Merge branch 'bing_issue#188_restructure_ambs' into develop

foo.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "\n",
+    "filenames_t850 = [\n",
+    "    \"data_t850/temperature_850hPa_1979_5.625deg.nc\",\n",
+    "    \"data_t850/temperature_850hPa_1980_5.625deg.nc\"\n",
+    "]\n",
+    "filenames_z500 = [\n",
+    "    \"data_z500/geopotential_500hPa_1979_5.625deg.nc\",\n",
+    "    \"data_z500/geopotential_500hPa_1980_5.625deg.nc\"\n",
+    "]\n",
+    "filenames = [*filenames_t850, *filenames_z500]\n",
+    "ds = xr.open_mfdataset(filenames, coords=\"minimal\", compat=\"override\")\n",
+    "ds = ds.drop_vars(\"level\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32, 64, 2)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da = ds.to_array(dim=\"variables\").squeeze()\n",
+    "\n",
+    "dims = [\"time\", \"lat\", \"lon\", \"variables\"]\n",
+    "da = da.transpose(*dims)\n",
+    "\n",
+    "def generator(iterable):\n",
+    "    iterator = iter(iterable)\n",
+    "    yield from iterator\n",
+    "\n",
+    "da.shape[1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ambs",
+   "language": "python",
+   "name": "ambs"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "341ba53bbba0a6f1cf5ae0d50bab29c5266302a4d2a8950e418cc5f54c6f95ff"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

test/run_pytest.sh

 #!#bin/bash
 
-# Name of virtual environment 
-#VIRT_ENV_NAME="vp_new_structure"
-VIRT_ENV_NAME="env_hdfml"
+# Name of virtual environment
+VIRT_ENV_NAME="venv2_hdfml"
 
 if [ -z ${VIRTUAL_ENV} ]; then
    if [[ -f ../video_prediction_tools/${VIRT_ENV_NAME}/bin/activate ]]; then
@@ -21,6 +20,7 @@ fi
 #python -m pytest  test_prepare_era5_data.py 
 ##Test for preprocess_step1
 #python -m pytest  test_process_netCDF_v2.py
+#source ../video_prediction_tools/env_setup/modules_preprocess+extract.sh
 source ../video_prediction_tools/env_setup/modules_train.sh
 ##Test for preprocess moving mnist
 #python -m pytest test_prepare_moving_mnist_data.py
@@ -33,5 +33,5 @@ source ../video_prediction_tools/env_setup/modules_train.sh
 #rm /p/project/deepacf/deeprain/video_prediction_shared_folder/models/test/* 
 #python -m pytest test_train_model_era5.py
 #python -m pytest test_vanilla_vae_model.py
-python -m pytest test_visualize_postprocess.py
+python -m pytest test_gzprcp_data.py
 #python -m pytest test_meta_postprocess.py

test/run_pytest_era5_data_preprocess.sh

+
+
+# Name of virtual environment
+VIRT_ENV_NAME="venv_hdfml"
+
+
+CONTAINER_IMG="../video_prediction_tools/HPC_scripts/tensorflow_21.09-tf1-py3.sif"
+WRAPPER="./wrapper_container.sh"
+
+# sanity checks
+if [[ ! -f ${CONTAINER_IMG} ]]; then
+  echo "ERROR: Cannot find required TF1.15 container image '${CONTAINER_IMG}'."
+  exit 1
+fi
+
+if [[ ! -f ${WRAPPER} ]]; then
+  echo "ERROR: Cannot find wrapper-script '${WRAPPER}' for TF1.15 container image."
+  exit 1
+fi
+
+#source ../video_prediction_tools/env_setup/modules_preprocess+extract.sh
+singularity exec --nv "${CONTAINER_IMG}" "${WRAPPER}" ${VIRT_ENV_NAME} python3 -m pytest test_era5_data.py
+

test/test_era5_data.py

 
-
 __email__ = "b.gong@fz-juelich.de"
-__author__ = "Bing Gong, Scarlet Stadtler,Michael Langguth"
-
-
+__author__ = "Bing Gong"
 
