sys.path.insert(0, cmd_subfolder)
import process2pandas
import plot_pandas
if __name__ == '__main__':
# --------------------------------------------------------------------------------------
# user inputs
# --------------------------------------------------------------------------------------
file_folder = '../data/SLICED_171020141500_130420150600/hydrographs/'
file_name = 'Farge_mean_after_Serfes1991.csv'
col_names = ['GW_2_averaging3', 'GW_3_averaging3', 'GW_4_averaging3', 'W_1_averaging3']
legend_names = ['GW_2 mean water-level', 'GW_3 mean water-level', 'GW_4 mean water-level', 'W_1 mean water-level']
# --------------------------------------------------------------------------------------
# END user inputs END
# --------------------------------------------------------------------------------------
path = os.path.dirname(sys.argv[0])
fname = os.path.abspath(os.path.join(path, file_folder, file_name) )
data = process2pandas.read_mean_hydrographs_into_pandas(fname, datetime_indexes=True, decimal=',', na_values=['---'])
if _sns:
with sns.axes_style("whitegrid"):
plot_pandas.plot_mean_waterlevel(data, col_names, legend_names , saveName=None)
else:
plot_pandas.plot_mean_waterlevel(data, col_names, legend_names , saveName=None)
def plot(df, df_names, legend_names, saveName=None,
axeslabel_fontsize=10., title_fontsize=20., axesvalues_fontsize=10., annotation_fontsize=10., legend_fontsize=8.):
"""
df - PandasDataFrame timeseries for original hydrographs
names - list with column names
legend_names - list with strings
"""
print "plotting timeseries data..."
fig = plt.figure(tight_layout=True, figsize=(40, 10))
# ---------------------------
# SUBPLOT 1
# ---------------------------
ax1 = fig.add_subplot(211)
plot_pandas.plot_mean_waterlevel(df, df_names, legend_names, saveName=saveName, ax=ax1)
ax1.tick_params(axis='both', labelsize=axesvalues_fontsize)
ax1.grid(True, which='major')
ax1.set_ylabel("Waterlevel [m AMSL]", fontsize=axeslabel_fontsize)
ax1.set_title('Averaged waterlevel after Serfes', fontsize=title_fontsize)
handles, labels = ax1.get_legend_handles_labels()
ax1.legend(handles, labels, fontsize=legend_fontsize)
# ---------------------------
# SUBPLOT 2
# ---------------------------
ax2 = fig.add_subplot(212)
distance_W_GW2 = 46.5 # this parameter has been found by regression analysis....
distance_GW2_GW3 = 9.49 # this has been calculated through coordinates
distance_GW3_GW4 = 13.6 # this has been calculated through coordinates
print 'calculating gradient....', df_names[-1], '-', df_names[0]
df['gradient_W_GW2'] = (df[df_names[-1]] - df[df_names[0]])/distance_W_GW2
print 'calculating gradient....', df_names[0], '-', df_names[1]
df['gradient_GW2_GW3'] = (df[df_names[0]] - df[df_names[1]])/distance_GW2_GW3
print 'calculating gradient....', df_names[1], '-', df_names[2]
df['gradient_GW3_GW4'] = (df[df_names[1]] - df[df_names[2]])/distance_GW3_GW4
df['gradient_W_GW2'].plot( ax=ax2, legend=True, lw=0.8)
df['gradient_GW2_GW3'].plot(ax=ax2, legend=True, lw=0.8)
df['gradient_GW3_GW4'].plot(ax=ax2, legend=True, lw=0.8)
handles, labels = ax2.get_legend_handles_labels()
for i, l_name, dist in zip([0, 1, 2], labels, [distance_W_GW2, distance_GW2_GW3, distance_GW3_GW4]):
labels[i] = l_name+': distance = {0} m'.format(dist)
ax2.legend(handles, labels, fontsize=legend_fontsize,)
ax2.grid(True, which='major')
ax2.set_title("Mean hydraulic gradients", fontsize=title_fontsize)
ax2.set_ylabel("Mean hydraulic gradient [-]", fontsize=axeslabel_fontsize)
ax2.set_xlabel("Datetime", fontsize=axeslabel_fontsize)
ax2.tick_params(axis='both', labelsize=axesvalues_fontsize)
ax2.set_ylim([-0.015, 0.015])
#figManager = plt.get_current_fig_manager()
#figManager.window.showMaximized()
if saveName:
fig.savefig(saveName, dpi=300, tight_layout=True)#, format='pdf')
print 'saving figure... :', saveName
plt.show()
