"""
mmcdata.py
 
This Python source defines the MMCData class 

An instance of this class is a given set of 'observed' (via instrument
or model) timeseries of U,V,W, and other state variables, along with
associated location (lat/lon), time, and elevation characteristics of
the data stream 
"""

from math import *
import collections
import numpy as np
import datetime as dt
import pandas as pd
import xarray
import pickle

from matplotlib import pyplot as plt
from matplotlib import rcParams, cycler
import matplotlib.dates as mdates
from matplotlib.ticker import AutoMinorLocator


# legacy file format
header = """INSTITUTION:{institution:s}
   LOCATION:{location:s}
   LATITUDE:{latitude:10.4f}
  LONGITUDE:{longitude:10.4f}
   CODENAME:{codename:s}
   CODETYPE:{codetype:s}
   CASENAME:{casename:s}
  BENCHMARK:{benchmark:s}
     LEVELS:{levels:7d}
"""
record = """
       DATE:{date:s}
       TIME:{time:s}
FRICTION VELOCITY [m/s] = {ustar:10.5f}
SURFACE ROUGHNESS [m]   = {z0:10.5f}
SKIN TEMPERATURE [K]    = {T0:10.5f}
SURFACE FLUX [Km/s]     = {qwall:10.5f}
             Z [m]           U [m/s]           V [m/s]           W [m/s]            TH [K]           P [mbar]    TKE [m^2/s^2]   TAU11 [m^2/s^2]   TAU12 [m^2/s^2]   TAU13 [m^2/s^2]   TAU22 [m^2/s^2]   TAU23 [m^2/s^2]   TAU33 [m^2/s^2]      HFLUX [Km/s]
"""
datarow = 4*'{:18.3f}' + 2*'{:18.2f}' + '{:18.3f}' + 7*'{:18.5f}' + '\n'


class MMCData():
    """A given set of 'observed' (via instrument or model) timeseries of
    U,V,W, and other state variables and any 'derived' (via calculation
    methods) data like mean, perturbation, variance, correlations, etc...
    that are attributes (could be defined or missing a value) in a given
    MMCData instance
    """
    def __init__(self,asciifile=None,pklfile=None,pkldata=None,**kwargs):
        """Read ascii data in the legacy MMC format from `asciifile` or
        pickled data in list form from `pklfile`. **kwargs can include
        convert_ft_to_m=True, or specified_date="YYYY-MM-DD", e.g.
        specified_date='2013-11-08' if necessary for specific legacy data 
        files.
        """
        self.description = None
        self.records = []
        self.dataDict = collections.defaultdict(list)
        if asciifile:
            with open(asciifile,'r') as f:
                data = self._read_ascii(f)
            if self.dataSetLength > 0:
                self._process_data(data,**kwargs)
        elif pklfile or pkldata:
            if pkldata is None:
                with open(pklfile,'rb') as f:
                    pkldata = pickle.load(f)
            # first item is a dictionary with metadata
            self.dataSetLength = len(pkldata) - 1
            self.description = pkldata[0]
            if self.dataSetLength > 0:
                self._process_data(pkldata[1:],**kwargs)
        else:
            raise ValueError('Need to specify asciifile, pklfile, or pkldata')

    def _read_ascii(self,f):
        """Read entire legacy MMC file"""
        self.description = read_ascii_header(f)
        self.dataSetLength = 0
        data = []
        while True:
            recordheader = read_ascii_recordheader(f);
            if (len(recordheader)==0) or (recordheader is None): #is recordheader={} or Null value? if so break, otherwise read a record
                break
            else:  
                recordarray = read_ascii_records(f,self.description['levels'])
                data.append([recordheader, recordarray])
                self.dataSetLength += 1
        return data

    def _process_data(self,data,convert_ft_to_m=False,specified_date=None,map_to_met_coords=False):
        """Updates dataset description, records, and dataDict"""
        time=[]
        datetime=[]
        z=[]
        u=[]
        v=[]
        w=[]
        theta=[]
        pres=[]
        tke=[]
        tau11=[]
        tau12=[]
        tau13=[]
        tau22=[]
        tau23=[]
        tau33=[]
        hflux=[]
        for record in data:
            recordheader, recordarray = record
            self.records.append(recordheader)
            time.append(recordheader['time'].strip())
            if specified_date is None:
                dtstr = recordheader['date'] + "_" + recordheader['time'].strip()
            else: 
                dtstr = '{:s}_{:s}'.format(specified_date,recordheader['time'].strip())
            datetime.append(dt.datetime.strptime(dtstr, '%Y-%m-%d_%H:%M:%S'))
            z.append(recordarray[:,0])
            u.append(recordarray[:,1])
            v.append(recordarray[:,2])
            w.append(recordarray[:,3])
            theta.append(recordarray[:,4])
            pres.append(recordarray[:,5])
            tke.append(recordarray[:,6])
            tau11.append(recordarray[:,7])
            tau12.append(recordarray[:,8])
            tau13.append(recordarray[:,9])
            tau22.append(recordarray[:,10])
            tau23.append(recordarray[:,11])
            tau33.append(recordarray[:,12])
            hflux.append(recordarray[:,13])
        assert len(z) == self.dataSetLength

