I'm having trouble displaying some data from Globcolour (1), due to the projection used with the matplotlib and cartopy definition of the image.
I downloaded a Total Suspended Matter image in NetCDF format (here is the data enter link description here), and when I tried to display it, along with a coastline from the cartopy package, there is a notorious gap between the coastline and the data. As you can see below, the pixels should be next to the coastline (black line), and not surpassed into the land (yellow pixels in the flags image)
This shouldn't happen. I check using QGIS and loading directly the netcdf file the coastline is set correctly.
Initially I used a PlateeCarrer projection for the image, considering that if the image was in WGS84 they would match, but clearly they don't. I've tried using the transform option in the matplotlib function but I haven't made it work. Either the gap remains, or the coordinates of the figure change to projected ones and my data (which is in geographical coordinates) disappear.
The attributes of the NetCDF file are:
'grid_type': 'Equirectangular',
'spatial_resolution': 4.6383123,
'nb_equ_bins': 55,
'registration': 5,
'lat_step': 0.041666668,
'lon_step': 0.041666668,
'earth_radius': 6378.137,
'max_north_grid': 11.124998,
'max_south_grid': 9.27,
'max_west_grid': -86.25,
'max_east_grid': -83.97,
'northernmost_latitude': 11.124998,
'southernmost_latitude': 9.249998,
'westernmost_longitude': -86.25,
'easternmost_longitude': -84.0,
'nb_grid_bins': 2475,
'nb_bins': 2475,
'pct_bins': 100.0,
'nb_valid_bins': 1089,
'pct_valid_bins': 44.0,
'netcdf_version': '4.3.3.1 of Jul 8 2016 18:15:50 $',
'DPM_reference': 'GC-UD-ACRI-PUG',
'IODD_reference': 'GC-UD-ACRI-PUG'}
The code that I'm using to plot the image is:
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import cartopy.crs as ccrs
import dill as pickel
def paint_maps(df_std=None, fecha=1, attributes=None,
savefol='/media/felipe/TOSHIBA EXT/iMARES/Investigacion/2019_MariculturaPacifico/DB/figures/',
disp_fig=0):
"""Función para dibujar los datos contenidos en los archivos netCDF de SST, Salinidad y propiedad ópticas del agua.
Recibe el dataframe con la información en formato de Pandas Dataframe, y selecciona según una fecha establecida,
el conjunto de datos con coordenadas Lat-Lon que debe dibujar. Esos los dibuja y transforma a formato raster. Unido
se dibuja también la línea de costa proveniente de un archivo shapefile. La función dibuja toda la información
contenida en el dataframe aportado (datos, anomalías, flags, y cualquier otro dato que tenga.
Recibe:
df_std: dataframe con la información a dibujar. Debe venir indexado por fecha, lat y lon.
fecha: día que se elige dibujar. Formato string 'yyyymmdd'. Valor 1 significa que grafica el valor promedio de todas las fechas en cada
píxel. Promedio simple ignorando NaN's
attributes: diccionario con los atributos del netcdf de donde se obtiene nombre de variable y unidades. Creado
con open_netcdf.py
savefol: carpeta donde se guardan las imágenes dibujadas
disp_fig: booleano para imprimir figura en pantalla.
Devuelve:
Nada. Solo crea y guarda figuras"""
# Identifica la fecha solicitada (cuando se ha especificado) y confirma que sea parte del registro. Extrae la
# información del Dataframe en la fecha que se solicitó, o calcula el promedio de todas las fechas para graficar
# el valor promedio.
if fecha != 1:
if isinstance(fecha, str):
fecha = pd.to_datetime(fecha + '120000')
else:
print('La fecha indicada no está en formato String. Reinicie la ejecución.')
try:
idx = pd.IndexSlice
df_map = df_std.loc[idx[:, :, fecha], :]
except:
print('Se generó un error. Posiblemente fecha no está dentro del registro. La fecha debe estar entre el ' + df_std.index[0][-1].strftime('%d/%m/%Y') + ' y el ' + df_std.index[-1][-1].strftime('%d/%m/%Y'))
raise
else:
df_map = df_std.groupby(['lat', 'lon']).mean()
