def update_bar_graph(start_date, end_date, selectedCrime, selectedLocation,
start_date_fut, end_date_fut, selectedCrime_fut, selectedLocation_fut):
crime_code = selectedCrime
location_code = selectedLocation
location = "London"
if selectedLocation:
zoom = 15.0
latInitial = cm.list_of_locations[selectedLocation]["lat"]
lonInitial = cm.list_of_locations[selectedLocation]["lon"]
location_code = cm.location_codes[selectedLocation]
location = selectedLocation
if selectedCrime:
crime_code = cm.crime_mapping[selectedCrime]
if start_date and end_date and not selectedLocation:
start = dt.strptime(start_date, "%Y-%m-%d")
end = dt.strptime(end_date, "%Y-%m-%d")
start = start.strftime("%b %Y")
end = end.strftime("%b %Y")
# get the data set
start_time = time.time()
database = Connection()
dataset = cm.barchart_database_pull(start_date, end_date, crime_code,
database)
print(time.time() - start_time, " update_bar_graph()")
dataset.replace({"lsoa_name": cm.location_mapping_reverse},
inplace=True)
locations = list(dataset["lsoa_name"])
num_crimes = list(dataset["count"])
fig = go.Figure(go.Bar(x=locations, y=num_crimes, name='Borough 1'))
fig.update_layout(template="plotly_dark", )
if not selectedCrime:
selectedCrime = ""
else:
selectedCrime += " "
if start == end:
return fig, 'Boroughs with most cases ({})'.format(start)
else:
return fig, 'Boroughs with most cases ({} - {})'.format(
start, end)
else:
x = []
fig = go.Figure(go.Bar(x=x, y=[], name='Borough 1'))
fig.update_layout(template="plotly_dark", )
return fig, 'Select London and date range to see analysis'
def display_predictions():
mapbox_key = "pk.eyJ1IjoicGxvdGx5bWFwYm94IiwiYSI6ImNqdnBvNDMyaTAxYzkzeW5ubWdpZ2VjbmMifQ.TXcBE-xg9BFdV2ocecc_7g"
database = Connection()
predicted_merged_2 = prediction_pull(database)
colors = ["blue", "purple", "red"]
with urlopen(
'https://raw.githubusercontent.com/mikeypatt/json_file_crime/master/LSOA_2011.json'
) as response:
j_file = json.load(response)
i = 1
for feature in j_file["features"]:
feature['id'] = str(i).zfill(2)
i += 1
print(j_file["features"])
print(predicted_merged_2['id'])
fig = go.Figure(
go.Choroplethmapbox(geojson=j_file,
locations=predicted_merged_2.id,
z=predicted_merged_2.prediction,
showscale=False,
colorscale=colors,
zmin=0,
zmax=20,
ids=predicted_merged_2.LSOA11NM,
text=predicted_merged_2.LSOA11NM,
hoverinfo='z+text'))
fig.update_layout(mapbox_style="dark",
mapbox_accesstoken=mapbox_key,
mapbox_zoom=10,
mapbox_center={
"lat": 51.5074,
"lon": -0.1278
})
fig.update_layout(autosize=True,
height=1300,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
})
return fig
def abrirPopup(self):
con = Connection()
texto = QString(self.selectedFeature.attributes()[0])
print " id selecr is " + texto
from Writes import Writes
wr = Writes()
wr.writefile("C:/Data/Python/read.txt", texto);
self.dockWidget.readDatabyId(texto)
from QuickWKT.QuickWKTDialog import QuickWKTDialog
self.QuickWKTDialog = QuickWKTDialog()
self.QuickWKTDialog.show()
def update_graph(n_clicks, end_date_fut, start_date, end_date, selectedCrime,
selectedLocation, start_date_fut):
zoom = 10.0
latInitial = 51.507879
lonInitial = -0.087732
bearing = 0
crime_code = selectedCrime
location_code = selectedLocation
if start_date_fut or end_date_fut:
return go.Figure(cm.display_predictions())
if selectedLocation:
zoom = 15.0
latInitial = cm.list_of_locations[selectedLocation]["lat"]
lonInitial = cm.list_of_locations[selectedLocation]["lon"]
location_code = cm.location_codes[selectedLocation]
if selectedCrime:
crime_code = cm.crime_mapping[selectedCrime]
