def main():
print("Starting ETL processes on SPA v1 outputs")
# Create a SPA ETL object
spa_etl = SPAoutput_ETL()
print("The script will now convert raw CSV outputs into netCDF4 files\n")
# Get the directory and file path information
file_path_list = spa_etl.get_spa_filepaths(DIRPATH)
# Load outputs files and process into dictionaries of dataframes
save_paths = ["{0}/spa_hws_exp{1}.nc".format(SAVEPATH, i + 1) for i in range(len(file_path_list))]
# [using pathos]
# setup processing pool
pool = mp.Pool(cpu_count())
pool.map(spa_etl.process_outputs, zip(file_path_list, save_paths))
pool.close()
pool.join()
# [boring sequential]
# [spa_etl.process_outputs([flist, sp]) \
# for (flist, sp) in zip(file_path_list, save_paths)]
print("ETL Finished")
def main():
print("Starting ETL processes on SPA v1 outputs")
# Create a SPA ETL object
spa_etl = SPAoutput_ETL()
print("The script will now convert raw CSV outputs into netCDF4 files\n")
# Get the directory and file path information
file_path_list = spa_etl.get_spa_filepaths(DIRPATH)
# Load outputs files and process into dictionaries of dataframes
save_paths = ["{0}/spa_hws_exp{1}.nc".format(SAVEPATH, i + 1) \
for i in range(len(file_path_list))]
# [using pathos]
# setup processing pool
pool = mp.Pool(cpu_count())
pool.map(spa_etl.process_outputs, zip(file_path_list, save_paths))
pool.close()
pool.join()
# [boring sequential]
# [spa_etl.process_outputs([flist, sp]) \
# for (flist, sp) in zip(file_path_list, save_paths)]
print("ETL Finished")
def main():
print("Test the canopy reduction process")
# Create a SPA ETL object
spa = SPAoutput_ETL()
# Get the directory and file path information
fpath_list = spa.get_spa_filepaths(DIRPATH)[1]
for fp in fpath_list:
if re.match(r'^.*_phen_+.csv', os.path.basename(fp)):
print(fp)
print("")
# check LAI from canopy outputs
phen = pd.read_csv(fpath_list[2], sep=r',')
phen['DT'] = pd.date_range(start="2001-01-01", freq='D', periods=len(phen))
phen['c3frac'] = phen.ix[:, 3:8].sum(axis=1)
phen['c4frac'] = phen.ix[:, 8:13].sum(axis=1)
phen['c3lai'] = phen['lai'] * phen['c3frac']
phen['c4lai'] = phen['lai'] * phen['c4frac']
phen.set_index(['DT'], inplace=True)
canopaths = [fp for fp in fpath_list if \
#re.search(r'_soils', os.path.basename(fp))]
re.search(r'l.*[0-9]+.csv$', os.path.basename(fp))]
for cp in canopaths:
print(cp)
return 1
leaf = spa.load_gasex_raw(canopaths[2:])
leaf_d = leaf.resample('D').apply('mean')
fig = plt.figure(figsize=(11, 7))
ax1 = plt.subplot(311)
ax2 = plt.subplot(312)
ax3 = plt.subplot(313)
ax1.plot(phen["lai"], 'g-', label='input')
ax1.plot(leaf_d["trees_LAI"] + leaf_d["grass_LAI"], \
c='darkgreen', lw=2, alpha=0.5, label='output')
ax2.plot(phen["c4lai"], 'r-', label='input')
ax2.plot(leaf_d["grass_LAI"],
'-',
c='darkred',
lw=2,
alpha=0.5,
label='output')
ax3.plot(phen["c3lai"], 'b-', label='input')
ax3.plot(leaf_d["trees_LAI"],
'-',
c='darkblue',
lw=2,
alpha=0.5,
label='output')
ax1.legend(loc="upper center", ncol=2)
ax2.legend(loc="upper center", ncol=2)
ax3.legend(loc="upper center", ncol=2)
ax1.set_ylim(0, 3)
ax2.set_ylim(0, 1.5)
ax3.set_ylim(0, 1.5)
plt.show()
return 1
def main():
print("Test the canopy reduction process")
# Create a SPA ETL object
spa = SPAoutput_ETL()
# Get the directory and file path information
fpath_list = spa.get_spa_filepaths(DIRPATH)[1]
for fp in fpath_list:
if re.match(r'^.*_phen_+.csv', os.path.basename(fp)):
print(fp)
print("")
# check LAI from canopy outputs
phen = pd.read_csv(fpath_list[2], sep=r',')
phen['DT'] = pd.date_range(start="2001-01-01", freq='D', periods=len(phen))
phen['c3frac'] = phen.ix[:, 3:8].sum(axis=1)
phen['c4frac'] = phen.ix[:, 8:13].sum(axis=1)
phen['c3lai'] = phen['lai']*phen['c3frac']
phen['c4lai'] = phen['lai']*phen['c4frac']
phen.set_index(['DT'], inplace=True)
canopaths = [fp for fp in fpath_list if \
#re.search(r'_soils', os.path.basename(fp))]
re.search(r'l.*[0-9]+.csv$', os.path.basename(fp))]
for cp in canopaths:
print(cp)
return 1
leaf = spa.load_gasex_raw(canopaths[2:])
leaf_d = leaf.resample('D').apply('mean')
fig = plt.figure(figsize=(11, 7))
ax1 = plt.subplot(311)
ax2 = plt.subplot(312)
ax3 = plt.subplot(313)
ax1.plot(phen["lai"], 'g-', label='input')
ax1.plot(leaf_d["trees_LAI"] + leaf_d["grass_LAI"], \
c='darkgreen', lw=2, alpha=0.5, label='output')
ax2.plot(phen["c4lai"], 'r-', label='input')
ax2.plot(leaf_d["grass_LAI"], '-', c='darkred', lw=2, alpha=0.5, label='output')
ax3.plot(phen["c3lai"], 'b-', label='input')
ax3.plot(leaf_d["trees_LAI"], '-', c='darkblue', lw=2, alpha=0.5, label='output')
ax1.legend(loc="upper center", ncol=2)
ax2.legend(loc="upper center", ncol=2)
ax3.legend(loc="upper center", ncol=2)
ax1.set_ylim(0, 3)
ax2.set_ylim(0, 1.5)
ax3.set_ylim(0, 1.5)
plt.show()
return 1