Notebook

In [1]:

import numpy as np
import xarray as xr
import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
from salishsea_tools import metric_tools_5x5 as met

%matplotlib inline
plt.rcParams['image.cmap'] = 'jet'
plt.rc('xtick', labelsize=20) 
plt.rc('ytick', labelsize=20) 

In [2]:

# Parent directory for the results of this run
group_results_directory = '/data/jpetrie/MEOPAR/SalishSea/results/nampisrem_old_IC_june_16/' + '/'
tracer_file = 'SS5x5_1h_20150201_20150501_ptrc_T.nc'
mesh_mask_file = 'mesh_mask.nc'
individual_directories = []
param_vals = []
param_name = "nampisrem_zz_remin_d_pon"

for file in os.listdir(group_results_directory):
    if os.path.isfile(group_results_directory + file + '/' + tracer_file):
        last_underscore = file.rfind("_")
        file_param_name = file[:last_underscore]
        if file_param_name == param_name:
            val = float(file[(last_underscore + 1):])
            individual_directories.append(file)
            param_vals.append(val)

tracer_datasets = [xr.open_dataset(group_results_directory + '/' + file +'/' + tracer_file) for file in individual_directories]
dataset_dict = dict(zip(param_vals, tracer_datasets))

In [3]:

plot_tracers = ['NO3', 'NH4', 'POC', 'DOC']

param_metrics = pd.DataFrame()
for param in param_vals:
    for tracer in plot_tracers:
        mean_tracer = met.mean_tracer_at_depth(dataset_dict[param], tracer)
        param_metrics = param_metrics.append(pd.DataFrame({param_name:[param], "METRIC_NAME":['MEAN_'+tracer + "_AT_DEPTH"], "METRIC_VAL": [mean_tracer]}))
    bloom_time = met.time_surface_NO3_drops_below_4(dataset_dict[param])
    param_metrics = param_metrics.append(pd.DataFrame({param_name:[param], "METRIC_NAME":["BLOOM_TIME"], "METRIC_VAL": [bloom_time]}))

   
sns.set(font_scale = 2)
plt.rcParams['image.cmap'] = 'jet'

fg = sns.FacetGrid(data=param_metrics, col = "METRIC_NAME",  sharey=False, col_wrap = 2, size = 6)
fg.set(xlim=(min(param_metrics[param_name]) - 0.1*max(param_metrics[param_name]),1.1*max(param_metrics[param_name])))
fg.map(plt.scatter, param_name, "METRIC_VAL").add_legend()
fg.set_xticklabels(rotation=-35)
fg.set_titles("{col_name}")

Out[3]:

<seaborn.axisgrid.FacetGrid at 0x7f75484c4518>

In [4]:

def plot_tracer_heatmaps(dataset_dict, tracer, vmin = None, vmax = None):
    plt.rcParams['image.cmap'] = 'jet'
    keys = (list(dataset_dict.keys()))
    keys.sort()

    for param_val in keys:
        z = dataset_dict[param_val].deptht.values
        t= np.array([float(x) for x in dataset_dict[param_val].time_centered.values])
        tz,zt=np.meshgrid((t[:] - t[0])/10**9/3600/24,z[:])

        fig, ax = plt.subplots(1, 1,figsize=(10,4))
        
        mesh=plt.pcolormesh(tz,zt,dataset_dict[param_val][tracer].values[:,:,2,2].T, vmin = vmin, vmax = vmax)
        fig.colorbar(mesh)
        plt.gca().invert_yaxis()
        plt.title(tracer +  " (" + param_name + " = " + str(param_val)+")", size = 20)
        plt.xlabel("Days", size =18)
        plt.ylabel("Depth", size = 18)
        
def plot_tracer_dif_heatmap(dataset_dict, tracer):
    param_vals = list(dataset_dict.keys())
    param_1 = min(param_vals)
    param_2 = max(param_vals)
    
    z = dataset_dict[param_1].deptht.values
    t= np.array([float(x) for x in dataset_dict[param_1].time_centered.values])
    tz,zt=np.meshgrid((t[:] - t[0])/10**9/3600/24,z[:])
    
    fig, ax = plt.subplots(1, 1,figsize=(15,6))
    mesh=plt.pcolormesh(tz,zt,dataset_dict[param_1][tracer].values[:,:,2,2].T - dataset_dict[param_2][tracer].values[:,:,2,2].T)
    fig.colorbar(mesh)
    plt.gca().invert_yaxis()
    plt.xlabel("Days", size =18)
    plt.ylabel("Depth", size = 18)
    plt.title(tracer + " difference (" + param_name + "=" + str(param_1) + " - " + param_name + "=" +str(param_2)+ ")")

In [5]:

plot_tracer_heatmaps(dataset_dict, "PHY2", 0, 1.8)
plot_tracer_dif_heatmap(dataset_dict, "PHY2")