rdmtinez

WPA1 = (4.093+(3.2113*(r/1000)))/(1-(0.009733*(r/1000))-(.01205*25))

# WPA2 has 3 equations conditional on risistance and according to
# https://www.kimberly.uidaho.edu/water/swm/Calibration_Watermark2.htm#_ftnref2

if (r >= 0 and r < 1000):
    WPA2 = -20*((r/1000)*(1+.018*(25-T))-.55)

elif(r >=1000 and r < 8000):
    WPA2 = (-3.213*(r/1000) - 4.093) / (1-.009733*(r/1000)-.01205*T)

rdmtinez

// Ricardo Martinez
// Vinduino Sketch Re-write for WaterMark Sensor
#include <math.h>
#include <Time.h>
#include <Time.h>
#include <TimeLib.h>


const int analogInPin1 = A0;            // rename Analog Pins
const int analogInPin2 = A1;

rdmtinez

# coding: utf-8

# This uses DWD data (Temp, Humidity, Wind, Solar) to Calculate ETo
# Then compares it against the ETo given by DWD. The hope is to check
# whether the OwnData_ETo script is sufficiently good.

# In[122]:

import os

rdmtinez

# coding: utf-8

# In[343]:

##the following will combine the datatables from DWD in Stuttgart, as
##it is the only station which collects Solar Radiation data for BW

##for the first iteration only the data from Bosch sensors in BW will

rdmtinez

#import os
#import pandas as pd
#import datetime as dt

####you want to optimize the conversion and create a summary report####

def raw_converter_main(raw_dir, wrt_dir, wrt_massisve=False):
    """This function is designed to condense the raw csv files, made by the
    'download_csv.py' function held by Christian Glunk and the Deepfiled 
    Connect team, into a more maneagable form"""

rdmtinez

import os
import pandas as pd
import numpy as np

from matplotlib import pyplot as plt, gridspec
from matplotlib.backends.backend_pdf import PdfPages
from collections import OrderedDict
from datetime import datetime, timedelta

rdmtinez

BW
['1348458', '1351266', '1351816', '1352180', 
'1353126', '1353308', '1353330', '1353332', 
'1353340', '1353340', '1353346', '1353350', 
'1348526', '1351816', '1350718', '1350718', 
'1350658', '1406166', '1406166', '1406166', 
'1406166', '1410410', '1413900', '1420666', 
'1434353', '1351816', '1439896', '1439896', 
'1406166']

rdmtinez

1. Affine transform of an image — Matplotlib 2.1.0.post231+gb4b4794 documentation
https://matplotlib.org/devdocs/gallery/api/affine_image.html#sphx-glr-gallery-api-affine-image-py

2. Numerical & Scientific Computing with Python: Creating Subplots with Python and Matplotlib
https://www.python-course.eu/matplotlib_multiple_figures.php

3. matplotlib - group multiple plot in one figure python - Stack Overflow
https://stackoverflow.com/questions/41492681/group-multiple-plot-in-one-figure-python

4. Tight Layout guide — Matplotlib 2.0.2 documentation

rdmtinez

import os
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt, gridspec
from matplotlib.backends.backend_pdf import PdfPages
from collections import OrderedDict

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

rdmtinez

import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.plotly as py
import plotly.graph_objs as go

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"