# Reestructura la información para tenerla en forma de matriz y dibujarla de forma más simple. Extrae los valores y
# las latitudes y longitudes correspondientes, así como los valores de la variable y sus flags.
df_map2 = df_map.unstack(level=0)
vari = df_map2['mean_val'].values
flags = df_map2['flag_val'].values
lat = df_map2['mean_val'].columns.get_level_values('lat')
lon = df_map2['mean_val'].index.get_level_values('lon')
# Extrae de los atributos del netcdf el nombre de la variable a graficar y las unidades
variable_str = attributes['variable']['long_name']
variable_units = attributes['variable']['units']
# Dibuja el mapa que se haya seleccionado según fecha (valor promedio del valor o fecha específica)
fig, ax = plt.subplots(1, 2, figsize=(10, 10), subplot_kw={'projection': ccrs.PlateCarree()})
extend = [lon[1], lon[-1], lat[1], lat[-1]]
# Primera figura. Variable a graficar. Usa línea de costa del cartopy y coloca una leyenda abajo
ax[0].set_extent(extend)
ax[0].coastlines(resolution='10m')
#cs = ax[0].pcolormesh(lon, lat, vari.T)
cs = ax[0].pcolormesh(lon, lat, vari.T, transform=ccrs.PlateCarree())
ax[0].set_title(variable_str)
cax, kw = matplotlib.colorbar.make_axes(ax[0], location='bottom', pad=0.05, shrink=0.7)
out = fig.colorbar(cs, cax=cax, extend='both', **kw)
out.set_label('Units: '+variable_units, size=10)
# Segunda figura. Flags de la figura. Usa la leyenda directamente de los datos usados.
ax[1].set_extent(extend)
ax[1].coastlines(resolution='10m')
cs2 = ax[1].pcolormesh(lon, lat, flags.T)
ax[1].set_title('Flags')
cax, kw = matplotlib.colorbar.make_axes(ax[1], location='bottom', pad=0.05, shrink=0.7)
out = fig.colorbar(cs2, cax=cax, extend='both', **kw)
out.set_label('Flags', size=10)
# Salva la figura
plt.savefig(savefol+variable_str+'.jpg', bbox_inches='tight')
with open(savefol+'fig_'+variable_str+'.pickel', 'wb') as f:
pickel.dump(fig, f)
# Imprime figura si se elige opción con disp_fig
if disp_fig == 1:
plt.show()
return
It receives the data as a Pandas dataframe. The NetCDF was opened using xarray.open_dataset and then transforming it to Pandas with to_dataframe()
I'm using Python 3.7 in Ubuntu.
Last thing. When loading the cartopy.crs package, this error occurs:
ERROR 1: PROJ: proj_create_from_database: Open of /home/felipe/anaconda3/envs/personal/share/proj failed
Could it be affecting?
we answered to Felipe by email, I copy/paste here:
A small Python script to create a map on your area from a TSM GlobColour Product (I used a monthly product to have a good coverage):
import netCDF4 as nc
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
fig, ax = plt.subplots(figsize=(5, 5), subplot_kw=dict(projection=ccrs.PlateCarree()))
# my region of interest
ax.set_extent([-86, -84, 9, 11])
ax.coastlines(resolution='10m', color='red')
nc_dst = nc.Dataset('L3m_20100101-20100131__GLOB_4_AV-MER_TSM_MO_00.nc')
# extent of the product
data_extent = [nc_dst.max_west_grid, nc_dst.max_east_grid,
nc_dst.max_south_grid, nc_dst.max_north_grid]
data = nc_dst.variables['TSM_mean'][:]
flags = nc_dst.variables['TSM_flags'][:]
land = flags & 8 # LAND == 3rd bit == 2^3 == 8
data_noland = np.ma.masked_where(land, data)
ax.imshow(data_noland, origin='upper', extent=data_extent)
plt.savefig('TSM_noland.png')
ax.imshow(data, origin='upper', extent=data_extent)
plt.savefig('TSM.png')
I think you are facing to 2 problems:
1) Our products may overlap some land areas because of the Level-3 rebinning during the GlobColour processing: if a 4km pixel has only the corner on the water we will fill the full pixel. We keep them because they may be usefull for some needs (for example areas where the land/water limit is varying), but in the quality flags we provide a LAND mask which could be used to remove these pixels. You can also use your own LAND mask if you prefer. The Python example below shows how to use the LAND mask.
2) I suspect that your Python code introduces an east/south shift of at least half a pixel maybe because the lat/lon arrays are for the center of each pixel but the extent needed by cartopy is the exterior limit.
GlobColour flags are defined in the Product User Guide http://www.globcolour.info/CDR_Docs/GlobCOLOUR_PUG.pdf page 76.
