diff --git a/docs/_source/_plots/data_availability_histogram_hist.png b/docs/_source/_plots/data_availability_histogram_hist.png
new file mode 100644
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diff --git a/docs/_source/_plots/data_availability_histogram_hist_cum.png b/docs/_source/_plots/data_availability_histogram_hist_cum.png
new file mode 100644
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diff --git a/docs/_source/_plots/skill_score_bootstrap_separated.png b/docs/_source/_plots/skill_score_bootstrap_separated.png
new file mode 100644
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diff --git a/mlair/configuration/defaults.py b/mlair/configuration/defaults.py
index ce42fc0eed6e891bc0a0625666da3dccfcc8a3ee..1862d6734430d42a2d0cda0b199acef97b58bebb 100644
--- a/mlair/configuration/defaults.py
+++ b/mlair/configuration/defaults.py
@@ -48,7 +48,7 @@ DEFAULT_CREATE_NEW_BOOTSTRAPS = False
DEFAULT_NUMBER_OF_BOOTSTRAPS = 20
DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries",
"PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
- "PlotAvailability", "PlotSeparationOfScales"]
+ "PlotAvailability", "PlotAvailabilityHistogram", "PlotSeparationOfScales"]
DEFAULT_SAMPLING = "daily"
DEFAULT_DATA_ORIGIN = {"cloudcover": "REA", "humidity": "REA", "pblheight": "REA", "press": "REA", "relhum": "REA",
"temp": "REA", "totprecip": "REA", "u": "REA", "v": "REA", "no": "", "no2": "", "o3": "",
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index f0b6baeb0b56126ccccb80c9da993fb406428d93..5383914d6229e2c7017dada2bdcb9c315e327cf7 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -1,6 +1,6 @@
"""Collection of plots to evaluate a model, create overviews on data or forecasts."""
__author__ = "Lukas Leufen, Felix Kleinert"
-__date__ = '2019-12-17'
+__date__ = '2020-11-23'
import logging
import math
@@ -8,10 +8,10 @@ import os
import warnings
from typing import Dict, List, Tuple
-
import matplotlib
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
import numpy as np
import pandas as pd
import seaborn as sns
@@ -75,7 +75,7 @@ class AbstractPlotClass:
"""
- def __init__(self, plot_folder, plot_name, resolution=500):
+ def __init__(self, plot_folder, plot_name, resolution=500, rc_params=None):
"""Set up plot folder and name, and plot resolution (default 500dpi)."""
plot_folder = os.path.abspath(plot_folder)
if not os.path.exists(plot_folder):
@@ -83,6 +83,15 @@ class AbstractPlotClass:
self.plot_folder = plot_folder
self.plot_name = plot_name
self.resolution = resolution
+ if rc_params is None:
+ rc_params = {'axes.labelsize': 'large',
+ 'xtick.labelsize': 'large',
+ 'ytick.labelsize': 'large',
+ 'legend.fontsize': 'large',
+ 'axes.titlesize': 'large',
+ }
+ self.rc_params = rc_params
+ self._update_rc_params()
def _plot(self, *args):
"""Abstract plot class needs to be implemented in inheritance."""
@@ -95,6 +104,24 @@ class AbstractPlotClass:
plt.savefig(plot_name, dpi=self.resolution, **kwargs)
plt.close('all')
+ def _update_rc_params(self):
+ plt.rcParams.update(self.rc_params)
+
+ @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):
@@ -113,18 +140,19 @@ 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 (default= ppb)
"""
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'):
"""Set attributes and create plot."""
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:
@@ -176,7 +204,12 @@ class PlotMonthlySummary(AbstractPlotClass):
window_lead_time = ahead_steps
return min(ahead_steps, window_lead_time)
- def _plot(self, target_var: str):
+ @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"
+
+ def _plot(self, target_var: str, target_var_unit: str):
"""
Create a monthly grouped box plot over all stations but with separate boxes for each lead time step.
@@ -189,7 +222,8 @@ 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()
@@ -453,7 +487,8 @@ class PlotConditionalQuantiles(AbstractPlotClass):
def _plot(self):
"""Start plotting routines: overall plot and seasonal (if enabled)."""
