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refactor

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This commit is contained in:
Pritimay Sarkar
2023-08-16 15:17:28 +05:30
parent d302e7bc1b
commit 6baeea5820
1 changed files with 14 additions and 30 deletions
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44
scripts/device_qc_report.py
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@@ -11,13 +11,9 @@ precision_tolerance = 0.009
accuracy_tolerance = 0.02
def perform_calculations(curdir, path_delim, report_file):
# print(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx")
df = pd.read_excel(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx", sheet_name="qc_data")
df_ref = pd.read_excel(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx", sheet_name="reference_values")
# df_ref['device - con'] = ""
# for idx, row in df_ref.iterrows():
# df_ref["device - con"][idx] = str(row["deviceId"]) + ", " + row["sol"] + ", " + row["wavelength"]
print(df_ref)
writer = pd.ExcelWriter(report_file, engine = 'xlsxwriter')
@@ -27,47 +23,40 @@ def perform_calculations(curdir, path_delim, report_file):
# duplicate columns
df["device"] = df["deviceId"]
df["device - con"] = ""
df["identifier"] = ""
for idx, row in df.iterrows():
df['device - con'][idx] = row["deviceId"] + ", " + row["sol"] + ", " + row["wavelength"]
df['identifier'][idx] = row["deviceId"] + ", " + row["sol"] + ", " + row["wavelength"]
# print(df)
df_max = df.groupby(['deviceId', "sol"]).max()
df_max['device - con'] = ""
# print(df_max.iloc[1])
df_max['identifier'] = ""
df_min = df.groupby(['deviceId', "sol"]).min()
df_min['device - con'] = ""
df_min['identifier'] = ""
for group, row in df_max.iterrows():
df_max["device - con"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]
df_max["identifier"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]
df_max.to_excel(writer, sheet_name="max", index=False)
for group, row in df_min.iterrows():
df_min["device - con"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]
df_min["identifier"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]
# TODO: combine 427 mean and 555 mean using df.groupby(['device - con'])[["Abs 427", "Abs555"]].mean()
df_427nm_mean = df.groupby(['device - con'])["Abs 427"].mean().to_frame()
# TODO: combine 427 mean and 555 mean using df.groupby(['identifier'])[["Abs 427", "Abs555"]].mean()
df_427nm_mean = df.groupby(['identifier'])["Abs 427"].mean().to_frame()
df_427nm_mean.rename(columns={'Abs 427': "Abs427_mean"}, inplace = True)
df_555nm_mean = df.groupby(['device - con'])["Abs555"].mean().to_frame()
df_555nm_mean = df.groupby(['identifier'])["Abs555"].mean().to_frame()
df_555nm_mean.rename(columns={'Abs555': "Abs555_mean"}, inplace = True)
# df_mean = df.copy()
df_mean = df_427nm_mean.merge(df_555nm_mean, on="device - con")
df_mean = df_427nm_mean.merge(df_555nm_mean, on="identifier")
df_describe = df.groupby(['deviceId', "sol"]).describe()
# print(df.groupby(['deviceId', "sol"]).describe())
df_describe.to_excel(writer, sheet_name="describe")
# for group, row in df_describe.iterrows():
# df_mean["Abs555_mean"] = row["Abs 427"]["mean"]
# print(df_mean)
df_max_min = df_max.merge(df_min, on="device - con")
df_max_min_mean = df_max_min.merge(df_mean, on="device - con")
df_max_min_mean_ref = df_max_min_mean.merge(df_ref, on="device - con")
df_max_min = df_max.merge(df_min, on="identifier")
df_max_min_mean = df_max_min.merge(df_mean, on="identifier")
df_max_min_mean_ref = df_max_min_mean.merge(df_ref, on="identifier")
df_min.to_excel(writer, sheet_name="min", index=False)
df_max_min_mean_ref.to_excel(writer, sheet_name="df_max_min_mean_ref", index=False)
@@ -79,12 +68,7 @@ def perform_calculations(curdir, path_delim, report_file):
df_result["accuracy_555nm"] = df_max_min_mean_ref["ref_device_abs"] - df_max_min_mean_ref["Abs555_mean"]
df_result = df_result.drop(["Abs 427_x", "Abs555_x", "wavelength_x", "Abs 427_y", "Abs555_y", "wavelength_y", "device_y"], axis=1)
df_result = df_result.groupby('device_x')[['device_x', "device - con", "precision_427nm", "precision_555nm", "accuracy_427nm", "accuracy_555nm"]].apply(lambda x: x)
# print(df_result)
# for idx, row in df_result.iterrows():
# print(idx, row)
# df_result["precision_427nm"][1] = df_max.loc["Abs 427"][1] + df_min["Abs 427"][1]
df_result = df_result.groupby('device_x')[['device_x', "identifier", "precision_427nm", "precision_555nm", "accuracy_427nm", "accuracy_555nm"]].apply(lambda x: x)
df_result.to_excel(writer, sheet_name="precision", index=False)
workbook = writer.book