The GlobColour Team
Are you sure your data are in WGS84? Looking at the metadata, I only see:
'earth_radius': 6378.137
which I imply means assume a spherical Earth with radius 6378.137 km. I don't have access to your data, but I would try setting up a cartopy.crs.Globe instance with that radius.
Related
How can I look for similar keys in a Python dictionary and that its values are saved in a single key by using a list? That is, for example, the user enters a name and two different phone numbers; I'd like to store those two telephone numbers in the same key.
agenda = {}
for i in range(3):
nombre = input("Ingrese el nombre de usuario: ")
numero = int(input("Ingrese el numero de telefono del usuario: "))
agenda[nombre] = numero
for key in agenda.keys():
if key in agenda.keys():
lista = []
agenda[key] = lista.append(numero)
print(agenda)
I tried it this way but if there is any repeated name the dictionary only takes into account once beforehand. Thank you very much.
I think you mean this
agenda = {}
for i in range(3):
nombre = input("Ingrese el nombre de usuario: ")
numero = int(input("Ingrese el numero de telefono del usuario: "))
if nombre not in agenda:
agenda[nombre] = []
agenda[nombre].append(numero)
Example run:
>>> Ingrese el nombre de usuario: Pablo
>>> Ingrese el numero de telefono del usuario: 123
>>> Ingrese el nombre de usuario: Pablo
>>> Ingrese el numero de telefono del usuario: 456
>>> Ingrese el nombre de usuario: Camilo
>>> Ingrese el numero de telefono del usuario: 321
Now, if you print agenda, you get
>>> agenda
{'Pablo': [123, 456], 'Camilo': [321]}
As a complement to #Camilo's answer, you could make use of the defaultdict module (https://docs.python.org/3/library/collections.html#collections.defaultdict) to create a list by default. Additionally, you could check for repeated phone numbers.
from collections import defaultdict
agenda = defaultdict(list)
for i in range(3):
nombre = input("Ingrese el nombre de usuario: ")
numero = int(input("Ingrese el numero de telefono del usuario: "))
if numero not in agenda[nombre]:
agenda[nombre].append(numero)
I have a small big problem with an app I have created, using Python and Streamlit.
The problem is that when I want to deploy it to the Heroku services, it builds, but when I try to access it, it tells me that there is an applicaiton error.
I have looked at my code, whcih on the local machine works pefectly.
Do you have any idea?
Code:
# Loading packages ##########################################################################
import streamlit as st
import numpy as np
import networkx as nx
from pyvis.network import Network
# import plotly.express as px
###############################################################################################
st.set_page_config(layout="wide")
##### Creazione Sidebar ####################################################################
st.sidebar.title('Ottimizzazione')
st.sidebar.header('Creazione matrice')
st.sidebar.write('Scrivere la matrice quadrata come da esempio Matrice: 1,2;4,5')
st.sidebar.write('Usare la , per separe le varie colonne e il ; per andare alla prossima riga')
input_matrice=st.sidebar.text_area('Scrivere Matrice:')
# Creazione matrice
matrice = np.matrix(input_matrice)
matrice_array = np.asarray(matrice)
##### Creazione Due colonne per output ##############################################################
left_column1, right_column1 = st.beta_columns(2)
left_column2, right_column2 = st.beta_columns(2)
## Visualizzazione matrice
with left_column1:
st.header('Matrice Quadrata Creata:')
matrice_array
with right_column1:
st.header('Visualizzazione del grafo:')
st.write('Il grafo verrà visualizzato in una apgina separata, in quanto al momento non riuslta possible integrarlo nella pagina pricipale')
##### Creaizone Input per calcolo #######################################################
st.sidebar.header('Calcolo percorso:')
st.sidebar.write('Calcolo del percorso più corto, Nx nodo di partenza e Ny nodo di arrivo. I nodi della matrice corrispondono agli indici della colonna.')
selezione = st.sidebar.radio("Seleziona tipo di calcolo",('Da Nx a tutti più vicini','Da Nx a Ny'))
if selezione == 'Da Nx a tutti più vicini':
nodo_partenza=np.int(st.sidebar.number_input('Scrivere nodo di partenza (Numero intero):'))
bottone_calcolo = st.sidebar.button('Calcola percorso', key=1)
if bottone_calcolo:
grafo_matrice = nx.from_numpy_matrix(matrice_array)
percorso = nx.single_source_dijkstra_path(grafo_matrice, nodo_partenza, weight='weight')
lunghezza = nx.single_source_dijkstra_path_length(grafo_matrice, nodo_partenza, weight='weight')
with left_column2:
st.header('Percorsi:')
st.write('Qui vengono mostrati i vari percorsi che sono stati trovati. I valori a destra corrispondono al ordine di successione, mentre I valori a destra i vari nodi.')