        # Re-cast fields as numpy arrays and add to 'dataDict' object attribute 
        self.dataDict['datetime'] = np.asarray(datetime)
        if convert_ft_to_m:
            # Convert TTU/SWiFT height to meters from feet ;-(
            self.dataDict['z'] = np.asarray(z)*0.3048
        else:
            # Otherwise expect heights in meters as they should be
            self.dataDict['z'] = np.asarray(z)
        if map_to_met_coords:      #map TTU-sonic (unorth, vwest) coords to standard meteorology coordinates
            self.dataDict['u']     = np.asarray(v)
            self.dataDict['v']     = -np.asarray(u)
        else:
            self.dataDict['u']     = np.asarray(u)
            self.dataDict['v']     = np.asarray(v)
        self.dataDict['w']     = np.asarray(w)
        self.dataDict['theta'] = np.asarray(theta)
        self.dataDict['pres']  = np.asarray(pres)
        self.dataDict['tke']   = np.asarray(tke)
        self.dataDict['tau11'] = np.asarray(tau11)
        self.dataDict['tau12'] = np.asarray(tau12)
        self.dataDict['tau13'] = np.asarray(tau13)
        self.dataDict['tau22'] = np.asarray(tau22)
        self.dataDict['tau23'] = np.asarray(tau23)
        self.dataDict['tau33'] = np.asarray(tau33)
        self.dataDict['hflux'] = np.asarray(hflux)
        self.dataDict['wspd']  = np.sqrt(self.dataDict['u']**2
                                       + self.dataDict['v']**2)
        self.dataDict['wdir']  = (270.0-np.arctan2(self.dataDict['v'],self.dataDict['u'])*180./np.pi)%360 
        ### The follwing will yield correct results, but sneaky usage of arctan2 where first argument is defined as y-oriented
        ### self.dataDict['wdir']  = 180. + np.arctan2(self.dataDict['v'],self.dataDict['u'])*180./np.pi

        #Declare and initialize to 0 the *_mean arrays
        self.dataDict['u_mean']     = np.zeros(self.dataDict['u'].shape)
        self.dataDict['v_mean']     = np.zeros(self.dataDict['u'].shape)
        self.dataDict['w_mean']     = np.zeros(self.dataDict['u'].shape)
        self.dataDict['theta_mean'] = np.zeros(self.dataDict['u'].shape)
        self.dataDict['tke_mean']   = np.zeros(self.dataDict['u'].shape)
        self.dataDict['hflux_mean'] = np.zeros(self.dataDict['u'].shape)
        self.dataDict['uu_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['uv_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['uw_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['vv_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['vw_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['ww_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['wt_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['wspd_mean']  = np.zeros(self.dataDict['u'].shape)
        self.dataDict['wdir_mean']  = np.zeros(self.dataDict['u'].shape)
        self.dataDict['shear_mean'] = np.zeros(self.dataDict['u'].shape)

    def to_pickle(self,pklfile):
        """pickle the entire class instance"""
        with open(pklfile,'wb') as f:
            pickle.dump(self,f) 

    def to_dataframe(self):
        """return a multi-indexed pandas dataframe with standard
        variables
        """
        df = self.to_xarray().to_dataframe()
        # the resulting dataframe has an integer multiindex formed by
        # range(num_samples) and range(num_levels)
        df = df.reset_index().drop(columns=['Times','bottom_top'])
        return df.set_index(['datetime','height'])
          
    def to_xarray(self,timedim='Times',heightdim='bottom_top'):
        """return an xarray dataset with standard variables"""
        coords = {
            'datetime': xarray.DataArray(self.dataDict['datetime'],
                                  name='datetime',
                                  dims=[timedim]),
            'height': xarray.DataArray(self.dataDict['z'],
                                  name='height',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'m'}),
        }
        data_vars = {
            'u': xarray.DataArray(self.dataDict['u'],
                                  name='west-east velocity',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'m s-1'}),
            'v': xarray.DataArray(self.dataDict['v'],
                                  name='south-north velocity',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'m s-1'}),
            'w': xarray.DataArray(self.dataDict['w'],
                                  name='vertical velocity',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'m s-1'}),
            'theta': xarray.DataArray(self.dataDict['theta'],
                                  name='potential temperature',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'K'}),
            'pres': xarray.DataArray(self.dataDict['pres'],
                                  name='pressure',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'mbar'}),
        }
        #ds=xarray.decode_cf(xarray.Dataset(data_vars,coords))
        ds=xarray.Dataset(data_vars,coords)
        #Remove the time dependence of heights by setting heights from each level as their temporal mean
        heights=ds['height'].groupby('bottom_top').mean()
        Nt,Nz = ds['height'].shape
        for i in range(Nt):
            ds['height'][i,:]=heights
        return ds

