Debugging of RAPIDS.

rapids
junos 2021-12-15 18:25:53 +01:00
parent c4aacfffe1
commit 7e8e922d71
3 changed files with 95 additions and 2 deletions

View File

@ -4,4 +4,17 @@
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View File

@ -227,7 +227,7 @@ phone_data_yield_per_segment.tail()
# # A workaround # # A workaround
# %% # %%
phone_data_yield_per_segment = phone_data_yield_per_segment[ phone_data_yield_per_segment["local_segment_start_datetimes", "minutes_after_segment_start"] = phone_data_yield_per_segment[
["local_segment_start_datetimes", "minutes_after_segment_start"] ["local_segment_start_datetimes", "minutes_after_segment_start"]
].drop_duplicates(keep="first") ].drop_duplicates(keep="first")
@ -240,4 +240,84 @@ phone_data_yield_per_segment.set_index(
# %% # %%
phone_data_yield_per_segment.head() phone_data_yield_per_segment.head()
# %% [markdown]
# # Retry
# %%
def getDataForPlot(phone_data_yield_per_segment):
# calculate the length (in minute) of per segment instance
phone_data_yield_per_segment["length"] = (
phone_data_yield_per_segment["timestamps_segment"]
.str.split(",")
.apply(lambda x: int((int(x[1]) - int(x[0])) / (1000 * 60)))
)
# calculate the number of sensors logged at least one row of data per minute.
phone_data_yield_per_segment = (
phone_data_yield_per_segment.groupby(
["local_segment", "length", "local_date", "local_hour", "local_minute"]
)[["sensor", "local_date_time"]]
.max()
.reset_index()
)
# extract local start datetime of the segment from "local_segment" column
phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(
phone_data_yield_per_segment["local_segment"].apply(
lambda x: x.split("#")[1].split(",")[0]
)
)
# calculate the number of minutes after local start datetime of the segment
phone_data_yield_per_segment["minutes_after_segment_start"] = (
(
phone_data_yield_per_segment["local_date_time"]
- phone_data_yield_per_segment["local_segment_start_datetimes"]
)
/ pd.Timedelta(minutes=1)
).astype("int")
# impute missing rows with 0
columns_for_full_index = phone_data_yield_per_segment[
["local_segment_start_datetimes", "length"]
].drop_duplicates(keep="first")
columns_for_full_index = columns_for_full_index.apply(
lambda row: [
[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)
],
axis=1,
)
full_index = []
for columns in columns_for_full_index:
full_index = full_index + columns
full_index = pd.MultiIndex.from_tuples(
full_index,
names=("local_segment_start_datetimes", "minutes_after_segment_start"),
)
phone_data_yield_per_segment = phone_data_yield_per_segment.drop_duplicates(subset=["local_segment_start_datetimes", "minutes_after_segment_start"],keep="first")
phone_data_yield_per_segment = (
phone_data_yield_per_segment.set_index(
["local_segment_start_datetimes", "minutes_after_segment_start"]
)
.reindex(full_index)
.reset_index()
.fillna(0)
)
# transpose the dataframe per local start datetime of the segment and discard the useless index layer
phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(
"local_segment_start_datetimes"
)[["minutes_after_segment_start", "sensor"]].apply(
lambda x: x.set_index("minutes_after_segment_start").transpose()
)
phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values(
"local_segment_start_datetimes"
)
return phone_data_yield_per_segment
# %%
phone_data_yield_per_segment = filter_data_by_segment(phone_data_yield, time_segment)
# %%
data_for_plot_per_segment = getDataForPlot(phone_data_yield_per_segment)
# %% # %%

2
rapids

@ -1 +1 @@
Subproject commit d2ed73dccfac65ce503c1b510182fe5ef1516508 Subproject commit e5cc02501f629c96641dfd1bcd1f7fcfd0d55462