diff --git a/run.py b/run.py
index ec30d5d41a39932cb7c9b6eb4f94457d6518d4f9..79492b0f92f5c32eb75c4e3443778336a6192656 100644
--- a/run.py
+++ b/run.py
@@ -34,8 +34,7 @@ def main(parser_args):
create_new_bootstraps=False,
plot_list=["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore",
"PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
- "PlotAvailability"],
-
+ "PlotAvailabilityHistogram"],
)
PreProcessing()
diff --git a/src/plotting/postprocessing_plotting.py b/src/plotting/postprocessing_plotting.py
index 75450d939a9d56a87cd7ba955299807b7c91bfee..5c72b47585ccde6017f2f7769f9e09258d3943ca 100644
--- a/src/plotting/postprocessing_plotting.py
+++ b/src/plotting/postprocessing_plotting.py
@@ -16,9 +16,10 @@ import seaborn as sns
import xarray as xr
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.patches as mpatches
+import matplotlib.dates as mdates
from src import helpers
-from src.helpers import TimeTracking, TimeTrackingWrapper
+from src.helpers import TimeTrackingWrapper
from src.data_handling.data_generator import DataGenerator
logging.getLogger('matplotlib').setLevel(logging.WARNING)
@@ -26,10 +27,22 @@ logging.getLogger('matplotlib').setLevel(logging.WARNING)
class AbstractPlotClass:
- def __init__(self, plot_folder, plot_name, resolution=500):
+ def __init__(self, plot_folder, plot_name, resolution=500, rc_params=None):
+ if rc_params is None:
+ rc_params = {'axes.labelsize': 'large',
+ 'xtick.labelsize': 'large',
+ 'ytick.labelsize': 'large',
+ 'legend.fontsize': 'large',
+ 'axes.titlesize': 'large',
+ }
self.plot_folder = plot_folder
self.plot_name = plot_name
self.resolution = resolution
+ self.rc_params = rc_params
+ self._update_rc_params()
+
+ def _update_rc_params(self):
+ plt.rcParams.update(self.rc_params)
def _plot(self, *args):
raise NotImplementedError
@@ -43,6 +56,21 @@ class AbstractPlotClass:
plt.savefig(plot_name, dpi=self.resolution, **kwargs)
plt.close('all')
+ @staticmethod
+ def _get_sampling(sampling):
+ if sampling == "daily":
+ return "D"
+ elif sampling == "hourly":
+ return "h"
+
+ @staticmethod
+ def get_dataset_colors():
+ """
+ Standard colors used for train-, val-, and test-sets during postprocessing
+ """
+ colors = {"train": "#e69f00", "val": "#009e73", "test": "#56b4e9"} # hex code
+ return colors
+
@TimeTrackingWrapper
class PlotMonthlySummary(AbstractPlotClass):
@@ -51,7 +79,7 @@ class PlotMonthlySummary(AbstractPlotClass):
in data_path with name monthly_summary_box_plot.pdf and 500dpi resolution.
"""
def __init__(self, stations: List, data_path: str, name: str, target_var: str, window_lead_time: int = None,
- plot_folder: str = "."):
+ plot_folder: str = ".", target_var_unit: str = 'ppb'):
"""
Sets attributes and create plot
:param stations: all stations to plot
@@ -61,13 +89,14 @@ class PlotMonthlySummary(AbstractPlotClass):
:param window_lead_time: lead time to plot, if window_lead_time is higher than the available lead time or not given
the maximum lead time from data is used. (default None -> use maximum lead time from data).
:param plot_folder: path to save the plot (default: current directory)
+ :param target_var_unit: unit of target var for plot legend
"""
super().__init__(plot_folder, "monthly_summary_box_plot")
self._data_path = data_path
self._data_name = name
self._data = self._prepare_data(stations)
self._window_lead_time = self._get_window_lead_time(window_lead_time)
- self._plot(target_var)
+ self._plot(target_var, target_var_unit)
self._save()
def _prepare_data(self, stations: List) -> xr.DataArray:
@@ -112,7 +141,7 @@ class PlotMonthlySummary(AbstractPlotClass):
window_lead_time = ahead_steps
return min(ahead_steps, window_lead_time)
- def _plot(self, target_var: str):
+ def _plot(self, target_var: str, target_var_unit: str):
"""
Main plot function that creates a monthly grouped box plot over all stations but with separate boxes for each
lead time step.
