formating in black style, add dependencies to .yml file

3528735a · Marco Kulüke · 415e3f10 · 3528735a · 3528735a
Commit 3528735a authored 4 years ago by Marco Kulüke
--- a/environment.yml
+++ b/environment.yml
-name: tutorials-and-use-cases
+name: /work/bk1088/k204228/kernels/test_env
 channels:
  - anaconda
  - conda-forge
@@ -7,12 +7,16 @@ dependencies:
  - _libgcc_mutex=0.1=main
  - aiohttp=3.6.2=py38h516909a_0
  - appdirs=1.4.3=py_1
+  - argon2-cffi=20.1.0=py38h7b6447c_1
  - asciitree=0.3.3=py_2
  - async-timeout=3.0.1=py_1000
+  - async_generator=1.10=py_0
  - attrs=20.2.0=pyh9f0ad1d_0
  - backcall=0.2.0=py_0
  - black=20.8b1=py_1
+  - bleach=3.2.1=py_0
  - bokeh=2.2.1=py38h32f6830_0
+  - branca=0.4.1=py_0
  - brotlipy=0.7.0=py38h1e0a361_1000
  - bzip2=1.0.8=h516909a_3
  - c-ares=1.16.1=h516909a_3
@@ -32,27 +36,36 @@ dependencies:
  - dask-core=2.26.0=py_0
  - dataclasses=0.7=py38_0
  - decorator=4.4.2=py_0
+  - defusedxml=0.6.0=py_0
  - distributed=2.26.0=py38h32f6830_0
  - entrypoints=0.3=py38h32f6830_1001
  - fastprogress=1.0.0=py_0
+  - folium=0.11.0=py_0
  - freetype=2.10.2=he06d7ca_0
  - fsspec=0.8.2=py_0
+  - geographiclib=1.50=py_0
+  - geopy=2.0.0=pyh9f0ad1d_0
  - geos=3.8.0=he6710b0_0
  - hdf4=4.2.13=hf30be14_1003
  - hdf5=1.10.6=nompi_h3c11f04_101
  - heapdict=1.0.1=py_0
  - idna=2.10=pyh9f0ad1d_0
+  - importlib-metadata=1.7.0=py38_0
+  - importlib_metadata=1.7.0=0
  - iniconfig=1.0.1=py_0
  - intake=0.6.0=py_0
  - intake-esm=2020.8.15=py_0
  - ipykernel=5.3.4=py38h5ca1d4c_0
  - ipython=7.18.1=py38h5ca1d4c_0
  - ipython_genutils=0.2.0=py38_0
+  - ipywidgets=7.5.1=py_1
  - jedi=0.17.2=py38_0
  - jinja2=2.11.2=pyh9f0ad1d_0
  - jpeg=9d=h516909a_0
+  - jsonschema=3.2.0=py38_1
  - jupyter_client=6.1.6=py_0
  - jupyter_core=4.6.3=py38_0
+  - jupyterlab_pygments=0.1.2=py_0
  - kiwisolver=1.2.0=py38hbf85e49_0
  - krb5=1.17.1=hfafb76e_3
  - lcms2=2.11=hbd6801e_0
@@ -80,13 +93,19 @@ dependencies:
  - markupsafe=1.1.1=py38h1e0a361_1
  - matplotlib=3.3.2=0
  - matplotlib-base=3.3.2=py38h91b0d89_0
+  - mistune=0.8.4=py38h7b6447c_1000
  - more-itertools=8.5.0=py_0
  - msgpack-python=1.0.0=py38hbf85e49_1
  - multidict=4.7.5=py38h1e0a361_1
  - mypy_extensions=0.4.3=py38h32f6830_1
  - nb_black=1.0.7=py_0
+  - nbclient=0.5.0=py_0
+  - nbconvert=6.0.6=py38_0
+  - nbformat=5.0.7=py_0
  - ncurses=6.2=he6710b0_1
+  - nest-asyncio=1.4.1=py_0
  - netcdf4=1.5.4=nompi_py38hec8b9af_102
+  - notebook=6.1.4=py38_0
  - numcodecs=0.7.1=py38h950e882_0
  - numpy=1.19.1=py38hbc27379_2
  - olefile=0.46=py_0
@@ -94,6 +113,8 @@ dependencies:
  - owslib=0.20.0=py_0
  - packaging=20.4=pyh9f0ad1d_0
  - pandas=1.1.2=py38h950e882_0
+  - pandoc=2.10.1=0
+  - pandocfilters=1.4.2=py38_1
  - parso=0.7.0=py_0
  - partd=1.1.0=py_0
  - pathspec=0.8.0=pyh9f0ad1d_0
@@ -103,6 +124,7 @@ dependencies:
  - pip=20.2.2=py38_0
  - pluggy=0.13.1=py38_0
  - proj=6.2.1=haa6030c_0
+  - prometheus_client=0.8.0=py_0
  - prompt-toolkit=3.0.7=py_0
  - psutil=5.7.2=py38h1e0a361_0
  - ptyprocess=0.6.0=py38_0
@@ -114,6 +136,7 @@ dependencies:
  - pyopenssl=19.1.0=py_1
  - pyparsing=2.4.7=pyh9f0ad1d_0
  - pyproj=2.6.1.post1=py38hd003209_1
+  - pyrsistent=0.17.3=py38h7b6447c_0
  - pyshp=2.1.2=py_0
  - pysocks=1.7.1=py38h32f6830_1
  - pytest=6.0.1=py38_0
@@ -129,12 +152,15 @@ dependencies:
  - requests=2.24.0=pyh9f0ad1d_0
  - scipy=1.5.2=py38h8c5af15_0
  - seaborn=0.11.0=py_0
+  - send2trash=1.5.0=py38_0
  - setuptools=49.6.0=py38_0
  - shapely=1.7.1=py38h98ec03d_0