- logging.info(f"start plotting {self.__class__.__name__}, scheduled number of plots: {(len(self._seasons) + 1) * 2}")
+ logging.info(
+ f"start plotting {self.__class__.__name__}, scheduled number of plots: {(len(self._seasons) + 1) * 2}")
if len(self._seasons) > 0:
self._plot_seasons()
@@ -504,7 +539,7 @@ class PlotConditionalQuantiles(AbstractPlotClass):
# add histogram of the segmented data (pred_name)
handles, labels = ax.get_legend_handles_labels()
segmented_data.loc[x_model, d, :].to_pandas().hist(bins=self._bins, ax=ax2, color='k', alpha=.3, grid=False,
- rwidth=1)
+ rwidth=1)
# add legend
plt.legend(handles[:3] + [handles[-1]], self._opts["legend"], loc='upper left', fontsize='large')
# adjust limits and set labels
@@ -688,25 +723,37 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
(score_only=True, default). Y-axis is adjusted following the data and not hard coded. The plot is saved under
plot_folder path with name skill_score_clim_{extra_name_tag}{model_setup}.pdf and resolution of 500dpi.
+ By passing a list `separate_vars` containing variable names, a second plot is created showing the `separate_vars`
+ and the remaining variables side by side with different scaling.
+
.. image:: ../../../../../_source/_plots/skill_score_bootstrap.png
:width: 400
+ .. image:: ../../../../../_source/_plots/skill_score_bootstrap_separated.png
+ :width: 400
+
"""
- def __init__(self, data: Dict, plot_folder: str = ".", model_setup: str = ""):
+ def __init__(self, data: Dict, plot_folder: str = ".", model_setup: str = "", separate_vars: List = None):
"""
Set attributes and create plot.
:param data: dictionary with station names as keys and 2D xarrays as values, consist on axis ahead and terms.
:param plot_folder: path to save the plot (default: current directory)
:param model_setup: architecture type to specify plot name (default "CNN")
+ :param separate_vars: variables to plot separated (default: ['o3'])
"""
super().__init__(plot_folder, f"skill_score_bootstrap_{model_setup}")
+ if separate_vars is None:
+ separate_vars = ['o3']
self._labels = None
self._x_name = "boot_var"
self._data = self._prepare_data(data)
self._plot()
self._save()
+ self.plot_name += '_separated'
+ self._plot(separate_vars=separate_vars)
+ self._save(bbox_inches='tight')
def _prepare_data(self, data: Dict) -> pd.DataFrame:
"""
@@ -719,11 +766,30 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
:return: pre-processed data set
"""
data = helpers.dict_to_xarray(data, "station").sortby(self._x_name)
+ new_boot_coords = self._return_vars_without_number_tag(data.coords['boot_var'].values, split_by='_', keep=1)
+ data = data.assign_coords({'boot_var': new_boot_coords})
self._labels = [str(i) + "d" for i in data.coords["ahead"].values]
if "station" not in data.dims:
data = data.expand_dims("station")
return data.to_dataframe("data").reset_index(level=[0, 1, 2])
+ def _return_vars_without_number_tag(self, values, split_by, keep):
+ arr = np.array([v.split(split_by) for v in values])
+ num = arr[:, 0]
+ new_val = arr[:, keep]
+ if self._all_values_are_equal(num, axis=0):
+ return new_val
+ else:
+ raise NotImplementedError
+
+
+ @staticmethod
+ def _all_values_are_equal(arr, axis=0):
+ if np.all(arr == arr[0], axis=axis):
+ return True
+ else:
+ return False
+
def _label_add(self, score_only: bool):
"""
Add the phrase "terms and " if score_only is disabled or empty string (if score_only=True).
@@ -733,12 +799,111 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
"""
return "" if score_only else "terms and "
- def _plot(self):
+ def _plot(self, separate_vars=None):
"""Plot climatological skill score."""
+ if separate_vars is None:
+ self._plot_all_variables()
+ else:
+ self._plot_selected_variables(separate_vars)
+
+ def _plot_selected_variables(self, separate_vars: List):
+ # if separate_vars is None:
+ # separate_vars = ['o3']
+ data = self._data
+ self.raise_error_if_separate_vars_do_not_exist(data, separate_vars)
+ all_variables = self._get_unique_values_from_column_of_df(data, 'boot_var')
+ # remaining_vars = helpers.list_pop(all_variables, separate_vars) #remove_items
+ remaining_vars = helpers.remove_items(all_variables, separate_vars)
+ data_first = self._select_data(df=data, variables=separate_vars, column_name='boot_var')
+ data_second = self._select_data(df=data, variables=remaining_vars, column_name='boot_var')
+
+ fig, ax = plt.subplots(nrows=1, ncols=2,
+ gridspec_kw={'width_ratios': [len(separate_vars),
+ len(remaining_vars)
+ ]
+ }
+ )
+ if len(separate_vars) > 1:
+ first_box_width = .8
+ else:
+ first_box_width = 2.