    def getDataSetDict(self):
        return self.description
    
    def getDataSetFieldShape(self):
        return self.dataDict['u'].shape
 
    def getRecordDict(self,recNum):
        return self.records[recNum]
 
    def setRunningMeans(self,windowLength,levels):
    #def getDataSetRunningMean(self,windowLength,levels, start_datetime,stop_datetime):
        for k in range(levels):
           #print("setRunningMeans: k = {:d}".format(k))
           self.dataDict['u_mean'][:,k] = running_mean(self.dataDict['u'][:,k],windowLength)
           self.dataDict['v_mean'][:,k] = running_mean(self.dataDict['v'][:,k],windowLength)
           self.dataDict['w_mean'][:,k] = running_mean(self.dataDict['w'][:,k],windowLength)
           self.dataDict['theta_mean'][:,k] = running_mean(self.dataDict['theta'][:,k],windowLength) 
           self.dataDict['tke_mean'][:,k] = running_mean(self.dataDict['tke'][:,k],windowLength)
           self.dataDict['hflux_mean'][:,k] = running_mean(self.dataDict['hflux'][:,k],windowLength)
           self.dataDict['uu_mean'][:,k] = running_mean( np.square(np.subtract(self.dataDict['u'][:,k],self.dataDict['u_mean'][:,k])),windowLength)
           self.dataDict['uv_mean'][:,k] = running_mean( np.multiply(np.subtract(self.dataDict['u'][:,k],self.dataDict['u_mean'][:,k]),
                                                                 np.subtract(self.dataDict['v'][:,k],self.dataDict['v_mean'][:,k])) ,windowLength)
           self.dataDict['uw_mean'][:,k] = running_mean( np.multiply(np.subtract(self.dataDict['u'][:,k],self.dataDict['u_mean'][:,k]),
                                                                 np.subtract(self.dataDict['w'][:,k],self.dataDict['w_mean'][:,k])) ,windowLength)
           self.dataDict['vv_mean'][:,k] = running_mean( np.square(np.subtract(self.dataDict['v'][:,k],self.dataDict['v_mean'][:,k])),windowLength)
           self.dataDict['vw_mean'][:,k] = running_mean( np.multiply(np.subtract(self.dataDict['v'][:,k],self.dataDict['v_mean'][:,k]),
                                                                 np.subtract(self.dataDict['w'][:,k],self.dataDict['w_mean'][:,k])) ,windowLength)
           self.dataDict['ww_mean'][:,k] = running_mean( np.square(np.subtract(self.dataDict['w'][:,k],self.dataDict['w_mean'][:,k])),windowLength)
           self.dataDict['wt_mean'][:,k] = running_mean( np.multiply(np.subtract(self.dataDict['w'][:,k],self.dataDict['w_mean'][:,k]),
                                                                 np.subtract(self.dataDict['theta'][:,k],self.dataDict['theta_mean'][:,k])) ,windowLength)
        self.dataDict['wspd_mean'] = np.sqrt(np.square(self.dataDict['u_mean'])+np.square(self.dataDict['v_mean']))
        #self.dataDict['wdir_mean'] = np.arctan2(self.dataDict['v_mean'],self.dataDict['u_mean'])*180./np.pi+180.0   #From Branko's original, but this seems incorrect...
        self.dataDict['wdir_mean'] = (270.0-np.arctan2(self.dataDict['v_mean'],self.dataDict['u_mean'])*180./np.pi)%360  
 #       self.dataDict['shear_mean']=np.sqrt(np.square(self.uw_mean)+np.square(self.vw_mean))
 
    #
    # Plotting functions (TODO: move to plotting.py)
    #
 
    def plotDataSetByKey(self,xVarKey,yVarKey):
        plt.figure()
        plt.plot(self.dataDict[xVarKey],self.dataDict[yVarKey],'bo-')
        #plt.show(block=False)
        plt.draw()
        #plt.show()
        #plt.pause(0.0001) 
 
