diff --git a/video_prediction_tools/main_scripts/main_visualize_postprocess.py b/video_prediction_tools/main_scripts/main_visualize_postprocess.py
index 6557c454284c47614db96ad8fdf0f12d4f3ba018..1a8f1a704de274e2ecc821dabca1d09155b62303 100644
--- a/video_prediction_tools/main_scripts/main_visualize_postprocess.py
+++ b/video_prediction_tools/main_scripts/main_visualize_postprocess.py
@@ -16,12 +16,8 @@ import tensorflow as tf
import pickle
import datetime as dt
import json
-import matplotlib
from typing import Union, List
-
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-from mpl_toolkits.basemap import Basemap
+# own modules
from normalization import Norm_data
from general_utils import check_dir
from metadata import MetaData as MetaData
@@ -29,6 +25,7 @@ from main_scripts.main_train_models import *
from data_preprocess.preprocess_data_step2 import *
from model_modules.video_prediction import datasets, models, metrics
from statistical_evaluation import perform_block_bootstrap_metric, avg_metrics, Scores
+from plotting import plot_avg_eval_metrics, create_plot
class Postprocess(TrainModel):
@@ -726,8 +723,8 @@ class Postprocess(TrainModel):
Postprocess.save_ds_to_netcdf(self.eval_metrics_ds, nc_fname)
# also save averaged metrics to JSON-file and plot it for diagnosis
- _ = Postprocess.plot_avg_eval_metrics(self.eval_metrics_ds, self.eval_metrics, self.fcst_products,
- self.vars_in[self.channel], self.results_dir)
+ _ = plot_avg_eval_metrics(self.eval_metrics_ds, self.eval_metrics, self.fcst_products,
+ self.vars_in[self.channel], self.results_dir)
# auxiliary methods (not necessarily bound to class instance)
@staticmethod
@@ -1053,7 +1050,7 @@ class Postprocess(TrainModel):
.format(varname, date_init.strftime("%Y%m%dT%H00"), metric,
int(quantiles[i]*100.)))
- Postprocess.create_plot(data_fcst, data_diff, varname, plt_fname_base)
+ create_plot(data_fcst, data_diff, varname, plt_fname_base)
@staticmethod
def init_metric_ds(fcst_products, eval_metrics, varname, nsamples, nlead_steps):
@@ -1090,151 +1087,6 @@ class Postprocess(TrainModel):
return indexes
- @staticmethod
- def plot_avg_eval_metrics(eval_ds, eval_metrics, fcst_prod_dict, varname, out_dir):
- """
- Plots error-metrics averaged over all predictions to file incl. 90%-confidence interval that is estimated by
- block bootstrapping.
- :param eval_ds: The dataset storing all evaluation metrics for each forecast (produced by init_metric_ds-method)
- :param eval_metrics: list of evaluation metrics
- :param fcst_prod_dict: dictionary of forecast products, e.g. {"persistence": "pfcst"}
- :param varname: the variable name for which the evaluation metrics are available
- :param out_dir: output directory to save the lots
- :return: a bunch of plots as png-files
- """
- method = Postprocess.plot_avg_eval_metrics.__name__
-
- # settings for block bootstrapping
- # sanity checks
- if not isinstance(eval_ds, xr.Dataset):
- raise ValueError("%{0}: Argument 'eval_ds' must be a xarray dataset.".format(method))
-
- if not isinstance(fcst_prod_dict, dict):
- raise ValueError("%{0}: Argument 'fcst_prod_dict' must be dictionary with short names of forecast product" +
- "as key and long names as value.".format(method))
-
- try:
- nhours = np.shape(eval_ds.coords["fcst_hour"])[0]
- except Exception as err:
- print("%{0}: Input argument 'eval_ds' appears to be unproper.".format(method))
- raise err
-
- nmodels = len(fcst_prod_dict.values())
- colors = ["blue", "red", "black", "grey"]
- for metric in eval_metrics:
- # create a new figure object
- fig = plt.figure(figsize=(6, 4))
- ax = plt.axes([0.1, 0.15, 0.75, 0.75])
- hours = np.arange(1, nhours+1)
-
- for ifcst, fcst_prod in enumerate(fcst_prod_dict.keys()):
- metric_name = "{0}_{1}_{2}".format(varname, fcst_prod, metric)
- try:
- metric2plt = eval_ds[metric_name+"_avg"]
- metric_boot = eval_ds[metric_name+"_bootstrapped"]
- except Exception as err:
- print("%{0}: Could not retrieve {1} and/or {2} from evaluation metric dataset."
