I have in my project a directory named uts in static/ folder that I want to
list the content and send it in HttpResponse but the glob.glob() function
doesn't work.
Here you have the structure of my project. The utility function
*list_dir_content* which call glob.glob is located in the utils folder:
*utils/data.py*
import glob
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# def get_remote_dataset():
# def get_local_dataset():
def list_dir_content(dir_path, file_extension=''):
files_pattern = dir_path + '/*.' + file_extension
print("files_pattern : ", files_pattern)
#files_results_paths = glob.glob(files_pattern)
files_results_paths =
glob.glob('../tsanalysisapp/static/tsanalysisapp/datasets/uts/*.')
print("files_results_paths : ", files_results_paths)
return files_results_paths
## in models.py
from django.db import models
from django.conf import settings
# utils methods import
import utils.data as data
import os
from django.conf import settings
# Create your models here.
class Dataset(models.Model):
"""docstring for dataset"""
dataset_path = models.CharField(max_length=256)
dataset_name = models.CharField(max_length=256)
dataset_nb_instances = models.CharField(max_length=256)
dataset_type = models.CharField(max_length=3)
dataset_adding_date = models.DateField()
def __str__(self):
return "dataset_path : {0} - dataset_name : {1} - number of instances :
{2}".format(self.dataset_path, self.dataset_name, self.dataset_nb_instances)
@classmethod
def get_uts_datasets(cls):
#uts_datasets_path = os.path.join(settings.DATASETS_DIR, 'uts')
#print("pathname of uts datasets repositorie : ", uts_datasets_path)
uts_datasets_files = data.list_dir_content(settings.UTS_DATASETS_DIR)
print("UTS list of files : ", uts_datasets_files)
uts_datasets = []
for uts_datasets_file in uts_datasets_files:
dataset_type = 'uts'
dataset_path = uts_datasets_file
dataset_name = data.get_dataset_name(uts_datasets_file)
dataset_nb_instances = data.get_nb_instances(uts_datasets_file)
uts_dataset = Dataset(dataset_path = dataset_path, dataset_name =
dataset_name, dataset_nb_instances = dataset_nb_instances, dataset_type =
dataset_type)
uts_datasets.append(uts_dataset)
print("UTS datasets : ", uts_datasets)
return uts_datasets
@classmethod
def get_mts_datasets(cls):
mts_datasets_path = settings.DATASETS_DIR + '/mts'
mts_datasets_files = data.list_dir_content(mts_datasets_path)
mts_datasets = []
for mts_datasets_file in mts_datasets_files:
dataset_type = 'mts'
dataset_path = mts_datasets_file
dataset_name = data.get_dataset_name(mts_datasets_file)
dataset_nb_instances = data.get_nb_instances(mts_datasets_file)
mts_dataset = Dataset(dataset_path = dataset_path, dataset_name =
dataset_name, dataset_nb_instances = dataset_nb_instances, dataset_type =
dataset_type)
mts_datasets.append(mts_dataset)
return mts_datasets
## in views.py
def server_uts_datasets(request):
if request.method == 'GET':
uts_datasets = Dataset.get_uts_datasets()
uts_datasets_serializer = DatasetSerializer(uts_datasets, many=True)
print(uts_datasets)
return JsonResponse(uts_datasets_serializer.data, safe=False)
Thanks for your kindly help in advance...
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import glob
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# def get_remote_dataset():
# def get_local_dataset():
def list_dir_content(dir_path, file_extension=''):
files_pattern = dir_path + '/*.' + file_extension
print("files_pattern : ", files_pattern)
#files_results_paths = glob.glob(files_pattern)
files_results_paths = glob.glob('../tsanalysisapp/static/tsanalysisapp/datasets/uts/*.')
print("files_results_paths : ", files_results_paths)
return files_results_paths
def get_dataset_name(file_path):
dataset_filename = os.path.split(file_path)[-1] # -> (head, tail); retrie e the last componnent, the tail, of the pathname path. Head is ignored.
