#!/usr/bin/env python # coding: utf-8 # Usar o ambiente SCANPLOT_PANEL # @cfbastarz, Jun/2023 (carlos.bastarz@inpe.br) import os import io import intake import requests import colorcet as cc import xarray as xr import numpy as np import pandas as pd import geopandas as gpd import seaborn as sns import panel as pn import param import hvplot.xarray import hvplot.pandas import hvplot as hv import holoviews as hvs import cartopy.crs as ccrs import cartopy.feature as cfeature import xyzservices.providers as xyz from datetime import datetime from matplotlib import pyplot as plt from monitor_texts import MonitoringAppTexts from monitor_dates import MonitoringAppDates hvs.extension('bokeh') pn.extension('katex') pn.extension('floatpanel') pn.extension('texteditor') pn.extension(notifications=True) pn.extension(sizing_mode='stretch_width') # SCANPLOT_V2.0.0a1 # @cfbastarz, Jun/2023 (carlos.bastarz@inpe.br) monitor_app_texts = MonitoringAppTexts() monitor_warning_bottom_main = monitor_app_texts.warnings() def url_exists(url): try: response = requests.head(url, allow_redirects=True, timeout=5) # códigos 200-399 indicam que a URL está acessível if response.status_code < 400: print(f"✅ [OBJ EVALUATION] Arquivo acessível: {url}") catalog_obj = intake.open_catalog(url) return True, catalog_obj else: print(f"❌ [OBJ EVALUATION] Arquivo não encontrado: {url} (status {response.status_code})") return False, None except requests.RequestException: return False, None data_catalog_file = 'https://dataserver.cptec.inpe.br/dataserver_dimnt/das/carlos.bastarz/SMNAMonitoringApp/objeval/catalog-scantec-s0.yml' data_exists = url_exists(data_catalog_file) if data_exists[0]: data_catalog = url_exists(data_catalog_file)[1] Vars = [ ('VTMP:925', 'Virtual Temperature @ 925 hPa [K]'), ('VTMP:850', 'Virtual Temperature @ 850 hPa [K]'), ('VTMP:500', 'Virtual Temperature @ 500 hPa [K]'), ('VTMP:250', 'Virtual Temperature @ 250 hPa [K]'), ('VTMP:200', 'Virtual Temperature @ 200 hPa [K]'), ('VTMP:150', 'Virtual Temperature @ 150 hPa [K]'), ('VTMP:070', 'Virtual Temperature @ 70 hPa [K]'), ('VTMP:050', 'Virtual Temperature @ 50 hPa [K]'), ('TEMP:850', 'Absolute Temperature @ 850 hPa [K]'), ('TEMP:500', 'Absolute Temperature @ 500 hPa [K]'), ('TEMP:250', 'Absolute Temperature @ 250 hPa [K]'), ('TEMP:200', 'Absolute Temperature @ 200 hPa [K]'), ('TEMP:150', 'Absolute Temperature @ 150 hPa [K]'), ('TEMP:070', 'Absolute Temperature @ 70 hPa [K]'), ('TEMP:050', 'Absolute Temperature @ 50 hPa [K]'), ('PSNM:000', 'Mean Sea Level Pressure [hPa]'), ('UMES:925', 'Specific Humidity @ 925 hPa [g/Kg]'), ('UMES:850', 'Specific Humidity @ 850 hPa [g/Kg]'), ('UMES:500', 'Specific Humidity @ 500 hPa [g/Kg]'), ('UMES:250', 'Specific Humidity @ 250 hPa [g/Kg]'), ('UMES:200', 'Specific Humidity @ 200 hPa [g/Kg]'), ('UMES:150', 'Specific Humidity @ 150 hPa [g/Kg]'), ('UMES:070', 'Specific Humidity @ 70 hPa [g/Kg]'), ('UMES:050', 'Specific Humidity @ 50 hPa [g/Kg]'), ('AGPL:000', 'Inst. Precipitable Water @ 1000 hPa [Kg/m2]'), ('ZGEO:850', 'Geopotential height @ 850 hPa [gpm]'), ('ZGEO:500', 'Geopotential height @ 500 hPa [gpm]'), ('ZGEO:250', 'Geopotential height @ 250 hPa [gpm]'), ('UVEL:850', 'Zonal Wind @ 850 hPa [m/s]'), ('UVEL:500', 'Zonal Wind @ 500 hPa [m/s]'), ('UVEL:250', 'Zonal Wind @ 250 hPa [m/s]'), ('UVEL:200', 'Zonal Wind @ 200 hPa [m/s]'), ('UVEL:150', 'Zonal Wind @ 150 hPa [m/s]'), ('UVEL:070', 