get_png_from_db.py
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import urllib.request
import json
import os
import datetime
from imageio import imread
import cv2
import numpy as np
import loca_data
import duamel_model
import matplotlib.pyplot as plt
import sher_duam_class
import dateutil.parser
import psycopg2
import pathlib
def get_connection():
DBConnection = psycopg2.connect(
host="localhost",
port = "15432",
database="wf71",
user="wf71",
password="wf71")
print("Connect is success!")
DBConnection.autocommit = True
return DBConnection
def write_png(filename = "test.png", px_array = None):
if px_array is None:
px_array = np.zeros(10,10)
path = pathlib.Path(__file__).parent.absolute()
path = str(path) + filename
cv2.imwrite(path, px_array)
def get_png_data_from_db():
DBConnection = get_connection()
print("Connect is success!")
cur = DBConnection.cursor()
query = "select measuretime, additional_info from md_precipitation where measurer_uuid = '1bc2b71c-d505-4034-a939-df0252b3f7c6' and measuretime >= '2020-07-08 07:00:00' and measuretime <= '2020-07-09 07:00:00'"
cur.execute(query)
result = cur.fetchall()
radar_a = 200
radar_n = 1.6
for_txt = []
for var in result:
matrix = []
m_dt = var[0]
mx = json.loads(var[1])
value = 0.0
error = mx.get("error", False)
if error == "Radar matrix is not exist":
value = 0.0
else:
matrix = mx["matrix"]
if len(matrix) > 1:
_dbz = matrix[143][107]
if _dbz == 0 or _dbz > 127:
value = 0.0
else:
step1 = (_dbz - 32) / 10
step2 = 10 ** step1
step3 = step2 / radar_a
step4 = step3 ** (1.0/radar_n)
step5 = step4 / 6.0
value = step5
#delta_dt = datetime.timedelta(hours=3)
#m_dt = datetime.datetime.strptime(str(m_dt), '%Y-%m-%d %H:%M:%S') - delta_dt
for_txt.append([str(m_dt), value])
cur.close()
f = open('result_from_db.txt', 'w')
f.write(str(for_txt))
f.close()
def get_png_picture_from_db():
DBConnection = get_connection()
print("Connect is success!")
cur = DBConnection.cursor()
query = "select measuretime, additional_info from md_precipitation where measurer_uuid = '1bc2b71c-d505-4034-a939-df0252b3f7c6' and measuretime >= '2020-07-08 07:00:00' and measuretime <= '2020-07-09 07:00:00'"
cur.execute(query)
result = cur.fetchall()
i = -1
for var in result:
matrix = []
m_dt = var[0]
td = datetime.timedelta(hours=3)
m_dt = m_dt+td
print(m_dt)
mx = json.loads(var[1])
img = np.zeros((256,256, 4),dtype=np.uint8)
error = mx.get("error", False)
if error == "Radar matrix is not exist":
continue
else:
matrix = mx["matrix"]
if len(matrix) > 1:
#png_data = []
#for line in matrix:
# new_line = []
# for column in line:
# new_line.append([column, column, column, 255])
# png_data.append(new_line)
#png_data = np.array(png_data)
x = -1
for line in matrix:
x = x + 1
y = -1
for column in line:
y = y + 1
img[x][y] = [column, column, column, 255]
fn_dt = '{}_{}_{}__{}_{}.png'.format(m_dt.timetuple()[0], m_dt.timetuple()[1], m_dt.timetuple()[2], m_dt.timetuple()[3], m_dt.timetuple()[4])
#write_png(filename=r"/png_07/{}".format(fn_dt), px_array=png_data)
write_png(filename=r"/png_07/{}".format(fn_dt), px_array=img)
cur.close()
#get_png_picture_from_db
#get_png_data_from_db()
get_png_picture_from_db()