  - six=1.15.0=py_0
  - sortedcontainers=2.2.2=pyh9f0ad1d_0
  - sqlite=3.33.0=h62c20be_0
  - tblib=1.6.0=py_0
+  - terminado=0.8.3=py38_0
+  - testpath=0.4.4=py_0
  - tk=8.6.10=hbc83047_0
  - toml=0.10.1=py_0
  - toolz=0.10.0=py_0
@@ -144,7 +170,9 @@ dependencies:
  - typing_extensions=3.7.4.2=py_0
  - urllib3=1.25.10=py_0
  - wcwidth=0.2.5=py_0
+  - webencodings=0.5.1=py38_1
  - wheel=0.35.1=py_0
+  - widgetsnbextension=3.5.1=py38_0
  - xarray=0.16.0=py_0
  - xz=5.2.5=h7b6447c_0
  - yaml=0.2.5=h516909a_0
@@ -152,9 +180,10 @@ dependencies:
  - zarr=2.4.0=py_0
  - zeromq=4.3.2=he6710b0_3
  - zict=2.0.0=py_0
+  - zipp=3.1.0=py_0
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.4.5=h6597ccf_2
  - pip:
    - fasteners==0.15
    - monotonic==1.5
-prefix: tutorial-and-use-cases
+prefix: /work/bk1088/k204228/kernels/test_env
--- a/notebooks/use-case_count_summer_days.ipynb
+++ b/notebooks/use-case_count_summer_days.ipynb
@@ -18,6 +18,16 @@
    "    - choose different index"
   ]
  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get formating done automatically according to style `black`\n",
+    "%load_ext lab_black"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": null,
@@ -47,7 +57,22 @@
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "382b84a479a848a68db019c179d1c228",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Text(value='')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
   "source": [
    "place = widgets.Text()\n",
    "display(place)"
@@ -70,7 +95,7 @@
    "location = geolocator.geocode(place.value)\n",
    "print(location.address)\n",
    "print((location.latitude, location.longitude))\n",
-    "#print(location.raw)"
+    "# print(location.raw)"
   ]
  },
  {
@@ -96,7 +121,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Choose Year"
+    "## Choose Year you want the number of summer days"
   ]
  },
  {
@@ -105,11 +130,11 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "x = range(1850, 2020) # TBD: needs to be historical time frame\n",
+    "x = range(1850, 2020)  # TBD: needs to be historical time frame\n",
    "\n",
    "year = widgets.Dropdown(\n",
    "    options=x,\n",
-    "    description='Select year: ',\n",
+    "    description=\"Select year: \",\n",
    "    disabled=False,\n",
    ")\n",
    "year"
@@ -140,7 +165,10 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "col_url = \"https://swift.dkrz.de/v1/dkrz_a44962e3ba914c309a7421573a6949a6/intake-esm/mistral-cmip6.json\"\n",
+    "# col_url = \"https://swift.dkrz.de/v1/dkrz_a44962e3ba914c309a7421573a6949a6/intake-esm/mistral-cmip6.json\"\n",
+    "# or\n",
+    "col_url = \"/work/ik1017/Catalogs/mistral-cmip6.json\"\n",
+    "\n",
    "col = intake.open_esm_datastore(col_url)"
   ]
  },
@@ -174,10 +202,11 @@
   "outputs": [],
   "source": [
    "query = dict(\n",
-    "    source_id = 'MPI-ESM1-2-HR',\n",
+    "    source_id=\"MPI-ESM1-2-LR\",\n",
    "    variable_id=\"tasmax\",\n",
    "    table_id=\"day\",\n",
-    "    experiment_id=[\"historical\"])\n",
+    "    experiment_id=[\"historical\"],\n",
+    ")\n",
    "cat = col.search(**query)"
   ]
  },
@@ -203,9 +232,25 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "ds_tasmax = xr.open_dataset(cat.df['path'][0]) # TBD: open data set of selected year\n",
+    "cat.df[\"path\"][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "open data set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_tasmax = xr.open_dataset(cat.df[\"path\"][0])  # TBD: open data set of selected year\n",
