import csv
import os
import json
import functools
import sqlite3
import tempfile

# add local modules folder
# file_path = os.path.join('..', 'Python_Modules')
# sys.path.append(file_path)

import click
import pandas as pd
import xarray as xr
import ogr2ogr
from DateTime import DateTime


# asking for parameters in command line
@click.command()
@click.option('--path', prompt="File path", help='Path to file')
@click.option('--name', prompt="File name", help="File name with extension")
@click.option('--clear', '-c', default=False, is_flag=True, help='Clear screen before showing results')
def main(path, name, clear):
    res = getTimeExtent(name, path)
    if clear:
        click.clear()
    if res[0] is not None:
        click.echo(res[0])
    else:
        click.echo(res[1])


def getTimeExtent(name, path):
    """
    returns the bounding Box of supported Datatypes and standards in WGS84.

    supported data: Shapefile (.shp), GeoJson (.json/.geojson), netCDF (.nc),
                    all ISO19xxx standardisiete Formate, CSV on the web

    @param path Path to the file
    @param name name of the file with extension
    @returns a boundingbox as an array in WGS84, formated like [minLong, minLat, maxLong, maxLat]
    """
    # connect name and path to file
    filepath = os.path.join(path, name)
    # get file extension
    filename, file_extension = os.path.splitext(filepath)

    #################################################################

    def netCDFCase(filepath):
        try:
            # https://gis.stackexchange.com/questions/270165/gdal-to-acquire-netcdf-like-metadata-structure-in-python
            ds = xr.open_dataset(filepath)
            # transform coordinates section in a dictionary
            time = ds.to_dict()['coords']['time']['data']
            isoTimeSeq = list(map(DateTime, (list(map(str, time)))))
            isoTimeSeq.sort()
            avgInt = 0
            if len(isoTimeSeq) > 1:
                interval = []

                for i in range(len(isoTimeSeq)-1):
                    interval.append(isoTimeSeq[i+1] - isoTimeSeq[i])

                avgInt = functools.reduce(
                    lambda x, y: x + y, interval) / float(len(interval))
                # avgInt = math.floor(avgInt*1000)/1000
                # print(avgInt)

            return ([isoTimeSeq[0].Date(), isoTimeSeq[-1].Date(), avgInt], None)

        # errors
        except KeyError:
            return (None, "'time' may be spelled wrong: should be 'time")
        except:
            return (None, "File Error!")

    def CSVCase(filepath):
        # column name should be either date, time or timestamp
        # @see https://stackoverflow.com/questions/16503560/read-specific-columns-from-a-csv-file-with-csv-module
        try:  # finding the correct columns for latitude and longitude
            csvfile = open(filepath)
            head = csv.reader(csvfile, delimiter=' ', quotechar='|')
            # get the headline and convert, if possible, ';' to ','
            # and seperate each word devided by a ',' into an array
            header = next(head)[0].replace(";", ",").split(",")
            time = None
            # searching for valid names for latitude and longitude
            for t in header:
                if t.lower() in ("date", "time", "timestamp"):
                    time = t
            csvfile.close()

            csvfile = open(filepath)
            correctCSV = csvfile.read()
            correctCSV = correctCSV.replace(";", ",")
            csvfile.close()

            # if there is no valid name or coordinates, an exception is thrown an cought with an errormassage
            if(time is None):
                raise ValueError(
                    "pleas rename timestamp to: date/time/timestamp")

        # errors
        except ValueError as e:
            return (None, e)
        except:
            return (None, "File Error!")

        # if no error accured
        else:
            try:
                with tempfile.TemporaryDirectory() as tmpdirname:
                    curDir = os.getcwd()
                    os.chdir(tmpdirname)
                    newFile = open("newCSV.csv", "w+")
                    newFile.write(correctCSV)
                    newFile.close()
                    df = pd.read_csv(os.path.join(
                        tmpdirname, "newCSV.csv"), header=0)
                    os.chdir(curDir)

                # get time from found columns
                timestamp = df[time].tolist()

                isoTimeSeq = list(map(DateTime, (list(map(str, timestamp)))))
                isoTimeSeq.sort()
                avgInt = 0
                if len(isoTimeSeq) > 1:
                    interval = []

                for i in range(len(isoTimeSeq)-1):
                    interval.append(isoTimeSeq[i+1] - isoTimeSeq[i])

                avgInt = functools.reduce(
                    lambda x, y: x + y, interval) / float(len(interval))

                return ([isoTimeSeq[0].Date(), isoTimeSeq[-1].Date(), avgInt], None)