+
+ sns.boxplot(x=self._x_name, y="data", hue="ahead", data=data_first, ax=ax[0], whis=1., palette="Blues_d",
+ showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
+ flierprops={"marker": "."}, width=first_box_width
+ )
+ ax[0].set(ylabel=f"skill score", xlabel="")
+
+ sns.boxplot(x=self._x_name, y="data", hue="ahead", data=data_second, ax=ax[1], whis=1., palette="Blues_d",
+ showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
+ flierprops={"marker": "."},
+ )
+ ax[1].set(ylabel="", xlabel="")
+ ax[1].yaxis.tick_right()
+ handles, _ = ax[1].get_legend_handles_labels()
+ for sax in ax:
+ matplotlib.pyplot.sca(sax)
+ sax.axhline(y=0, color="grey", linewidth=.5)
+ plt.xticks(rotation=45, ha='right')
+ sax.legend_.remove()
+
+ fig.legend(handles, self._labels, loc='upper center', ncol=len(handles) + 1, )
+
+ def align_yaxis(ax1, ax2):
+ """
+ Align zeros of the two axes, zooming them out by same ratio
+
+ This function is copy pasted from https://stackoverflow.com/a/41259922
+ """
+ axes = (ax1, ax2)
+ extrema = [ax.get_ylim() for ax in axes]
+ tops = [extr[1] / (extr[1] - extr[0]) for extr in extrema]
+ # Ensure that plots (intervals) are ordered bottom to top:
+ if tops[0] > tops[1]:
+ axes, extrema, tops = [list(reversed(l)) for l in (axes, extrema, tops)]
+
+ # How much would the plot overflow if we kept current zoom levels?
+ tot_span = tops[1] + 1 - tops[0]
+
+ b_new_t = extrema[0][0] + tot_span * (extrema[0][1] - extrema[0][0])
+ t_new_b = extrema[1][1] - tot_span * (extrema[1][1] - extrema[1][0])
+ axes[0].set_ylim(extrema[0][0], b_new_t)
+ axes[1].set_ylim(t_new_b, extrema[1][1])
+
+ align_yaxis(ax[0], ax[1])
+ align_yaxis(ax[0], ax[1])
+
+ @staticmethod
+ def _select_data(df: pd.DataFrame, variables: List[str], column_name: str) -> pd.DataFrame:
+ for i, variable in enumerate(variables):
+ if i == 0:
+ selected_data = df.loc[df[column_name] == variable]
+ else:
+ tmp_var = df.loc[df[column_name] == variable]
+ selected_data = pd.concat([selected_data, tmp_var], axis=0)
+ return selected_data
+
+ def raise_error_if_separate_vars_do_not_exist(self, data, separate_vars):
+ if not self._variables_exist_in_df(df=data, variables=separate_vars):
+ raise ValueError(f"At least one entry of `separate_vars' does not exist in `self.data' ")
+
+ @staticmethod
+ def _get_unique_values_from_column_of_df(df: pd.DataFrame, column_name: str) -> List:
+ return list(df[column_name].unique())
+
+ def _variables_exist_in_df(self, df: pd.DataFrame, variables: List[str], column_name: str = 'boot_var'):
+ vars_in_df = set(self._get_unique_values_from_column_of_df(df, column_name))
+ return set(variables).issubset(vars_in_df)
+
+ def _plot_all_variables(self):
+ """
+
+ """
fig, ax = plt.subplots()
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)
@@ -859,6 +1024,7 @@ class PlotTimeSeries:
def _get_time_range(data):
def f(x, f_x):
return pd.to_datetime(f_x(x.index.values)).year
+
return f(data, min), f(data, max)
@staticmethod
@@ -911,8 +1077,10 @@ class PlotAvailability(AbstractPlotClass):
# create standard Gantt plot for all stations (currently in single pdf file with single page)
super().__init__(plot_folder, "data_availability")
self.dim = time_dimension
- self.linewidth = None
self.sampling = self._get_sampling(sampling)
+ self.linewidth = None
+ if self.sampling == 'h':
+ self.linewidth = 0.001
plot_dict = self._prepare_data(generators)