 from video_prediction.datasets.era5_dataset import *
 import pytest
+import xarray as xr
+import os
+import tensorflow as tf
 import numpy as np
-import json
-import datetime
-
-input_dir =  "/p/project/deepacf/deeprain/video_prediction_shared_folder/preprocessedData/test"
-datasplit_config = "/p/project/deepacf/deeprain/bing/ambs/video_prediction_tools/data_split/cv_test.json"
-hparams_dict_config = "/p/project/deepacf/deeprain/bing/ambs/video_prediction_tools/hparams/era5/convLSTM/model_hparams.json"
-sequences_per_file = 10
-mode = "val"
+input_dir = "/p/project/deepacf/deeprain/video_prediction_shared_folder/test_data_roshni"
+datasplit_config = "/p/project/deepacf/deeprain/bing/ambs/video_prediction_tools/data_split/test/cv_test.json"
+hparams_dict_config = "/p/project/deepacf/deeprain/bing/ambs/video_prediction_tools/hparams/era5/convLSTM/model_hparams_template.json"
+mode = "test"
 
 
 @pytest.fixture(scope="module")
-def era5_dataset_case2():
-    return ERA5Dataset(input_dir=input_dir,mode=mode,
-                       datasplit_config=datasplit_config,hparams_dict_config=hparams_dict_config,seed=1234)  
 
-def test_init_era5_dataset(era5_dataset_case2):
-    assert era5_dataset_case2.hparams.max_epochs == 20
-    assert era5_dataset_case2.mode == mode
+def era5_dataset_case1():
+    return ERA5Dataset(input_dir=input_dir, datasplit_config=datasplit_config, hparams_dict_config=hparams_dict_config,
+                 mode="test", seed=1234, nsamples_ref=1000)
 
-def test_get_tfrecords_filesnames(era5_dataset_case2):
-    era5_dataset_case2.get_tfrecords_filesnames_base_datasplit()
-    assert era5_dataset_case2.filenames[0] ==  os.path.join(input_dir,"tfrecords","sequence_Y_2017_M_2_0_to_9.tfrecords")# def test_check_pkl_tfrecords_consistency(era5_dataset_case1):
-
-def test_get_example_info(era5_dataset_case2):
-    era5_dataset_case2.get_tfrecords_filesnames_base_datasplit()
-    era5_dataset_case2.get_example_info()
-    assert era5_dataset_case2.image_shape[0] == 160
-    assert era5_dataset_case2.image_shape[1] == 128
-    assert era5_dataset_case2.image_shape[2] == 3
+def test_init_era5_dataset(era5_dataset_case1):
+    era5_dataset_case1.get_hparams()
+    assert era5_dataset_case1.max_epochs == 20
+    assert era5_dataset_case1.mode == mode
+    assert era5_dataset_case1.batch_size == 4
 
 
+def test_get_filenames_from_datasplit(era5_dataset_case1):
+    flname= os.path.join(era5_dataset_case1.input_dir, "era5_vars4ambs_201901.nc")
+    n_files = len(era5_dataset_case1.filenames)
+    check = flname in era5_dataset_case1.filenames
+    assert check == True
+    assert n_files == 12
 
+def test_make_dataset(era5_dataset_case1):
+    # Get the data from nc files directly
+    data_arr = era5_dataset_case1.load_data_from_nc()
+    assert len(data_arr) !=0
+    ds = xr.open_mfdataset(era5_dataset_case1.filenames)
+    len_dt = len(ds["time"].values) # count number of images/samples in the test dataset
+    da = ds.to_array(dim = "variables").squeeze()
+    dims = ["time", "lat", "lon"]
+    data_arr = np.squeeze(da.values) #[vars,samples,lat,lon]
+    max_vars, min_vars = da.max(dim=dims).values, da.min(dim=dims).values #three dimension
+    print("data_arr shape",data_arr.shape)
+    #normalise the data for the first variable
+    def norm_var(x, min_value, max_value):
+        return (x - min_value) / (max_value - min_value)
    