dataset = pd.DataFrame(columns=[
'crime_type', 'date', 'latitude', 'longitude', 'Crime_type_num'
])
if start_date and end_date:
# get the data set
start_time = time.time()
database = Connection()
dataset = cm.database_pull_accelerated(start_date, end_date,
crime_code, location_code,
database)
dataset["Crime_type_num"] = dataset["crime_type"]
dataset.replace({"crime_type": cm.crime_mapping_reverse}, inplace=True)
return go.Figure(
data=[
# Data for all points plotted
Scattermapbox(lat=dataset["latitude"],
lon=dataset["longitude"],
mode="markers",
hoverinfo="lat+lon+text",
text=dataset["crime_type"],
marker=go.scattermapbox.Marker(
size=5,
color=dataset["Crime_type_num"],
showscale=False)),
Scattermapbox(
lat=[
cm.list_of_locations[i]["lat"]
for i in cm.list_of_locations
],
lon=[
cm.list_of_locations[i]["lon"]
for i in cm.list_of_locations
],
mode="markers",
hoverinfo="text",
text=[i for i in cm.list_of_locations],
marker=dict(size=8, color="#ffa0a0"),
),
],
layout=Layout(
autosize=True,
margin=go.layout.Margin(l=0, r=0, t=0, b=0),
showlegend=False,
mapbox=dict(
accesstoken=mapbox_access_token,
center=dict(lat=latInitial,
lon=lonInitial), # 40.7272 # -73.991251
style="dark",
bearing=bearing,
zoom=zoom,
),
updatemenus=[
dict(
buttons=([
dict(
args=[{
"mapbox.zoom": zoom,
"mapbox.center.lon": lonInitial,
"mapbox.center.lat": latInitial,
"mapbox.bearing": bearing,
"mapbox.style": "dark",
}],
label="Reset Zoom",
method="relayout",
)
]),
direction="left",
pad={
"r": 0,
"t": 0,
"b": 0,
"l": 0
},
showactive=False,
type="buttons",
x=0.45,
y=0.02,
xanchor="left",
yanchor="bottom",
bgcolor="#323130",
borderwidth=1,
bordercolor="#6d6d6d",
font=dict(color="#FFFFFF"),
)
],
),
)
def update_pie_chart(start_date, end_date, selectedCrime, selectedLocation,
start_date_fut, end_date_fut, selectedCrime_fut,
selectedLocation_fut):
crime_code = selectedCrime
location_code = selectedLocation
location = "London"
if selectedLocation:
zoom = 15.0
latInitial = cm.list_of_locations[selectedLocation]["lat"]
lonInitial = cm.list_of_locations[selectedLocation]["lon"]
location_code = cm.location_codes[selectedLocation]
location = selectedLocation
if selectedCrime:
crime_code = cm.crime_mapping[selectedCrime]
if start_date and end_date and not selectedCrime:
start = dt.strptime(start_date, "%Y-%m-%d")
end = dt.strptime(end_date, "%Y-%m-%d")
start = start.strftime("%b %Y")
end = end.strftime("%b %Y")
# get the data set
start_time = time.time()
database = Connection()
dataset = cm.database_pull_accelerated(start_date, end_date,
crime_code, location_code,
database)
print(time.time() - start_time, " update_pie_chart()")
dataset["Crime_type_num"] = dataset["crime_type"]
dataset.replace({"crime_type": cm.crime_mapping_reverse}, inplace=True)
# count number of crimes
crime_count = [0] * 16
crime_counts = dataset["Crime_type_num"].value_counts()
values = list(crime_counts.values.astype(int))
index = list(crime_counts.index.astype(int))
labels = [cm.crime_mapping_reverse[i] for i in index]
colors = [
'rgb(61,73,154)', 'rgb(67,80,168)', 'rgb(72,86,182)',
'rgb(86,99,188)', 'rgb(100,112,193)', 'rgb(114,125,199)',
'rgb(128,138,205)', 'rgb(142,151,210)', 'rgb(156,164,216)',
'rgb(170,177,221)', 'rgb(184,190,227)', 'rgb(198,203,232)',
'rgb(212,216,238)', 'rgb(226,229,244)', 'rgb(240,241,249)',
'rgb(251,253,254)'
]
colors = colors[0:len(labels)]
layout = go.Layout(
margin=dict(l=20, r=20, b=20, t=20, pad=4),
template="plotly_dark",
showlegend=False,
)
trace1 = {
"labels": labels,
"values": values,
"textinfo": "none",
"marker": {
