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]')