+    assert np.max(data_arr[0]) == max_vars[0]
+    #mannualy calculate the normalization of the data
+    dt_norm = norm_var(data_arr[0],np.min(data_arr[0]), np.max(data_arr[0]))
+    
+    print("dt_norm",dt_norm.shape)
+    s1 = dt_norm[0] #the first sample, first timestamp
+    s2 = dt_norm[23] #the first sample, last timestamp 
+    s3 = dt_norm[1] # the second sample, first timestamp
+    s4 = dt_norm[24] # the second sample, last timestamp
+    # Get the data from make_dataset function
+    test_dataset = era5_dataset_case1.make_dataset()
+    test_iterator = test_dataset.make_one_shot_iterator()
+    # The `Iterator.string_handle()` method returns a tensor that can be evaluated
+    # and used to feed the `handle` placeholder.
+    test_handle = test_iterator.string_handle()
+    iterator = tf.data.Iterator.from_string_handle(test_handle, test_dataset.output_types, test_dataset.output_shapes)
+    inputs = iterator.get_next()
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        sess.run(tf.local_variables_initializer())
+        #get the batch size samples from dataset
+        dt = sess.run(inputs) #[batch_size,sequence_len,n_vars,lon,lat]
+        dt.shape[0] == 4
+        dt.shape[1] == 24
+        print("shape of dt",dt.shape)
+        s1t = dt[0,0,0]
+        s2t = dt[0,23,0]
+        
+        #get the second sample from dataset
+        s3t = dt[1,0,0]
+        s4t = dt[1,23,0]
+         
+        #s2t = sess.run(inputs)[0,:,0]
+    assert np.sum(s1-s1t) < 0.0001
+    assert np.sum(s2-s2t) < 0.0001
+    assert np.sum(s3-s3t) < 0.0001
+    assert np.sum(s4 -s4t) < 0.0001
+    #compare the data from nc files and make_dataset
+
+ 
+

test/test_gzprcp_data.py

+
+__email__ = "b.gong@fz-juelich.de"
+
+from video_prediction.datasets.gzprcp_dataset import *
+import pytest
+import tensorflow as tf
+import xarray as xr
+
+input_dir = "/p/largedata/jjsc42/project/deeprain/project_data/10min_AWS_prcp"
+datasplit_config = "/p/project/deepacf/deeprain/bing/ambs/video_prediction_tools/data_split/gzprcp/datasplit.json"
+hparams_dict_config = "/p/project/deepacf/deeprain/bing/ambs/video_prediction_tools/hparams/gzprcp/convLSTM_gan/model_hparams_template.json"
+sequences_per_file = 10
+mode = "test"
+
+
+@pytest.fixture(scope="module")
+def gzprcp_dataset_case1():
+    dataset =  GzprcpDataset(input_dir=input_dir, datasplit_config=datasplit_config, hparams_dict_config=hparams_dict_config,
+                 mode="test", seed=1234, nsamples_ref=1000)
+    dataset.get_hparams()
+    dataset.get_filenames_from_datasplit()
+    dataset.load_data_from_nc()
+    return dataset
+
+def test_init_gzprcp_dataset(gzprcp_dataset_case1):
+    # gzprcp_dataset_case1.get_hparams()
+    print('gzprcp_dataset_case1.max_epochs: {}'.format(gzprcp_dataset_case1.max_epochs))
+    print('gzprcp_dataset_case1.mode: {}'.format(gzprcp_dataset_case1.mode))
+    print('gzprcp_dataset_case1.batch_size: {}'.format(gzprcp_dataset_case1.batch_size))
+    print('gzprcp_dataset_case1.k: {}'.format(gzprcp_dataset_case1.k))
+    print('gzprcp_dataset_case1.filenames: {}'.format(gzprcp_dataset_case1.filenames))
+    
+    assert gzprcp_dataset_case1.max_epochs == 8
+    assert gzprcp_dataset_case1.mode == mode
+    assert gzprcp_dataset_case1.batch_size == 32
+    assert gzprcp_dataset_case1.k == 0.01
+    # assert gzprcp_dataset_case1.filenames[0] == 'GZ_prcp_2019.nc'
+
+def test_load_data_from_nc(gzprcp_dataset_case1):
+    train_tf_dataset = gzprcp_dataset_case1.make_dataset()
+    train_iterator = train_tf_dataset.make_one_shot_iterator()
+    # The `Iterator.string_handle()` method returns a tensor that can be evaluated
+    # and used to feed the `handle` placeholder.
+    train_handle = train_iterator.string_handle()
+    iterator = tf.data.Iterator.from_string_handle(train_handle, train_tf_dataset.output_types, train_tf_dataset.output_shapes)
+    inputs = iterator.get_next()
+
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        sess.run(tf.local_variables_initializer())
+        for step in range(2):
+            sess.run(inputs)
+    
+    # df = xr.open_mfdataset(era5_dataset_case1.filenames)
+    
+# if __name__ == '__main__':
+#     dataset = ERA5Dataset(input_dir: str = None, datasplit_config: str = None, hparams_dict_config: str = None,
+#                  mode: str = "train", seed: int = None, nsamples_ref: int = None)
+#     for next_element in dataset.take(2):
+#         # time_s = time.time()
+#         # tf.print(next_element.shape)
+#         pass
+
+
+
+
+
+
+
+
+
+
+
+
+