percorso
with right_column2:
st.header('Tempi percorsi:')
st.write('Qui vengono mostrati i vari tempi dei vari percorsi')
lunghezza
with right_column1:
nt=Network("1000px","1000px")
nt.from_nx(grafo_matrice)
nt.show("nx.html")
elif selezione == 'Da Nx a Ny':
nodo_partenza=np.int(st.sidebar.number_input('Scrivere nodo di partenza (Numero intero):'))
nodo_arrivo=np.int(st.sidebar.number_input('Scrivere nodo di arrivo (Numero intero):'))
bottone_calcolo = st.sidebar.button('Calcola percorso', key=2)
if bottone_calcolo:
grafo_matrice = nx.from_numpy_matrix(matrice_array)
percorso = nx.shortest_path(grafo_matrice, source=nodo_partenza, target=nodo_arrivo, weight='weight')
lunghezza = nx.shortest_path_length(grafo_matrice, source=nodo_partenza, target=nodo_arrivo, weight='weight')
with left_column2:
st.header('Percorso:')
st.write('Qui viene mostrato il percorso trovato. I valori a destra corrispondono al ordine di successione, mentre I valori a destra i vari nodi.')
percorso
with right_column2:
st.header('Tempo percorso:')
st.write('Qui viene mostrato il tempo totale del pecrorso più breve')
lunghezza
with right_column1:
nt=Network("1000px","1000px")
nt.from_nx(grafo_matrice)
nt.show("nx.html")
Requirements:
streamlit==0.75.0
numpy==1.19.2
networkx==2.5.0
pyvis==0.1.9
Thank you for the help!
I solved the issue, it appear the problem was not in the code, but inside the command of the code for running the webapp
I'm working on old French Napoleonian cadastre, I've vectorized it, and now I've been studying parcels' neighborhood relation. I want to know which polygon is next to which polygon.
I tried NetworkX python library, but I did not succeed to convert my shapefile to a graph. I want to extract centroids from my polygons and trace relation between them.
I can use line shapefile or area shapefile to represent my parcels.
There is my python code:
import networkx as nx
import matplotlib.pyplot as plt
G=nx.read_shp('path/to/shp') #Read shapefile as graph
pos = {xy: xy for xy in G.nodes()}
nx.draw_networkx_nodes(G,pos,node_size=10,node_color='r')
nx.draw_networkx_edges(G,pos,edge_color='b')
plt.show()
This is my shapefile:
All right, I've fixed my problem with PySal library.
There is my code if someone need to generate some graphs!
##Définition des relations d'adjacence
qW = ps.queen_from_shapefile(str(planche)+".shp")
dataframe = ps.pdio.read_files(str(planche)+".shp")
## Affiche la matrice de voisinage complète.
Wmatrix, ids = qW.full() #ou ps.full(qW)
print("Matrice d'adjacence complète:\n", Wmatrix)
print("\n")
## pour compter le nombre de voisins que possède une parcelle:
n_neighbors = Wmatrix.sum(axis=1)
for i in range (len(n_neighbors)):
if n_neighbors[i] != 0:
print("La parcelle %i a %i voisins" %(i,n_neighbors[i]))
else:
print("La parcelle %i n'a pas de voisin" %i)
print("")
## Affiche [parcelle choisie, ses voisins]:
for i in range (len(n_neighbors)):
self_and_neighbors = [i]
self_and_neighbors.extend(qW.neighbors[i])
if self_and_neighbors[1:] == []:
print("La parcelle %i n'a pas de voisin" %i)
else:
print("Les voisins de la parcelle %i sont les parcelles " %i, self_and_neighbors[1:])
##Extractions des coordonnées des centroïds:
centroids = np.array([list(poly.centroid) for poly in dataframe.geometry])
plt.plot(centroids[:,0], centroids[:,1],'.')
for k,neighs in qW.neighbors.items():
#print(k,neighs)
#origin = centroids[k]
for neigh in neighs:
segment = centroids[[k,neigh]]
plt.plot(segment[:,0], segment[:,1], '-')
##Affichage des numéros des centroïds sur le graph:
for i in range (len(centroids)):
plt.text(centroids[i][0],centroids[i][1],str(i))
plt.title('Graph de la planche '+str(planche)+ " de l'année "+str(annee))
print("\nDuree: %.2f sec" %(time.time()-deb))
show()
I try to represent a budget with gnuplot with rowstacked histograms.
