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Issue #383 logging support
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@bikegeek
bikegeek committed Mar 7, 2024
1 parent 3352da0 commit 2ebe3be
Showing 1 changed file with 43 additions and 24 deletions.
67 changes: 43 additions & 24 deletions metplotpy/plots/tcmpr_plots/skill/mean/tcmpr_skill_mean.py
100644 → 100755
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Original file line number Diff line number Diff line change
@@ -1,38 +1,44 @@
import os
from datetime import datetime

import numpy as np
import plotly.graph_objects as go

from metcalcpy.util import utils
from metplotpy.plots.tcmpr_plots.skill.mean.tcmpr_series_skill_mean import TcmprSeriesSkillMean
from metplotpy.plots.tcmpr_plots.skill.tcmpr_skill import TcmprSkill
import metplotpy.plots.util as util


class TcmprSkillMean(TcmprSkill):
def __init__(self, config_obj, column_info, col, case_data, input_df, baseline_data):
super().__init__(config_obj, column_info, col, case_data, input_df, baseline_data)
print("--------------------------------------------------------")
print(f"Plotting SKILL_MN time series by {self.config_obj.series_val_names[0]}")
def __init__(self, config_obj, column_info, col, case_data, input_df, stat_name, baseline_data):
super().__init__(config_obj, column_info, col, case_data, input_df, baseline_data, stat_name)

self._adjust_titles()
# Set up Logging
self.skill_logger = util.get_common_logger(self.config_obj.log_level, self.config_obj.log_filename)

self.skill_logger.info("--------------------------------------------------------")
self.skill_logger.info(f"Plotting SKILL_MN time series by {self.config_obj.series_val_names[0]}")

self._adjust_titles(stat_name)
self.cur_baseline = baseline_data['cur_baseline']
self.cur_baseline_data = baseline_data['cur_baseline_data']
self._init_hfip_baseline_for_plot()
self.series_list = self._create_series(self.input_df)
self.series_list = self._create_series(self.input_df, stat_name)
self.case_data = None
if self.config_obj.prefix is None or len(self.config_obj.prefix) == 0:
self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{self.config_obj.list_stat_1[0]}_skill_mn.png"
self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{stat_name}_skill_mn.png"
else:
self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{self.config_obj.prefix}.png"
self.plot_filename = f"{self.config_obj.plot_dir}{os.path.sep}{self.config_obj.prefix}_{stat_name}_skill_mn.png"

# remove the old file if it exist
# remove the old file if it exists
if os.path.exists(self.plot_filename):
os.remove(self.plot_filename)
self._create_figure()
self._create_figure(stat_name)

def _adjust_titles(self):
def _adjust_titles(self, stat_name):
if self.yaxis_1 is None or len(self.yaxis_1) == 0:
self.yaxis_1 = self.config_obj.list_stat_1[0] + '(' + self.col['units'] + ')'
self.yaxis_1 = 'Skill for ' + stat_name + ' (' + self.col['units'] + ')'

if self.title is None or len(self.title) == 0:
self.title = 'Mean Skill Scores of ' + self.col['desc'] + ' by ' \
Expand All @@ -41,15 +47,16 @@ def _adjust_titles(self):

def _init_hfip_baseline_for_plot(self):
if 'Water Only' in self.title:
print("Plot HFIP Baseline:" + self.cur_baseline)
self.skill_logge.info(f"Plot HFIP Baseline: {self.cur_baseline}")
else:
self.cur_baseline = self.cur_baseline.replace('Error', 'Skill')
self.cur_baseline = self.cur_baseline.replace('HFIP Baseline ', 'HFIP Skill Baseline')
print('Plot HFIP Baseline:' + self.cur_baseline.replace('Error ', ''))
self.skill_logger.info(f"Plot HFIP Baseline: {self.cur_baseline.replace('Error ', '')}")

