<jupyter_start><jupyter_text>read Naip rasters<jupyter_code>with rasterio.open(address_NAIP_2010,'r') as rst:
    albedo2010_ar=rst.read(1).astype('float64')
    kwds = rst.meta.copy()
    cellSize= kwds['transform'][0]
    albedo2010_nodata= rst.nodata
    albedo2010_ar[albedo2010_ar==albedo2010_nodata]= -100

with rasterio.open(address_NAIP_2013,'r') as rst:
    albedo2013_ar=rst.read(1).astype('float64')
    albedo2013_nodata= rst.nodata
    albedo2013_ar[albedo2013_ar==albedo2013_nodata]= -100

    
with rasterio.open(address_NAIP_2018,'r') as rst:
    albedo2018_ar=rst.read(1).astype('float64')
    albedo2018_nodata= rst.nodata
    albedo2018_ar[albedo2018_ar==albedo2018_nodata]= -100<jupyter_output><empty_output><jupyter_text>Read ortho rasters<jupyter_code>with rasterio.open(address_ortho80_2010,'r') as rst:
    albedo_ortho80_2010_ar=rst.read(1).astype('float64')
    kwds = rst.meta.copy()
    cellSize= kwds['transform'][0]
    nodata= rst.nodata
    albedo_ortho80_2010_ar[albedo_ortho80_2010_ar==nodata]= -100

with rasterio.open(address_ortho80_2013,'r') as rst:
    albedo2013_ortho_2013_ar=rst.read(1).astype('float64')
    nodata= rst.nodata
    albedo2013_ortho_2013_ar[albedo2013_ortho_2013_ar==nodata]= -100

    
with rasterio.open(address_ortho80_2018,'r') as rst:
    albedo2018_Orhto_2018_ar=rst.read(1).astype('float64')
    nodata= rst.nodata
    albedo2018_Orhto_2018_ar[albedo2018_Orhto_2018_ar==nodata]= -100

albedo2018_Orhto_2018_ar.max()
df = pd.DataFrame({'albedo_NAIP_10': albedo2010_ar.flatten(), 'albedo_NAIP_13': albedo2013_ar.flatten(),'albedo_NAIP_18': albedo2018_ar.flatten(),
                   'albedo_ortho_10': albedo_ortho80_2010_ar.flatten(),'albedo_ortho_13': albedo2013_ortho_2013_ar.flatten(),'albedo_ortho_18': albedo2018_Orhto_2018_ar.flatten()
                  })
df.sample(10)
dfFiltered_NAIP=df[(df['albedo_NAIP_10']>=0)&(df['albedo_NAIP_13']>=0)&(df['albedo_NAIP_18']>=0)]
dfFiltered_Ortho=df[(df['albedo_ortho_10']>=0)&(df['albedo_ortho_13']>=0)&(df['albedo_ortho_18']>=0)]
dfClean = df[(df['albedo_NAIP_10']>=0)&(df['albedo_NAIP_13']>=0)&(df['albedo_NAIP_18']>=0)&(df['albedo_ortho_10']>=0)&(df['albedo_ortho_13']>=0)&(df['albedo_ortho_18']>=0)]
dfFiltered_Ortho<jupyter_output><empty_output><jupyter_text>Albedo NAIP<jupyter_code>sns.set(rc={'figure.figsize':(12,8)})
sns.distplot( dfFiltered["albedo10"] , color="skyblue", label="Albedo 2010")
sns.distplot( dfFiltered["albedo13"] , color="green", label="Albedo 2013")
sns.distplot( dfFiltered["albedo18"] , color="red", label="Albedo 2018")
#sns.distplot( playDf["tc_predict2"] , color="navy", label="Corrected Tree Cover2")

plt.tick_params(axis='x',labelsize='large' )
plt.tick_params(axis='y',labelsize='large' )
plt.xlabel ('Albedo',fontsize='x-large')
plt.legend(loc='upper right',prop={'size': 18})
plt.show()
dfFiltered_Ortho.sample(2)
sns.set(rc={'figure.figsize':(12,8)})
sns.distplot( dfFiltered_Ortho["albedo_ortho_10"] , color="skyblue", label="Albedo 2010")
sns.distplot( dfFiltered_Ortho["albedo_ortho_13"] , color="green", label="Albedo 2013")
sns.distplot( dfFiltered_Ortho["albedo_ortho_18"] , color="red", label="Albedo 2018")
#sns.distplot( playDf["tc_predict2"] , color="navy", label="Corrected Tree Cover2")

plt.tick_params(axis='x',labelsize='large' )
plt.tick_params(axis='y',labelsize='large' )
plt.xlabel ('Albedo',fontsize='x-large')
plt.legend(loc='upper right',prop={'size': 18})
plt.show()
sns.set(rc={'figure.figsize':(12,8)})
# sns.distplot( dfClean["albedo_ortho_10"] , color="skyblue", label="Ortho 2010")
# sns.distplot( dfClean["albedo_ortho_13"] , color="green", label="Ortho 2013")
# sns.distplot( dfClean["albedo_ortho_18"] , color="red", label="Ortho 2018")

# sns.distplot( dfClean["albedo_NAIP_10"] , color="yellow", label="NAIP 2010")
sns.distplot( dfClean["albedo_NAIP_13"] , color="blue", label="NAIP 2013")
sns.distplot( dfClean["albedo_NAIP_18"] , color="gold", label="NAIP 2018")
#sns.distplot( playDf["tc_predict2"] , color="navy", label="Corrected Tree Cover2")

plt.tick_params(axis='x',labelsize='large' )
plt.tick_params(axis='y',labelsize='large' )
plt.xlabel ('Albedo',fontsize='x-large')
plt.legend(loc='upper right',prop={'size': 18})
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18171133051245886, 'fraction_numerical': 0.08932769158439116, 'mean_word_length': 8.561797752808989, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 25, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '120150', 'license': 'MIT', 'n_tokens_mistral': 2108, 'n_tokens_neox': 1829, 'n_words': 188, 'path': 'Compare.ipynb', 'repo_name': 'mehdiheris/UrbanAlbedoMeasurment', 'size': '4254'}