dataset_name = dataset_filename.split('.')[0] # get the name without the extension file
return dataset_name
def get_nb_instances(file_path):
return 200
#from django.shortcuts import render
from django.http import HttpResponse #, HttpResponseRedirect
#from django.template import RequestContext
#from django.urls import reverse
# DRF imports
from rest_framework import viewsets, status
from rest_framework.parsers import JSONParser
from django.http.response import JsonResponse
from django.views.decorators.csrf import csrf_exempt
# models imports
from .models import Dataset
# serializers import
from .serializers import DatasetSerializer
# import of python ML libraries
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg
#import base64
from io import BytesIO
from matplotlib.figure import Figure
import pandas as pd
from pandas.plotting import lag_plot
from pandas.plotting import autocorrelation_plot
# Other imports
import os
from django.conf import settings
# For testing purpose
DATASET_PATH = os.path.join(settings.BASE_DIR, 'tsanalysisapp/static/tsanalysisapp/datasets/daily-min-temperatures.csv')
# Create your views here.
### (Data) visualization views
def histogramplot(request):
#fig = Figure()
fig = plt.figure()
# get the time serie data saved in session
#timeseries = pd.read_json(request.session['ts_dataset'], orient='index')
timeseries = pd.read_csv(DATASET_PATH, header=0, index_col=0, parse_dates=True, squeeze=True)
print("DATASET_PATH : ", DATASET_PATH, "BASE_DIR : ", settings.BASE_DIR)
# Build the graphic
timeseries.hist()
plt.title('Histogram')
plt.grid(True)
buf = BytesIO()
#FigureCanvasAgg(fig)
#plt.savefig(buf, format="png")
#plt.close(fig)
canvas = FigureCanvasAgg(fig)
canvas.print_png(buf)
response = HttpResponse(buf.getvalue(), content_type="image/png")
fig.clear()
return response
def lineplot(request):
#fig = Figure()
fig = plt.figure()
# get the time serie data saved in session
#timeseries = pd.read_json(request.session['ts_dataset'], orient='index')
timeseries = pd.read_csv(DATASET_PATH, header=0, index_col=0, parse_dates=True, squeeze=True)
# Build the graphic
timeseries.plot()
plt.title('Line plot')
plt.grid(True)
buf = BytesIO()
#FigureCanvasAgg(fig)
#plt.savefig(buf, format="png")
#plt.close(fig)
canvas = FigureCanvasAgg(fig)
canvas.print_png(buf)
response = HttpResponse(buf.getvalue(), content_type="image/png")
fig.clear()
return response
def densityplot(request):
#fig = Figure()
fig = plt.figure()
# get the time serie data saved in session
#timeseries = pd.read_json(request.session['ts_dataset'], orient='index')
timeseries = pd.read_csv(DATASET_PATH, header=0, index_col=0, parse_dates=True, squeeze=True)
# Build the graphic
timeseries.plot(kind='kde')
plt.title('Density plot')
plt.grid(True)
buf = BytesIO()
#FigureCanvasAgg(fig)
#plt.savefig(buf, format="png")
#plt.close(fig)
canvas = FigureCanvasAgg(fig)
canvas.print_png(buf)
response = HttpResponse(buf.getvalue(), content_type="image/png")
fig.clear()
return response
def lagplot(request):
#fig = Figure()
fig = plt.figure()
# get the time serie data saved in session
#timeseries = pd.read_json(request.session['ts_dataset'], orient='index')
timeseries = pd.read_csv(DATASET_PATH, header=0, index_col=0, parse_dates=True, squeeze=True)
# Build the graphic
lag_plot(timeseries)
plt.title('Lag plot')
plt.grid(True)
#FigureCanvasAgg(fig)
#plt.savefig(buf, format="png")
#plt.close(fig)
buf = BytesIO()
canvas = FigureCanvasAgg(fig)
canvas.print_png(buf)
response = HttpResponse(buf.getvalue(), content_type="image/png")
fig.clear()
return response
def scatterplot(request, lag=1):
fig = plt.figure()
# get the time serie data saved in session
#timeseries = pd.read_json(request.session['ts_dataset'], orient='index')
timeseries = pd.read_csv(DATASET_PATH, header=0, index_col=0, parse_dates=True, squeeze=True)
values = pd.DataFrame(timeseries.values)
valuesAgo = values.shift(lag)