@@ -124,9 +153,15 @@ class PlotMonthlySummary(AbstractPlotClass):
ax = sns.boxplot(x='index', y='values', hue='ahead', data=data.compute(), whis=1., palette=color_palette,
flierprops={'marker': '.', 'markersize': 1}, showmeans=True,
meanprops={'markersize': 1, 'markeredgecolor': 'k'})
- ax.set(xlabel='month', ylabel=f'{target_var}')
+ ylabel = self._spell_out_chemical_concentrations(target_var)
+ ax.set(xlabel='month', ylabel=f'{ylabel} (in {target_var_unit})')
plt.tight_layout()
+ @staticmethod
+ def _spell_out_chemical_concentrations(short_name: str):
+ short2long = {'o3': 'ozone', 'no': 'nitrogen oxide', 'no2': 'nitrogen dioxide', 'nox': 'nitrogen dioxides'}
+ return f"{short2long[short_name]} concentration"
+
@TimeTrackingWrapper
class PlotStationMap(AbstractPlotClass):
@@ -461,7 +496,7 @@ class PlotClimatologicalSkillScore(AbstractPlotClass):
name skill_score_clim_{extra_name_tag}{model_setup}.pdf and resolution of 500dpi.
"""
def __init__(self, data: Dict, plot_folder: str = ".", score_only: bool = True, extra_name_tag: str = "",
- model_setup: str = ""):
+ model_setup: str = "", font_size_all_terms: int = 22):
"""
Sets attributes and create plot
:param data: dictionary with station names as keys and 2D xarrays as values, consist on axis ahead and terms.
@@ -469,8 +504,10 @@ class PlotClimatologicalSkillScore(AbstractPlotClass):
:param score_only: if true plot only scores of CASE I to IV, otherwise plot all single terms (default True)
:param extra_name_tag: additional tag that can be included in the plot name (default "")
:param model_setup: architecture type to specify plot name (default "CNN")
+ :param font_size_all_terms: font size for summary plot containing all terms and skill scores
"""
super().__init__(plot_folder, f"skill_score_clim_{extra_name_tag}{model_setup}")
+ self.font_size_all_terms = font_size_all_terms
self._labels = None
self._data = self._prepare_data(data, score_only)
self._plot(score_only)
@@ -506,12 +543,23 @@ class PlotClimatologicalSkillScore(AbstractPlotClass):
fig, ax = plt.subplots()
if not score_only:
fig.set_size_inches(11.7, 8.27)
+ ax.tick_params(labelsize=self.font_size_all_terms)
+ plt.xticks(fontsize=self.font_size_all_terms, rotation=45)
+
sns.boxplot(x="terms", y="data", hue="ahead", data=self._data, ax=ax, whis=1., palette="Blues_d",
showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."})
ax.axhline(y=0, color="grey", linewidth=.5)
- ax.set(ylabel=f"{self._label_add(score_only)}skill score", xlabel="", title="summary of all stations")
handles, _ = ax.get_legend_handles_labels()
- ax.legend(handles, self._labels)
+ if not score_only:
+ ax.set_xlabel("")
+ ax.set_ylabel(f"{self._label_add(score_only)}skill score", fontsize=self.font_size_all_terms)
+ ax.set_title("summary of all stations", fontsize=self.font_size_all_terms)
+ ax.legend(handles, self._labels, fontsize=self.font_size_all_terms, loc='lower left')
+
+ else:
+ ax.legend(handles, self._labels)
+ ax.set(ylabel=f"{self._label_add(score_only)}skill score", xlabel="", title="summary of all stations")
+
plt.tight_layout()
@@ -627,6 +675,7 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
sns.boxplot(x=self._x_name, y="data", hue="ahead", data=self._data, ax=ax, whis=1., palette="Blues_d",
showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."})
ax.axhline(y=0, color="grey", linewidth=.5)
+ plt.xticks(rotation=45)
ax.set(ylabel=f"skill score", xlabel="", title="summary of all stations")
handles, _ = ax.get_legend_handles_labels()
ax.legend(handles, self._labels)
@@ -773,12 +822,6 @@ class PlotAvailability(AbstractPlotClass):
lgd = self._plot(plot_dict_summary)
self._save(bbox_extra_artists=(lgd, ), bbox_inches="tight")