'Zonal Wind @ 70 hPa [m/s]'), ('UVEL:050', 'Zonal Wind @ 50 hPa [m/s]'), ('VVEL:850', 'Meridional Wind @ 850 hPa [m/s]'), ('VVEL:500', 'Meridional Wind @ 500 hPa [m/s]'), ('VVEL:250', 'Meridional Wind @ 250 hPa [m/s]'), ('VVEL:200', 'Meridional Wind @ 200 hPa [m/s]'), ('VVEL:150', 'Meridional Wind @ 150 hPa [m/s]'), ('VVEL:070', 'Meridional Wind @ 70 hPa [m/s]'), ('VVEL:050', 'Meridional Wind @ 50 hPa [m/s]'), ] list_var = [ltuple[0].lower() for ltuple in Vars] tmp_list = list(data_catalog) Regs = list(set(item.split('-')[1] for item in tmp_list)) Stats = list(set(item.split('-')[2].upper() for item in tmp_list)) Exps = list(set(item.split('-')[3].upper() for item in tmp_list)) Refs = list(set(item.split('-')[4] for item in tmp_list)) # # Widgets # # Widgets Datas (das distribuições espaciais) tmp = list(data_catalog)[0] ds = data_catalog[tmp].read() ti = ds.time.values[0] tf = ds.time.values[-1] datei = pd.Timestamp(ti).to_pydatetime() datef = pd.Timestamp(tf).to_pydatetime() date = pn.widgets.DateSlider(name='Data', start=datei, end=datef, value=datei, format='%Y-%m-%d', width=230) #fcts = pn.widgets.IntSlider(name='Previsão (horas)', start=0, end=264, step=24, value=0, width=230) # Widget de Notificações #silence = pn.widgets.Toggle(name='🔔 Silenciar Notificações', button_type='primary', button_style='outline', value=False) #read_catalog = pn.widgets.Button(name='🎲 Ler Catálogo de Dados', button_type='primary') #file_input = pn.widgets.FileInput(name='Escolher Catálogo de Dados', accept='yml', mime_type='text/yml', multiple=False) # Widgets Série Temporal (_st) varlev_st = pn.widgets.Select(name='Variable', options=[i[0] for i in Vars], value=[i[0] for i in Vars][0], disabled=False, width=230) reg_st = pn.widgets.Select(name='Region', options=Regs, value=Regs[3], disabled=False, width=230) ref_st = pn.widgets.Select(name='Reference', options=Refs, value=Refs[1], disabled=False, width=230) expt_st_tb = pn.widgets.Select(name='Experiment (Table)', options=Exps, value=Exps[0], disabled=True, width=230) expt_st = pn.widgets.MultiChoice(name='Experiments (Plots)', options=Exps, value=[Exps[0]], disabled=False, solid=False, width=230) # Widgets Scorecard (_sc) Tstats = ['Percentual Gain', 'Fractional Change'] colormap_sc = pn.widgets.ColorMap(name='Colormap', options=cc.palette_n, value_name='coolwarm', width=250, margin=(0, 0, 0, 0)) invert_colors_sc = pn.widgets.Checkbox(name='Invert Colors', value=True, width=230) statt_sc = pn.widgets.Select(name='Statistic', options=Stats, value=Stats[0], disabled=False, width=230) tstat = pn.widgets.Select(name='Type', options=Tstats, value=Tstats[1], disabled=False, width=230) reg_sc = pn.widgets.Select(name='Region', options=Regs, value=Regs[3], disabled=False, width=230) ref_sc = pn.widgets.Select(name='Reference', options=Refs, value=Refs[1], disabled=False, width=230) expt1 = pn.widgets.Select(name='Experiment 1', options=Exps, value=Exps[0], disabled=False, width=230) expt2 = pn.widgets.Select(name='Experiment 2', options=Exps, value=Exps[-1], disabled=False, width=230) # Widgets Distribuição Espacial (_de) Fills = ['image', 'contour'] fill_de = pn.widgets.Select(name='Fill', options=Fills, width=230) colormap_de = pn.widgets.ColorMap(name='Colormap', options=cc.palette_n, value_name='coolwarm', width=250, margin=(0, 0, 0, 0)) invert_colors_de = pn.widgets.Checkbox(name='Invert Colors', value=True, width=230) interval = pn.widgets.IntInput(name='Intervals', value=10, step=1, start=5, end=30, width=230, disabled=True) state = pn.widgets.Select(name='Statistic', options=Stats, value=Stats[0], disabled=False, width=230) stat_de = pn.widgets.Select(name='Statistic', options=Stats, value=Stats[0], disabled=False, width=230) varlev_de = pn.widgets.Select(name='Variable', options=[i[0] for i in Vars], value=[i[0] for i in Vars][0], disabled=False, width=230) reg_de = pn.widgets.Select(name='Region', options=Regs, value=Regs[3], disabled=False, width=230) ref_de = pn.widgets.Select(name='Reference', options=Refs, value=Refs[1], disabled=False, width=230) expe_de = pn.widgets.MultiChoice(name='Experiments', options=Exps, value=[Exps[0]], disabled=False, solid=False, width=230) # Variável lógica para determinar se o arquivo de catálogo já foi lido (True) ou não (False) loaded = pn.widgets.Checkbox(name='Catálogo Carregado', value=True, disabled=True) # # Funções de plotagem # def get_min_max_ds(ds): return ds.compute().min().item(), ds.compute().max().item() def get_df(reg, exp, stat, ref, varlev): kname = 'scantec-' + reg + '-' + stat + '-' + exp.lower() + '-' + ref + '-table' df = data_catalog[kname].read() df.set_index('Unnamed: 0', inplace=True) df.index.name = '' return df #def colorbar_bounds(vmin, vmax, n_levels): # vals = np.linspace(vmin, vmax, n_levels) # vals_int = np.round(vals).astype(int) # print(np.unique(vals_int)) # return np.unique(vals_int) tabs_layout = None @pn.depends(varlev_st, reg_st, ref_st, expt_st, expt_st_tb, loaded) def plotCurves(varlev_st, reg_st, ref_st, expt_st, expt_st_tb, loaded): global tabs_layout if loaded and varlev_st and reg_st and ref_st and expt_st: for i in Vars: if i[0] == varlev_st: nexp_ext = i[1] varlev_st = varlev_st.lower() height=500 for count, i in enumerate(expt_st): if count == 0: exp = expt_st[count] exp_tb = expt_st_tb df_vies = get_df(reg_st, exp, 'vies', ref_st, varlev_st) if df_vies is not None: ax_vies = df_vies.hvplot.line(x='%Previsao', y=varlev_st, xlabel='Horas', ylabel='VIES', shared_axes=False, grid=True, line_width=3, label=str(exp), fontsize={'ylabel': '12px', 'ticks': 10}, responsive=True, height=height, title='VIES' + ' - ' + str(nexp_ext)) ax_vies_s = df_vies.hvplot.scatter(x='%Previsao', y=varlev_st, label=str(exp), height=height, shared_axes=False, persist=True, responsive=True).opts(size=5, marker='o') df_rmse = get_df(reg_st, exp, 'rmse', ref_st, varlev_st) ax_rmse = df_rmse.hvplot.line(x='%Previsao', y=varlev_st, xlabel='Horas', ylabel='RMSE', shared_axes=False, grid=True, line_width=3, label=str(exp), fontsize={'ylabel': '12px', 'ticks': 10}, responsive=True, height=height, title='RMSE' + ' - ' + str(nexp_ext)) ax_rmse_s = df_rmse.hvplot.scatter(x='%Previsao', y=varlev_st, label=str(exp), height=height, shared_axes=False, persist=True, responsive=True).opts(size=5, marker='o') df_acor = get_df(reg_st, exp, 'acor', ref_st, varlev_st) ax_acor = df_acor.hvplot.line(x='%Previsao', y=varlev_st, xlabel='Horas', ylabel='ACOR', shared_axes=False, grid=True, line_width=3, label=str(exp), fontsize={'ylabel': '12px', 'ticks': 10}, responsive=True, height=height, title='ACOR' + ' - ' + str(nexp_ext)) ax_acor_s = df_acor.hvplot.scatter(x='%Previsao', y=varlev_st, label=str(exp), height=height, shared_axes=False, persist=True, responsive=True).opts(size=5, marker='o') else: exp = expt_st[count] df_vies = get_df(reg_st, exp, 'vies', ref_st, varlev_st) if df_vies is not None: ax_vies *= df_vies.hvplot.line(x='%Previsao', y=varlev_st, xlabel='Horas', ylabel='VIES', shared_axes=False, grid=True, line_width=3, label=str(exp), fontsize={'ylabel': '12px', 'ticks': 10}, responsive=True, height=height, title='VIES' + ' - ' + str(nexp_ext)) ax_vies_s *= df_vies.hvplot.scatter(x='%Previsao', y=varlev_st, label=str(exp), height=height, shared_axes=False, persist=True, responsive=True).opts(size=5, marker='o') df_rmse = get_df(reg_st, exp, 'rmse', ref_st, varlev_st) ax_rmse *= df_rmse.hvplot.line(x='%Previsao', y=varlev_st, xlabel='Horas', ylabel='RMSE', shared_axes=False, grid=True, line_width=3, label=str(exp), fontsize={'ylabel': '12px', 'ticks': 10}, responsive=True, height=height, title='RMSE' + ' - ' + str(nexp_ext)) ax_rmse_s *= df_rmse.hvplot.scatter(x='%Previsao', y=varlev_st, label=str(exp), height=height, shared_axes=False, persist=True, responsive=True).opts(size=5, marker='o') df_acor = get_df(reg_st, exp, 'acor', ref_st, varlev_st) ax_acor *= df_acor.hvplot.line(x='%Previsao', y=varlev_st, xlabel='Horas', ylabel='ACOR', shared_axes=False, grid=True, line_width=3, label=str(exp), fontsize={'ylabel': '12px', 'ticks': 10}, responsive=True, height=height, title='ACOR' + ' - ' + str(nexp_ext)) ax_acor_s *= df_acor.hvplot.scatter(x='%Previsao', y=varlev_st, label=str(exp), height=height, shared_axes=False, persist=True, responsive=True).opts(size=5, marker='o') if ax_vies is not None: ax_vies *= hvs.HLine(0).opts(line_width=1, shared_axes=False, responsive=True, height=height, line_color='black', line_dash='dashed') ax_rmse *= hvs.HLine(0).opts(line_width=1, shared_axes=False, responsive=True, height=height, line_color='black', line_dash='dashed') ax_acor *= hvs.HLine(0.6).opts(line_width=1, shared_axes=False, responsive=True, height=height, line_color='black', line_dash='dashed') stylesheet = """ .tabulator-cell { font-size: 12px; } """ df_vies = get_df(reg_st, exp_tb, 'vies', ref_st, varlev_st) df_vies_table = pn.widgets.Tabulator(df_vies, #selectable=False, show_index=False, theme='bootstrap4', text_align='center', layout='fit_data_table', #widths=250, selectable='toggle', #layout='fit_data_fill', #width=250, stylesheets=[stylesheet], disabled=True) df_rmse = get_df(reg_st, exp_tb, 'rmse', ref_st, varlev_st) df_rmse_table = pn.widgets.Tabulator(df_rmse, #selectable=False, show_index=False, theme='bootstrap4', text_align='center', layout='fit_data_table', #widths=250, selectable='toggle', #layout='fit_data_fill', #width=250, stylesheets=[stylesheet], disabled=True) df_acor = get_df(reg_st, exp_tb, 'acor', ref_st, varlev_st) df_acor_table = pn.widgets.Tabulator(df_acor, #selectable=False, show_index=False, theme='bootstrap4', text_align='center', layout='fit_data_table', #widths=250, selectable='toggle', #layout='fit_data_fill', #width=250, stylesheets=[stylesheet], disabled=True) if ax_vies is not None: ax_vies.opts(axiswise=True, legend_position='bottom_left') ax_rmse.opts(axiswise=True, legend_position='top_left') ax_acor.opts(axiswise=True, legend_position='bottom_left') def get_csv_vies(): io_buffer = io.BytesIO() df_vies.to_csv(io_buffer, index=False) io_buffer.seek(0) return io_buffer file_download_vies = pn.widgets.FileDownload( icon='download', callback=get_csv_vies, filename=f'vies_table_{exp_tb}.csv', button_type='success', width=310 ) def