test/test_prepare_era5_data.py

@@ -4,11 +4,8 @@ __author__ = "Bing Gong"
 __date__ = "2021-03-03"
 
 
-
 from data_preprocess.prepare_era5_data import *
 import pytest
-import numpy as np
-import json
 import os
 
 year="2007"
@@ -23,8 +20,6 @@ def dataExtraction_case1(year=year,job_name=job_name,src_dir=src_dir,target_dir=
     return ERA5DataExtraction(year,job_name,src_dir,target_dir,varslist_json)
 
 
-
-
 def test_init(dataExtraction_case1):
     assert dataExtraction_case1.job_name == 1
     assert dataExtraction_case1.src_dir == src_dir

video_prediction_tools/HPC_scripts/preprocess_data_era5_step1_template.sh → video_prediction_tools/HPC_scripts/data_extraction_weatherbench_template.sh

 #!/bin/bash -x
-## Controlling Batch-job
-#SBATCH --account=<your_project>
+#SBATCH --account=deepacf
 #SBATCH --nodes=1
-#SBATCH --ntasks=13
-##SBATCH --ntasks-per-node=12
-#SBATCH --cpus-per-task=1
-#SBATCH --output=DataPreprocess_era5_step1-out.%j
-#SBATCH --error=DataPreprocess_era5_step1-err.%j
-#SBATCH --time=04:20:00
 #SBATCH --gres=gpu:0
+#SBATCH --output=log_out.%j
+#SBATCH --error=log_err.%j
+#SBATCH --time=00:10:00
 #SBATCH --partition=batch
-#SBATCH --mail-type=ALL
-#SBATCH --mail-user=me@somewhere.com
 
 ######### Template identifier (don't remove) #########
 echo "Do not run the template scripts"
 exit 99
 ######### Template identifier (don't remove) #########
 
+ml Stages/2022
+ml GCCcore/.11.2.0
+ml GCC/11.2.0
+ml ParaStationMPI/5.5.0-1
+
+ml Python/3.9.6
+ml SciPy-bundle/2021.10
+ml xarray/0.20.1
+ml netcdf4-python/1.5.7
+ml dask/2021.9.1
+
 # Name of virtual environment 
 VIRT_ENV_NAME="my_venv"
 
 # Activate virtual environment if needed (and possible)
+"""
 if [ -z ${VIRTUAL_ENV} ]; then
    if [[ -f ../virtual_envs/${VIRT_ENV_NAME}/bin/activate ]]; then
       echo "Activating virtual environment..."
@@ -33,27 +39,25 @@ if [ -z ${VIRTUAL_ENV} ]; then
 fi
 # Loading modules
 source ../env_setup/modules_preprocess+extract.sh
+"""
 
