def get_monthly_normals(data_folder_path, start_date = None, end_date = None):
"""
returns numpy array of the monthly normals of the shape (12, nx, ny)
"""
result = [0.0 for i in xrange(12)]
record_counts = [0.0 for i in xrange(12)]
for file_name in os.listdir(data_folder_path):
date_month = ccc_util.get_month_date_from_name(file_name)
#focus only on the interval of interest
if start_date is not None:
if date_month < start_date:
continue
if end_date is not None:
if date_month > end_date:
continue
data = champ_ccc(os.path.join(data_folder_path, file_name)).charge_champs()
m = date_month.month - 1
for record in data:
field = record["field"]
result[m] += field
record_counts[m] += 1.0
result = np.array(result)
record_counts = [ count * np.ones(result.shape[1:]) for count in record_counts ]
record_counts = np.array(record_counts)
result /= np.array(record_counts)
return result
pass
def get_seasonal_mean(data_folder_path, start_date = None, end_date = None, months = None):
"""
returns seasonal mean
"""
result = 0.0
record_count = 0.0
for file_name in os.listdir(data_folder_path):
date_month = ccc_util.get_month_date_from_name(file_name)
#take only months of interest
if date_month.month not in months:
continue
#focus only on the interval of interest
if start_date is not None:
if date_month < start_date:
continue
if end_date is not None:
if date_month > end_date:
continue
data = champ_ccc(os.path.join(data_folder_path, file_name)).charge_champs()
for record in data:
field = record["field"]
result += field
record_count += 1.0
return result / record_count
def get_seasonal_means_for_year_range(data_folder, year_range, months = None):
"""
Using the fact that the data for each month are in a separate file
return data (n_years, nx, ny) - numpy array
"""
path_map = ccc_util.get_yearmonth_to_path_map(data_folder)
result = []
for the_year in year_range:
print("year = {0}".format(the_year))
data = []
for the_month in months:
key = (the_year, the_month)
ccc_obj = champ_ccc(path_map[key])
records = ccc_obj.charge_champs()
data.extend(map(lambda x: x["field"],records))
result.append(np.mean(data, axis=0))
return np.array(result)
def main():
data_path = "data/permafrost/p1perma"
cccObj = champ_ccc(data_path)
pData = cccObj.charge_champs()[0]["field"]
plot_utils.apply_plot_params()
basemap = polar_stereographic.basemap
x, y = basemap(polar_stereographic.lons, polar_stereographic.lats)
pData = pData[10:190,10:182]
pData = np.ma.masked_where(pData < 0, pData)
x1d = x[~pData.mask]
y1d = y[~pData.mask]
xmin = np.min(x1d)
xmax = np.max(x1d)
ymin, ymax = np.min(y1d), np.max(y1d)
#basemap.contourf(x,y, pData)
# basemap.drawcoastlines()
# basemap.drawcountries()
# basemap.drawstates()
# plt.colorbar()
path_to_shapefile = "data/shape/canada_provinces_shape/PROVINCE"
basemap.readshapefile(path_to_shapefile, name = "provinces")
gdal.UseExceptions()
driver = ogr.GetDriverByName("ESRI Shapefile")
dataStore = driver.Open(path_to_shapefile + ".SHP", 0)
layer = dataStore.GetLayer(0)
print dataStore.GetLayerCount()
geometries = []
for feature in layer:
name = feature.GetFieldAsString('NAME').lower()
geom = feature.GetGeometryRef()
geometries.append(geom.ExportToWkt())
centroid = geom.Centroid()
lon, lat = centroid.GetX(), centroid.GetY()
abbr = name_to_abbr[name]
if abbr == "YT":
lon -= 1
if abbr == "PE":
lon -= 1.5
if abbr == "NB":
lat -= 0.5
lon -= 1.5
if abbr == "NS":
lat -= 0.5
lon += 1
if abbr == "NU":
lat -= 8
lon -= 10
if abbr == "NT":
lat -= 3
if abbr == "NL":
lon, lat = basemap(9.18639e6, 3.36468e6, True)
x1, y1 = basemap(lon, lat)
plt.annotate(name_to_abbr[name], xy = (x1,y1))
print name
geometries = map(ogr.CreateGeometryFromWkt, geometries)
lons = polar_stereographic.lons
lats = polar_stereographic.lats
nx, ny = lons.shape
for i in xrange(nx):
for j in xrange(ny):
if pData.mask[i, j]:
continue
else:
pData.mask[i, j] = not inside_region(lons[i,j], lats[i,j], geometries)
basemap.pcolormesh(x,y, pData, cmap = mpl.cm.get_cmap(name = "gist_yarg", lut = 5),
alpha = 0.8, linewidth = 0, shading='flat', vmin = 0, vmax = 5)
# basemap.contourf(x, y, pData, levels = [0.0, 1.0,2.0,3.0,4.0, 5.0],
# cmap = mpl.cm.get_cmap(name = "gist_yarg", lut = 5), alpha = 0.6)
plt.colorbar(ticks = LinearLocator(numticks = 6), format = "%.1f")
plot_stations(basemap)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.show()
pass