get_csv_rmse(): io_buffer = io.BytesIO() df_rmse.to_csv(io_buffer, index=False) io_buffer.seek(0) return io_buffer file_download_rmse = pn.widgets.FileDownload( icon='download', callback=get_csv_rmse, filename=f'rmse_table_{exp_tb}.csv', button_type='success', width=310 ) def get_csv_acor(): io_buffer = io.BytesIO() df_acor.to_csv(io_buffer, index=False) io_buffer.seek(0) return io_buffer file_download_acor = pn.widgets.FileDownload( icon='download', callback=get_csv_acor, filename=f'acor_table_{exp_tb}.csv', button_type='success', width=310 ) tabulators = pn.Tabs(('VIES', pn.Column(df_vies_table, file_download_vies)), ('RMSE', pn.Column(df_rmse_table, file_download_rmse)), ('ACOR', pn.Column(df_acor_table, file_download_acor)), tabs_location='left')#, dynamic=True) #layout = hvs.Layout(ax_vies*ax_vies_s + ax_rmse*ax_rmse_s + ax_acor*ax_acor_s).cols(3) tabs_layout = pn.Tabs(('PLOTS', hvs.Layout(ax_vies*ax_vies_s + ax_rmse*ax_rmse_s + ax_acor*ax_acor_s).cols(3)), ('TABLES', pn.Column('Statistics tables from the SCANTEC objective evaluation.', tabulators)))#, dynamic=True) # Callback to enable/disable the expt_st_tb widget based on the active tab def update_expt_st_tb_widget(event): global expt_st_tb if event.new == 1: # Tab index for "TABLES" expt_st_tb.disabled = False else: expt_st_tb.disabled = True # Attach the callback to the active parameter of the tabs tabs_layout.param.watch(update_expt_st_tb_widget, 'active') else: tabs_layout = pn.Column( pn.pane.Markdown(""" # Série Temporal A avaliação por meio de série temporal permite verificar o comportamento de parâmetros (variáveis) do modelo ao longo do tempo, seja por meio da verificação dos erros aleatórios, sistemáticos e habilidade de previsão. """), pn.pane.Alert('⛔ **Atenção:** Nada para mostrar! Para começar, selecione um catálogo de dados ou aguarde a execução da função de plotagem.', alert_type='danger') ) return tabs_layout @pn.depends(statt_sc, tstat, reg_sc, ref_sc, expt1, expt2, colormap_sc, invert_colors_sc, loaded) def plotScorecard(statt_sc, tstat, reg_sc, ref_sc, expt1, expt2, colormap_sc, invert_colors_sc, loaded): if loaded and statt_sc and tstat and reg_sc and ref_sc and expt1 and expt2 and colormap_sc and invert_colors_sc: dfs = globals()['data_catalog'] kname1 = 'scantec-' + reg_sc + '-' + statt_sc.lower() + '-' + expt1.lower() + '-' + ref_sc + '-table' kname2 = 'scantec-' + reg_sc + '-' + statt_sc.lower() + '-' + expt2.lower() + '-' + ref_sc + '-table' df1 = dfs[kname1].read() df2 = dfs[kname2].read() df1.set_index('Unnamed: 0', inplace=True) df1.index.name = '' df2.set_index('Unnamed: 0', inplace=True) df2.index.name = '' p_table1 = pd.pivot_table(df1, index='%Previsao', values=list_var) p_table2 = pd.pivot_table(df2, index='%Previsao', values=list_var) if invert_colors_sc == True: cmap = colormap_sc[::-1] else: cmap = colormap_sc if tstat == 'Percentual Gain': # Porcentagem de ganho if statt_sc == 'ACOR': #score_table = ((p_table2[1:].T - p_table1[1:].T) / (1.0 - p_table1[1:].T)) * 100 score_table = ((p_table2.T - p_table1.T) / (1.0 - p_table1.T)) * 100 elif statt_sc == 'RMSE' or statt_sc == 'VIES': #score_table = ((p_table2[1:].T - p_table1[1:].T) / (0.0 - p_table1[1:].T)) * 100 score_table = ((p_table2.T - p_table1.T) / (0.0 - p_table1.T)) * 100 elif tstat == 'Fractional Change': # Mudança fracional #score_table = (1.0 - (p_table2[1:].T / p_table1[1:].T)) score_table = (1.0 - (p_table2.T / p_table1.T)) if score_table.isnull().values.any(): # Tentativa de substituir os NaN - que aparecem quando vies e rmse são iguais a zero score_table = score_table.fillna(0.0000001) # Tentativa de substituir valores -inf por um número não muito grande score_table.replace([np.inf, -np.inf], 1000000, inplace=True) #if silence.value is False: pn.state.notifications.info('NaN or Inf values may have been replaced by other values.', duration=5000) ## Figura plt.figure(figsize = (9,6)) sns.set(style='whitegrid', font_scale=0.450) sns.set_context(rc={'xtick.major.size': 1.5, 'ytick.major.size': 1.5, 'xtick.major.pad': 0.05, 'ytick.major.pad': 0.05, 'xtick.major.width': 0.5, 'ytick.major.width': 0.5, 'xtick.minor.size': 1.5, 'ytick.minor.size': 1.5, 'xtick.minor.pad': 0.05, 'ytick.minor.pad': 0.05, 'xtick.minor.width': 0.5, 'ytick.minor.width': 0.5}) if tstat == 'Percentual Gain': ax = sns.heatmap(score_table, annot=True, fmt='.3f', cmap=cmap, vmin=-100, vmax=100, center=0, linewidths=0.25, square=False, cbar_kws={'shrink': 1.0, 'ticks': np.arange(-100,110,10), 'pad': 0.01, 'orientation': 'vertical'}) cbar = ax.collections[0].colorbar cbar.set_ticks([-100, -50, 0, 50, 100]) cbar.set_ticklabels(['pior', '-50%', '0', '50%', 'melhor']) cbar.ax.tick_params(labelsize=7) plt.title('Ganho ' + str(statt_sc) + ' (%) - ' + expt1 + ' Vs. ' + expt2, fontsize=8) elif tstat == 'Fractional Change': ax = sns.heatmap(score_table, annot=True, fmt='.3f', cmap=cmap, vmin=-1, vmax=1, center=0, linewidths=0.25, square=False, cbar_kws={'shrink': 1.0, 'ticks': np.arange(-1,2,1), 'pad': 0.01, 'orientation': 'vertical'}) cbar = ax.collections[0].colorbar cbar.set_ticks([-1, -0.5, 0, 0.5, 1]) cbar.set_ticklabels(['pior', '-0.5', '0', '0.5', 'melhor']) cbar.ax.tick_params(labelsize=7) plt.title('Fractional Change ' + str(statt_sc) + " - " + expt1 + ' Vs. ' + expt2, fontsize=8) plt.xlabel('Forecast Length') plt.yticks(fontsize=7) #plt.xticks(rotation=90, fontsize=6) plt.xticks(fontsize=7) plt.tight_layout() layout = ax.get_figure() plt.close() else: layout = pn.Column( pn.pane.Markdown(""" # Scorecard Para uma variável alpha (e.g., pressão, temperatura, umidade, componentes do vento etc.), podem ser calculadas duas métricas que permitem quantificar a variação relativa entre dois experimentos avaliados pelo SCANTEC. As métricas aplicadas são o Ganho Percentual e a Mudança Fracional* e ambas podem ser calculadas com base nas tabelas de estatisticas do SCANTEC. Estas métricas podem ser utilizadas quando se quiser ter uma visão imediata sobre as melhorias obtidas entre duas versões de um modelo ou entre dois experimentos de um mesmo modelo. """), pn.pane.Alert('⛔ **Atenção:** Nada para mostrar! Para começar, selecione um catálogo de dados ou aguarde a execução da função de plotagem.', alert_type='danger') ) return layout @pn.depends(stat_de, varlev_de, reg_de, ref_de, date, colormap_de, invert_colors_de, interval, expe_de, fill_de) def plotFields(stat_de, varlev_de, reg_de, ref_de, date, colormap_de, invert_colors_de, interval, expe_de, fill_de): date = str(date) + ' 12:00' # consertar... var = varlev_de.replace(':', '').lower() for i in Vars: if i[0] == varlev_de: nexp_ext = i[1] if invert_colors_de == True: cmap = colormap_de[::-1] else: cmap = colormap_de if reg_de == 'as': data_aspect=1 frame_height=600 elif (reg_de == 'hn') or (reg_de == 'hs'): data_aspect=1 frame_height=225 