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Fixed same measurement with different param for export bug

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l_samenv
2024-08-28 10:23:07 +02:00
parent 144a6aa763
commit fcee45817f
2 changed files with 15 additions and 13 deletions
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20
influxdb.py
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@ -142,7 +142,7 @@ class InfluxDataGetter:
components = self._get_device_name_components(time) components = self._get_device_name_components(time)
return "/".join(components) return "/".join(components)
def get_curves_data_frame(self, variables, times, interval): def get_curves_data_frame(self, variables, times, interval, variables_name_label_map=None):
""" """
Gets the curves for the given variables within a timerange times, as a pandas dataframe. Gets the curves for the given variables within a timerange times, as a pandas dataframe.
All curves are on a single common time axis. All curves are on a single common time axis.
@ -156,7 +156,7 @@ class InfluxDataGetter:
variables ([(str)]) : an array of variable names (Influx) to get the curves for. variables ([(str)]) : an array of variable names (Influx) to get the curves for.
times ([int]) : the timerange we want the values in. It consists of two values which are Unix timestamps in seconds, first included, second excluded. times ([int]) : the timerange we want the values in. It consists of two values which are Unix timestamps in seconds, first included, second excluded.
interval (int) : the interval (resolution) of the values to get (in seconds). Allows data binning. interval (int) : the interval (resolution) of the values to get (in seconds). Allows data binning.
variables_name_label_map ({(str):(str)} | None) : a dictionnary containing curve labels, indexed by INFLUX names. The corresponding label will be used in the TSV header for each variable if found, else the influx name.
Returns : Returns :
pandas.DataFrame : the curves in a single pandas DataFrame pandas.DataFrame : the curves in a single pandas DataFrame
""" """
@ -213,25 +213,27 @@ class InfluxDataGetter:
if data_frame.empty: if data_frame.empty:
continue continue
variable_df_column_name = variables_name_label_map.get(variable, variable) if not variables_name_label_map == None else variable
data_frame.rename(columns={variable_name_for_query : variable_df_column_name}, inplace=True)
data_frame.drop(["result", "table"], axis=1, inplace=True) data_frame.drop(["result", "table"], axis=1, inplace=True)
data_frame.sort_values(by=["timestamp"], inplace=True) data_frame.sort_values(by=["timestamp"], inplace=True)
data_frame.reset_index() data_frame.reset_index()
variables_info[variable] = {} variables_info[variable_df_column_name] = {}
variables_info[variable]["expired_ranges"] = [] variables_info[variable_df_column_name]["expired_ranges"] = []
# Identify time windows for which the curve is expired # Identify time windows for which the curve is expired
for index, row in data_frame.iterrows(): for index, row in data_frame.iterrows():
if row["expired"] == "True": if row["expired"] == "True":
data_frame.loc[index, variable] = NaN data_frame.loc[index, variable_df_column_name] = NaN
variables_info[variable]["expired_ranges"].append([row["timestamp"]]) variables_info[variable_df_column_name]["expired_ranges"].append([row["timestamp"]])
elif row["expired"] == "False": elif row["expired"] == "False":
if len(variables_info[variable]["expired_ranges"]) > 0 and len(variables_info[variable]["expired_ranges"][-1]) == 1: if len(variables_info[variable_df_column_name]["expired_ranges"]) > 0 and len(variables_info[variable_df_column_name]["expired_ranges"][-1]) == 1:
variables_info[variable]["expired_ranges"][-1].append(row["timestamp"]) variables_info[variable_df_column_name]["expired_ranges"][-1].append(row["timestamp"])
data_frame.reset_index() data_frame.reset_index()
data_frame.drop(["expired"], axis=1, inplace=True) data_frame.drop(["expired"], axis=1, inplace=True)
variables_info[variable]["df"] = data_frame variables_info[variable_df_column_name]["df"] = data_frame
res = None res = None
non_empty_variables = list(variables_info.keys()) non_empty_variables = list(variables_info.keys())

8
influxgraph.py
View File

@ -32,7 +32,7 @@ class InfluxGraph:
self.livemode = self.HISTORICAL self.livemode = self.HISTORICAL
self.end_query = 0 self.end_query = 0
self.lastvalues = {} self.lastvalues = {}
self.variables = [] self.variables = {} # name:label
def milliseconds_to_nano(self, milliseconds): def milliseconds_to_nano(self, milliseconds):
""" """
@ -141,7 +141,7 @@ class InfluxGraph:
blocks = self.influx_data_getter.get_available_variables_at_time([start_time, end_time]) blocks = self.influx_data_getter.get_available_variables_at_time([start_time, end_time])
device_name = self.influx_data_getter.get_device_name(end_time) device_name = self.influx_data_getter.get_device_name(end_time)
# updates the self.variables attribute to keep track of the available variables # updates the self.variables attribute to keep track of the available variables
self.variables = [variable["name"] for block in blocks for variable in block["curves"]] self.variables = {variable["name"]:variable["label"] for block in blocks for variable in block["curves"]}
assign_colors_to_curves(blocks) assign_colors_to_curves(blocks)
result = dict(type='var_list') result = dict(type='var_list')
@ -185,7 +185,7 @@ class InfluxGraph:
queried_variables = variables.split(',') queried_variables = variables.split(',')
if interval != "None" : interval = int(interval) if interval != "None" : interval = int(interval)
df = self.influx_data_getter.get_curves_data_frame(queried_variables, [start, end], interval) df = self.influx_data_getter.get_curves_data_frame(queried_variables, [start, end], interval, self.variables)
mem = io.BytesIO() mem = io.BytesIO()
df.to_csv(mem, sep="\t", index=False, float_format="%.15g", na_rep=nan) df.to_csv(mem, sep="\t", index=False, float_format="%.15g", na_rep=nan)
@ -207,7 +207,7 @@ class InfluxGraph:
return None return None
now, = self.get_abs_time([0]) now, = self.get_abs_time([0])
result = self.influx_data_getter.poll_last_values(self.variables, self.lastvalues, now) result = self.influx_data_getter.poll_last_values(list(self.variables.keys()), self.lastvalues, now)
for variable in self.lastvalues.keys(): for variable in self.lastvalues.keys():
if variable in result.keys(): if variable in result.keys():