- .format(method, metric_name, metric_name+"_boot"))
- raise err
- # plot the data
- metric2plt_min = metric_boot.quantile(0.05, dim="iboot")
- metric2plt_max = metric_boot.quantile(0.95, dim="iboot")
- plt.plot(hours, metric2plt, label=fcst_prod, color=colors[ifcst], marker="o")
- plt.fill_between(hours, metric2plt_min, metric2plt_max, facecolor=colors[ifcst], alpha=0.3)
- # configure plot
- plt.xticks(hours)
- # automatic y-limits for PSNR wich can be negative and positive
- if metric != "psnr": ax.set_ylim(0., None)
- legend = ax.legend(loc="upper right", bbox_to_anchor=(1.15, 1))
- ax.set_xlabel("Lead time [hours]")
- ax.set_ylabel(metric.upper())
- plt_fname = os.path.join(out_dir, "evaluation_{0}".format(metric))
- print("Saving basic evaluation plot in terms of {1} to '{2}'".format(method, metric, plt_fname))
- plt.savefig(plt_fname)
-
- plt.close()
-
- return True
-
- @staticmethod
- def create_plot(data, data_diff, varname, plt_fname):
- """
- Creates filled contour plot of forecast data and also draws contours for differences.
- ML: So far, only plotting of the 2m temperature is supported (with 12 predicted hours/frames)
- :param data: the forecasted data array to be plotted
- :param data_diff: the reference data ('ground truth')
- :param varname: the name of the variable
- :param plt_fname: the filename to the store the plot
- :return: -
- """
- method = Postprocess.create_plot.__name__
-
- try:
- coords = data.coords
- # handle coordinates and forecast times
- lat, lon = coords["lat"], coords["lon"]
- date0 = pd.to_datetime(coords["init_time"].data)
- fhhs = coords["fcst_hour"].data
- except Exception as err:
- print("%{0}: Could not retrieve expected coordinates lat, lon and time_forecast from data.".format(method))
- raise err
-
- lons, lats = np.meshgrid(lon, lat)
-
- date0_str = date0.strftime("%Y-%m-%d %H:%M UTC")
-
- # check data to be plotted since programme is not generic so far
- if np.shape(fhhs)[0] != 12:
- raise ValueError("%{0}: Currently, only 12 hour forecast can be handled properly.".format(method))
-
- if varname != "2t":
- raise ValueError("%{0}: Currently, only 2m temperature is plotted nicely properly.".format(method))
-
- # define levels
- clevs = np.arange(-10., 40., 1.)
- clevs_diff = np.arange(0.5, 10.5, 2.)
- clevs_diff2 = np.arange(-10.5, -0.5, 2.)
-
- # create fig and subplot axes
- fig, axes = plt.subplots(2, 6, sharex=True, sharey=True, figsize=(12, 6))
- axes = axes.flatten()
-
- # create all subplots
- for t, fhh in enumerate(fhhs):
- m = Basemap(projection='cyl', llcrnrlat=np.min(lat), urcrnrlat=np.max(lat),
- llcrnrlon=np.min(lon), urcrnrlon=np.max(lon), resolution='l', ax=axes[t])
- m.drawcoastlines()
- x, y = m(lons, lats)
- if t%6 == 0:
- lat_lab = [1, 0, 0, 0]
- axes[t].set_ylabel(u'Latitude', labelpad=30)
- else:
- lat_lab = list(np.zeros(4))
- if t/6 >= 1:
- lon_lab = [0, 0, 0, 1]
- axes[t].set_xlabel(u'Longitude', labelpad=15)
- else:
- lon_lab = list(np.zeros(4))
- m.drawmapboundary()
- m.drawparallels(np.arange(0, 90, 5),labels=lat_lab, xoffset=1.)
- m.drawmeridians(np.arange(5, 355, 10),labels=lon_lab, yoffset=1.)
- cs = m.contourf(x, y, data.isel(fcst_hour=t)-273.15, clevs, cmap=plt.get_cmap("jet"), ax=axes[t],
- extend="both")
- cs_c_pos = m.contour(x, y, data_diff.isel(fcst_hour=t), clevs_diff, linewidths=0.5, ax=axes[t],
- colors="black")
- cs_c_neg = m.contour(x, y, data_diff.isel(fcst_hour=t), clevs_diff2, linewidths=1, linestyles="dotted",
- ax=axes[t], colors="black")
- axes[t].set_title("{0} +{1:02d}:00".format(date0_str, int(fhh)), fontsize=7.5, pad=4)
-
- fig.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=-0.7,
- wspace=0.05)
- # add colorbar.
- cbar_ax = fig.add_axes([0.3, 0.22, 0.4, 0.02])
- cbar = fig.colorbar(cs, cax=cbar_ax, orientation="horizontal")
- cbar.set_label('°C')
- # save to disk
- plt.savefig(plt_fname, bbox_inches="tight")
-
def main():
parser = argparse.ArgumentParser()
diff --git a/video_prediction_tools/postprocess/plotting.py b/video_prediction_tools/postprocess/plotting.py
index 67e68171b87ceb181d32314a271f8493b9c0c460..3f19d0c3db7df021b7b5214daa6be1bdba9267f8 100644
--- a/video_prediction_tools/postprocess/plotting.py
+++ b/video_prediction_tools/postprocess/plotting.py
@@ -83,7 +83,6 @@ def plot_avg_eval_metrics(eval_ds, eval_metrics, fcst_prod_dict, varname, out_di
return True
-
def create_plot(data, data_diff, varname, plt_fname):
"""
Creates filled contour plot of forecast data and also draws contours for differences.