    "\n",
-    "ds_tasmax['tasmax']"
+    "ds_tasmax[\"tasmax\"]"
   ]
  },
  {
@@ -223,24 +268,20 @@
   "source": [
    "# Find nearest model coordinate\n",
    "\n",
-    "# First, find the index of the grid point nearest a specific lat/lon.   \n",
-    "abslat = np.abs(ds_tasmax['lat']-location.latitude)\n",
-    "abslon = np.abs(ds_tasmax['lon']-location.longitude)\n",
+    "# First, find the index of the grid point nearest a specific lat/lon.\n",
+    "abslat = np.abs(ds_tasmax[\"lat\"] - location.latitude)\n",
+    "abslon = np.abs(ds_tasmax[\"lon\"] - location.longitude)\n",
    "c = np.maximum(abslon, abslat)\n",
    "\n",
-    "([xloc], [yloc]) = np.where(c == np.min(c))\n",
-    "\n",
-    "# Now I can use that index location to get the values at the x/y diminsion\n",
-    "\n",
-    "tas_max_hamburg = ds_tasmax['tasmax'][0, yloc, xloc] -273.15"
+    "([xloc], [yloc]) = np.where(c == np.min(c))"
   ]
  },
  {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
   "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Draw Map"
+   ]
  },
  {
   "cell_type": "code",
@@ -250,17 +291,31 @@
   "source": [
    "m = folium.Map(location=[location.latitude, location.longitude], zoom_start=8)\n",
    "tooltip = location.latitude, location.longitude\n",
-    "folium.Marker([ds_tasmax['lat'][yloc], ds_tasmax['lon'][xloc]], tooltip=tooltip, popup='Model Grid Cell Center').add_to(m)\n",
-    "folium.Marker([location.latitude, location.longitude], tooltip=tooltip, popup='Location selected by User').add_to(m)\n",
+    "folium.Marker(\n",
+    "    [ds_tasmax[\"lat\"][yloc], ds_tasmax[\"lon\"][xloc]],\n",
+    "    tooltip=tooltip,\n",
+    "    popup=\"Model Grid Cell Center\",\n",
+    ").add_to(m)\n",
+    "folium.Marker(\n",
+    "    [location.latitude, location.longitude],\n",
+    "    tooltip=tooltip,\n",
+    "    popup=\"Location selected by User\",\n",
+    ").add_to(m)\n",
    "\n",
    "# Define rectangle\n",
-    "rect_lat1 = (ds_tasmax['lat'][yloc -1] +ds_tasmax['lat'][yloc])/2\n",
-    "rect_lon1 = (ds_tasmax['lon'][xloc -1] +ds_tasmax['lon'][xloc])/2\n",
-    "rect_lat2 = (ds_tasmax['lat'][yloc +1] +ds_tasmax['lat'][yloc])/2\n",
-    "rect_lon2 = (ds_tasmax['lon'][xloc +1] +ds_tasmax['lon'][xloc])/2\n",
+    "rect_lat1 = (ds_tasmax[\"lat\"][yloc - 1] + ds_tasmax[\"lat\"][yloc]) / 2\n",
+    "rect_lon1 = (ds_tasmax[\"lon\"][xloc - 1] + ds_tasmax[\"lon\"][xloc]) / 2\n",
+    "rect_lat2 = (ds_tasmax[\"lat\"][yloc + 1] + ds_tasmax[\"lat\"][yloc]) / 2\n",
+    "rect_lon2 = (ds_tasmax[\"lon\"][xloc + 1] + ds_tasmax[\"lon\"][xloc]) / 2\n",
    "#\n",
    "\n",
-    "folium.Rectangle(bounds=[[rect_lat1,rect_lon1], [rect_lat2,rect_lon2]], color='#ff7800', fill=True, fill_color='#ffff00', fill_opacity=0.2).add_to(m)\n",
+    "folium.Rectangle(\n",
+    "    bounds=[[rect_lat1, rect_lon1], [rect_lat2, rect_lon2]],\n",
+    "    color=\"#ff7800\",\n",
+    "    fill=True,\n",
+    "    fill_color=\"#ffff00\",\n",
+    "    fill_opacity=0.2,\n",
+    ").add_to(m)\n",
    "m"
   ]
  },
@@ -278,8 +333,8 @@
   "outputs": [],
   "source": [
    "# Now I can use that index location to get the values at the lat/lon location\n",
+    "tas_max_hamburg = ds_tasmax[\"tasmax\"][:, yloc, xloc] - 273.15\n",
    "\n",
-    "tas_max_hamburg = ds_tasmax['tasmax'][:, yloc, xloc] -273.15\n",
    "matplotlib.pyplot.plot(tas_max_hamburg)"
   ]
  },