elif reg_de == 'tr': data_aspect=1 frame_height=150 elif reg_de == 'gl': data_aspect=1 frame_height=590 for count, i in enumerate(expe_de): exp = expe_de[count] kname = 'scantec-' + reg_de + '-' + stat_de.lower() + '-' + exp.lower() + '-' + ref_de + '-field' if data_catalog is not None: ds = data_catalog[kname].to_dask() vmin, vmax = get_min_max_ds(ds[var]) #colorbar_bounds = (vmin, vmax, interval) if fill_de == 'image': if count == 0: ax = ds.sel(time=date).hvplot.image(x='lon', y='lat', z=var, data_aspect=data_aspect, frame_height=frame_height, cmap=cmap, projection=ccrs.PlateCarree(), coastline=True, rasterize=True, clim=(vmin,vmax), title=str(stat_de) + ' (' + str(exp) + ') - ' + str(nexp_ext) + ' (' + str(date) + ')') else: ax += ds.sel(time=date).hvplot.image(x='lon', y='lat', z=var, data_aspect=data_aspect, frame_height=frame_height, cmap=cmap, projection=ccrs.PlateCarree(), coastline=True, rasterize=True, clim=(vmin,vmax), title=str(stat_de) + ' (' + str(exp) + ') - ' + str(nexp_ext) + ' (' + str(date) + ')') elif fill_de == 'contour': if count == 0: ax = ds.sel(time=date).hvplot.contour(x='lon', y='lat', z=var, data_aspect=data_aspect, frame_height=frame_height, cmap=cmap, projection=ccrs.PlateCarree(), coastline=True, rasterize=True, clim=(vmin,vmax), levels=interval, line_width=2, title=str(stat_de) + ' (' + str(exp) + ') - ' + str(nexp_ext) + ' (' + str(date) + ')') else: ax += ds.sel(time=date).hvplot.contour(x='lon', y='lat', z=var, data_aspect=data_aspect, frame_height=frame_height, cmap=cmap, projection=ccrs.PlateCarree(), coastline=True, rasterize=True, clim=(vmin,vmax), levels=interval, line_width=2, title=str(stat_de) + ' (' + str(exp) + ') - ' + str(nexp_ext) + ' (' + str(date) + ')') if reg_de == 'as': if count > 0: layout = hvs.Layout([ax]).cols(count+1) else: layout = hvs.Layout([ax]).cols(1) else: layout = hvs.Layout([ax]).cols(1) return layout def LayoutSidebarObjEval(): card1 = pn.Card(pn.Row(varlev_st, pn.widgets.TooltipIcon(value='Choose a variable and level', align='start')), pn.Row(reg_st, pn.widgets.TooltipIcon(value='Choose a region', align='start')), pn.Row(ref_st, pn.widgets.TooltipIcon(value='Choose a reference', align='start')), pn.Row(expt_st_tb, pn.widgets.TooltipIcon(value='Choose one experiment', align='start')), pn.Row(expt_st, pn.widgets.TooltipIcon(value='Choose one or more experiments', align='start')), title='Time Series', collapsed=False) card2 = pn.Card(pn.Row(statt_sc, pn.widgets.TooltipIcon(value='Choose a statistic', align='start')), pn.Row(tstat, pn.widgets.TooltipIcon(value='Choose a score', align='start')), pn.Row(reg_sc, pn.widgets.TooltipIcon(value='Choose a region', align='start')), pn.Row(ref_sc, pn.widgets.TooltipIcon(value='Choose a reference', align='start')), pn.Row(expt1, pn.widgets.TooltipIcon(value='Choose a experiment', align='start')), pn.Row(expt2, pn.widgets.TooltipIcon(value='Choose a experiment', align='start')), pn.Row(colormap_sc, pn.widgets.TooltipIcon(value='Choose a colormap', align='start')), pn.Row(invert_colors_sc, pn.widgets.TooltipIcon(value='Invert the color range in the colormap', align='start')), title='Scorecard', collapsed=True) card3 = pn.Card(pn.Row(date, pn.widgets.TooltipIcon(value='Choose a date', align='start')), pn.Row(state, pn.widgets.TooltipIcon(value='Choose a statistic', align='start')), pn.Row(varlev_de, pn.widgets.TooltipIcon(value='Choose a variable