# Build the graphic
plt.scatter(x=values, y=valuesAgo)
plt.title('Scatter plot t vs. t+' + str(lag))
plt.grid(True)
buf = BytesIO()
#FigureCanvasAgg(fig)
#plt.savefig(buf, format="png")
#plt.close(fig)
canvas = FigureCanvasAgg(fig)
canvas.print_png(buf)
response = HttpResponse(buf.getvalue(), content_type="image/png")
fig.clear()
response['Content-Length'] = str(len(response.content))
return response
def autocorrelationplot(request):
#fig = Figure()
fig = plt.figure()
# get the time serie data saved in session
#timeseries = pd.read_json(request.session['ts_dataset'], orient='index')
timeseries = pd.read_csv(DATASET_PATH, header=0, index_col=0, parse_dates=True, squeeze=True)
# Build the graphic
autocorrelation_plot(timeseries)
plt.title('Autocorrelation plot')
plt.grid(True)
buf = BytesIO()
#FigureCanvasAgg(fig)
#plt.savefig(buf, format="png")
#plt.close(fig)
canvas = FigureCanvasAgg(fig)
canvas.print_png(buf)
response = HttpResponse(buf.getvalue(), content_type="image/png")
fig.clear()
response['Content-Length'] = str(len(response.content))
return response
## Acces datasets views
#@csrf_exempt
def server_uts_datasets(request):
if request.method == 'GET':
uts_datasets = Dataset.get_uts_datasets()
uts_datasets_serializer = DatasetSerializer(uts_datasets, many=True)
print(uts_datasets)
return JsonResponse(uts_datasets_serializer.data, safe=False)
#@csrf_exempt
def server_mts_datasets(request):
if request.method == 'GET':
mts_datasets = Dataset.get_mts_datasets()
mts_datasets_serializer = DatasetSerializer(mts_datasets, many=True)
print(mts_datasets)
return JsonResponse(mts_datasets_serializer.data, safe=False)
from django.db import models
from django.conf import settings
# utils methods import
import utils.data as data
import os
from django.conf import settings
# Create your models here.
class Dataset(models.Model):
"""docstring for dataset"""
dataset_path = models.CharField(max_length=256)
dataset_name = models.CharField(max_length=256)
dataset_nb_instances = models.CharField(max_length=256)
dataset_type = models.CharField(max_length=3)
dataset_adding_date = models.DateField()
def __str__(self):
return "dataset_path : {0} - dataset_name : {1} - number of instances : {2}".format(self.dataset_path, self.dataset_name, self.dataset_nb_instances)
@classmethod
def get_uts_datasets(cls):
#uts_datasets_path = os.path.join(settings.DATASETS_DIR, 'uts')
#print("pathname of uts datasets repositorie : ", uts_datasets_path)
uts_datasets_files = data.list_dir_content(settings.UTS_DATASETS_DIR)
print("UTS list of files : ", uts_datasets_files)
uts_datasets = []
for uts_datasets_file in uts_datasets_files:
dataset_type = 'uts'
dataset_path = uts_datasets_file
dataset_name = data.get_dataset_name(uts_datasets_file)
dataset_nb_instances = data.get_nb_instances(uts_datasets_file)
uts_dataset = Dataset(dataset_path = dataset_path, dataset_name = dataset_name, dataset_nb_instances = dataset_nb_instances, dataset_type = dataset_type)
uts_datasets.append(uts_dataset)
print("UTS datasets : ", uts_datasets)
return uts_datasets
@classmethod
def get_mts_datasets(cls):
mts_datasets_path = settings.DATASETS_DIR + '/mts'
mts_datasets_files = data.list_dir_content(mts_datasets_path)
mts_datasets = []
for mts_datasets_file in mts_datasets_files:
dataset_type = 'mts'
dataset_path = mts_datasets_file
dataset_name = data.get_dataset_name(mts_datasets_file)
dataset_nb_instances = data.get_nb_instances(mts_datasets_file)
mts_dataset = Dataset(dataset_path = dataset_path, dataset_name = dataset_name, dataset_nb_instances = dataset_nb_instances, dataset_type = dataset_type)
mts_datasets.append(mts_dataset)
return mts_datasets
from rest_framework import serializers
# import Models here
from .models import Dataset
# Define ModelSerializers below
class DatasetSerializer(serializers.HyperlinkedModelSerializer):
class Meta :
model = Dataset
fields = '__all__'
