charts script Krigring

service/chart.py

@@ -0,0 +1,117 @@
+#!/usr/bin/python3
+import numpy as np
+import gstools as gs
+import math
+from pykrige.uk import UniversalKriging
+from pykrige.ok import OrdinaryKriging 
+from matplotlib import ticker
+from matplotlib import pyplot as plt
+from datetime import datetime
+plt.style.use('ggplot')
+
+
+def round_up(n, decimals= -1):
+    multiplier = 10 ** decimals
+    return int (math.ceil(n * multiplier) / multiplier)
+
+def round_down(n, decimals= -1):
+    multiplier = 10 ** decimals
+    return int (math.floor(n * multiplier) / multiplier)
+
+def get_v(vmax, vmin): 
+    outMax = round_up(vmax)
+    outMin = round_down(vmin)
+    return outMax, outMin 
+
+def myPlot(varLabel=0, varData=[34,5,10,22,200]):
+
+    folder = ''
+    labelName = ''
+    units = ''
+    if varLabel == 0:
+        folder = 'at/'
+        labelName = 'Temperatura Aire'
+        units = ' (°C)'
+    elif varLabel == 1:
+        folder = 'ah/'
+        labelName = 'Humedad Aire'
+        units = ' (%)'
+    elif varLabel == 2:
+        folder = 'et/'
+        labelName = 'Temperatura Tierra'
+        units = ' (°C)'
+    elif varLabel == 3:
+        folder = 'eh/'
+        labelName = 'Humedad Tierra'
+        units = ' (%)'
+    elif varLabel == 4:
+        folder = 'l/'
+        labelName = 'Luz'
+        units = ' (Lux - lm/m2)'
+
+
+
+
+
+    # conditioning data (x, y, values)
+    data = np.array([
+            [5.8, 2.0, varData[0]], #Nodo 1
+            [0.1, 1.4, varData[1]], #Nodo 2
+            [3.0, 2.3, varData[2]], #Nodo 3
+            [5.1, 4.1, varData[3]], #Nodo 4
+            [0.1, 5.1, varData[4]], #Nodo 5
+        ])       
+    # convert conditioning data
+    x, y, val = data[:, 0], data[:, 1], data[:, 2]
+    # grid definition for output field
+    gridx = np.arange(0.0, 6.0, 0.01)
+    gridy = np.arange(0.0, 5.6, 0.01)
+    # a gaussian covariance model
+    cov_model = gs.Gaussian( dim=2, len_scale=2.8340093019680803, anis=1.0, angles=-0, var=0.7320789975351657, nugget=0.0)  
+
+    pk_kwargs = cov_model.pykrige_kwargs
+    OK1 = OrdinaryKriging(x, y, val, **pk_kwargs)
+    z1, ss1 = OK1.execute("grid", gridx, gridy)
+
+    fig = plt.figure(figsize=(6.4*1.7, 4.8*1.7))
+    #Vmax Vmin
+    #vMax, vMin = get_v(int(z1.max()), int(z1.min()))
+    #print(vMax, vMin) 
+
+    ss = plt.imshow(z1, origin="lower",  cmap=plt.cm.get_cmap('jet', 30), vmin=z1.min(),vmax=z1.max())
+    v1 = np.linspace(z1.min(), z1.max(), 16, endpoint=True) 
+    cbar = plt.colorbar(ss, label=labelName + units, ticks=v1)
+
+    cbar.ax.tick_params(labelsize=10)
+    dt_string = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+    title = labelName + units + " - Campo: 6mx5.6m " + dt_string
+    fig.suptitle(title, fontsize=18)
+    #plt.show() 
+    plt.close() 
+
+    #Guardar Imagen
+    dt_string = dt_string.replace(":", "-").replace("/", "-")
+    nameFile = "charts/" + folder +  labelName + " " + dt_string +  '.png'
+    fig.savefig(nameFile, dpi=200, format='png' )
+
+    return nameFile
+
+
+
+if __name__ == "__main__":
+    myPlot(0,[55,45,60,40,55])
+
+
+# #Si es positivo aproxima arriba cada 10 
+# #143 => 150
+# #Si es negativo aproxima hacia arriba cada 10
+# #-143 => -140
+# print(-a)
+# print(round_up(-a, -1))
+
+# #Si es positivo aproxima hacia abajo cada 10
+# #25 => 20
+# #Si es Negativo aproxima hacia abajo cada 10
+# #-24 => -30
+# print(-b)
+# print(round_down(-b, -1))

service/variogram.py

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+#!/usr/bin/python3
+import numpy as np
+import gstools as gs
+from matplotlib import pyplot as plt
+plt.style.use('ggplot')
+
+def main():
+    data = np.array([
+        [0.1, 5.1],   #Nodo 5
+        [0.1, 1.4],   #Nodo 2
+        [3.0, 2.3],   #Nodo 3
+        [5.1, 4.1],   #Nodo 4
+        [5.8, 2.0],   #Nodo 1
+        ])
+
+    # convert conditioning data
+    x, y = data[:, 0], data[:, 1]
+
+    model = gs.Gaussian(dim=2, var=2, len_scale=3.6)
+    srf = gs.SRF(model, mean=2, seed=42000)#seed=19970221)
+    field = srf((x, y))
+    # estimate the variogram of the field with 40 bins
+    bins = np.arange(12)
+    bin_center, gamma = gs.vario_estimate_unstructured((x, y), field, bins, sampling_size=10, sampling_seed=10)
+    # fit the variogram with a stable model. (no nugget fitted)
+    fit_model = gs.Stable(dim=2)
+    fit_model.fit_variogram(bin_center, gamma, nugget=False)
+    # output
+    ax = fit_model.plot(x_max=max(bin_center))
+    ax.plot(bin_center, gamma)
+    plt.show()
+    print(fit_model)
+
+if __name__ == "__main__":
+    main()