def _add_hfip_baseline(self):
# Add HFIP baseline for each lead time
if self.cur_baseline_data is not None:
self.skill_logger.info(f"Adding HFIP baseline: {datetime.now()}")
baseline_x_values = []
baseline_y_values = []
lead_times = np.unique(self.series_list[0].series_data[self.config_obj.indy_var].tolist())
Expand All @@ -59,16 +66,13 @@ def _add_hfip_baseline(self):
ocd5_data = self.cur_baseline_data.loc[
(self.cur_baseline_data['LEAD'] == lead) & (self.cur_baseline_data['TYPE'] == "OCD5")][
'VALUE'].tolist()
if len(ocd5_data) > 1:
raise ValueError(
f"ERROR: Can't crate HFIP baseline for lead time {lead} : too many values of OCD5 in .dat file")
ocd5_data = ocd5_data[0]
cons_data = self.cur_baseline_data.loc[
(self.cur_baseline_data['LEAD'] == lead) & (self.cur_baseline_data['TYPE'] == "CONS")][
'VALUE'].tolist()
if len(cons_data) > 1:
raise ValueError(
f"ERROR: Can't crate HFIP baseline for lead time {lead} : too many values of CONS in .dat file")
f"ERROR: Can't create HFIP baseline for lead time {lead} : too many values of CONS in .dat file")
cons_data = cons_data[0]

baseline_lead = utils.round_half_up(100 * (ocd5_data - cons_data) / ocd5_data, 1)
Expand All @@ -93,7 +97,7 @@ def _add_hfip_baseline(self):
)
)

def _create_series(self, input_data):
def _create_series(self, input_data, stat_name):
"""
Generate all the series objects that are to be displayed as specified by the plot_disp
setting in the config file. The points are all ordered by datetime. Each series object
Expand All @@ -103,27 +107,39 @@ def _create_series(self, input_data):
Args:
input_data: The input data in the form of a Pandas dataframe.
This data will be subset to reflect the series data of interest.
stat_name: The current value of the list_stat_1 specified in the configuration file. This represents
type of plot (e.g. TK_ERR, ABS(AMAX_WIND-BMAX-WIND), etc.).
Returns:
a list of series objects that are to be displayed
"""

start_time = datetime.now()
all_fields_values = {'AMODEL': [utils.GROUP_SEPARATOR.join(self.config_obj.skill_ref)],
'fcst_var': self.config_obj.list_stat_1}
permutations = utils.create_permutations_mv(all_fields_values, 0)
ref_model_data_series = TcmprSeriesSkillMean(self.config_obj, 0,
input_data, [], permutations[0])
input_data, [], permutations[0], stat_name, None)
ref_model_data = ref_model_data_series.series_data

series_list = []

# add series for y1 axis
num_series_y1 = len(self.config_obj.get_series_y(1))
for i, name in enumerate(self.config_obj.get_series_y(1)):
unique_series = []
for cur in self.config_obj.get_series_y(1):
if cur[0] not in unique_series:
unique_series.append(cur[0])

num_series_y1 = len(unique_series)
# num_series_y1 = len(self.config_obj.get_series_y(1))
# for i, name in enumerate(self.config_obj.get_series_y(1)):
for i, name in enumerate(unique_series):
if not isinstance(name, list):
name = [name]
series_obj = TcmprSeriesSkillMean(self.config_obj, i, input_data, series_list, name, ref_model_data)

series_obj = TcmprSeriesSkillMean(self.config_obj, i, input_data, series_list, name, stat_name, ref_model_data)
series_list.append(series_obj)

# add derived for y1 axis
Expand All @@ -133,10 +149,13 @@ def _create_series(self, input_data):
name.append("DIFF")
# include the series only if the name is valid
if len(name) == 3:
series_obj = TcmprSeriesSkillMean(self.config_obj, num_series_y1 + i, input_data, series_list, name)
series_obj = TcmprSeriesSkillMean(self.config_obj, num_series_y1 + i, input_data, series_list, name, stat_name)
series_list.append(series_obj)

# reorder series
series_list = self.config_obj.create_list_by_series_ordering(series_list)

end_time = datetime.now()
total_time = end_time - start_time
self.skill_logger.info(f"Total time to create series list for {stat_name}: {total_time} milliseconds ")
return series_list

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