def plot(df, df_names, legendlabels=[None], saveName=None):
"""
df - PandasDataFrame timeseries for original hydrographs
df_names - list with column names
saveName - None, or string with figure name to be saved
legendlabels - List of legendnames or [None]. If default ([None]) - standart names are used
"""
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
plot_pandas.plot_pandas_scatter(
df,
x=["W_1_averaging3", "W_1_averaging3", "W_1_averaging3"],
y=["gradient_W_GW2", "gradient_GW2_GW3", "gradient_GW3_GW4"],
saveName=saveName,
xlabel="Mean river waterlevel [m AMSL]",
title="Mean Hydraulic Gradient VS. Mean River Waterlevel",
ylabel="Mean hydraulic gradient [-]",
legendlabels=legendlabels,
trendlinemode=1,
ylim=[-0.015, 0.015],
xlim=[-0.5, 2.5],
df_scatter_kwargs={
"marker": "o",
"markersize": 2.0,
"style": ".",
"markeredgecolor": "black",
"markeredgewidth": 0.0,
"legend": False,
},
axeslabel_fontsize=18.0,
title_fontsize=20.0,
axesvalues_fontsize=18.0,
annotation_fontsize=18.0,
legend_fontsize=18.0,
)
fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(nrows=2, ncols=3, figsize=(11.69, 8.27))
for ax, x, y, legendlabel, borders, ylabel, l_loc in zip(
[ax1, ax4],
[['x', 'x', 'x'], ['x', 'x']],
[['E_1', 'E_2', 'E_3'], ['T_1', 'T_2']],
[['Method 1', 'Method 2', 'Method 3'], ['Method 1', 'Method 2']],
[[-100., 100., -1.8, 0.], [-100., 100, 0., 40.]],
['Log Tidal Efficiency [-]', 'Timelag [min]'],
['upper right', 'upper left']
):
plot_pandas.plot_pandas_scatter(df1, x=x, y=y, saveName=None,
xlabel='Distance from shore [m]', title='Wells GWM1-GWM6', ylabel=ylabel,
trendlinemode=1, legendlabels=legendlabel,
xlim=[borders[0], borders[1]], ylim=[borders[2], borders[3]],
legend_location=l_loc, ax=ax, draw_axes=True,
df_scatter_kwargs={'marker': "o", 'markersize': 6., 'style': '.', 'markeredgecolor': 'black', 'markeredgewidth': 0.2, 'legend': False},
axeslabel_fontsize=18., title_fontsize=20., axesvalues_fontsize=18., annotation_fontsize=18., legend_fontsize=18.)
for ax, x, y, legendlabel, borders, ylabel, l_loc in zip(
[ax2, ax5],
[['x', 'x', 'x'], ['x', 'x']],
[['E_1', 'E_2', 'E_3'], ['T_1', 'T_2']],
[['Method 1', 'Method 2', 'Method 3'], ['Method 1', 'Method 2']],
[[-100., 100., -1.8 , 0.], [-100., 100, 0., 40.]],
['Log Tidal Efficiency [-]', 'Timelag [min]'],
['upper right', 'upper left']
):
plot_pandas.plot_pandas_scatter(df2, x=x, y=y, saveName=None,
xlabel='Distance from shore [m]', title='Wells GWM1-GWM5', ylabel=ylabel,
['Log Tidal Efficiency [-]', 'Timelag [min]'],
['upper right', 'upper left']):
plot_pandas.plot_pandas_scatter(df1,
x=x,
y=y,
saveName=None,
xlabel='Distance from shore [m]',
title='Wells GWM1-GWM6',
ylabel=ylabel,
trendlinemode=1,
legendlabels=legendlabel,
xlim=[borders[0], borders[1]],
ylim=[borders[2], borders[3]],
legend_location=l_loc,
ax=ax,
draw_axes=True,
df_scatter_kwargs={
'marker': "o",
'markersize': 6.,
'style': '.',
'markeredgecolor': 'black',
'markeredgewidth': 0.2,
'legend': False
},
axeslabel_fontsize=18.,
title_fontsize=20.,
axesvalues_fontsize=18.,
annotation_fontsize=18.,
legend_fontsize=18.)
for ax, x, y, legendlabel, borders, ylabel, l_loc in zip(
'H_gw3_at_W_high[m AMSL]',
'H_gw4_at_W_high[m AMSL]',
'H_gw5_at_W_high[m AMSL]',
'H_gw6_at_W_high[m AMSL]']
)
figure_savename = list([
'GW_1_vs_W_1.png',
'GW_2_vs_W_1.png',
'GW_3_vs_W_1.png',
'GW_4_vs_W_1.png',
'GW_5_vs_W_1.png',
'GW_6_vs_W_1.png']
)
# --------------------------------------------------------------------------------------
# END user inputs END
# --------------------------------------------------------------------------------------
path = os.path.dirname(sys.argv[0])
fname = os.path.abspath(os.path.join(path, file_folder, file_name) )
fign = os.path.abspath(os.path.join(path, figure_name))
if mode == 'XLS': # WORKING WITH EXCEL SHEET
# read excel file into pandas dataframe
data = process2pandas.read_xlx_into_pandas(fname, sheetname=0)
for Y_name1, Y_name2, sn in zip(overhead_names1, overhead_names2, figure_savename):
sn = os.path.abspath(os.path.join(path, 'out/', sn))
plot_pandas.plot_pandas_scatter(data, x=[X_name1, X_name2], y=[Y_name1, Y_name2], saveName=None, trendlinemode=trendlinemode,
xlabel='River water level [m AMSL]', title='WATER LEVEL IN OBSERVATION WELL VS RIVER WATERLEVEL', ylabel='Well water level [m AMSL]')