Power, water charts and plotting in own module

chart.py

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+from plot import df_plot
+import pandas as pd
+from functools import lru_cache
+
+def get_colour_code():
+
+    colorcode = pd.read_csv('./techcodes.csv', sep=',')
+    colorcode1 = colorcode.drop('colour', axis=1)
+    return dict([(a,b) for a,b in zip(colorcode1.tech_code, colorcode1.tech_name)])
+
+@lru_cache(maxsize=64)
+def get_country_code(Country):
+
+    country_code = pd.read_csv('./countrycode.csv', sep=',')
+    return country_code[country_code['Country Name'] == Country]['Country code'].tolist()[0]    
+
+def water_chart(Country, all_params, t_include):
+    """Plots water abstraction results
+    """
+    cc = get_country_code(Country)
+    det_col = get_colour_code()
+    agg_col = pd.read_csv('./agg_col.csv', sep=',').to_dict('list')
+
+    # water withdrawal detailed
+    wat_w_df = all_params['UseByTechnologyAnnual']
+    wat_w_df = wat_w_df[wat_w_df['f'].str[:6] == cc + 'WAT1'].copy()
+
+    wat_w_df['t'] = wat_w_df['t'].str[2:10]
+    wat_w_df['value'] = wat_w_df['value'].astype('float64')
+    wat_w_df = wat_w_df.pivot_table(index='y', 
+                                    columns='t',
+                                    values='value', 
+                                    aggfunc='sum').reset_index().fillna(0)
+    wat_w_df = wat_w_df.reindex(sorted(wat_w_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)
+    # Water Withdrawal (Aggregated)
+    watw_agg_df = pd.DataFrame(columns=agg_col)
+    watw_agg_df.insert(0,'y',wat_w_df['y'])
+    watw_agg_df = watw_agg_df.fillna(0.00)
+    for each in agg_col:
+        for tech_exists in agg_col[each]:
+            if tech_exists in wat_w_df.columns:
+                watw_agg_df[each] = watw_agg_df[each] + wat_w_df[tech_exists]
+                watw_agg_df[each] = watw_agg_df[each].round(2)
+
+    df_plot(watw_agg_df, 'Million cubic metres (Mm^3)', cc + "-" + 'Water Withdrawal')
+
+    # water output detailed
+    wat_o_df = all_params['ProductionByTechnologyAnnual']
+    wat_o_df = wat_o_df[wat_o_df['f'].str[:6]==cc+'WAT2'].copy()
+    wat_o_df['t'] = wat_o_df['t'].str[2:10].copy()
+    wat_o_df['value'] = wat_o_df['value'].astype('float64')
+    wat_o_df = wat_o_df.pivot_table(index='y', 
+                                    columns='t',
+                                    values='value', 
+                                    aggfunc='sum').reset_index().fillna(0)
+    wat_o_df = wat_o_df.reindex(sorted(wat_o_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)
+
+    #Water consumption missing row additions
+    for wd in wat_w_df.columns:
+        for wc in wat_o_df.columns:
+            if wd in wat_o_df.columns:
+                pass
+            else:
+                wat_o_df[wd] = 0
+    #####
+    ####Water consumption (Detailed)
+    wat_c_df = wat_w_df.set_index('y') - wat_o_df.set_index('y')
+    wat_c_df = wat_c_df.fillna(0.00)
+    wat_c_df.reset_index(inplace=True)
+    # Water consumption (Aggregate)
+    watc_agg_df = pd.DataFrame(columns=agg_col)
+    watc_agg_df.insert(0,'y',wat_c_df['y'])
+    watc_agg_df  = watc_agg_df.fillna(0.00)
+    for each in agg_col:
+        for tech_exists in agg_col[each]:
+            if tech_exists in wat_c_df.columns:
+                watc_agg_df[each] = watc_agg_df[each] + wat_c_df[tech_exists]
+                watc_agg_df[each] = watc_agg_df[each].round(2)
+    df_plot(watc_agg_df, 'Million cubic metres (Mm^3)',cc+'-'+'Water consumption aggregated')
+
+
+def power_chart(Country, all_params, agg_pow_col, t_include):
+
+    cc = get_country_code(Country)
+    det_col = get_colour_code()
+
+    # colorcode = pd.read_csv('./techcodes.csv', sep=',')
+    # colorcode2 = colorcode.drop('tech_code', axis=1)
+    # color_dict = dict([(a,b) for a,b in zip(colorcode2.tech_name, colorcode2.colour)])
+
+    # Power capacity (detailed)
+    cap_df = all_params['TotalCapacityAnnual']
+    cap_df = cap_df[cap_df['t'].str[:2]==cc].copy()
+    cap_df['t'] = cap_df['t'].str[2:10]
+    cap_df['value'] = cap_df['value'].astype('float64')
+    cap_df = cap_df[cap_df['t'].isin(t_include)].pivot_table(index='y',
+                                               columns='t',
+                                               values='value',
+                                               aggfunc='sum').reset_index().fillna(0)