+source_dir=/p/scratch/deepacf/inbound_data/weatherbench
+destination_dir=/p/project/deepacf/deeprain/video_prediction_shared_folder/weatherbench_test/extracted
+data_extraction_dir=/p/project/deepacf/deeprain/grasse/ambs/video_prediction_tools/data_preprocess
+variables='[{"name":"temperature","lvl":[850],"interpolation":"p"},{"name":"geopotential","lvl":[500],"interpolation":"p"}]'
+years=("2013" "2014" "2015" "2016" "2017")
 
-# select years and variables for dataset and define target domain 
-years=( "2015" )
-variables=( "t2" "t2" "t2" )
-sw_corner=( -999.9 -999.9)
-nyx=( -999 -999 )
+cd ${data_extraction_dir}
 
-# set some paths
-# note, that destination_dir is adjusted during runtime based on the data
-source_dir=/my/path/to/extracted/data/
-destination_dir=/my/path/to/pickle/files
-
-# execute Python-scripts
-for year in "${years[@]}"; do
-  echo "start preprocessing data for year ${year}"
-	srun python ../main_scripts/main_preprocess_data_step1.py \
-        --source_dir ${source_dir} --destination_dir ${destination_dir} --years "${year}" \
-       	--vars "${variables[0]}" "${variables[1]}" "${variables[2]}" \
-       	--sw_corner "${sw_corner[0]}" "${sw_corner[1]}" --nyx "${nyx[0]}" "${nyx[1]}"
-done
+# Name of virtual environment 
+venv_dir=".venv"
+python -m venv --system-site-packages ${venv_dir}
+. ${venv_dir}/bin/activate
+#pip3 install --no-cache-dir pytz
+#pip3 install --no-cache-dir python-dateutil
+export PYTHONPATH=${data_extraction_dir}:$PYTHONPATH
+export PYTHONPATH="${data_extraction_dir}/..":$PYTHONPATH
 
+python3 ../main_scripts/main_data_extraction.py ${source_dir} ${dest_dir} ${years[@]} ${variables}
 
-#srun python ../../workflow_parallel_frame_prediction/DataPreprocess/mpi_split_data_multi_years.py --destination_dir ${destination_dir} --varnames T2 MSL gph500    
+rm -r ${venv_dir}

video_prediction_tools/HPC_scripts/data_extraction_era5_template.sh → video_prediction_tools/HPC_scripts/era5_data_extraction_template.sh

@@ -3,13 +3,13 @@
 #SBATCH --account=<your_project>
 #SBATCH --nodes=1
 #SBATCH --ntasks=13
-##SBATCH --ntasks-per-node=13
+##SBATCH --ntasks-per-node=12
 #SBATCH --cpus-per-task=1
-#SBATCH --output=data_extraction_era5-out.%j
-#SBATCH --error=data_extraction_era5-err.%j
+#SBATCH --output=DataExtraction_era5_step1-out.%j
+#SBATCH --error=DataExtraction_era5_step1-err.%j
 #SBATCH --time=04:20:00
-#SBATCH --partition=batch
 #SBATCH --gres=gpu:0
+#SBATCH --partition=batch
 #SBATCH --mail-type=ALL
 #SBATCH --mail-user=me@somewhere.com
 
@@ -22,7 +22,7 @@ exit 99
 VIRT_ENV_NAME="my_venv"
 
 # Activate virtual environment if needed (and possible)
-if [ -z ${VIRTUAL_ENV} ]; then
+if [ -z "${VIRTUAL_ENV}" ]; then
    if [[ -f ../virtual_envs/${VIRT_ENV_NAME}/bin/activate ]]; then
       echo "Activating virtual environment..."
       source ../virtual_envs/${VIRT_ENV_NAME}/bin/activate
@@ -34,16 +34,21 @@ fi
 # Loading modules
 source ../env_setup/modules_preprocess+extract.sh
 