    def plotObsVsModelProfileAsSubplot(self,fig,axs,fldString,obsData,obsIndepVar,obsLabel,modelData,modelIndepVar,modelLabel):
        #Set the Marker styles
        obs_marker_style = dict(color='r', linestyle='None', marker='s', markersize=5, markerfacecolor='None')
        model_marker_style = dict(color='b', linestyle='--', marker='o', markersize=3, markerfacecolor='None')
        #Setup up the shared y-axis ticks and labels
        yticks=np.arange(0,251,50)
        ylabels=[]
        ylabels.extend(str(z) for z in range(0,251,50))
        #Compute the standard deviation of obsField
        obs_std = np.std(obsData,axis=0)
        model_std = np.std(modelData,axis=0)
        #Find the x-axis Min,Max, and Interval
        deltaXp = np.nanmax(np.append(np.abs(np.nanmax(np.mean(obsData,axis=0))- \
                                             np.nanmax(np.mean(modelData,axis=0))) \
                             ,np.nanmax(obs_std)))
        deltaXm = np.nanmax(np.append(np.abs(np.nanmin(np.mean(obsData,axis=0))- \
                                             np.nanmin(np.mean(modelData,axis=0))) \
                             ,np.nanmax(obs_std)))
        deltaX = np.nanmax(np.append(deltaXp,deltaXm))
        xTickMin = np.floor(np.nanmin(np.mean(obsData,axis=0))-deltaX)
        xTickMax = np.ceil(np.nanmax(np.mean(obsData,axis=0))+deltaX)
        xTickInterval = np.round_((xTickMax-xTickMin)/3.0,0)
        #print the x-axis characteristics
        print('{:s}'.format(fldString+" x-axis traits..."))
        print('{:s}'.format("xTickMin ="+str(xTickMin)))
        print('{:s}'.format("xTickMax ="+str(xTickMax)))
        print('{:s}'.format("xTickInterval ="+str(xTickInterval)))
        #Setup the x-axis ticks and labels
        xticks=np.arange(xTickMin,xTickMax+xTickInterval,xTickInterval)
        xlabels=[]
        xlabels.extend(str(u).split('.')[0] for u in xticks.tolist())
        #Plot the observations (and uncertainty via errorbars)
        axs.errorbar(np.mean(obsData,axis=0), obsIndepVar, xerr=obs_std, capsize=2, \
                     label=obsLabel, **obs_marker_style)
        axs.errorbar(np.mean(modelData,axis=0), modelIndepVar, xerr=model_std, capsize=2, \
                     label=modelLabel, **model_marker_style)
        #axs.plot(np.mean(modelData,axis=0), modelIndepVar, \
        #         label=modelLabel, **model_marker_style)
        axs.set(xticks=xticks,xticklabels=xlabels,yticks=yticks,yticklabels=ylabels)
        #Format and beautify the axes ticks and limits
        axs.yaxis.set_minor_locator(AutoMinorLocator(4))
        axs.xaxis.set_minor_locator(AutoMinorLocator(4))
        axs.tick_params(direction='in',top=True,right=True,length=10, width=2, which='major')
        axs.tick_params(direction='in',top=True,right=True,length=5, width=1, which='minor')
        axs.tick_params(axis='both', which='major', labelsize=8)
        axs.tick_params(axis='both', which='minor', labelsize=6)
        axs.set_ylim(0.0,np.max(obsIndepVar+50))
        axs.set_xlim(xTickMin,xTickMax)
       
    def plotObsVsModelTimeSeriesAsSubplot(self,fig,axs,fldString, \
                                          obsData,obsIndepVar,obsLabel,obsLevs, \
                                          modelData,modelIndepVar,modelLabel,modelLevs):
        obs_marker_style = dict(linestyle='-')
        model_marker_style = dict(linestyle=':')
        for lev in range(obsData.shape[1]):
           axs.plot(obsIndepVar,obsData[:,lev],label = obsLabel+": "+str(obsLevs[0,lev]),**obs_marker_style)
        for lev in range(modelData.shape[1]):
           axs.plot(modelIndepVar,modelData[:,lev],label = modelLabel+": "+str(modelLevs[0,lev]),**model_marker_style)
 
    def plotSingleSourceTimeSeriesAsSubplot(self,fig,axs,fldString, \
                                            fldData,fldIndepVar,fldLabel,fldLevs):
        fld_marker_style = dict(linestyle='-')
        for lev in range(fldData.shape[1]):
           axs.plot(fldIndepVar,fldData[:,lev],label = fldLabel+": "+str(fldLevs[0,lev]),**fld_marker_style)

#####END OF the MMC_CLASS


### Readers for legacy MMC data

def read_ascii_header(f):
    """Read header from legacy MMC file, called by _read_ascii()"""
    head1 = f.readline()
    head2 = f.readline()
    head3 = f.readline()
    head4 = f.readline()
    head5 = f.readline()
    head6 = f.readline()
    head7 = f.readline()
    head8 = f.readline()
    head9 = f.readline()
    lab = head1[12:25].strip()
    print("lab: {:s}".format(lab))
    location = head2[12:25].strip()
    latitude = float(head3[12:25].strip())
    longitude = float(head4[12:25].strip())
    codename = head5[12:25].strip()
    print("codename: {:s}".format(codename))
    codetype = head6[12:25].strip()
    casename = head7[12:25].strip()
    benchmark = head8[12:25].strip()
    levels = int(head9[12:25].strip())
    print("levels: {:d}".format(levels))