+    cap_df = cap_df.reindex(sorted(cap_df.columns), axis=1
+        ).set_index('y'
+        ).reset_index(
+        ).rename(columns=det_col)
+    #***********************************************
+    # Power capacity (Aggregated)
+    cap_agg_df = pd.DataFrame(columns=agg_pow_col)
+    cap_agg_df.insert(0,'y',cap_df['y'])
+    cap_agg_df = cap_agg_df.fillna(0.00)
+    #
+    for each in agg_pow_col:
+        for tech_exists in agg_pow_col[each]:
+            if tech_exists in cap_df.columns:
+                cap_agg_df[each] = cap_agg_df[each] + cap_df[tech_exists]
+                cap_agg_df[each] = cap_agg_df[each].round(3)
+    #
+    df_plot(cap_agg_df, 'Gigawatts (GW)', 
+            cc + "-" + 'Power Generation Capacity (Aggregate)')
+    ## Power generation (Detailed)
+    gen_df = all_params['ProductionByTechnologyAnnual'].copy()
+    gen_df = gen_df[(gen_df['f'].str[:2] == cc)].copy()
+    gen_df = gen_df[(gen_df['f'].str[2:6] == 'EL01') | (gen_df['f'].str[2:6] == 'EL03')].copy()
+    gen_df = gen_df[(gen_df['t'].str[2:10] != 'EL00T00X') & (gen_df['t'].str[2:10] != 'EL00TDTX')].copy()
+    gen_df['value'] = gen_df['value'].astype('float64')
+    gen_df = gen_df.pivot_table(index='y', 
+                                columns='t',
+                                values='value', 
+                                aggfunc='sum').reset_index().fillna(0)
+    for each in gen_df.columns:
+        if len(each)!=1:
+            if (each[2:4]=='EL') & (each[6:10]=='BP00'):
+                pass
+            else:
+                gen_df.rename(columns={each:each[2:10]},inplace=True)
+        else:
+            pass
+    gen_df = gen_df.reindex(sorted(gen_df.columns), axis=1
+                            ).set_index('y'
+                            ).reset_index(
+                            ).rename(columns=det_col)
+    #####
+    # Power generation (Aggregated)
+    gen_agg_df = pd.DataFrame(columns=agg_pow_col)
+    gen_agg_df.insert(0,'y',gen_df['y'])
+    gen_agg_df = gen_agg_df.fillna(0.00)
+    for each in agg_pow_col:
+        for tech_exists in agg_pow_col[each]:
+            if tech_exists in gen_df.columns:
+                gen_agg_df[each] = gen_agg_df[each] + gen_df[tech_exists]
+                gen_agg_df[each] = gen_agg_df[each].round(2)
+    df_plot(gen_agg_df, 'Petajoules (PJ)', cc+"-"+'Power Generation (Aggregate)')
+    # New capacity (detailed)
+    cap_new_df = all_params['NewCapacity']
+    cap_new_df = cap_new_df[cap_new_df['t'].str[:2]==cc].copy()
+    cap_new_df['t'] = cap_new_df['t'].str[2:10]
+    cap_new_df['value'] = cap_new_df['value'].astype('float64')
+    cap_new_df = cap_new_df[cap_new_df['t'].isin(t_include)].pivot_table(
+        index='y', columns='t', values='value', aggfunc='sum'
+        ).reset_index().fillna(0)
+    cap_new_df = cap_new_df.reindex(sorted(cap_new_df.columns), axis=1
+        ).set_index('y'
+        ).reset_index(
+
+        ).rename(columns=det_col)
+    #***********************************************
+    # Power capacity (Aggregated)
+    cap_new_agg_df = pd.DataFrame(columns=agg_pow_col)
+    cap_new_agg_df.insert(0, 'y', cap_new_df['y'])
+    cap_new_agg_df  = cap_new_agg_df.fillna(0.00)
+    #
+    for each in agg_pow_col:
+        for tech_exists in agg_pow_col[each]:
+            if tech_exists in cap_new_df.columns:
+                cap_new_agg_df[each] = cap_new_agg_df[each] + cap_new_df[tech_exists]
+                cap_new_agg_df[each] = cap_new_agg_df[each].round(3)
+                ##
+    df_plot(cap_new_agg_df, 'Gigawatts (GW)', 
+            cc + "-" + 'New power generation capacity (Aggregate)')

plot.py

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+import pandas as pd
+
+def df_plot(df, y_title, p_title):
+
+    colorcode = pd.read_csv('./techcodes.csv', sep=',')
+    colorcode2 = colorcode.drop('tech_code', axis=1)
+
+    color_dict = dict([(a,b) for a,b in zip(colorcode2.tech_name, colorcode2.colour)])
+
+    if len(df.columns) == 1:
+        print('There are no values for the result variable that you want to plot')
+    else:
+         return df.iplot(x='y',
+                         kind='bar', 
+                         barmode='stack',
+                         xTitle='Year',
+                         yTitle=y_title,
+                         color=[color_dict[x] for x in df.columns if x != 'y'],
+                         title=p_title, 
+                         showlegend=True)