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precision_factor should be an int, not a float #823

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precision_factor should be an int, not a float #823

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57 changes: 27 additions & 30 deletions influxdb/_dataframe_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
import numpy as np

from .client import InfluxDBClient
from .line_protocol import _escape_tag
from .line_protocol import _escape_tag, _to_nanos


def _pandas_time_unit(time_precision):
Expand Down Expand Up @@ -267,19 +267,19 @@ def _convert_dataframe_to_json(dataframe,

precision_factor = {
"n": 1,
"u": 1e3,
"ms": 1e6,
"s": 1e9,
"m": 1e9 * 60,
"h": 1e9 * 3600,
"u": 10 ** 3,
"ms": 10 ** 6,
"s": 10 ** 9,
"m": 10 ** 9 * 60,
"h": 10 ** 9 * 3600,
}.get(time_precision, 1)

if not tag_columns:
points = [
{'measurement': measurement,
'fields':
rec.replace([np.inf, -np.inf], np.nan).dropna().to_dict(),
'time': np.int64(ts.value / precision_factor)}
'time': np.int64(ts.value) // precision_factor}
for ts, (_, rec) in zip(
dataframe.index,
dataframe[field_columns].iterrows()
Expand All @@ -293,7 +293,7 @@ def _convert_dataframe_to_json(dataframe,
'tags': dict(list(tag.items()) + list(tags.items())),
'fields':
rec.replace([np.inf, -np.inf], np.nan).dropna().to_dict(),
'time': np.int64(ts.value / precision_factor)}
'time': np.int64(ts.value) // precision_factor}
for ts, tag, (_, rec) in zip(
dataframe.index,
dataframe[tag_columns].to_dict('record'),
Expand Down Expand Up @@ -354,20 +354,20 @@ def _convert_dataframe_to_lines(self,

precision_factor = {
"n": 1,
"u": 1e3,
"ms": 1e6,
"s": 1e9,
"m": 1e9 * 60,
"h": 1e9 * 3600,
"u": 10 ** 3,
"ms": 10 ** 6,
"s": 10 ** 9,
"m": 10 ** 9 * 60,
"h": 10 ** 9 * 3600,
}.get(time_precision, 1)

# Make array of timestamp ints
if isinstance(dataframe.index, pd.PeriodIndex):
time = ((dataframe.index.to_timestamp().values.astype(np.int64) /
precision_factor).astype(np.int64).astype(str))
time = ((dataframe.index.to_timestamp().values.astype(np.int64) //
precision_factor).astype(str))
else:
time = ((pd.to_datetime(dataframe.index).values.astype(np.int64) /
precision_factor).astype(np.int64).astype(str))
time = ((pd.to_datetime(dataframe.index).values.astype(np.int64) //
precision_factor).astype(str))

# If tag columns exist, make an array of formatted tag keys and values
if tag_columns:
Expand Down Expand Up @@ -473,16 +473,13 @@ def _stringify_dataframe(dframe, numeric_precision, datatype='field'):
return dframe

def _datetime_to_epoch(self, datetime, time_precision='s'):
seconds = (datetime - self.EPOCH).total_seconds()
if time_precision == 'h':
return seconds / 3600
elif time_precision == 'm':
return seconds / 60
elif time_precision == 's':
return seconds
elif time_precision == 'ms':
return seconds * 1e3
elif time_precision == 'u':
return seconds * 1e6
elif time_precision == 'n':
return seconds * 1e9
nanos = _to_nanos(datetime)
precision_factor = {
"n": 1,
"u": 10 ** 3,
"ms": 10 ** 6,
"s": 10 ** 9,
"m": 10 ** 9 * 60,
"h": 10 ** 9 * 3600,
}.get(time_precision, 1)
return nanos // precision_factor