 %% Cell type:markdown id: tags:

 # Count Annual Summer Days for a particular Location

 %% Cell type:markdown id: tags:

 TBD:
 - choose year by drop-down menu and select correspondig dataset
 - future:
    - compare two places
    - choose different index

 %% Cell type:code id: tags:

 ``` python
+# get formating done automatically according to style `black`
+%load_ext lab_black
+```
+
+%% Cell type:code id: tags:
+
+``` python
 import intake
 import requests
 import folium
 import xarray as xr
 import matplotlib
 from IPython.display import display
 from ipywidgets import widgets
 from geopy.geocoders import Nominatim
 import numpy as np
 ```

 %% Cell type:markdown id: tags:

 ## Choose Location
 If ambiguous the more likely location will be chosen

 %% Cell type:code id: tags:

 ``` python
 place = widgets.Text()
 display(place)
 ```

+%% Output
+
+
 %% Cell type:markdown id: tags:

 Find Place by using GeoPy

 %% Cell type:code id: tags:

 ``` python
 geolocator = Nominatim(user_agent="any_agent")
 location = geolocator.geocode(place.value)
 print(location.address)
 print((location.latitude, location.longitude))
-#print(location.raw)
+# print(location.raw)
 ```

 %% Cell type:markdown id: tags:

 ## Draw Map

 %% Cell type:code id: tags:

 ``` python
 m = folium.Map(location=[location.latitude, location.longitude])
 tooltip = location.latitude, location.longitude
 folium.Marker([location.latitude, location.longitude], tooltip=tooltip).add_to(m)
 m
 ```

 %% Cell type:markdown id: tags:

-## Choose Year
+## Choose Year you want the number of summer days

 %% Cell type:code id: tags:

 ``` python
-x = range(1850, 2020) # TBD: needs to be historical time frame
+x = range(1850, 2020)  # TBD: needs to be historical time frame

 year = widgets.Dropdown(
    options=x,
-    description='Select year: ',
+    description="Select year: ",
    disabled=False,
 )
 year
 ```

 %% Cell type:code id: tags:

 ``` python
 year.value
 ```

 %% Cell type:markdown id: tags:

 ## Load Intake Catalogue
 The catalog files are uploaded to the cloud. There is a gsv.gz file and a json file under
 https://swiftbrowser.dkrz.de/public/dkrz_a44962e3ba914c309a7421573a6949a6/intake-esm/
 We load the catalog descriptor with intake:

 %% Cell type:code id: tags:

 ``` python
-col_url = "https://swift.dkrz.de/v1/dkrz_a44962e3ba914c309a7421573a6949a6/intake-esm/mistral-cmip6.json"
+# col_url = "https://swift.dkrz.de/v1/dkrz_a44962e3ba914c309a7421573a6949a6/intake-esm/mistral-cmip6.json"
+# or
+col_url = "/work/ik1017/Catalogs/mistral-cmip6.json"
+
 col = intake.open_esm_datastore(col_url)
 ```

 %% Cell type:markdown id: tags:

 Let's see what is in the intake catalog. The underlying data base is given as a panda dataframe which we can access with 'col.df'. col.df.head() shows us the first rows of the table:

 %% Cell type:code id: tags:

 ``` python
 col.df.head()
 ```

 %% Cell type:markdown id: tags:

 ## Browse Intake Catalogue and open Variable

 %% Cell type:code id: tags:

 ``` python
 query = dict(
-    source_id = 'MPI-ESM1-2-HR',
+    source_id="MPI-ESM1-2-LR",
    variable_id="tasmax",
    table_id="day",
-    experiment_id=["historical"])
+    experiment_id=["historical"],
+)
 cat = col.search(**query)
 ```