"colors": colors
}
}
if start == end:
return go.Figure(
data=[go.Pie(**trace1)], layout=layout
), "Total crime in " + location + " (" + start + ")"
else:
return go.Figure(
data=[go.Pie(**trace1)], layout=layout
), "Total crime in " + location + " (" + start + " - " + end + ")"
else:
labels = []
values = []
fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
fig.update_layout(template="plotly_dark", )
# Please return figure data and new title: x is amount of crimes, Date is the date
return fig, "Select All Crimes and date range to see analysis"
def update_avg_serious(start_date, end_date, selectedCrime, selectedLocation,
start_date_fut, end_date_fut, selectedCrime_fut,
selectedLocation_fut):
crime_code = selectedCrime
location_code = selectedLocation
if selectedLocation:
zoom = 15.0
latInitial = cm.list_of_locations[selectedLocation]["lat"]
lonInitial = cm.list_of_locations[selectedLocation]["lon"]
location_code = cm.location_codes[selectedLocation]
if selectedCrime:
crime_code = cm.crime_mapping[selectedCrime]
if start_date and end_date:
# get the data set
start_time = time.time()
database = Connection()
dataset = cm.database_pull_accelerated(start_date, end_date,
crime_code, location_code,
database)
print(time.time() - start_time, " update_avg_serious()")
dataset["Crime_type_num"] = dataset["crime_type"]
dataset.replace({"crime_type": cm.crime_mapping_reverse}, inplace=True)
# count number of crimes
num_crimes = len(dataset.index)
days, months = cm.date_interval(start_date, end_date)
crimes_per_month = round(num_crimes / months, 1)
layout = go.Layout(
annotations=[
dict(text=str(crimes_per_month),
x=0.5,
y=0.5,
font_size=14,
showarrow=False),
],
margin=dict(l=20, r=20, b=20, t=20, pad=4),
template="plotly_dark",
showlegend=False,
hovermode=False,
)
trace1 = {
"hole": 0.8,
"labels": ["Crimes per Month: " + str(crimes_per_month)],
"values": [crimes_per_month],
"textinfo": "none"
}
else:
layout = go.Layout(
annotations=[
dict(text=str(0), x=0.5, y=0.5, font_size=14, showarrow=False),
],
margin=dict(l=20, r=20, b=20, t=20, pad=4),
template="plotly_dark",
showlegend=False,
hovermode=False,
)
trace1 = {
"hole": 0.8,
"labels": ["No Interval Chosen"],
"values": [0],
"textinfo": "none"
}
return go.Figure(data=[go.Pie(**trace1)], layout=layout),
def update_timeline(start_date, end_date, selectedCrime, selectedLocation,
start_date_fut, end_date_fut, selectedCrime_fut,
selectedLocation_fut):
first_date = dt(2010, 12, 1)
last_date = dt(2019, 11, 1)
crime_code = selectedCrime
location_code = selectedLocation
location = "London"
crime_str = "all crime"
if selectedLocation:
zoom = 15.0
latInitial = cm.list_of_locations[selectedLocation]["lat"]
lonInitial = cm.list_of_locations[selectedLocation]["lon"]
location_code = cm.location_codes[selectedLocation]
location = selectedLocation
if selectedCrime:
crime_code = cm.crime_mapping[selectedCrime]
crime_str = selectedCrime
if start_date and end_date:
start = dt.strptime(start_date, "%Y-%m-%d")
end = dt.strptime(end_date, "%Y-%m-%d")
start = start.strftime("%b %Y")
end = end.strftime("%b %Y")
if (start_date and end_date) or (not start_date and not end_date):
# get the data set
start_time = time.time()
database = Connection()
dataset = cm.timeline_database_pull(start_date, end_date, crime_code,
location_code, database)
print(time.time() - start_time, " update_timeline()")
dates = list(dataset[0]["date"])
crimes = list(dataset[0]["count"])
if isinstance(dataset[1], pd.DataFrame):
# When a date range has been entered
average_crimes = list(dataset[1]["avg"])[0]