and level', align='start')), pn.Row(reg_de, pn.widgets.TooltipIcon(value='Choose a region', align='start')), pn.Row(ref_de, pn.widgets.TooltipIcon(value='Choose a reference', align='start')), pn.Row(fill_de, pn.widgets.TooltipIcon(value='Choose a experiment', align='start')), pn.Row(interval, pn.widgets.TooltipIcon(value='Choose the contour invervals (min=5, max=30)', align='start')), pn.Row(colormap_de, pn.widgets.TooltipIcon(value='Choose a colormap', align='start')), pn.Row(invert_colors_de, pn.widgets.TooltipIcon(value='Invert the color range in the colormap', align='start')), pn.Row(expe_de, pn.widgets.TooltipIcon(value='Choose a experiment', align='start')), title='Spatial Distribution', collapsed=True) def toggle_widgets(event): if event.new == 'contour': interval.disabled = False elif event.new == 'image': interval.disabled = True fill_de.param.watch(toggle_widgets, 'value') def toggle_cards(event): if event.new == False: if event.obj is card1: card2.collapsed = True card3.collapsed = True elif event.obj is card2: card1.collapsed = True card3.collapsed = True elif event.obj is card3: card1.collapsed = True card2.collapsed = True card1.param.watch(toggle_cards, 'collapsed') card2.param.watch(toggle_cards, 'collapsed') card3.param.watch(toggle_cards, 'collapsed') global cards_objeval cards_objeval = [card1, card2, card3] card_parameters = pn.Card('Click to expand cards.', card1, card2, card3, title='Parameters', collapsed=False) return pn.Column(card_parameters) def LayoutMainObjEval(): main_text = pn.Column(""" # Objective Evaluation Set the parameters on the left to update the map below and explore our analysis features. Click on the `TABLE` tab to get an overview of the calculated statistics. """) tabs_objeval = pn.Tabs(('TIME SERIES', pn.Column('Time series of bias, root mean square error and anomaly correlation for a variable and level for a particular region with respect to a climatology.', plotCurves)), ('SCORECARD', pn.Column('Scorecard of bias, root mean square error and anomaly correlation for all variables and levels for a time range over a particular region with respect to a climatology.', plotScorecard)), ('SPATIAL DISTRIBUTION', pn.Column('Spatial distribution of bias and root square error for a variable and level for a particular region with respect to a climatology.', plotFields)), dynamic=True) # Função para abrir o card correspondente e fechar os outros def on_tab_change(event): index = event.new # Obtém o índice da aba selecionada for i, card in enumerate(cards_objeval): card.collapsed = i != index # Abre o card correspondente e fecha os outros # Monitorando mudanças na aba selecionada tabs_objeval.param.watch(on_tab_change, "active") return pn.Column(main_text, tabs_objeval, monitor_warning_bottom_main, sizing_mode="stretch_both") else: def LayoutSidebarObjEval(): main_text = pn.Column(""" # Objective Evaluation Set the parameters on the left to update the map below and explore our analysis features. Click on the `TABLE` tab to get an overview of the calculated statistics. """) return pn.Column(main_text, sizing_mode="stretch_both") def LayoutMainObjEval(): main_text = pn.Column(""" # Objective Evaluation Set the parameters on the left to update the map below and explore our analysis features. Click on the `TABLE` tab to get an overview of the calculated statistics. """) return pn.Column(main_text, sizing_mode="stretch_both")