    fileheader = {
        'lab':lab,
        'location':location,
        'latitude':latitude,
        'longitude':longitude,
        'codename':codename,
        'codetype':codetype,
        'casename':casename,
        'benchmark':benchmark,
        'levels':levels,
    }

    return fileheader

def read_ascii_recordheader(f):
    """Read a record from legacy MMC file, called by _read_ascii()"""
    try:
        pos0=f.tell()
        head0 = f.readline()
        pos1=f.tell()
        if pos1==pos0:
            recordheader = {}
        else:
            head1 = f.readline()
            head2 = f.readline()
            head3 = f.readline()
            head4 = f.readline()
            head5 = f.readline()
            head6 = f.readline()
            head7 = f.readline()
            date  = head1[12:22]
            time  = head2[12:22]
            ustar = float(head3[26:36].strip())
            z0    = float(head4[26:36].strip())
            tskin = float(head5[26:36])
            hflux = float(head6[26:36])
            varlist = head7.split()

            varnames=[]
            varunits=[]

            for i in range(len(varlist)):
                if (i % 2) == 0:
                    varnames.append(varlist[i])
                if (i % 2) == 1:
                    varunits.append(varlist[i])

            recordheader = {
                'date':date,
                'time':time,
                'ustar':ustar,
                'z0':z0,
                'tskin':tskin,
                'hflux':hflux,
                'varnames':varnames,
                'varunits':varunits,
            }

    except:
        print("Error in readrecordheader... Check your datafile for bad records!!\n Lines read are")
        print("head1 = ",head1)
        print("head2 = ",head2)
        print("head3 = ",head3)
        print("head4 = ",head4)
        print("head5 = ",head5)
        print("head6 = ",head6)
        print("head7 = ",head7)

    return recordheader

def read_ascii_records(f,Nlevels):
    """Read specified number of records from legacy MMC file, called
    by _read_ascii().
    """
    record=[]
    for i in range(Nlevels):
        line = f.readline()
        #data = map(float,line.split())
        for data in map(float,line.split()):
            record.append(data)
        #print("len(data) = {:d}",len(data))
        #record.append(data)
        #print("len(record) = {:d}",len(record))
    recordarray=np.array(record).reshape(Nlevels,floor(len(record)/Nlevels))
    #print("recordarray.shape = ",recordarray.shape)
    return recordarray


### Utility functions for MMC class

def linearly_interpolate_nans(y):
    # Fit a linear regression to the non-nan y values

    # Create X matrix for linreg with an intercept and an index
    X = np.vstack((np.ones(len(y)), np.arange(len(y))))

    # Get the non-NaN values of X and y
    X_fit = X[:, ~np.isnan(y)]
    y_fit = y[~np.isnan(y)].reshape(-1, 1)

    # Estimate the coefficients of the linear regression
    beta = np.linalg.lstsq(X_fit.T, y_fit, rcond=None)[0]

    # Fill in all the nan values using the predicted coefficients
    y.flat[np.isnan(y)] = np.dot(X[:, np.isnan(y)].T, beta)
    return y

def running_mean(x, N):
    M = len(x)
    bad = np.where(np.isnan(x))
    B = bad[0].size
    if (B > 0) and (float(B)/float(M) <= 0.1):
       x = linearly_interpolate_nans(x)
    if (B > 0) and (float(B)/float(M) > 0.1):
       sys.exit("More than 10% data is NaN!")
    y = x
    cumsum = np.cumsum(np.insert(x, 0, 0))
    xavg = (cumsum[N:] - cumsum[:-N]) / N
    for i in range(0,floor(N/2)):
        xavg = np.insert(xavg,i,np.nanmean(y[i:i+N]))
    for i in range(M-floor(N/2)+1,M):
        xavg = np.append(xavg,np.nanmean(y[i-N:i]))
    return xavg

def running_mean2(x,N):
    xavg=[]
    M=len(x)
    for i in range(0,floor(N/2)):
        xavg.append(np.nanmean(x[i:i+N]))
    for i in range(floor(N/2),M-floor(N/2)):
        xavg.append(np.nanmean(x[i-floor(N/2):i+floor(N/2)]))
    for i in range(M-floor(N/2),M):
        xavg.append(np.nanmean(x[i-N:i]))
    return xavg



"""
mmcdata.py
 
This Python source defines the MMCData class 

An instance of this class is a given set of 'observed' (via instrument
or model) timeseries of U,V,W, and other state variables, along with
associated location (lat/lon), time, and elevation characteristics of
the data stream 
"""

from math import *
import collections
import numpy as np
import datetime as dt
import pandas as pd
import xarray
import pickle

from matplotlib import pyplot as plt
from matplotlib import rcParams, cycler
import matplotlib.dates as mdates
from matplotlib.ticker import AutoMinorLocator