lgd = self._plot(plot_dict)
self._save(bbox_extra_artists=(lgd,), bbox_inches="tight")
@@ -927,13 +1095,6 @@ class PlotAvailability(AbstractPlotClass):
lgd = self._plot(plot_dict_summary)
self._save(bbox_extra_artists=(lgd,), bbox_inches="tight")
- def _get_sampling(self, sampling):
- if sampling == "daily":
- return "D"
- elif sampling == "hourly":
- self.linewidth = 0.001
- return "h"
-
def _prepare_data(self, generators: Dict[str, DataCollection]):
plt_dict = {}
for subset, data_collection in generators.items():
@@ -978,9 +1139,7 @@ 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 = []
@@ -1025,6 +1184,169 @@ class PlotSeparationOfScales(AbstractPlotClass):
self._save()
+@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)
+
+ .. image:: ../../../../../_source/_plots/data_availability_histogram_hist.png
+ :width: 400
+
+ .. image:: ../../../../../_source/_plots/data_availability_histogram_hist_cum.png
+ :width: 400
+
+ """
+
+ def __init__(self, generators: Dict[str, DataCollection], plot_folder: str = ".",
+ subset_dim: str = 'DataSet', history_dim: str = 'window',
+ station_dim: str = 'Stations',):
+
+ super().__init__(plot_folder, "data_availability_histogram")
+
+ self.subset_dim = subset_dim
+ self.history_dim = history_dim
+ self.station_dim = station_dim
+
+ self.freq = None
+ self.temporal_dim = None
+ self.target_dim = None
+ 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
+
+ def _set_dims_from_datahandler(self, data_handler):
+ self.temporal_dim = data_handler.id_class.time_dim
+ self.target_dim = data_handler.id_class.target_dim
+ self.freq = self._get_sampling(data_handler.id_class.sampling)
+
+ @property
+ def allowed_plot_types(self):
+ plot_types = ['hist', 'hist_cum']
+ return plot_types
+
+ def _prepare_data(self, generators: Dict[str, DataCollection]):
+ """
+ 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:
+ self._set_dims_from_datahandler(data_handler=station)
+ station_data_x = station.get_X(as_numpy=False)[0]
+ station_data_x = station_data_x.loc[{self.history_dim: 0, # select recent window frame
+ self.target_dim: station_data_x[self.target_dim].values[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/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 3dc91cbd54094f116f0d959fb9c845751e998464..21fbb3edfa2c9665db12f7ea6bb0f9310ca324a1 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -19,8 +19,8 @@ from mlair.helpers import TimeTracking, statistics, extract_value
from mlair.model_modules.linear_model import OrdinaryLeastSquaredModel
from mlair.model_modules.model_class import AbstractModelClass
from mlair.plotting.postprocessing_plotting import PlotMonthlySummary, PlotStationMap, PlotClimatologicalSkillScore, \
- PlotCompetitiveSkillScore, PlotTimeSeries, PlotBootstrapSkillScore, PlotAvailability, PlotConditionalQuantiles, \
- PlotSeparationOfScales
+ PlotCompetitiveSkillScore, PlotTimeSeries, PlotBootstrapSkillScore, PlotAvailability, PlotAvailabilityHistogram, \
+ PlotConditionalQuantiles, PlotSeparationOfScales
from mlair.run_modules.run_environment import RunEnvironment
@@ -293,6 +293,10 @@ 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, time_dimension=time_dimension)
+ 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, )# time_dimension=time_dimension)
+
def calculate_test_score(self):
"""Evaluate test score of model and save locally."""