            # errors
            except:
                return (None, "File Error: File not found or check if your csv file is valid to 'csv on the web'")

    def geojsonCase(filepath):
        try:
            ds = open(filepath)
            jsonDict = json.load(ds)
            isoTimeSeq = []
            if jsonDict["type"] == "FeatureCollection":
                prop = ""
                                    
                if "time" in jsonDict["features"][0]["properties"]:
                    prop = "time"
                elif "date" in jsonDict["features"][0]["properties"]:
                    prop = "date"
                elif "dateTime" in jsonDict["features"][0]["properties"]:
                    prop = "dateTime"
                else:
                    return (None, "no time data available")

                timeext = []
                for feature in jsonDict["features"]:
                    timeext.append(feature["properties"][prop])
                isoTimeSeq = list(map(DateTime, timeext))

                isoTimeSeq.sort()
                avgInt = 0
                if len(isoTimeSeq) > 1:
                    interval = []

                    for i in range(len(isoTimeSeq)-1):
                        interval.append(isoTimeSeq[i+1] - isoTimeSeq[i])

                    avgInt = functools.reduce(
                        lambda x, y: x + y, interval) / float(len(interval))

                return ([isoTimeSeq[0].Date(), isoTimeSeq[-1].Date(), avgInt], None)
            else:
                prop = ""
                if "time" in jsonDict:
                    prop = "time"
                elif "date" in jsonDict:
                    prop = "date"
                elif "dateTime" in jsonDict:
                    prop = "dateTime"
                else:
                    return (None, "no time data available")

                timeext = jsonDict["properties"][prop]
                timeext = DateTime(timeext)
                return ([timeext.Date(), timeext.Date(), 0], None)
        except:
            return (None, "File Error!")
        finally:
            ds.close()

    # def geoPackageCase(filepath):
    #     try:
    #         conn = sqlite3.connect(filepath)
    #         c = conn.cursor()
    #         c.execute("""   SELECT last_change 
    #                         FROM gpkg_contents 
    #                 """)
    #         row = c.fetchone()
    #         row = list(map(DateTime, row))
    #         # print(row)
    #         return ([str(row[0]), str(row[0]), 0], None)
    #     except:
    #         return (None, "File Error!")
    #     finally:
    #         try:
    #             conn.close()
    #         except:
    #             pass

    def ISOCase(filepath):
        try:
            # @see https://gis.stackexchange.com/questions/39080/using-ogr2ogr-to-convert-gml-to-shapefile-in-python
            # convert the gml file to a GeoJSON file
            with tempfile.TemporaryDirectory() as tmpdirname:
                curDir = os.getcwd()
                os.chdir(tmpdirname)
                ogr2ogr.main(["", "-f", "GeoJSON", "output.json", filepath])
                # get boundingbox from generated GeoJSON file
                result = getTimeExtent("output.json", "")
                os.chdir(curDir)
                # delete generated GeoJSON file
            return result
        # errors
        except:
            return (None, "file not found or your gml/xml/kml data is not valid")


#################################################################

    # netCDF handeling
    if file_extension == ".nc":
        return netCDFCase(filepath)

    # csv handeling
    elif file_extension in (".csv", ".txt"):
        return CSVCase(filepath)

    # json handeling
    elif file_extension in (".json", ".geojson"):
        return geojsonCase(filepath)

    # geopackage handeling
    # elif file_extension == ".gpkg":
    #     return geoPackageCase(filepath)

    elif file_extension in (".gml", ".xml", ".kml"):
        return ISOCase(filepath)

    # unsupported files handeling
    else:
        return (None, "Filetype %s not yet supported" % file_extension)


# Main method
if __name__ == '__main__':
    main()

import csv
import os
import json
import functools
import sqlite3
import tempfile

# add local modules folder
# file_path = os.path.join('..', 'Python_Modules')
# sys.path.append(file_path)

import click
import pandas as pd
import xarray as xr
import ogr2ogr
from DateTime import DateTime


# asking for parameters in command line
@click.command()
@click.option('--path', prompt="File path", help='Path to file')
@click.option('--name', prompt="File name", help="File name with extension")
@click.option('--clear', '-c', default=False, is_flag=True, help='Clear screen before showing results')
def main(path, name, clear):
    res = getTimeExtent(name, path)
    if clear:
        click.clear()
    if res[0] is not None:
        click.echo(res[0])
    else:
        click.echo(res[1])


def getTimeExtent(name, path):
    """
    returns the bounding Box of supported Datatypes and standards in WGS84.

    supported data: Shapefile (.shp), GeoJson (.json/.geojson), netCDF (.nc),
                    all ISO19xxx standardisiete Formate, CSV on the web