-# Declare path-variables (dest_dir will be set and configured automatically via generate_runscript.py)
-source_dir=/my/path/to/era5
+
+# select years and variables for dataset and define target domain 
+years=( 2017 )
+months=( "all" )
+var_dict='{"2t": {"sf": ""}, "tcc": {"sf": ""}, "t": {"ml": "p85000."}}'
+sw_corner=(38.4 0.0)
+nyx=(56 92)
+
+# set some paths
+# note, that destination_dir is adjusted during runtime based on the data
+source_dir=/my/path/to/era5/data
 destination_dir=/my/path/to/extracted/data
-varmap_file=/my/path/to/varmapping/file
 
-years=( "2015" )
+# execute Python-script
+srun python ../main_scripts/main_era5_data_extraction.py -src_dir "${source_dir}" \
+     -dest_dir "${destination_dir}" -y "${years[@]}" -m "${months[@]}" \
+     -swc "${sw_corner[@]}" -nyx "${nyx[@]}" -v "${var_dict}"
 
-# Run data extraction
-for year in "${years[@]}"; do
-  echo "Perform ERA5-data extraction for year ${year}"
-  srun python ../main_scripts/main_data_extraction.py  --source_dir ${source_dir} --target_dir ${destination_dir} \
-                                                       --year ${year} --varslist_path ${varmap_file}
-done

video_prediction_tools/HPC_scripts/train_model_weatherbench_template.sh

+#!/bin/bash -x
+#SBATCH --account=<your_project>
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --output=train_model_era5-out.%j
+#SBATCH --error=train_model_era5-err.%j
+#SBATCH --time=24:00:00
+#SBATCH --gres=gpu:1
+#SBATCH --partition=some_partition
+#SBATCH --mail-type=ALL
+#SBATCH --mail-user=me@somewhere.com
+
+######### Template identifier (don't remove) #########
+echo "Do not run the template scripts"
+exit 99
+######### Template identifier (don't remove) #########
+
+# auxiliary variables
+WORK_DIR="$(pwd)"
+BASE_DIR=$(dirname "$WORK_DIR")
+# Name of virtual environment
+VIRT_ENV_NAME="my_venv"
+# !!! ADAPAT DEPENDING ON USAGE OF CONTAINER !!!
+# For container usage, comment in the follwoing lines
+# Name of container image (must be available in working directory)
+CONTAINER_IMG="${WORK_DIR}/tensorflow_21.09-tf1-py3.sif"
+WRAPPER="${BASE_DIR}/env_setup/wrapper_container.sh"
+
+# sanity checks
+if [[ ! -f ${CONTAINER_IMG} ]]; then
+  echo "ERROR: Cannot find required TF1.15 container image '${CONTAINER_IMG}'."
+  exit 1
+fi
+
+if [[ ! -f ${WRAPPER} ]]; then
+  echo "ERROR: Cannot find wrapper-script '${WRAPPER}' for TF1.15 container image."
+  exit 1
+fi
+
+# clean-up modules to avoid conflicts between host and container settings
+module purge
+
+# declare directory-variables which will be modified by generate_runscript.py
+source_dir=/my/path/to/tfrecords/files
+destination_dir=/my/model/output/path
+
+# valid identifiers for model-argument are: convLSTM, savp, mcnet and vae
+model=convLSTM
+datasplit_dict=${destination_dir}/data_split.json
+model_hparams=${destination_dir}/model_hparams.json
+
+# run training in container
+export CUDA_VISIBLE_DEVICES=0
+## One node, single GPU 
+srun --mpi=pspmix --cpu-bind=none \
+     singularity exec --nv "${CONTAINER_IMG}" "${WRAPPER}" ${VIRT_ENV_NAME} \
+     python3 "${BASE_DIR}"/main_scripts/main_train_models.py --input_dir ${source_dir} --datasplit_dict ${datasplit_dict} \
+     --dataset weatherbench --model ${model} --model_hparams_dict ${model_hparams} --output_dir ${destination_dir}/
+
+# WITHOUT container usage, comment in the follwoing lines (and uncomment the lines above)
+# Activate virtual environment if needed (and possible)
+#if [ -z ${VIRTUAL_ENV} ]; then
+#   if [[ -f ../virtual_envs/${VIRT_ENV_NAME}/bin/activate ]]; then