diff --git a/mlair/run_modules/pre_processing.py b/mlair/run_modules/pre_processing.py
index 13a4b38184ea3c00c9cf5990c35c6d7bdcb6225d..7f546865f3ab3ebae8b48f375300035f5be766a4 100644
--- a/mlair/run_modules/pre_processing.py
+++ b/mlair/run_modules/pre_processing.py
@@ -115,9 +115,47 @@ class PreProcessing(RunEnvironment):
precision = 4
path = os.path.join(self.data_store.get("experiment_path"), "latex_report")
path_config.check_path_and_create(path)
- set_names = ["train", "val", "test"]
- df = pd.DataFrame(columns=meta_data + set_names)
- for set_name in set_names:
+ names_of_set = ["train", "val", "test"]
+ df = self.create_info_df(meta_data, meta_round, names_of_set, precision)
+ column_format = self.create_column_format_for_tex(df)
+ self.save_to_tex(path=path, filename="station_sample_size.tex", column_format=column_format, df=df)
+ self.save_to_md(path=path, filename="station_sample_size.md", df=df)
+ df_nometa = df.drop(meta_data, axis=1)
+ self.save_to_tex(path=path, filename="station_sample_size_short.tex", column_format=column_format, df=df_nometa)
+ self.save_to_md(path=path, filename="station_sample_size_short.md", df=df_nometa)
+ # df_nometa.to_latex(os.path.join(path, "station_sample_size_short.tex"), na_rep='---',
+ # column_format=column_format)
+ df_descr = self.create_describe_df(df_nometa)
+ column_format = self.create_column_format_for_tex(df_descr)
+ self.save_to_tex(path=path, filename="station_describe_short.tex", column_format=column_format, df=df_descr)
+ self.save_to_md(path=path, filename="station_describe_short.md", df=df_descr)
+ # df_descr.to_latex(os.path.join(path, "station_describe_short.tex"), na_rep='---', column_format=column_format)
+
+ @staticmethod
+ def create_describe_df(df, percentiles=None, ignore_last_lines: int = 2):
+ if percentiles is None:
+ percentiles = [.05, .1, .25, .5, .75, .9, .95]
+ df_descr = df.iloc[:-ignore_last_lines].astype('float32').describe(
+ percentiles=percentiles).astype('int32')
+ df_descr = pd.concat([df.loc[['# Samples']], df_descr]).T
+ df_descr.rename(columns={"# Samples": "no. samples", "count": "no. stations"}, inplace=True)
+ df_descr_colnames = list(df_descr.columns)
+ df_descr_colnames = [df_descr_colnames[1]] + [df_descr_colnames[0]] + df_descr_colnames[2:]
+ df_descr = df_descr[df_descr_colnames]
+ return df_descr
+
+ @staticmethod
+ def save_to_tex(path, filename, column_format, df, na_rep='---'):
+ df.to_latex(os.path.join(path, filename), na_rep=na_rep, column_format=column_format)
+
+ @staticmethod
+ def save_to_md(path, filename, df, mode="w", encoding='utf-8', tablefmt="github"):
+ df.to_markdown(open(os.path.join(path, filename), mode=mode, encoding=encoding),
+ tablefmt=tablefmt)
+
+ def create_info_df(self, meta_data, meta_round, names_of_set, precision):
+ df = pd.DataFrame(columns=meta_data + names_of_set)
+ for set_name in names_of_set:
data = self.data_store.get("data_collection", set_name)
for station in data:
station_name = str(station.id_class)
@@ -130,15 +168,21 @@ class PreProcessing(RunEnvironment):
df.sort_index(inplace=True)
df = df.reindex(df.index.drop(["# Stations", "# Samples"]).to_list() + ["# Stations", "# Samples"], )
df.index.name = 'stat. ID'
+ return df
+
+ @staticmethod
+ def create_column_format_for_tex(df: pd.DataFrame) -> str:
+ """
+ Creates column format for latex table based on the shape of a given DataFrame.
