list) -> list:
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"""
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Creates list of buttons with one for each mode to be used in showing/hiding mode traces.
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def output_figures(figs: list, group_name: str):
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"""
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Writes plotly figure to html file.
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Args:
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modes (list): list of modes used by main parser.
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fig_len (int): number of traces in the figure
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Args:
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settings (dict): settings passed down from click
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fig (Figure): input figure object
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group_name (str): controltype
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"""
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output = select_save_file(None, default_name=group_name, extension="html")
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with open(output, "w") as f:
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for fig in figs:
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Returns:
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list: list of buttons.
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"""
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fig_len = len(self.data)
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if len(modes) > 1:
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for ii, mode in enumerate(modes):
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# What I need to do is create a list of bools with the same length as the fig.data
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mode_vis = [True] * fig_len
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# And break it into {len(modes)} chunks
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mode_vis = list(divide_chunks(mode_vis, len(modes)))
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# Then, for each chunk, if the chunk index isn't equal to the index of the current mode, set to false
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for jj, sublist in enumerate(mode_vis):
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if jj != ii:
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mode_vis[jj] = [not elem for elem in mode_vis[jj]]
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# Finally, flatten list.
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mode_vis = [item for sublist in mode_vis for item in sublist]
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# Now, yield button to add to list
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yield dict(label=mode, method="update", args=[
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{"visible": mode_vis},
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{"yaxis.title.text": mode},
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])
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def save_figure(self, group_name: str = "plotly_output"):
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"""
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Writes plotly figure to html file.
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Args:
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figs ():
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settings (dict): settings passed down from click
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fig (Figure): input figure object
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group_name (str): controltype
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"""
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output = select_save_file(None, default_name=group_name, extension="html")
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with open(output, "w") as f:
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try:
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f.write(fig.to_html(full_html=False, include_plotlyjs='cdn'))
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f.write(self.to_html())
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except AttributeError:
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logger.error(f"The following figure was a string: {fig}")
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logger.error(f"The following figure was a string: {self}")
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def to_html(self) -> str:
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"""
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Creates final html code from plotly
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def construct_chart(df: pd.DataFrame, modes: list, ytitle: str | None = None) -> Figure:
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"""
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Creates a plotly chart for controls from a pandas dataframe
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Args:
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figure (Figure): input figure
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Args:
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df (pd.DataFrame): input dataframe of controls
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modes (list): analysis modes to construct charts for
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ytitle (str | None, optional): title on the y-axis. Defaults to None.
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Returns:
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Figure: output stacked bar chart.
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"""
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fig = Figure()
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for ii, mode in enumerate(modes):
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if "count" in mode:
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df[mode] = pd.to_numeric(df[mode], errors='coerce')
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color = "genus"
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color_discrete_sequence = None
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elif 'percent' in mode:
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color = "genus"
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color_discrete_sequence = None
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Returns:
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str: html string
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"""
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html = '<html><body>'
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if self is not None:
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html += plotly.offline.plot(self, output_type='div',
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include_plotlyjs='cdn') #, image = 'png', auto_open=True, image_filename='plot_image')
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else:
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color = "target"
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match get_unique_values_in_df_column(df, 'target'):
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case ['Target']:
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color_discrete_sequence = ["blue"]
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case ['Off-target']:
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color_discrete_sequence = ['red']
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case _:
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color_discrete_sequence = ['blue', 'red']
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bar = px.bar(df, x="submitted_date",
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y=mode,
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color=color,
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title=mode,
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barmode='stack',
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hover_data=["genus", "name", "target", mode],
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text="genera",
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color_discrete_sequence=color_discrete_sequence
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)
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bar.update_traces(visible=ii == 0)
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fig.add_traces(bar.data)
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return generic_figure_markers(fig=fig, modes=modes, ytitle=ytitle)
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def construct_html(figure: Figure) -> str:
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"""
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Creates final html code from plotly
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Args:
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figure (Figure): input figure
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Returns:
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str: html string
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"""
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html = '<html><body>'
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if figure is not None:
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html += plotly.offline.plot(figure, output_type='div',
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include_plotlyjs='cdn') #, image = 'png', auto_open=True, image_filename='plot_image')
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else:
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html += "<h1>No data was retrieved for the given parameters.</h1>"
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html += '</body></html>'
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return html
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html += "<h1>No data was retrieved for the given parameters.</h1>"
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html += '</body></html>'
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return html
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Reference in New Issue
Block a user