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| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Fri Mar 10 15:10:36 2023 | |
| @author: BorowyP | |
| """ | |
| import os | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import pandas as pd | |
| import hvplot.pandas | |
| import holoviews as hv | |
| from holoviews.operation.timeseries import rolling, rolling_outlier_std | |
| import xarray as xr | |
| import panel as pn | |
| import panel.widgets as pnw | |
| import hvplot.xarray | |
| cwd = os.getcwd() | |
| print(cwd) | |
| print(os.listdir(cwd)) | |
| os.chdir('hemi_fotos') | |
| pic_cwd = os.getcwd() | |
| print(pic_cwd) | |
| os.listdir(pic_cwd) | |
| os.chdir(pic_cwd) | |
| #os.listdir() | |
| list_pictures_files = os.listdir() | |
| rgb_list = [] | |
| for i in range(len(list_pictures_files)): | |
| im = plt.imread(list_pictures_files[i]) | |
| x = np.arange(len(im[0])) | |
| y = np.arange(len(im)) | |
| channel = np.arange(3) | |
| im_xr = xr.DataArray(im,coords={'y': y,'x': x,'channel': channel},dims=["y", "x", "channel"]) | |
| rgb_list.append(im_xr) | |
| #print(str(list_pictures_files[i]) + ' ' + | |
| # 'y-Achse: ' + ' ' + str(len(im)) + ', ' + | |
| # 'x-Achse: ' + ' ' + str(len(im[0])) + ', ' + 'channel: ' + ' ' + str(len(im[0][0]))) | |
| #print(' ') | |
| x = np.arange(len(rgb_list[0][0])) | |
| y = np.arange(len(rgb_list[0])) | |
| channel = np.arange(3) | |
| Stndrt = np.array([ | |
| 'Krausnick K', | |
| 'Glienig K', | |
| 'Glienig nK', | |
| 'Halbe K', | |
| 'Halbe nK', | |
| 'Hangelsberg K', | |
| 'Hangelsberg nK', | |
| 'Spreeau K', | |
| 'Spreeau nK', | |
| 'Groß Liebitz K', | |
| 'Groß Liebitz nK' | |
| ]) | |
| xar = xr.DataArray(rgb_list, | |
| dims=( | |
| 'Standort', | |
| "y", | |
| "x", | |
| "channel") | |
| , | |
| coords={ | |
| "x": x, | |
| "y": y, | |
| "channel": channel, | |
| 'Standort' : Stndrt, | |
| }, | |
| name="rgb") | |
| fisheye = xar.hvplot.rgb(x='x', y='y', bands='channel', data_aspect=1, flip_yaxis=True, xaxis=False, yaxis=None, width=500) | |
| os.chdir(cwd) | |
| df = pd.read_csv( | |
| 'Hemisphere_totals_holzdeko.csv' , | |
| encoding_errors='ignore', | |
| sep=';', | |
| #skiprows=[5,10], | |
| header=0, | |
| decimal=',' | |
| ) | |
| df['transmission'] = df['transmission'].str.strip('%') # entfernt Character '%' | |
| df['transm_gaps'] = df['transm_gaps'].str.strip('%') | |
| df['openness'] = df['openness'].str.strip('%') | |
| df['openn_gaps'] = df['openn_gaps'].str.strip('%') | |
| df['transmission'] = df['transmission'].replace(',', '.', regex=True) # wandelt , in . um | |
| df['transm_gaps'] = df['transm_gaps'].replace(',', '.', regex=True) | |
| df['openness'] = df['openness'].replace(',', '.', regex=True) | |
| df['openn_gaps'] = df['openn_gaps'].replace(',', '.', regex=True) | |
| df['transmission'] = df['transmission'].astype('float64', copy=False) | |
| df['transm_gaps'] = df['transm_gaps'].astype('float64', copy=False) | |
| df['openness'] = df['openness'].astype('float64', copy=False) | |
| df['openn_gaps'] = df['openn_gaps'].astype('float64', copy=False) | |
| params = pn.widgets.RadioButtonGroup( | |
| name='param', | |
| options=['white', | |
| 'black', | |
| 'transmission', | |
| 'transm_gaps', | |
| 'openness', | |
| 'openn_gaps' | |
| ], | |
| button_type='success') | |
| data_inter = df.interactive() | |
| mean_param = ( | |
| data_inter | |
| .groupby(['Standort', 'k_nk']).mean() | |
| ) | |
| mean_param_plot = mean_param[params].hvplot(x='Standort', y=params, by='k_nk', kind='bar', | |
| title=params, xlabel='Standort') | |
| param_discription = pd.DataFrame({'white': ['white pixels (sky)'], | |
| 'black' : ['black pixels (canopy)'], | |
| 'transmission' : ['light transmission in 2D (azimutal equidistant projection of the hemisphere)'], | |
| 'transm_gaps' : ['large gaps of transmission'], | |
| 'openness' : ['canopy openness in 3D, also called sky view factor (solid angles of the hemisphere)'], | |
| 'openn_gaps' : ['large gaps of openness']}) | |
| param_discription = param_discription.interactive() | |
| param_discription = param_discription[params].values[0] | |
| os.chdir(cwd) | |
| hd_logo = pn.pane.PNG('HD_Logo.png', width=100) | |
| lfe_logo = pn.pane.PNG('LFE_Logo.png', width=100) | |
| fnr_logo = pn.pane.PNG('fnr_logo.png', width=100) | |
| template = pn.template.FastListTemplate( | |
| title='Auswertung Hemisfere', | |
| sidebar=[hd_logo, | |
| pn.pane.Markdown("## Einstellungen"), | |
| #'Standort',Stndrt, | |
| #'gekalkt/ungekalkt', yaxis, | |
| lfe_logo, | |
| fnr_logo | |
| #'Boden', | |
| #'Gekalkt/ungekalkt', yaxis, | |
| ], | |
| main=[pn.pane.Markdown("## Hemisfere"), | |
| fisheye,#.panel(), | |
| #params, | |
| param_discription, | |
| mean_param_plot.panel(), | |
| #accent_base_color="#88d8b0", | |
| #header_background="#88d8b0", | |
| ]) | |
| template.servable(); | |
| print('fertig!') | |
| # To launch this dashboard as a web server, we can simply run | |
| # cd C:\Users\BorowyP\Desktop\Dashboard-Preasi\hemisfer | |
| # panel serve 2023_02_10_hemi_dashboard.ipynb --autoreload | |