 %% Cell type:markdown id: tags:

 show query

 %% Cell type:code id: tags:

 ``` python
 cat.df
 ```

 %% Cell type:code id: tags:

 ``` python
-ds_tasmax = xr.open_dataset(cat.df['path'][0]) # TBD: open data set of selected year
+cat.df["path"][0]
+```
+
+%% Cell type:markdown id: tags:

-ds_tasmax['tasmax']
+open data set
+
+%% Cell type:code id: tags:
+
+``` python
+ds_tasmax = xr.open_dataset(cat.df["path"][0])  # TBD: open data set of selected year
+
+ds_tasmax["tasmax"]
 ```

 %% Cell type:markdown id: tags:

 ## Compare Model Grid Cell with chosen Location

 %% Cell type:code id: tags:

 ``` python
 # Find nearest model coordinate

 # First, find the index of the grid point nearest a specific lat/lon.
-abslat = np.abs(ds_tasmax['lat']-location.latitude)
-abslon = np.abs(ds_tasmax['lon']-location.longitude)
+abslat = np.abs(ds_tasmax["lat"] - location.latitude)
+abslon = np.abs(ds_tasmax["lon"] - location.longitude)
 c = np.maximum(abslon, abslat)

 ([xloc], [yloc]) = np.where(c == np.min(c))
-
-# Now I can use that index location to get the values at the x/y diminsion
-
-tas_max_hamburg = ds_tasmax['tasmax'][0, yloc, xloc] -273.15
 ```

-%% Cell type:code id: tags:
+%% Cell type:markdown id: tags:

-``` python
-```
+Draw Map

 %% Cell type:code id: tags:

 ``` python
 m = folium.Map(location=[location.latitude, location.longitude], zoom_start=8)
 tooltip = location.latitude, location.longitude
-folium.Marker([ds_tasmax['lat'][yloc], ds_tasmax['lon'][xloc]], tooltip=tooltip, popup='Model Grid Cell Center').add_to(m)
-folium.Marker([location.latitude, location.longitude], tooltip=tooltip, popup='Location selected by User').add_to(m)
+folium.Marker(
+    [ds_tasmax["lat"][yloc], ds_tasmax["lon"][xloc]],
+    tooltip=tooltip,
+    popup="Model Grid Cell Center",
+).add_to(m)
+folium.Marker(
+    [location.latitude, location.longitude],
+    tooltip=tooltip,
+    popup="Location selected by User",
+).add_to(m)

 # Define rectangle
-rect_lat1 = (ds_tasmax['lat'][yloc -1] +ds_tasmax['lat'][yloc])/2
-rect_lon1 = (ds_tasmax['lon'][xloc -1] +ds_tasmax['lon'][xloc])/2
-rect_lat2 = (ds_tasmax['lat'][yloc +1] +ds_tasmax['lat'][yloc])/2
-rect_lon2 = (ds_tasmax['lon'][xloc +1] +ds_tasmax['lon'][xloc])/2
+rect_lat1 = (ds_tasmax["lat"][yloc - 1] + ds_tasmax["lat"][yloc]) / 2
+rect_lon1 = (ds_tasmax["lon"][xloc - 1] + ds_tasmax["lon"][xloc]) / 2
+rect_lat2 = (ds_tasmax["lat"][yloc + 1] + ds_tasmax["lat"][yloc]) / 2
+rect_lon2 = (ds_tasmax["lon"][xloc + 1] + ds_tasmax["lon"][xloc]) / 2
 #

-folium.Rectangle(bounds=[[rect_lat1,rect_lon1], [rect_lat2,rect_lon2]], color='#ff7800', fill=True, fill_color='#ffff00', fill_opacity=0.2).add_to(m)
+folium.Rectangle(
+    bounds=[[rect_lat1, rect_lon1], [rect_lat2, rect_lon2]],
+    color="#ff7800",
+    fill=True,
+    fill_color="#ffff00",
+    fill_opacity=0.2,
+).add_to(m)
 m
 ```

 %% Cell type:markdown id: tags:

 ## Draw Temperature Time Series and count Summer days

 %% Cell type:code id: tags:

 ``` python
 # Now I can use that index location to get the values at the lat/lon location
+tas_max_hamburg = ds_tasmax["tasmax"][:, yloc, xloc] - 273.15

-tas_max_hamburg = ds_tasmax['tasmax'][:, yloc, xloc] -273.15
 matplotlib.pyplot.plot(tas_max_hamburg)
 ```

 %% Cell type:code id: tags:

 ``` python
 tas_max_hamburg[tas_max_hamburg > 25]
 ```