def is_incremented(compCat, bigfilt, smallfilt, df, offset=0):
""" general equation is
compCat(bigCat:bigElem) = compCat(smallCat:smallElem) + offset
Same as is_ordered, but eq instead of gt
if bigfilt or smallfilt are strings instead of lambdas, turn them
into lambdas with the val_filter function
we also know, from this, that bigCat:bigElem != smallCat:smallElem
"""
compCat = common.comparison_category(compCat, df)
bigfilt = common.force_filter(bigfilt)
smallfilt = common.force_filter(smallfilt)
df2 = df.copy()
# take care of the != clause first
df2 = is_diff(bigfilt, smallfilt, df2)
# possible values
small = df2[smallfilt(df2)][compCat].unique()
big = df2[bigfilt(df2)][compCat].unique()
# find impossible values (small values with no corresponding big; vice versa)
badsmall = set(small).difference(big - offset)
badbig = set(big).difference(small + offset)
# drop impossible values
df2.loc[smallfilt(df2) & (df2[compCat].isin(badsmall)), common.STATUS] = common.REJECTED
df2.loc[bigfilt(df2) & (df2[compCat].isin(badbig)), common.STATUS] = common.REJECTED
return df2
def is_ordered(compCat, bigfilt, smallfilt, df, offset=0):
""" general equation is
compCat(bigCat:bigElem) > compCat(smallCat:smallElem) + offset
we also know, from this, that bigCat:bigElem != smallCat:smallElem
"""
compCat = common.comparison_category(compCat, df)
bigfilt = common.force_filter(bigfilt)
smallfilt = common.force_filter(smallfilt)
df2 = df.copy()
# take care of the != clause first
df2 = is_diff(bigfilt, smallfilt, df2)
# all vals of bigCat must be > the minium val of smallCat
minSmall = df2[smallfilt(df2)][compCat].min()
df2.loc[
bigfilt(df2) & (df2[compCat] <= (minSmall + offset)),
common.STATUS
] = common.REJECTED
# all vals of smallCat must be < the largest val of bigCat
maxBig = df2[bigfilt(df2)][compCat].max()
df2.loc[
smallfilt(df2) & (df2[compCat] >= (maxBig - offset)),
common.STATUS
] = common.REJECTED
return df2
def is_same(filt1, filt2, df):
""" Reject all rows where
cat1:elem1 != cat2:elem2
"""
filt1 = common.force_filter(filt1)
filt2 = common.force_filter(filt2)
df2 = df.copy()
df2.loc[filt1(df2) & (~filt2(df2)), common.STATUS] = common.REJECTED
df2.loc[(~filt1(df2)) & filt2(df2), common.STATUS] = common.REJECTED
return df2
def is_either_or(isfilt, eitherfilt, orfilt, df):
isfilt = common.force_filter(isfilt)
eitherfilt = common.force_filter(eitherfilt)
orfilt = common.force_filter(orfilt)
df2 = df.copy()
# we have one exclusion relation here
df2 = is_diff(eitherfilt, orfilt, df)
# reject all values which are is but not either or
df2.loc[(isfilt(df2) & ~((eitherfilt(df2)) | (orfilt(df2)))), common.STATUS] = common.REJECTED
return df2
def is_diff(filt1, filt2, df):
filt1 = common.force_filter(filt1)
filt2 = common.force_filter(filt2)
df2 = df.copy()
df2.loc[filt1(df2) & filt2(df2), common.STATUS] = common.REJECTED
return df2
