Create Marker Plots #61
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…t and one with custom y-axis scale
| n = 100 if max_reads > 1000 else 10 | ||
| max_yvalue = int(math.ceil(max_reads / n)) * n | ||
| increase_value = int(math.ceil((max_yvalue / 5)) / n) * n |
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This is to make nice round values (10, 20, 100, 200, etc.) when determining the max y value of the dataset and what the incremental increase should be for the plots. The first line determines whether to round to 10s or 100s depending on the highest # of reads.
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RebJ had requested the two separate pages: one showing the individual marker plots with its own y-axis scale (better for examining individual marker plots) and one showing the individual marker plots with the same y-axis scale (better for getting an idea of the profile as a whole). |
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This is ready for review @standage. I'm attaching two examples of the plots, one for the autosomal STRs and one for the sex chr STRs since you aren't able to see what they look like. |
README.md
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| @@ -165,7 +165,12 @@ Further, a second table (labeled as ```*_flanks.txt```) containing information r | |||
| * 3' Flanking Sequence Bracketed Notation | |||
| * Potential Issues (such as: Possible indel or partial sequence) | |||
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| The ```Potential Issues``` column in this report is to draw attention to potential problem sequences (due to perhaps an indel or partial sequence) and requires the attention of the user to further evaluate the sequence for it's authenticity. | |||
| The ```Potential Issues``` column in this report is to draw attention to potential problem sequences (due to perhaps an indel or partial sequence) and requires the attention of the user to further evaluate the sequence for it's authenticity. | |||
lusSTR/wrappers/convert.py
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| def make_plot(df, id, sex, sameyaxis): | ||
| sample_df = df[df["SampleID"] == id] | ||
| sample_id = f"{id}_sexchr" if sex else id |
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Suggestion for a marginal improvement in readability: make the sameyaxis argument a keyword argument by giving it a default value.
lusSTR/wrappers/convert.py
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| make_plot(df, id, sex, False) | ||
| pdf.savefig() | ||
| make_plot(df, id, sex, True) | ||
| pdf.savefig() |
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That way, when someone is messing with the code 18 months from now and they're trying to remember what that dang 4th argument to the make_plot function is, passing the argument as a key/value pair will provide a great contextual reminder.
make_plot(df, id, sex, sameyaxis=False)
lusSTR/wrappers/convert.py
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| @@ -202,6 +250,7 @@ def main(input, out, kit, uas, sex, nocombine): | |||
| autosomal_final_table.to_csv(out, sep="\t", index=False) | |||
| else: | |||
| autosomal_final_table.to_csv(out, sep="\t", index=False) | |||
| marker_plots(autosomal_final_table, output_name, False) | |||
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Similar suggestion with the sex argument here.
marker_plots(autosomal_final_table, output_name, sex_chr=False)
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Updated! |
This PR will create a single PDF file containing the reads vs. CE allele for each locus.