Here is my script
#Ce fichier permet de plotter les dépenses et les récettes sur une même image
#Les fichiers des données sont structurées comme suit
#Sur la première ligne qui commence par un croisillon suivi d'une espace, les textes des libellés. Les espaces à l'intérieur des libellés sont insécables
#Sur la deuxième ligne qui commence par un croisillon suivi d'une espace les valeurs en euros
#Sur la troisième ligne, le total de la section
#Sur la quatrième ligne les valeurs en pourcentage
#Dans un terminal, se placer dans le dossier où se trouve ce fichier, entrer la commande gnuplot
# puis au prompt de gnuplot taper : call "InvestissementRowStacked.gnu" "Fonctionnement" "2017" "545 000"
set decimalsign ','
unset multiplot
unset ylabel
set term pngcairo size 2048,800
set output ARG1.'.png'
#Le titre est commun aux deux plots
set multiplot title "Budget primitif - ".ARG1." ".ARG2." (Total ".ARG3." €)" font 'Arial,18'
#réglages communs aux deux plots
set style data histogram
set style histogram rowstacked
set boxwidth 2 absolute
set style fill solid border
#unset xtics
#unset ytics
#####################################
#DÉPENSES
######################################
#place le premier plot et le limite à la moitié de la largeur
set origin 0.01,0.05
set size 0.49,0.9
#titre pour le premier plot
set title "Dépenses" font 'Arial,16'
file ="Dépenses".ARG1.ARG2.".dat"
#regarde le nombre de données
unset xrange
unset yrange
stats file output
n=STATS_columns
set yrange [0:120]
#lit les valeurs en pourcentage sur la troisième ligne du fichier
getData(fName, row)=system(sprintf("sed -n -e %dp %s", row, fName))
values = getData(file, 3)
#lit les valeurs en euros sur la deuxième ligne
values2= getData(file, 2)
#utilise la première ligne pour les libellés
names=system(sprintf("head -1 '%s'",file))
#calcule le point d'arrivée des flèches en ordonnées
cumul_D=0
f(x)=(cumul_D=cumul_D+x, cur_D=cumul_D-x/2., cur_D)
#supprime la légende
unset key
#plot les dépenses avec les libellés et les flèches sur la gauche
set xrange [-14:2]
plot for [C=1:n] sprintf('%s',file) u C, \
for [C=1:n] sprintf('%s',file) u (-3.8):(2+9*C):(+2.8):(f(column(C))-2-9*C) w vectors lc rgb('black'), \
for [C=1:n] sprintf('%s',file) u (-4):(2+9*C):(word(names,C+1).' ('.word(values,C).'% | '.word(values2,C+1).' €) ') w labels right font 'Arial,14'
######################
# RECETTES
#######################
#plot les recettes avec les libellés et les flèches sur la droite
fileR ="Recettes".ARG1.ARG2.".dat"
#regarde le nombre de données
unset yrange
unset xrange
stats fileR output
n=STATS_columns
set yrange [0:120]
set xrange [-2:14]
set title "Recettes" font 'Arial,16'
#lit les valeurs en pourcentage dans la troisième ligne du fichier
values = getData(fileR, 3)
#lit les valeurs en euros sur la deuxième ligne du fichier
values2=getData(fileR, 2)
#utilise la première ligne pour les libellés
names=system(sprintf("head -1 '%s'",fileR))
#place le deuxième plot dans la seconde moitié en largeur du canvas
set origin 0.5,0.05
set size 0.49,0.9
#réinitialise le calcul des points d'arrivée des flèches de libellé
cumul_D=0
#plot les recettes avec les flèches et les libellés à droite
plot for [C=1:n] sprintf('%s',fileR) u C, \
for [C=1:n] sprintf('%s',fileR) u (3.8):(2+9*C):(-2.8):(f(column(C))-2-9*C) w vectors lc rgb('black'), \
for [C=1:n] sprintf('%s',fileR) u (4):(2+9*C):('('.word(values,C).'% | '.word(values2,C+1).' €) '.word(names,C+1)) w labels left font 'Arial,14'
unset multiplot; set output
the budget has two sections Investissement and Fonctionnement and I use the following command to plot the sections
call "BudgetRowStacked.gnu" "Fonctionnement" "2017" "460 000"
and
call "BudgetRowStacked.gnu" "Investissement" "2017" "545 000"
I get the following
There are some strangeness I have trouble to explain.
