obs_time = modules.date_process(obs_date,obs_time)
#process model
#----------------------------------------
#read in model data
model_dict = {'4x5':'/work/home/db876/plotting_tools/binary_logs/v90103_4x5_GRID_O3.npy','2x2.5':'/work/home/db876/plotting_tools/binary_logs/v90103_2x2.5_GRID_O3.npy'}
model_version = raw_input('\nChoose Model Version.\n%s\n'%(' '.join(i for i in model_dict)))
model_f = model_dict[model_version]
model_data = read = np.load(model_f)
model_time = np.arange(0,2191,1./24)
#get model grid dims. for sim. type
lat_c,lat_e,lon_c,lon_e = modules.model_grids(model_version)
gridbox_count = len(lat_c)*len(lon_c)
#get model gridbox for obs site
gridbox_n = modules.obs_model_gridbox(lat_e,lon_e,obs_lat,obs_lon)
model_var = model_data[gridbox_n::gridbox_count]
model_var = model_var*1e9
#----------------------------------------
model_time = model_time[valids]
model_var = model_var[valids]
#take lomb for obs and model
ofac = raw_input('\nChoose oversampling factor. (Typically 4.)\n')
model_dict = {
'4x5': 'binary_logs/4x5_GRID_O3.npy',
'2x2.5': 'binary_logs/2x2.5_GRID_O3.npy'
}
model_version = raw_input('\nChoose Model Version.\n%s\n' %
(' '.join(i for i in model_dict)))
model_f = model_dict[model_version]
model_data = read = np.load(model_f)
if model_version == '2x2.5':
model_time = np.arange(0, 2191, 1. / 24)
else:
model_time = np.arange(0, 2190, 1. / 24)
#get model grid dims. for sim. type
lat_c, lat_e, lon_c, lon_e = modules.model_grids(model_version)
gridbox_count = len(lat_c) * len(lon_c)
#get model gridbox for obs site
gridbox_n = modules.obs_model_gridbox(lat_e, lon_e, obs_lat, obs_lon)
model_var = model_data[gridbox_n::gridbox_count]
model_var = model_var * 1e9
#----------------------------------------
#align obs and model periods
#obs_time,model_time,obs_var,model_var = modules.align_periods(obs_time,model_time,obs_var,model_var)
#take lomb for obs and model
ofac = raw_input('\nChoose oversampling factor. (Typically 4.)\n')
import plotly.plotly as py
from plotly.graph_objs import *
from plotly import tools
import numpy as np
from mpl_toolkits.basemap import Basemap
import modules
# Create random data with numpy
import numpy as np
N = 1000
random_x = np.random.randn(N)
random_y = np.random.randn(N)
lat_c, lat_e, lon_c, lon_e = modules.model_grids('GEOSCHEM_4x5')
data = np.random.rand(len(lat_c), len(lon_c))
# Create a trace
trace1 = Heatmap(
z=data,
x=lon_e,
y=lat_e,
colorscale="RdBu",
zauto=False, # custom contour levels
zmin=0, # first contour level
zmax=1 # last contour level => colorscale is centered about 0
)
m = Basemap()