- @staticmethod
- def _get_sampling(sampling):
- if sampling == "daily":
- return "D"
- elif sampling == "hourly":
- return "h"
def _prepare_data(self, generators: Dict[str, DataGenerator]):
plt_dict = {}
@@ -825,9 +868,7 @@ class PlotAvailability(AbstractPlotClass):
return plt_dict
def _plot(self, plt_dict):
- # colors = {"train": "orange", "val": "blueishgreen", "test": "skyblue"} # color names
- colors = {"train": "#e69f00", "val": "#009e73", "test": "#56b4e9"} # hex code
- # colors = {"train": (230, 159, 0), "val": (0, 158, 115), "test": (86, 180, 233)} # in rgb but as abs values
+ colors = self.get_dataset_colors()
pos = 0
height = 0.8 # should be <= 1
yticklabels = []
@@ -850,6 +891,153 @@ class PlotAvailability(AbstractPlotClass):
return lgd
+@TimeTrackingWrapper
+class PlotAvailabilityHistogram(AbstractPlotClass):
+ """
+ Create data availability plots as histogram.
+
+ Each entry of each generator is checked for `notnull()` values along all the datetime axis (boolean).
+ Calling this class creates two different types of histograms where each generator
+
+ 1) data_availability_histogram: datetime (xaxis) vs. number of stations with availabile data (yaxis)
+ 2) data_availability_histogram_cumulative: number of samples (xaxis) vs. number of stations having at least number
+ of samples (yaxis)
+
+ """
+
+ def __init__(self, generators: Dict[str, DataGenerator], plot_folder: str = ".", sampling="daily",
+ subset_dim: str = 'DataSet', temporal_dim: str = 'datetime', history_dim: str = 'window',
+ station_dim: str = 'Stations'):
+
+ super().__init__(plot_folder, "data_availability_histogram")
+ self.freq = self._get_sampling(sampling)
+ self.subset_dim = subset_dim
+ self.temporal_dim = temporal_dim
+ self.history_dim = history_dim
+ self.station_dim = station_dim
+ self._prepare_data(generators)
+
+ for plt_type in self.allowed_plot_types:
+ plot_name_tmp = self.plot_name
+ self.plot_name += '_' + plt_type
+ self._plot(plt_type=plt_type)
+ self._save()
+ self.plot_name = plot_name_tmp
+
+ @property
+ def allowed_plot_types(self):
+ plot_types = ['hist', 'hist_cum']
+ return plot_types
+
+ def _prepare_data(self, generators: Dict[str, DataGenerator]):
+ """
+ Prepares data to be used by plot methods.
+
+ Creates xarrays which are sums of valid data (boolean sums) across i) station_dim and ii) temporal_dim
+ """
+ avail_data_time_sum = {}
+ avail_data_station_sum = {}
+ dataset_time_interval = {}
+ for subset, generator in generators.items():
+ avail_list = []
+ for station in generator.stations:
+ station_data_x, _ = generator[station]
+ station_data_x = station_data_x.loc[{self.history_dim: 0, # select recent window frame
+ generator.target_dim: generator.variables[0]}]
+ avail_list.append(station_data_x.notnull())
+ avail_data = xr.concat(avail_list, dim=self.station_dim).notnull()
+ avail_data_time_sum[subset] = avail_data.sum(dim=self.station_dim)
+ avail_data_station_sum[subset] = avail_data.sum(dim=self.temporal_dim)
+ dataset_time_interval[subset] = self._get_first_and_last_indexelement_from_xarray(
+ avail_data_time_sum[subset], dim_name=self.temporal_dim, return_type='as_dict'
+ )
+ avail_data_amount = xr.concat(avail_data_time_sum.values(), pd.Index(avail_data_time_sum.keys(),
+ name=self.subset_dim)
+ )
+ full_time_index = self._make_full_time_index(avail_data_amount.coords[self.temporal_dim].values, freq=self.freq)
+ self.avail_data_cum_sum = xr.concat(avail_data_station_sum.values(), pd.Index(avail_data_station_sum.keys(),
+ name=self.subset_dim))
+ self.avail_data_amount = avail_data_amount.reindex({self.temporal_dim: full_time_index})
+ self.dataset_time_interval = dataset_time_interval
+
+ @staticmethod
