Replace 'CHANGE' database with 'STATE'
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6ef88e9b23
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@ -56,7 +56,9 @@ CLOSED: [2020-01-03 Fri 00:44]
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- [X] Convert PU to Country (ISO 3166)
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- [ ] Arithmetic operations for yearly changes
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- [ ] Insert into database
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**** NEXT Flask framework
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-[[https://flask.palletsprojects.com/en/1.1.x/patterns/#patterns][ Patterns]]
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**** NEXT Flask framework [0/2] [0%]
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- [ ] Plots with pandas
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- [ ] Login for admin
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*** NEXT Documentation
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*** INACTIVE Frontend [0/1] [0%]
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- [ ] [[https://adminlte.io/][Adminlte]]
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@ -45,14 +45,14 @@ class Annual_Data(db.Model):
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db.Integer, db.ForeignKey("glacier.uid"), nullable=False, primary_key=True
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)
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surface = db.Column(db.Integer)
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volume = db.Column(db.Integer)
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thickness = db.Column(db.Integer)
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length = db.Column(db.Integer)
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elevation = db.Column(db.Integer)
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year = db.Column(db.Integer, primary_key=True)
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def __init__(self, surface, volume, thickness, year):
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def __init__(self, surface, length, elevation, year):
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self.surface = surface
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self.volume = volume
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self.thickness = thickness
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self.length = length
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self.elevation = elevation
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self.year = year
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@ -62,14 +62,14 @@ class Annual_Change(db.Model):
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db.Integer, db.ForeignKey("glacier.uid"), nullable=False, primary_key=True
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)
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surface_change = db.Column(db.Integer)
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volume_change = db.Column(db.Integer)
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thickness_change = db.Column(db.Integer)
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length_change = db.Column(db.Integer)
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elevation_change = db.Column(db.Integer)
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year = db.Column(db.Integer, primary_key=True)
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def __init__(self, surface_change, volume_change, thickness_change, year):
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def __init__(self, surface_change, length_change, elevation_change, year):
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self.surface_change = surface_change
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self.volume_change = volume_change
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self.thickness_change = thickness_change
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self.length_change = length_change
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self.elevation_change = elevation_change
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self.year = year
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@ -5,6 +5,8 @@ from constants import ADMIN_PW
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def country_conversion(political_unit) -> str:
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if political_unit == "99":
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return "99"
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codes = co.get(political_unit)
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return codes.name
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@ -16,13 +18,12 @@ def select_columns() -> DataFrame:
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"NAME",
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"WGMS_ID",
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"YEAR",
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"AREA_SURVEY_YEAR",
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"AREA_CHANGE",
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"THICKNESS_CHG",
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"VOLUME_CHANGE",
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"MEDIAN_ELEVATION",
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"AREA",
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"LENGTH",
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]
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iter_csv = read_csv(
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"../data/WGMS-FoG-2019-12-D-CHANGE.csv",
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"../data/WGMS-FoG-2019-12-B-STATE.csv",
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skipinitialspace=True,
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usecols=fields,
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iterator=True,
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@ -37,16 +38,14 @@ def create_databases(df):
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users = {"UID": [7843], "USERNAME": ["admin"], "PASSWORD": [ADMIN_PW]}
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files = {
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"glacier": "../data/glacier.csv",
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"annual_data": "../data/annual_data.csv",
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"annual_change": "../data/annual_change.csv",
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"annual__data": "../data/annual_data.csv",
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"annual__change": "../data/annual_change.csv",
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"users": "../data/users.csv",
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}
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dataframes = {
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"glacier": df[["POLITICAL_UNIT", "NAME", "WGMS_ID"]].drop_duplicates(),
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"annual_data": df[["WGMS_ID", "YEAR", "AREA_SURVEY_YEAR"]],
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"annual_change": df[
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["WGMS_ID", "YEAR", "AREA_CHANGE", "THICKNESS_CHG", "VOLUME_CHANGE"]
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],
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"annual__data": df[["WGMS_ID", "YEAR", "AREA", "MEDIAN_ELEVATION", "LENGTH"]],
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"annual__change": df[["WGMS_ID", "YEAR"]],
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"users": DataFrame(users),
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}
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for key, val in dataframes.items():
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@ -2,20 +2,28 @@ from db_setup import create_connection
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from pandas import DataFrame, read_csv, to_sql
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def create_dataframe() -> DataFrame:
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csv_file = "../data/igdb.csv"
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df = read_csv(csv_file)
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return df
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def create_dataframes() -> DataFrame:
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files = {
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"glacier": "../data/glacier.csv",
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"annual__data": "../data/annual_data.csv",
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"annual__change": "../data/annual_change.csv",
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"users": "../data/users.csv",
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}
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df_list = {}
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for csv in files.keys():
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df_list[csv] = read_csv(files[csv], index=False)
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return df_list
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def insert_data(df, conn):
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df.to_sql("glacier", con=conn, if_exists="replace", index=False)
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def insert_data(df_list, conn):
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for key, value in df_list.items():
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value.to_sql(key, con=conn, if_exists="replace", index=False)
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def main():
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conn = create_connection()
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df = create_dataframe()
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insert_data(df, conn)
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df_list = create_dataframes()
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insert_data(df_list, conn)
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if __name__ == "__main__":
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data/annual_change.csv
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data/annual_change.csv
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89904
data/annual_data.csv
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data/annual_data.csv
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data/glacier.csv
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data/glacier.csv
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@ -26,14 +26,14 @@ datos relevantes para estudios acerca del cambio climático, y acotando éstos a
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- ID del glaciar (Compatible con la WGMS) - /Entero de 5 dígitos/
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- Área - /Entero de 10 dígitos/
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- Volumen - /Entero de 10 dígitos/
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- Grosor - /Entero de 10 dígitos/
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- Altura - /Entero de 10 dígitos/
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- Año - /Entero de 10 dígitos/
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3. *RD3*: Datos de cambio de un glaciar
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- ID del glaciar (Compatible con la WGMS) - /Entero de 5 dígitos/
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- Variación de área - /Entero de 10 dígitos/
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- Variación de volumen - /Entero de 10 dígitos/
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- Variación de grosor - /Entero de 10 dígitos/
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- Variación de altura - /Entero de 10 dígitos/
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- Año - /Entero de 10 dígitos/
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4. *RD4*: Datos del administrador
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@ -56,7 +56,7 @@ datos relevantes para estudios acerca del cambio climático, y acotando éstos a
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3. *RF3*: Cálculo de las variaciones anuales
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Calcula las variaciones anuales de grosor, área y volumen para un glaciar
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Calcula las variaciones anuales de altura, área y longitud para un glaciar
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- Entrada: *RD2*
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- Manejo: *RD3*
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