Arts-of-coding commited on
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0f92a7d
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1 Parent(s): 28d14c3

Update dash_plotly_QC_scRNA.py

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  1. dash_plotly_QC_scRNA.py +6 -6
dash_plotly_QC_scRNA.py CHANGED
@@ -382,12 +382,12 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
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  pie_title = f'Percentage of Total Cells: {total_cells}' # Include total cells in the title
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  # Calculate the mean expression
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- round_precision = 2
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- category_counts = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).count().alias("count"))
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- category_sums = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).sum().alias("sum"))
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- total_count = len(dff)
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- category_means = category_sums.select((pl.col("sum") / total_count).round(round_precision).alias("mean"))
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- values_mean = category_means["mean"].to_list()
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  fig_pie = px.pie(names=labels, values=values, title=pie_title,template="seaborn")
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  pie_title = f'Percentage of Total Cells: {total_cells}' # Include total cells in the title
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  # Calculate the mean expression
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+ #round_precision = 2
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+ #category_counts = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).count().alias("count"))
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+ #category_sums = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).sum().alias("sum"))
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+ #total_count = len(dff)
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+ #category_means = category_sums.select((pl.col("sum") / total_count).round(round_precision).alias("mean"))
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+ #values_mean = category_means["mean"].to_list()
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  fig_pie = px.pie(names=labels, values=values, title=pie_title,template="seaborn")
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