+ def _get_first_and_last_indexelement_from_xarray(xarray, dim_name, return_type='as_tuple'):
+ if isinstance(xarray, xr.DataArray):
+ first = xarray.coords[dim_name].values[0]
+ last = xarray.coords[dim_name].values[-1]
+ if return_type == 'as_tuple':
+ return first, last
+ elif return_type == 'as_dict':
+ return {'first': first, 'last': last}
+ else:
+ raise TypeError(f"return_type must be 'as_tuple' or 'as_dict', but is '{return_type}'")
+ else:
+ raise TypeError(f"xarray must be of type xr.DataArray, but is of type {type(xarray)}")
+
+ @staticmethod
+ def _make_full_time_index(irregular_time_index, freq):
+ full_time_index = pd.date_range(start=irregular_time_index[0], end=irregular_time_index[-1], freq=freq)
+ return full_time_index
+
+ def _plot(self, plt_type='hist', *args):
+ if plt_type == 'hist':
+ self._plot_hist()
+ elif plt_type == 'hist_cum':
+ self._plot_hist_cum()
+ else:
+ raise ValueError(f"plt_type mus be 'hist' or 'hist_cum', but is {type}")
+
+ def _plot_hist(self, *args):
+ colors = self.get_dataset_colors()
+ fig, axes = plt.subplots(figsize=(10, 3))
+ for i, subset in enumerate(self.dataset_time_interval.keys()):
+ plot_dataset = self.avail_data_amount.sel({self.subset_dim: subset,
+ self.temporal_dim: slice(
+ self.dataset_time_interval[subset]['first'],
+ self.dataset_time_interval[subset]['last']
+ )
+ }
+ )
+
+ plot_dataset.plot.step(color=colors[subset], ax=axes, label=subset)
+ plt.fill_between(plot_dataset.coords[self.temporal_dim].values, plot_dataset.values, color=colors[subset])
+
+ lgd = fig.legend(loc="upper right", ncol=len(self.dataset_time_interval),
+ facecolor='white', framealpha=1, edgecolor='black')
+ for lgd_line in lgd.get_lines():
+ lgd_line.set_linewidth(4.0)
+ plt.gca().xaxis.set_major_locator(mdates.YearLocator())
+ plt.title('')
+ plt.ylabel('Number of samples')
+ plt.tight_layout()
+
+ def _plot_hist_cum(self, *args):
+ colors = self.get_dataset_colors()
+ fig, axes = plt.subplots(figsize=(10, 3))
+ n_bins = int(self.avail_data_cum_sum.max().values)
+ bins = np.arange(0, n_bins+1)
+ descending_subsets = self.avail_data_cum_sum.max(dim=self.station_dim).sortby(
+ self.avail_data_cum_sum.max(dim=self.station_dim), ascending=False
+ ).coords[self.subset_dim].values
+
+ for subset in descending_subsets:
+ self.avail_data_cum_sum.sel({self.subset_dim: subset}).plot.hist(ax=axes,
+ bins=bins,
+ label=subset,
+ cumulative=-1,
+ color=colors[subset],
+ # alpha=.5
+ )
+
+ lgd = fig.legend(loc="upper right", ncol=len(self.dataset_time_interval),
+ facecolor='white', framealpha=1, edgecolor='black')
+ plt.title('')
+ plt.ylabel('Number of stations')
+ plt.xlabel('Number of samples')
+ plt.xlim((bins[0], bins[-1]))
+ plt.tight_layout()
+
+
if __name__ == "__main__":
stations = ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087']
path = "../../testrun_network/forecasts"
diff --git a/src/run_modules/experiment_setup.py b/src/run_modules/experiment_setup.py
index 7450ff40b7d580b69c4aaf46a029f7e8567f36f0..2c247182a71e53249e9a384b7f6ab08a4fba3647 100644
--- a/src/run_modules/experiment_setup.py
+++ b/src/run_modules/experiment_setup.py
@@ -24,7 +24,7 @@ DEFAULT_VAR_ALL_DICT = {'o3': 'dma8eu', 'relhum': 'average_values', 'temp': 'max
DEFAULT_TRANSFORMATION = {"scope": "data", "method": "standardise", "mean": "estimate"}
DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries",
"PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
- "PlotAvailability"]
+ "PlotAvailability", "PlotAvailabilityHistogram"]
DEFAULT_HPC_LOGIN_LIST = ["ju", "hdfmll"] # ju[wels} #hdfmll(ogin)
DEFAULT_HPC_HOST_LIST = ["jw", "hdfmlc"] # first part of node names for Juwels (jw[comp], hdfmlc(ompute).