+
+ Calculates number of columns and uses 'c' as column position. First element is set to 'l', last to 'r'
+ """
column_format = np.repeat('c', df.shape[1] + 1)
column_format[0] = 'l'
column_format[-1] = 'r'
column_format = ''.join(column_format.tolist())
- df.to_latex(os.path.join(path, "station_sample_size.tex"), na_rep='---', column_format=column_format)
- df.to_markdown(open(os.path.join(path, "station_sample_size.md"), mode="w", encoding='utf-8'),
- tablefmt="github")
- df.drop(meta_data, axis=1).to_latex(os.path.join(path, "station_sample_size_short.tex"), na_rep='---',
- column_format=column_format)
+ return column_format
+
def split_train_val_test(self) -> None:
"""
diff --git a/test/test_configuration/test_defaults.py b/test/test_configuration/test_defaults.py
index fffe7c84075eeeab37ebf59d52bc42dbf87bf522..ae81ef2ef0a15ad08f14ad19312f04040ab71263 100644
--- a/test/test_configuration/test_defaults.py
+++ b/test/test_configuration/test_defaults.py
@@ -70,4 +70,5 @@ class TestAllDefaults:
assert DEFAULT_NUMBER_OF_BOOTSTRAPS == 20
assert DEFAULT_PLOT_LIST == ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore",
"PlotTimeSeries", "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore",
- "PlotConditionalQuantiles", "PlotAvailability", "PlotSeparationOfScales"]
+ "PlotConditionalQuantiles", "PlotAvailability", "PlotAvailabilityHistogram",
+ "PlotSeparationOfScales"]
diff --git a/test/test_run_modules/test_pre_processing.py b/test/test_run_modules/test_pre_processing.py
index 602c563fa9e346bd944b20aee2c038970791730e..e7d05bdb51aeb069beda002f9b53439853edff91 100644
--- a/test/test_run_modules/test_pre_processing.py
+++ b/test/test_run_modules/test_pre_processing.py
@@ -9,6 +9,8 @@ from mlair.helpers import PyTestRegex
from mlair.run_modules.experiment_setup import ExperimentSetup
from mlair.run_modules.pre_processing import PreProcessing
from mlair.run_modules.run_environment import RunEnvironment
+import pandas as pd
+import numpy as np
class TestPreProcessing:
@@ -117,3 +119,38 @@ class TestPreProcessing:
assert pre.transformation(data_preparation, stations) is None
class data_preparation_no_trans: pass
assert pre.transformation(data_preparation_no_trans, stations) is None
+
+ @pytest.fixture
+ def dummy_df(self):
+ data_dict = {'station_name': {'DEBW013': 'Stuttgart Bad Cannstatt', 'DEBW076': 'Baden-Baden',
+ 'DEBW087': 'Schwäbische_Alb', 'DEBW107': 'Tübingen',
+ 'DEBY081': 'Garmisch-Partenkirchen/Kreuzeckbahnstraße', '# Stations': np.nan,
+ '# Samples': np.nan},
+ 'station_lon': {'DEBW013': 9.2297, 'DEBW076': 8.2202, 'DEBW087': 9.2076, 'DEBW107': 9.0512,
+ 'DEBY081': 11.0631, '# Stations': np.nan, '# Samples': np.nan},
+ 'station_lat': {'DEBW013': 48.8088, 'DEBW076': 48.7731, 'DEBW087': 48.3458, 'DEBW107': 48.5077,
+ 'DEBY081': 47.4764, '# Stations': np.nan, '# Samples': np.nan},
+ 'station_alt': {'DEBW013': 235.0, 'DEBW076': 148.0, 'DEBW087': 798.0, 'DEBW107': 325.0,
+ 'DEBY081': 735.0, '# Stations': np.nan, '# Samples': np.nan},
+ 'train': {'DEBW013': 1413, 'DEBW076': 3002, 'DEBW087': 3016, 'DEBW107': 1782, 'DEBY081': 2837,
+ '# Stations': 6, '# Samples': 12050},
+ 'val': {'DEBW013': 698, 'DEBW076': 715, 'DEBW087': 700, 'DEBW107': 701, 'DEBY081': 456,
+ '# Stations': 6, '# Samples': 3270},
+ 'test': {'DEBW013': 1066, 'DEBW076': 696, 'DEBW087': 1080, 'DEBW107': 1080, 'DEBY081': 700,
+ '# Stations': 6, '# Samples': 4622}}
+ df = pd.DataFrame.from_dict(data_dict)
+ return df
+
+ def test_create_column_format_for_tex(self):
+ df = pd.DataFrame(np.ones((2, 1)))
+ df_col = PreProcessing.create_column_format_for_tex(df) # len: 1+1
+ assert df_col == 'lr'
+ assert len(df_col) == 2
+ df = pd.DataFrame(np.ones((2, 2)))
+ df_col = PreProcessing.create_column_format_for_tex(df) # len: 2+1
+ assert df_col == 'lcr'
+ assert len(df_col) == 3
+ df = pd.DataFrame(np.ones((2, 3)))
+ df_col = PreProcessing.create_column_format_for_tex(df) # len: 3+1
+ assert df_col == 'lccr'
+ assert len(df_col) == 4