corrected undefined variables detected by flake8 package. Two issue open: #77 and #76. [trigger test]

Author: ArcticSnow

TopoPyScale/topo_da.py

@@ -310,7 +310,7 @@ def pymodis_download(wdir, vert, horiz, STARTDATE, ENDDATE):
     hreg = ("h" + f"{horiz:02d}")  # re.compile("h2[4]")
     vreg = ("v" + f"{vert:02d}")  # re.compile("v0[5]")
     TILE = hreg + vreg
-    print("Downloading " + product + " from " + STARTDATE + " to " + ENDDATE)
+    print("Downloading " + PRODUCT + " from " + STARTDATE + " to " + ENDDATE)
     os.system("modis_download.py -U " +
               USER +
               " -P " + PWD +
@@ -877,7 +877,7 @@ def fsca_plots(wdir, plotDay, df_mean):
     for i in range(0, nclust):
         lookup[i] = df_mean[i]
 
-    with rasterio.open('landform.tif') as src:
+    with rio.open('landform.tif') as src:
         # Read as numpy array
         array = src.read()
         profile = src.profile
@@ -894,7 +894,7 @@ def fsca_plots(wdir, plotDay, df_mean):
 
     with rio.open('output_raster.tif', 'w', **profile) as dst:
         # Write to disk
-        dst.write(array.astype(rasterio.int16))
+        dst.write(array.astype(rio.int16))
 
     src1 = rio.open("output_raster.tif")
     rio.plot.show(src1, ax=axhist)
@@ -1003,7 +1003,7 @@ def da_compare_plot(wdir, plotDay, df_mean_open, df_mean_da):
     for i in range(0, nclust):
         lookup[i] = df_mean_open[i]
 
-    with rasterio.open('landform.tif') as src:
+    with rio.open('landform.tif') as src:
         # Read as numpy array
         array = src.read()
         profile = src.profile
@@ -1015,12 +1015,12 @@ def da_compare_plot(wdir, plotDay, df_mean_open, df_mean_da):
         #array[array > 0] = 1
 
 
-    # rasterio.plot.show(array, cmap='viridis')
+    # rio.plot.show(array, cmap='viridis')
     # plt.show()
 
     with rio.open('output_raster.tif', 'w', **profile) as dst:
         # Write to disk
-        dst.write(array.astype(rasterio.int16))
+        dst.write(array.astype(rio.int16))
 
     src1 = rio.open("output_raster.tif")
     rio.plot.show(src1, ax=axopen)
@@ -1040,7 +1040,7 @@ def da_compare_plot(wdir, plotDay, df_mean_open, df_mean_da):
     for i in range(0, nclust):
         lookup[i] = df_mean_da[i]
 
-    with rasterio.open('landform.tif') as src:
+    with rio.open('landform.tif') as src:
         # Read as numpy array
         array = src.read()
         profile = src.profile
@@ -1052,12 +1052,12 @@ def da_compare_plot(wdir, plotDay, df_mean_open, df_mean_da):
         #array[array > 0] = 1
 
 
-    # rasterio.plot.show(array, cmap='viridis')
+    # rio.plot.show(array, cmap='viridis')
     # plt.show()
 
     with rio.open('output_raster.tif', 'w', **profile) as dst:
         # Write to disk
-        dst.write(array.astype(rasterio.int16))
+        dst.write(array.astype(rio.int16))
 
     src1 = rio.open("output_raster.tif")
     rio.plot.show(src1, ax=axda)
@@ -1128,6 +1128,7 @@ def getModisbbox(Npixel, arr):
     bbox = [xmax,xmin, ymax, ymin]
     return bbox
 
+'''
 def getSamples_inModisPixel(map_path):
     """
     Identify samples that exist within a single bbox and proportianal cover. This can be used to construct a modelled fSCA in a MODIS pixel
@@ -1145,12 +1146,12 @@ def getSamples_inModisPixel(map_path):
     # # project
     # x2, y2 = proj.transform(x_coord, y_coord)
     # open map
-    dataset = rasterio.open(map_path)
+    dataset = rio.open(map_path)
     # get pixel x+y of the coordinate
     py, px = dataset.index(x2, y2)
     # create 1x1px window of the pixel
-    window = rasterio.windows.Window(px - 1 // 2, py - 1 // 2, 1, 1)
+    window = rio.windows.Window(px - 1 // 2, py - 1 // 2, 1, 1)
     # read rgb values of the window
     clip = dataset.read(window=window)
     return (int(clip))
-
+'''

TopoPyScale/topo_export.py

@@ -7,7 +7,7 @@
 - SPHY forcing (grids)
 """
 import pdb
-import sys
+import sys, csv
 import numpy as np
 import pandas as pd
 import datetime as dt

TopoPyScale/topo_utils.py

@@ -41,10 +41,10 @@ def FsmSnowParser(file, freq="1H", resample=False):
     return (df)
 
 
-def FsmPlot(df):
-    df.plot(subplots=True)
-    plt.title("Sample=" + str(sampleN) + " Elev=" + str(np.round(lp.elevation[sampleN])))
-    plt.show()
+#def FsmPlot(df):
+#    df.plot(subplots=True)
+#    plt.title("Sample=" + str(sampleN) + " Elev=" + str(np.round(lp.elevation[sampleN])))
+#    plt.show()
 
 def FsmPlot_ensemble(wdir, simvar, sampleN):
     """

TopoPyScale/topoclass.py

@@ -569,7 +569,7 @@ def get_WMO_observations(self):
         df['year'] = df.dates.dt.year
 
         bbox = [latS, lonW, latN, lonE]
-        for year in years:
+        for year in df.year.unique():
             # API only accept one year request
             tpo.fetch_WMO_insitu_observations(years=year,
                                               months=list(df.month.unique().astype(str)),