<jupyter_start><jupyter_text>MANUAL SKETCHBOOK PREPARACIÓN  Python 3.Xhttps://www.python.org/downloads/ Librerias necesarias- selenium- utm- requests- pandas- numpy Chrome-driverhttps://chromedriver.storage.googleapis.com/index.html?path=89.0.4389.23/ ORGANIZACIÓN DE LA BASE DE DATOS La realización de esto se hará en base al siguiente documento https://docs.gbif-uat.org/georeferencing-quick-reference-guide/1.0/en/georeferencing-quick-reference-guide-community-review-draft.en.pdf (punto 1.5)En resumen, tendremos dos posibles casos de georreferenciación.El primero corresponde a la existencia de un punto con coordenadas geograficas definidas, por lo que es necesario rellenar los siguientes campos- verbatimLatitude- verbatimLongitude- verbatimSRS PROCESAMIENTO DE LA BASE DE DATOS AUTOMATIZACIÓN DE "GEOREFERENCING CALCULATOR" POST PROCESAMIENTO DE LA BASE DE DATOS EXPORTAR BASE DE DATOS<jupyter_code>from GeoParser import *
import pandas as pd
pd.set_option("display.max_rows", None, "display.max_columns", None)
data=pd.read_csv("GeoRefTest.csv")
data
%%time
#Testing...
DBPrep(data).verbatim_coordinates_autocomplete()
DBPrep(data).verbatim_coordinate_system_autocomplete()
DBPrep(data).verbatim_latitude_longitude_autocomplete()
#DBPrep(data).coordinate_precision_autocomplete() Deprecated function implemented on GeorefCalculator
data_pep=data.query("decimalLatitude.notna() and decimalLongitude.notna() ")
for index in data_pep.index:
    verbatimLatitude=data_pep.loc[index,"decimalLatitude"]
    verbatimLongitude=data_pep.loc[index,"decimalLongitude"]
    dynamicProperties=data_pep.loc[index,"dynamicProperties"]
    verbatimCoordinteSystem="decimal degrees"
    verbatimSRS=data_pep.loc[index,"geodeticDatum"]
    SeleniumGeoref(verbatimCoordinteSystem,verbatimLatitude,verbatimLongitude,dynamicProperties,verbatimSRS).georeferencer()
    result_dict=SeleniumGeoref(verbatimCoordinteSystem,verbatimLatitude,verbatimLongitude,dynamicProperties,verbatimSRS).get_results()
    for keys in result_dict.keys():
        data.loc[index,keys]=result_dict[keys]
    
data_pep=data.query("verbatimLatitude.notna() and verbatimLongitude.notna() ")
for index in data_pep.index:
    verbatimLatitude=data_pep.loc[index,"verbatimLatitude"]
    verbatimLongitude=data_pep.loc[index,"verbatimLongitude"]
    dynamicProperties=data_pep.loc[index,"dynamicProperties"]
    verbatimCoordinteSystem=data_pep.loc[index,"verbatimCoordinateSystem"]
    verbatimSRS=data_pep.loc[index,"verbatimSRS"]
    SeleniumGeoref(verbatimCoordinteSystem,verbatimLatitude,verbatimLongitude,dynamicProperties,verbatimSRS).georeferencer()
    result_dict=SeleniumGeoref(verbatimCoordinteSystem,verbatimLatitude,verbatimLongitude,dynamicProperties,verbatimSRS).get_results()
    for keys in result_dict.keys():
        data.loc[index,keys]=result_dict[keys]
driver.quit()
%%time
# Testing ...
#coord_transform(data).utm_to_dg() Deprecated function implemented on GeorefCalculator
#CoordTransform(data).dms_to_dg() Deprecated function implemented on GeorefCalculator
#CoordTransform(data).geodetic_datum_autocomplete()  Deprecated function implemented on GeorefCalculator
CoordTransform(data).location_autocomplete() 
CoordTransform(data).continent_autocomplete() 
CoordTransform(data).higher_geography_autocomplete() 
list1=data.query('minimumElevationInMeters.isnull() and maximumElevationInMeters.isnull() and decimalLatitude.notnull() and decimalLongitude.notnull()').index.tolist()
CoordTransform(data).elevation_autocomplete() 
list2=data.query('minimumElevationInMeters.notnull() and maximumElevationInMeters.notnull() and decimalLatitude.notnull() and decimalLongitude.notnull()').index.tolist()
list(set(list1) - set(list2))
data<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09879839786381843, 'fraction_numerical': 0.004806408544726302, 'mean_word_length': 8.388471177944862, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 3, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '47659', 'license': 'MIT', 'n_tokens_mistral': 1297, 'n_tokens_neox': 1206, 'n_words': 189, 'path': 'GeoParser/.ipynb_checkpoints/GeoParser-checkpoint.ipynb', 'repo_name': 'marcelooyaneder/Date_GeoParser', 'size': '3745'}