    @param path Path to the file
    @param name name of the file with extension
    @returns a boundingbox as an array in WGS84, formated like [minLong, minLat, maxLong, maxLat]
    """
    # connect name and path to file
    filepath = os.path.join(path, name)
    # get file extension
    filename, file_extension = os.path.splitext(filepath)

    #################################################################

    def netCDFCase(filepath):
        try:
            # https://gis.stackexchange.com/questions/270165/gdal-to-acquire-netcdf-like-metadata-structure-in-python
            ds = xr.open_dataset(filepath)
            # transform coordinates section in a dictionary
            time = ds.to_dict()['coords']['time']['data']
            isoTimeSeq = list(map(DateTime, (list(map(str, time)))))
            isoTimeSeq.sort()
            avgInt = 0
            if len(isoTimeSeq) > 1:
                interval = []

                for i in range(len(isoTimeSeq)-1):
                    interval.append(isoTimeSeq[i+1] - isoTimeSeq[i])

                avgInt = functools.reduce(
                    lambda x, y: x + y, interval) / float(len(interval))
                # avgInt = math.floor(avgInt*1000)/1000
                # print(avgInt)

            return ([isoTimeSeq[0].Date(), isoTimeSeq[-1].Date(), avgInt], None)

        # errors
        except KeyError:
            return (None, "'time' may be spelled wrong: should be 'time")
        except:
            return (None, "File Error!")

    def CSVCase(filepath):
        # column name should be either date, time or timestamp
        # @see https://stackoverflow.com/questions/16503560/read-specific-columns-from-a-csv-file-with-csv-module
        try:  # finding the correct columns for latitude and longitude
            csvfile = open(filepath)
            head = csv.reader(csvfile, delimiter=' ', quotechar='|')
            # get the headline and convert, if possible, ';' to ','
            # and seperate each word devided by a ',' into an array
            header = next(head)[0].replace(";", ",").split(",")
            time = None
            # searching for valid names for latitude and longitude
            for t in header:
                if t.lower() in ("date", "time", "timestamp"):
                    time = t
            csvfile.close()

            csvfile = open(filepath)
            correctCSV = csvfile.read()
            correctCSV = correctCSV.replace(";", ",")
            csvfile.close()

            # if there is no valid name or coordinates, an exception is thrown an cought with an errormassage
            if(time is None):
                raise ValueError(
                    "pleas rename timestamp to: date/time/timestamp")

        # errors
        except ValueError as e:
            return (None, e)
        except:
            return (None, "File Error!")

        # if no error accured
        else:
            try:
                with tempfile.TemporaryDirectory() as tmpdirname:
                    curDir = os.getcwd()
                    os.chdir(tmpdirname)
                    newFile = open("newCSV.csv", "w+")
                    newFile.write(correctCSV)
                    newFile.close()
                    df = pd.read_csv(os.path.join(
                        tmpdirname, "newCSV.csv"), header=0)
                    os.chdir(curDir)

                # get time from found columns
                timestamp = df[time].tolist()

                isoTimeSeq = list(map(DateTime, (list(map(str, timestamp)))))
                isoTimeSeq.sort()
                avgInt = 0
                if len(isoTimeSeq) > 1:
                    interval = []

                for i in range(len(isoTimeSeq)-1):
                    interval.append(isoTimeSeq[i+1] - isoTimeSeq[i])

                avgInt = functools.reduce(
                    lambda x, y: x + y, interval) / float(len(interval))

                return ([isoTimeSeq[0].Date(), isoTimeSeq[-1].Date(), avgInt], None)

            # errors
            except:
                return (None, "File Error: File not found or check if your csv file is valid to 'csv on the web'")

    def geojsonCase(filepath):
        try:
            ds = open(filepath)
            jsonDict = json.load(ds)
            isoTimeSeq = []
            if jsonDict["type"] == "FeatureCollection":
                prop = ""
                                    
                if "time" in jsonDict["features"][0]["properties"]:
                    prop = "time"
                elif "date" in jsonDict["features"][0]["properties"]:
                    prop = "date"
                elif "dateTime" in jsonDict["features"][0]["properties"]:
                    prop = "dateTime"
                else:
                    return (None, "no time data available")

                timeext = []
                for feature in jsonDict["features"]:
                    timeext.append(feature["properties"][prop])
                isoTimeSeq = list(map(DateTime, timeext))

                isoTimeSeq.sort()
                avgInt = 0
                if len(isoTimeSeq) > 1:
                    interval = []

                    for i in range(len(isoTimeSeq)-1):
                        interval.append(isoTimeSeq[i+1] - isoTimeSeq[i])

                    avgInt = functools.reduce(
                        lambda x, y: x + y, interval) / float(len(interval))

                return ([isoTimeSeq[0].Date(), isoTimeSeq[-1].Date(), avgInt], None)
            else:
                prop = ""
                if "time" in jsonDict:
                    prop = "time"
                elif "date" in jsonDict:
                    prop = "date"
                elif "dateTime" in jsonDict:
                    prop = "dateTime"
                else:
                    return (None, "no time data available")

                timeext = jsonDict["properties"][prop]
                timeext = DateTime(timeext)
                return ([timeext.Date(), timeext.Date(), 0], None)
        except:
            return (None, "File Error!")
        finally:
            ds.close()