fig = go.Figure(
data=[
go.Scatter(x=dates, y=crimes,
name="# of crimes"), # actual data,
{
'x': [min(dates), max(dates)],
'y': [average_crimes, average_crimes],
'name': 'avg of period'
}
],
# horizontal line that represents crime level over selected period
layout=go.Layout(
xaxis=dict(rangeslider={'visible': True}, type="date"),
margin=dict(l=20, r=20, b=20, t=20, pad=4),
template="plotly_dark",
))
else:
# When no date range has been entered
fig = go.Figure(
data=[go.Scatter(x=dates, y=crimes, name="# of crimes")],
layout=go.Layout(
xaxis=dict(rangeslider={'visible': True}, type="date"),
margin=dict(l=20, r=20, b=20, t=20, pad=4),
template="plotly_dark",
))
return fig, "Crime timeline for {} in {}".format(crime_str, location)
else:
fig = go.Figure(data=[go.Scatter(x=[], y=[])],
layout=go.Layout(
xaxis=dict(rangeslider={'visible': True},
type="date"),
margin=dict(l=20, r=20, b=20, t=20, pad=4),
template="plotly_dark",
))
return fig, "Select full date range to see analysis"
def calculateInterdependenciesbyid(self):
print " calculate interdependencies 560 "
from PyQt4.QtCore import QSettings
import Logica
self.dockWidget.PredictionModel.setCurrentIndex(6)
''' set combo box'''
AllItems = [self.dockWidget.comboBoxFromLayer.itemText(i) for i in range(self.dockWidget.comboBoxFromLayer.count())]
texto = QString(self.selectedFeature.attributes()[0])
id = texto
print " Items 403 "
indice = 0
num = 0
for i in AllItems:
try:
if str(i).startswith(texto):
indice = num
except:
print " exceot 513 "
num = num + 1
self.dockWidget.comboBoxFromLayer.setCurrentIndex(indice)
''' set combo box'''
s = QSettings()
## possible values are: prompt, useProject, useGlobal
s.setValue("/Projections/defaultBehaviour", "useProject")
idforshow = "1"
id = str(self.selectedFeature.attributes()[0])
print " 475 id es " + id
Logica.calculateinterdependencyById(id)
# init postgres
# http://gis.stackexchange.com/questions/86983/how-to-properly-establish-a-postgresql-connection-using-qgscredentials
uri = QgsDataSourceURI()
# assign this information before you query the QgsCredentials data sQStringtore
from Connection import Connection
# maquina = " -h 127.0.0.1 -p 5434"
con2 = Connection()
con = con2.getConnection()
uri.setConnection(con.host, con.port, con.database, con.user, con.password)
connInfo = uri.connectionInfo()
(success, user, passwd) = QgsCredentials.instance().get(connInfo, con.user, con.password)
if success:
QgsCredentials.instance().put(connInfo, user, passwd)
print " line 501 connect "
uri.setPassword(passwd)
uri.setUsername(user)
uri.setDataSource("public", "interdependencies", "geom")
LYR = QgsVectorLayer(uri.uri(), "inter", "postgres")
if (LYR.isValid()):
print "Layer interdependencies valid"
else:
print "Layer interdependencies not valid"
display_name = "inter"
layerExiste = QgsMapLayerRegistry.instance().mapLayersByName(display_name)
try:
QgsMapLayerRegistry.instance().removeMapLayers([layerExiste[0].id()]) # primer elemento de la lista
except:
print " except LYR "
LYR.setCrs(QgsCoordinateReferenceSystem(4326, QgsCoordinateReferenceSystem.EpsgCrsId))
# QgsMapLayerRegistry.instance().addMapLayer(LYR)
root = QgsProject.instance().layerTreeRoot()
# mylayer = QgsVectorLayer("/Path/to/your/data.shp", "my layer", "ogr")
QgsMapLayerRegistry.instance().addMapLayer(LYR, False)
# root.addLayer(LYR)
root.insertLayer(1, LYR)
try:
symbols = LYR.rendererV2().symbols()
symbol = symbols[0]
symbol.setColor(PyQt4.QtGui.QColor.fromRgb(255, 0, 0))
except:
print " reviewing the symbols of layers "
self.iface.mapCanvas().refresh()