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Code cleanup, dependency update, various bug fixes

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This commit is contained in:
lwark
2024-05-24 13:02:46 -05:00
parent 2814be8980
commit e047d1a9ee
24 changed files with 403 additions and 561 deletions
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34
src/submissions/backend/excel/reports.py
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@@ -27,7 +27,7 @@ def make_report_xlsx(records:list[dict]) -> Tuple[DataFrame, DataFrame]:
# aggregate cost and sample count columns
df2 = df.groupby(["Submitting Lab", "Extraction Kit"]).agg({'Extraction Kit':'count', 'Cost': 'sum', 'Sample Count':'sum'})
df2 = df2.rename(columns={"Extraction Kit": 'Run Count'})
logger.debug(f"Output daftaframe for xlsx: {df2.columns}")
# logger.debug(f"Output daftaframe for xlsx: {df2.columns}")
df = df.drop('id', axis=1)
df = df.sort_values(['Submitting Lab', "Submitted Date"])
return df, df2
@@ -47,13 +47,13 @@ def make_report_html(df:DataFrame, start_date:date, end_date:date) -> str:
"""
old_lab = ""
output = []
logger.debug(f"Report DataFrame: {df}")
# logger.debug(f"Report DataFrame: {df}")
for ii, row in enumerate(df.iterrows()):
logger.debug(f"Row {ii}: {row}")
# logger.debug(f"Row {ii}: {row}")
lab = row[0][0]
logger.debug(type(row))
logger.debug(f"Old lab: {old_lab}, Current lab: {lab}")
logger.debug(f"Name: {row[0][1]}")
# logger.debug(type(row))
# logger.debug(f"Old lab: {old_lab}, Current lab: {lab}")
# logger.debug(f"Name: {row[0][1]}")
data = [item for item in row[1]]
kit = dict(name=row[0][1], cost=data[1], run_count=int(data[0]), sample_count=int(data[2]))
# if this is the same lab as before add together
@@ -67,7 +67,7 @@ def make_report_html(df:DataFrame, start_date:date, end_date:date) -> str:
adder = dict(lab=lab, kits=[kit], total_cost=kit['cost'], total_samples=kit['sample_count'], total_runs=kit['run_count'])
output.append(adder)
old_lab = lab
logger.debug(output)
# logger.debug(output)
dicto = {'start_date':start_date, 'end_date':end_date, 'labs':output}#, "table":table}
temp = env.get_template('summary_report.html')
html = temp.render(input=dicto)
@@ -91,14 +91,14 @@ def convert_data_list_to_df(input:list[dict], subtype:str|None=None) -> DataFram
for column in df.columns:
if "percent" in column:
count_col = [item for item in df.columns if "count" in item][0]
# The actual percentage from kraken was off due to exclusion of NaN, recalculating.
# NOTE: The actual percentage from kraken was off due to exclusion of NaN, recalculating.
df[column] = 100 * df[count_col] / df.groupby('name')[count_col].transform('sum')
if column not in safe:
if subtype != None and column != subtype:
del df[column]
# move date of sample submitted on same date as previous ahead one.
# NOTE: move date of sample submitted on same date as previous ahead one.
df = displace_date(df)
# ad hoc method to make data labels more accurate.
# NOTE: ad hoc method to make data labels more accurate.
df = df_column_renamer(df=df)
return df
@@ -131,8 +131,8 @@ def displace_date(df:DataFrame) -> DataFrame:
Returns:
DataFrame: output dataframe with dates incremented.
"""
logger.debug(f"Unique items: {df['name'].unique()}")
# get submitted dates for each control
# logger.debug(f"Unique items: {df['name'].unique()}")
# NOTE: get submitted dates for each control
dict_list = [dict(name=item, date=df[df.name == item].iloc[0]['submitted_date']) for item in sorted(df['name'].unique())]
previous_dates = []
for _, item in enumerate(dict_list):
@@ -157,10 +157,10 @@ def check_date(df:DataFrame, item:dict, previous_dates:list) -> Tuple[DataFrame,
check = False
previous_dates.append(item['date'])
if check:
logger.debug(f"We found one! Increment date!\n\t{item['date']} to {item['date'] + timedelta(days=1)}")
# get df locations where name == item name
# logger.debug(f"We found one! Increment date!\n\t{item['date']} to {item['date'] + timedelta(days=1)}")
# NOTE: get df locations where name == item name
mask = df['name'] == item['name']
# increment date in dataframe
# NOTE: increment date in dataframe
df.loc[mask, 'submitted_date'] = df.loc[mask, 'submitted_date'].apply(lambda x: x + timedelta(days=1))
item['date'] += timedelta(days=1)
passed = False
@@ -170,9 +170,9 @@ def check_date(df:DataFrame, item:dict, previous_dates:list) -> Tuple[DataFrame,
# logger.debug(f"DF: {type(df)}, previous_dates: {type(previous_dates)}")
# if run didn't lead to changed date, return values
if passed:
logger.debug(f"Date check passed, returning.")
# logger.debug(f"Date check passed, returning.")
return df, previous_dates
# if date was changed, rerun with new date
# NOTE: if date was changed, rerun with new date
else:
logger.warning(f"Date check failed, running recursion")
df, previous_dates = check_date(df, item, previous_dates)