    # def geoPackageCase(filepath):
    #     try:
    #         conn = sqlite3.connect(filepath)
    #         c = conn.cursor()
    #         c.execute("""   SELECT last_change 
    #                         FROM gpkg_contents 
    #                 """)
    #         row = c.fetchone()
    #         row = list(map(DateTime, row))
    #         # print(row)
    #         return ([str(row[0]), str(row[0]), 0], None)
    #     except:
    #         return (None, "File Error!")
    #     finally:
    #         try:
    #             conn.close()
    #         except:
    #             pass

    def ISOCase(filepath):
        try:
            # @see https://gis.stackexchange.com/questions/39080/using-ogr2ogr-to-convert-gml-to-shapefile-in-python
            # convert the gml file to a GeoJSON file
            with tempfile.TemporaryDirectory() as tmpdirname:
                curDir = os.getcwd()
                os.chdir(tmpdirname)
                ogr2ogr.main(["", "-f", "GeoJSON", "output.json", filepath])
                # get boundingbox from generated GeoJSON file
                result = getTimeExtent("output.json", "")
                os.chdir(curDir)
                # delete generated GeoJSON file
            return result
        # errors
        except:
            return (None, "file not found or your gml/xml/kml data is not valid")


#################################################################

    # netCDF handeling
    if file_extension == ".nc":
        return netCDFCase(filepath)

    # csv handeling
    elif file_extension in (".csv", ".txt"):
        return CSVCase(filepath)

    # json handeling
    elif file_extension in (".json", ".geojson"):
        return geojsonCase(filepath)

    # geopackage handeling
    # elif file_extension == ".gpkg":
    #     return geoPackageCase(filepath)

    elif file_extension in (".gml", ".xml", ".kml"):
        return ISOCase(filepath)

    # unsupported files handeling
    else:
        return (None, "Filetype %s not yet supported" % file_extension)


# Main method
if __name__ == '__main__':
    main()

# add local modules folder # file_path = os.path.join('..', 'Python_Modules') # sys.path.append(file_path) # asking for parameters in command line returns the bounding Box of supported Datatypes and standards in WGS84.      supported data: Shapefile (.shp), GeoJson (.json/.geojson), netCDF (.nc),                     all ISO19xxx standardisiete Formate, CSV on the web      @param path Path to the file     @param name name of the file with extension     @returns a boundingbox as an array in WGS84, formated like [minLong, minLat, maxLong, maxLat] # connect name and path to file # get file extension ################################################################# # https://gis.stackexchange.com/questions/270165/gdal-to-acquire-netcdf-like-metadata-structure-in-python # transform coordinates section in a dictionary # avgInt = math.floor(avgInt*1000)/1000 # print(avgInt) # errors # column name should be either date, time or timestamp # @see https://stackoverflow.com/questions/16503560/read-specific-columns-from-a-csv-file-with-csv-module # finding the correct columns for latitude and longitude # get the headline and convert, if possible, ';' to ',' # and seperate each word devided by a ',' into an array # searching for valid names for latitude and longitude # if there is no valid name or coordinates, an exception is thrown an cought with an errormassage # errors # if no error accured # get time from found columns # errors # def geoPackageCase(filepath): #     try: #         conn = sqlite3.connect(filepath) #         c = conn.cursor() #         c.execute("""   SELECT last_change #                         FROM gpkg_contents #                 """) #         row = c.fetchone() #         row = list(map(DateTime, row)) #         # print(row) #         return ([str(row[0]), str(row[0]), 0], None) #     except: #         return (None, "File Error!") #     finally: #         try: #             conn.close() #         except: #             pass # @see https://gis.stackexchange.com/questions/39080/using-ogr2ogr-to-convert-gml-to-shapefile-in-python # convert the gml file to a GeoJSON file # get boundingbox from generated GeoJSON file # delete generated GeoJSON file # errors ################################################################# # netCDF handeling # csv handeling # json handeling # geopackage handeling # elif file_extension == ".gpkg": #     return geoPackageCase(filepath) # unsupported files handeling # Main method