# legacy file format
header = """INSTITUTION:{institution:s}
   LOCATION:{location:s}
   LATITUDE:{latitude:10.4f}
  LONGITUDE:{longitude:10.4f}
   CODENAME:{codename:s}
   CODETYPE:{codetype:s}
   CASENAME:{casename:s}
  BENCHMARK:{benchmark:s}
     LEVELS:{levels:7d}
"""
record = """
       DATE:{date:s}
       TIME:{time:s}
FRICTION VELOCITY [m/s] = {ustar:10.5f}
SURFACE ROUGHNESS [m]   = {z0:10.5f}
SKIN TEMPERATURE [K]    = {T0:10.5f}
SURFACE FLUX [Km/s]     = {qwall:10.5f}
             Z [m]           U [m/s]           V [m/s]           W [m/s]            TH [K]           P [mbar]    TKE [m^2/s^2]   TAU11 [m^2/s^2]   TAU12 [m^2/s^2]   TAU13 [m^2/s^2]   TAU22 [m^2/s^2]   TAU23 [m^2/s^2]   TAU33 [m^2/s^2]      HFLUX [Km/s]
"""
datarow = 4*'{:18.3f}' + 2*'{:18.2f}' + '{:18.3f}' + 7*'{:18.5f}' + '\n'


class MMCData():
    """A given set of 'observed' (via instrument or model) timeseries of
    U,V,W, and other state variables and any 'derived' (via calculation
    methods) data like mean, perturbation, variance, correlations, etc...
    that are attributes (could be defined or missing a value) in a given
    MMCData instance
    """
    def __init__(self,asciifile=None,pklfile=None,pkldata=None,**kwargs):
        """Read ascii data in the legacy MMC format from `asciifile` or
        pickled data in list form from `pklfile`. **kwargs can include
        convert_ft_to_m=True, or specified_date="YYYY-MM-DD", e.g.
        specified_date='2013-11-08' if necessary for specific legacy data 
        files.
        """
        self.description = None
        self.records = []
        self.dataDict = collections.defaultdict(list)
        if asciifile:
            with open(asciifile,'r') as f:
                data = self._read_ascii(f)
            if self.dataSetLength > 0:
                self._process_data(data,**kwargs)
        elif pklfile or pkldata:
            if pkldata is None:
                with open(pklfile,'rb') as f:
                    pkldata = pickle.load(f)
            # first item is a dictionary with metadata
            self.dataSetLength = len(pkldata) - 1
            self.description = pkldata[0]
            if self.dataSetLength > 0:
                self._process_data(pkldata[1:],**kwargs)
        else:
            raise ValueError('Need to specify asciifile, pklfile, or pkldata')

    def _read_ascii(self,f):
        """Read entire legacy MMC file"""
        self.description = read_ascii_header(f)
        self.dataSetLength = 0
        data = []
        while True:
            recordheader = read_ascii_recordheader(f);
            if (len(recordheader)==0) or (recordheader is None): #is recordheader={} or Null value? if so break, otherwise read a record
                break
            else:  
                recordarray = read_ascii_records(f,self.description['levels'])
                data.append([recordheader, recordarray])
                self.dataSetLength += 1
        return data

    def _process_data(self,data,convert_ft_to_m=False,specified_date=None,map_to_met_coords=False):
        """Updates dataset description, records, and dataDict"""
        time=[]
        datetime=[]
        z=[]
        u=[]
        v=[]
        w=[]
        theta=[]
        pres=[]
        tke=[]
        tau11=[]
        tau12=[]
        tau13=[]
        tau22=[]
        tau23=[]
        tau33=[]
        hflux=[]
        for record in data:
            recordheader, recordarray = record
            self.records.append(recordheader)
            time.append(recordheader['time'].strip())
            if specified_date is None:
                dtstr = recordheader['date'] + "_" + recordheader['time'].strip()
            else: 
                dtstr = '{:s}_{:s}'.format(specified_date,recordheader['time'].strip())
            datetime.append(dt.datetime.strptime(dtstr, '%Y-%m-%d_%H:%M:%S'))
            z.append(recordarray[:,0])
            u.append(recordarray[:,1])
            v.append(recordarray[:,2])
            w.append(recordarray[:,3])
            theta.append(recordarray[:,4])
            pres.append(recordarray[:,5])
            tke.append(recordarray[:,6])
            tau11.append(recordarray[:,7])
            tau12.append(recordarray[:,8])
            tau13.append(recordarray[:,9])
            tau22.append(recordarray[:,10])
            tau23.append(recordarray[:,11])
            tau33.append(recordarray[:,12])
            hflux.append(recordarray[:,13])
        assert len(z) == self.dataSetLength