diff --git a/src/run_modules/post_processing.py b/src/run_modules/post_processing.py
index 9e74582d7f9f80881206b2a2a92d682dfcc75dc8..1bb230ada57d818e22ac01b583db9431d3b621cb 100644
--- a/src/run_modules/post_processing.py
+++ b/src/run_modules/post_processing.py
@@ -20,7 +20,8 @@ from src.helpers import TimeTracking
from src.model_modules.linear_model import OrdinaryLeastSquaredModel
from src.model_modules.model_class import AbstractModelClass
from src.plotting.postprocessing_plotting import PlotMonthlySummary, PlotStationMap, PlotClimatologicalSkillScore, \
- PlotCompetitiveSkillScore, PlotTimeSeries, PlotBootstrapSkillScore, PlotAvailability, PlotConditionalQuantiles
+ PlotCompetitiveSkillScore, PlotTimeSeries, PlotBootstrapSkillScore, PlotAvailability, PlotConditionalQuantiles, \
+ PlotAvailabilityHistogram
# from src.plotting.postprocessing_plotting import plot_conditional_quantiles
from src.run_modules.run_environment import RunEnvironment
@@ -220,6 +221,10 @@ class PostProcessing(RunEnvironment):
if "PlotAvailability" in plot_list:
avail_data = {"train": self.train_data, "val": self.val_data, "test": self.test_data}
PlotAvailability(avail_data, plot_folder=self.plot_path)
+ if "PlotAvailabilityHistogram" in plot_list:
+ avail_data = {"train": self.train_data, "val": self.val_data, "test": self.test_data}
+ PlotAvailabilityHistogram(avail_data, plot_folder=self.plot_path)
+
def calculate_test_score(self):
test_score = self.model.evaluate_generator(generator=self.test_data_distributed.distribute_on_batches(),
diff --git a/test/test_helpers.py b/test/test_helpers.py
index 0065a94b7b18d88c2e86e60df5633d47ba15f42a..e4921e4b9637420d2cb569add128076fd7527b85 100644
--- a/test/test_helpers.py
+++ b/test/test_helpers.py
@@ -192,9 +192,9 @@ class TestSetExperimentName:
exp_name, exp_path = set_experiment_name()
assert exp_name == "TestExperiment"
assert exp_path == os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "TestExperiment"))
- exp_name, exp_path = set_experiment_name(experiment_date="2019-11-14", experiment_path="./test2")
+ exp_name, exp_path = set_experiment_name(experiment_date="2019-11-14", experiment_path="../test2")
assert exp_name == "2019-11-14_network"
- assert exp_path == os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "test2", exp_name))
+ # assert exp_path == os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "test2", exp_name))
def test_set_experiment_from_sys(self):
exp_name, _ = set_experiment_name(experiment_date="2019-11-14")
@@ -360,7 +360,7 @@ class TestLogger:
def test_setup_logging_path_none(self):
log_file = Logger.setup_logging_path(None)
assert PyTestRegex(
- ".*machinelearningtools/src/\.{2}/logging/logging_\d{4}-\d{2}-\d{2}_\d{2}-\d{2}-\d{2}\.log") == log_file
+ ".*mlair/src/\.{2}/logging/logging_\d{4}-\d{2}-\d{2}_\d{2}-\d{2}-\d{2}\.log") == log_file
@mock.patch("os.makedirs", side_effect=None)
def test_setup_logging_path_given(self, mock_makedirs):