From e532578e60cea197691ab63e0023d631ec0116ef Mon Sep 17 00:00:00 2001
From: lukas leufen <l.leufen@fz-juelich.de>
Date: Tue, 7 Jan 2020 15:21:45 +0100
Subject: [PATCH] post processing can plot station_map(), but countries are not
filled with e.g. elevation data (just blank fill)
---
README.md | 8 ++++++-
requirements.txt | 10 +++++++-
src/plotting/postprocessing_plotting.py | 32 ++++++++++++++++++++++---
src/run_modules/post_processing.py | 5 +++-
4 files changed, 49 insertions(+), 6 deletions(-)
diff --git a/README.md b/README.md
index 329b1f16..e49362e9 100644
--- a/README.md
+++ b/README.md
@@ -6,4 +6,10 @@ This is a collection of all relevant functions used for ML stuff in the ESDE gro
See a description [here](https://towardsdatascience.com/a-simple-guide-to-the-versions-of-the-inception-network-7fc52b863202)
or take a look on the papers [Going Deeper with Convolutions (Szegedy et al., 2014)](https://arxiv.org/abs/1409.4842)
-and [Network In Network (Lin et al., 2014)](https://arxiv.org/abs/1312.4400).
\ No newline at end of file
+and [Network In Network (Lin et al., 2014)](https://arxiv.org/abs/1312.4400).
+
+
+# Installation
+
+* Install __proj__ on your machine using the console. E.g. for opensuse / leap `zypper install proj`
+* c++ compiler required for cartopy installation
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 496c6426..f6c1eb24 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,4 +14,12 @@ statsmodels
seaborn
dask==0.20.2
toolz # for dask
-cloudpickle # for dask
\ No newline at end of file
+cloudpickle # for dask
+cython
+pyshp
+six
+pyproj
+shapely
+cartopy==0.16.0
+matplotlib
+pillow
\ No newline at end of file
diff --git a/src/plotting/postprocessing_plotting.py b/src/plotting/postprocessing_plotting.py
index 4b196053..9ad7f87f 100644
--- a/src/plotting/postprocessing_plotting.py
+++ b/src/plotting/postprocessing_plotting.py
@@ -8,10 +8,9 @@ import xarray as xr
import matplotlib
import seaborn as sns
import matplotlib.pyplot as plt
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
-import dask.array as da
-import dask
-import dask.dataframe as ddf
logging.getLogger('matplotlib').setLevel(logging.WARNING)
@@ -53,3 +52,30 @@ def plot_monthly_summary(stations, data_path, name: str, window_lead_time, targe
plt.close('all')
+def plot_climsum_boxplot():
+ return
+
+
+def station_map(generators, plot_folder="."):
+
+ logging.debug("run station_map()")
+ fig = plt.figure(figsize=(10, 5))
+ ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
+ ax.set_extent([0, 20, 42, 58], crs=ccrs.PlateCarree())
+ ax.add_feature(cfeature.COASTLINE.with_scale("10m"), edgecolor='black')
+ ax.add_feature(cfeature.LAKES.with_scale("50m"))
+ ax.add_feature(cfeature.OCEAN.with_scale("50m"))
+ ax.add_feature(cfeature.RIVERS.with_scale("10m"))
+ ax.add_feature(cfeature.BORDERS.with_scale("10m"), facecolor='none', edgecolor='black')
+
+ if generators is not None:
+ for color, gen in generators.items():
+ for k, v in enumerate(gen):
+ station_coords = gen.get_data_generator(k).meta.loc[['station_lon', 'station_lat']]
+ station_names = gen.get_data_generator(k).meta.loc[['station_id']]
+ IDx, IDy = float(station_coords.loc['station_lon'].values), float(station_coords.loc['station_lat'].values)
+ ax.plot(IDx, IDy, mfc=color, mec='k', marker='s', markersize=6, transform=ccrs.PlateCarree())
+
+ plot_path = os.path.join(os.path.abspath(plot_folder), 'test_map_plot.pdf')
+ plt.savefig(plot_path)
+ plt.close('all')
diff --git a/src/run_modules/post_processing.py b/src/run_modules/post_processing.py
index 5d4c805a..96c8a40b 100644
--- a/src/run_modules/post_processing.py
+++ b/src/run_modules/post_processing.py
@@ -17,7 +17,7 @@ from src.model_modules.linear_model import OrdinaryLeastSquaredModel
from src import statistics
from src import helpers
from src.helpers import TimeTracking
-from src.plotting.postprocessing_plotting import plot_monthly_summary
+from src.plotting.postprocessing_plotting import plot_monthly_summary, plot_climsum_boxplot, station_map
class PostProcessing(RunEnvironment):
@@ -39,11 +39,14 @@ class PostProcessing(RunEnvironment):
self.plot()
def plot(self):
+ logging.debug("Run plotting routines...")
path = self.data_store.get("forecast_path", "general")
window_lead_time = self.data_store.get("window_lead_time", "general")
target_var = self.data_store.get("target_var", "general")
+ station_map(generators={'b': self.test_data}, plot_folder=self.plot_path)
plot_monthly_summary(self.test_data.stations, path, r"forecasts_%s_test.nc", window_lead_time, target_var,
plot_folder=self.plot_path)
+ # plot_climsum_boxplot()
def calculate_test_score(self):
test_score = self.model.evaluate(generator=self.test_data_distributed.distribute_on_batches(),
--
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