        # Re-cast fields as numpy arrays and add to 'dataDict' object attribute 
        self.dataDict['datetime'] = np.asarray(datetime)
        if convert_ft_to_m:
            # Convert TTU/SWiFT height to meters from feet ;-(
            self.dataDict['z'] = np.asarray(z)*0.3048
        else:
            # Otherwise expect heights in meters as they should be
            self.dataDict['z'] = np.asarray(z)
        if map_to_met_coords:      #map TTU-sonic (unorth, vwest) coords to standard meteorology coordinates
            self.dataDict['u']     = np.asarray(v)
            self.dataDict['v']     = -np.asarray(u)
        else:
            self.dataDict['u']     = np.asarray(u)
            self.dataDict['v']     = np.asarray(v)
        self.dataDict['w']     = np.asarray(w)
        self.dataDict['theta'] = np.asarray(theta)
        self.dataDict['pres']  = np.asarray(pres)
        self.dataDict['tke']   = np.asarray(tke)
        self.dataDict['tau11'] = np.asarray(tau11)
        self.dataDict['tau12'] = np.asarray(tau12)
        self.dataDict['tau13'] = np.asarray(tau13)
        self.dataDict['tau22'] = np.asarray(tau22)
        self.dataDict['tau23'] = np.asarray(tau23)
        self.dataDict['tau33'] = np.asarray(tau33)
        self.dataDict['hflux'] = np.asarray(hflux)
        self.dataDict['wspd']  = np.sqrt(self.dataDict['u']**2
                                       + self.dataDict['v']**2)
        self.dataDict['wdir']  = (270.0-np.arctan2(self.dataDict['v'],self.dataDict['u'])*180./np.pi)%360 
        ### The follwing will yield correct results, but sneaky usage of arctan2 where first argument is defined as y-oriented
        ### self.dataDict['wdir']  = 180. + np.arctan2(self.dataDict['v'],self.dataDict['u'])*180./np.pi

        #Declare and initialize to 0 the *_mean arrays
        self.dataDict['u_mean']     = np.zeros(self.dataDict['u'].shape)
        self.dataDict['v_mean']     = np.zeros(self.dataDict['u'].shape)
        self.dataDict['w_mean']     = np.zeros(self.dataDict['u'].shape)
        self.dataDict['theta_mean'] = np.zeros(self.dataDict['u'].shape)
        self.dataDict['tke_mean']   = np.zeros(self.dataDict['u'].shape)
        self.dataDict['hflux_mean'] = np.zeros(self.dataDict['u'].shape)
        self.dataDict['uu_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['uv_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['uw_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['vv_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['vw_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['ww_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['wt_mean']    = np.zeros(self.dataDict['u'].shape)
        self.dataDict['wspd_mean']  = np.zeros(self.dataDict['u'].shape)
        self.dataDict['wdir_mean']  = np.zeros(self.dataDict['u'].shape)
        self.dataDict['shear_mean'] = np.zeros(self.dataDict['u'].shape)

    def to_pickle(self,pklfile):
        """pickle the entire class instance"""
        with open(pklfile,'wb') as f:
            pickle.dump(self,f) 

    def to_dataframe(self):
        """return a multi-indexed pandas dataframe with standard
        variables
        """
        df = self.to_xarray().to_dataframe()
        # the resulting dataframe has an integer multiindex formed by
        # range(num_samples) and range(num_levels)
        df = df.reset_index().drop(columns=['Times','bottom_top'])
        return df.set_index(['datetime','height'])
          
    def to_xarray(self,timedim='Times',heightdim='bottom_top'):
        """return an xarray dataset with standard variables"""
        coords = {
            'datetime': xarray.DataArray(self.dataDict['datetime'],
                                  name='datetime',
                                  dims=[timedim]),
            'height': xarray.DataArray(self.dataDict['z'],
                                  name='height',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'m'}),
        }
        data_vars = {
            'u': xarray.DataArray(self.dataDict['u'],
                                  name='west-east velocity',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'m s-1'}),
            'v': xarray.DataArray(self.dataDict['v'],
                                  name='south-north velocity',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'m s-1'}),
            'w': xarray.DataArray(self.dataDict['w'],
                                  name='vertical velocity',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'m s-1'}),
            'theta': xarray.DataArray(self.dataDict['theta'],
                                  name='potential temperature',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'K'}),
            'pres': xarray.DataArray(self.dataDict['pres'],
                                  name='pressure',
                                  dims=[timedim, heightdim],
                                  attrs={'units':'mbar'}),
        }
        #ds=xarray.decode_cf(xarray.Dataset(data_vars,coords))
        ds=xarray.Dataset(data_vars,coords)
        #Remove the time dependence of heights by setting heights from each level as their temporal mean
        heights=ds['height'].groupby('bottom_top').mean()
        Nt,Nz = ds['height'].shape
        for i in range(Nt):
            ds['height'][i,:]=heights
        return ds