The bars doesn't reach 100 despite the fact that the sum of the data is always 100, moreover, in the Investissement section, the bars are of different heights.
I solved every thing using
set locale 'fr_FR'
set decimalsign locale
I have a Database Class built in Python 3.5.2, which I am built to have general functions other classes could use to connect to the DB.
I am using SQLite and SQLite Studio to check my work.
So far, I have successfully created create-table functions, as well as others that return the name of all tables in the database (in a list), and one that returns the names of all columns in any table of the DB.
The problem:
For some reason, the INSERT INTO method is crashing my DB (it takes a while, then it says it's locked). It creates a db-journal file in the same folder, I assume it's some kind of log.
I have substituted the execute lines for prints, to check that the query is ok. I have got the query it prints and put it into the query editor of sqlite Studio to see if they work, AND THEY DO!
The method uses a while loop to create a query for each insert I want to do, and I am quite sure there is something wrong with my method definition. It must be doing something that crashes de DB (maybe something memory related?)
Here is the code:
import sqlite3
class Database:
def __init__(self, name):
self.name=name
self.db_conn=sqlite3.connect(self.name+'.db')
self.cursor=self.db_conn.cursor()
self.commit=self.db_conn.commit()
def get_name(self):
return self.name
def get_tableNames(self):
#get table names into a list:
self.tables=[]
c=self.db_conn.execute("select name from sqlite_master where type = 'table'")
for row in c:
self.tables.append(row[0])
return self.tables
def get_tableColumns(self,tableName):
#get table columns into a list:
self.tableName=tableName
self.columns=[]
c=self.db_conn.execute("PRAGMA table_info("+ self.tableName+" );")
for row in c:
self.columns.append(row[1])
return self.columns
def create_table(self,table_name,*args):
#crear una tabla con nombre table_name y columns= cada uno de los argumentos de *fields.
#cada campo de args debe ser una lista.
self.table_name=table_name
fields=[]
for field in args:
fields.append(field)
i=0
try:
self.db_conn.execute("CREATE TABLE " +self.table_name+ "(ID INTEGER PRIMARY KEY AUTOINCREMENT);")
self.commit
except sqlite3.OperationalError:
print("La tabla "+self.table_name+" no se ha creado")
#añadimos las columnas a la tabla creada:
while i<len(fields):
if len(fields[i])==2:
try:
self.db_conn.execute("ALTER TABLE "+self.table_name+" ADD COLUMN "+fields[i][0]+" "+fields[i][1].upper()+";")
self.commit
i+=1
except sqlite3.OperationalError:
print("No se ha podido añadir la columna "+fields[i][0]+" a la tabla "+self.table_name)
i+=1
elif len(fields[i])==3:
try:
self.db_conn.execute("ALTER TABLE " +table_name+" ADD COLUMN " +fields[i][0]+" "+fields[i][1].upper()+" ("+str(fields[i][2])+") "+";")
self.commit
i+=1
except sqlite3.OperationalError:
print("No se ha podido añadir la columna "+fields[i][0]+" a la tabla "+self.table_name)
i+=1
else:
print('los argumentos deben ser listas donde el primer elemento será el nombre de la columna y los otros dos, el tipo y tamaño (de haberlo)')
def insert(self,tableName, *vals):
#insertar valores en tabla:
fields=self.get_tableColumns(tableName)
rows=[]
for row in vals:
rows.append(row)
r=0
while r<len(rows):
i=1
query="INSERT INTO "+self.tableName+" ("
while i<len(fields):
query=query+str(fields[i]+',')
i+=1
query=query[:-1]
query=query+') VALUES ('
i=0
while i<len(rows[r]):
if type(rows[r][i]) is str:
query=query+str("'"+rows[r][i]+"'"+',')
else:
query=query+str(rows[r][i])+','
i+=1
query=query[:-1]+');'
print(query)
print('commit')
try:
self.db_conn.execute(query)
self.commit
print("se han añadido los datos a la tabla")
except sqlite3.OperationalError:
print('no se han podido añadir los valores específicos a la tabla '+self.tableName)
r+=1
#esta función peta la DB y por eso no funcionan las queries.
def close(self):
self.db_conn.close()
closed= 'database closed'
return closed
In order to make it work:
db=Database('stock-data')
db.create_table('test',['name','text',50],['age','integer'])
db.insert('test',['john',20],['will',21])
This last expression is the one that crashes.