    def getDataSetDict(self):
        return self.description
    
    def getDataSetFieldShape(self):
        return self.dataDict['u'].shape
 
    def getRecordDict(self,recNum):
        return self.records[recNum]
 
    def setRunningMeans(self,windowLength,levels):
    #def getDataSetRunningMean(self,windowLength,levels, start_datetime,stop_datetime):
        for k in range(levels):
           #print("setRunningMeans: k = {:d}".format(k))
           self.dataDict['u_mean'][:,k] = running_mean(self.dataDict['u'][:,k],windowLength)
           self.dataDict['v_mean'][:,k] = running_mean(self.dataDict['v'][:,k],windowLength)
           self.dataDict['w_mean'][:,k] = running_mean(self.dataDict['w'][:,k],windowLength)
           self.dataDict['theta_mean'][:,k] = running_mean(self.dataDict['theta'][:,k],windowLength) 
           self.dataDict['tke_mean'][:,k] = running_mean(self.dataDict['tke'][:,k],windowLength)
           self.dataDict['hflux_mean'][:,k] = running_mean(self.dataDict['hflux'][:,k],windowLength)
           self.dataDict['uu_mean'][:,k] = running_mean( np.square(np.subtract(self.dataDict['u'][:,k],self.dataDict['u_mean'][:,k])),windowLength)
           self.dataDict['uv_mean'][:,k] = running_mean( np.multiply(np.subtract(self.dataDict['u'][:,k],self.dataDict['u_mean'][:,k]),
                                                                 np.subtract(self.dataDict['v'][:,k],self.dataDict['v_mean'][:,k])) ,windowLength)
           self.dataDict['uw_mean'][:,k] = running_mean( np.multiply(np.subtract(self.dataDict['u'][:,k],self.dataDict['u_mean'][:,k]),
                                                                 np.subtract(self.dataDict['w'][:,k],self.dataDict['w_mean'][:,k])) ,windowLength)
           self.dataDict['vv_mean'][:,k] = running_mean( np.square(np.subtract(self.dataDict['v'][:,k],self.dataDict['v_mean'][:,k])),windowLength)
           self.dataDict['vw_mean'][:,k] = running_mean( np.multiply(np.subtract(self.dataDict['v'][:,k],self.dataDict['v_mean'][:,k]),
                                                                 np.subtract(self.dataDict['w'][:,k],self.dataDict['w_mean'][:,k])) ,windowLength)
           self.dataDict['ww_mean'][:,k] = running_mean( np.square(np.subtract(self.dataDict['w'][:,k],self.dataDict['w_mean'][:,k])),windowLength)
           self.dataDict['wt_mean'][:,k] = running_mean( np.multiply(np.subtract(self.dataDict['w'][:,k],self.dataDict['w_mean'][:,k]),
                                                                 np.subtract(self.dataDict['theta'][:,k],self.dataDict['theta_mean'][:,k])) ,windowLength)
        self.dataDict['wspd_mean'] = np.sqrt(np.square(self.dataDict['u_mean'])+np.square(self.dataDict['v_mean']))
        #self.dataDict['wdir_mean'] = np.arctan2(self.dataDict['v_mean'],self.dataDict['u_mean'])*180./np.pi+180.0   #From Branko's original, but this seems incorrect...
        self.dataDict['wdir_mean'] = (270.0-np.arctan2(self.dataDict['v_mean'],self.dataDict['u_mean'])*180./np.pi)%360  
 #       self.dataDict['shear_mean']=np.sqrt(np.square(self.uw_mean)+np.square(self.vw_mean))
 
    #
    # Plotting functions (TODO: move to plotting.py)
    #
 
    def plotDataSetByKey(self,xVarKey,yVarKey):
        plt.figure()
        plt.plot(self.dataDict[xVarKey],self.dataDict[yVarKey],'bo-')
        #plt.show(block=False)
        plt.draw()
        #plt.show()
        #plt.pause(0.0001) 
 
    def plotObsVsModelProfileAsSubplot(self,fig,axs,fldString,obsData,obsIndepVar,obsLabel,modelData,modelIndepVar,modelLabel):
        #Set the Marker styles
        obs_marker_style = dict(color='r', linestyle='None', marker='s', markersize=5, markerfacecolor='None')
        model_marker_style = dict(color='b', linestyle='--', marker='o', markersize=3, markerfacecolor='None')
        #Setup up the shared y-axis ticks and labels
        yticks=np.arange(0,251,50)
        ylabels=[]
        ylabels.extend(str(z) for z in range(0,251,50))
        #Compute the standard deviation of obsField
        obs_std = np.std(obsData,axis=0)
        model_std = np.std(modelData,axis=0)
        #Find the x-axis Min,Max, and Interval
        deltaXp = np.nanmax(np.append(np.abs(np.nanmax(np.mean(obsData,axis=0))- \
                                             np.nanmax(np.mean(modelData,axis=0))) \
                             ,np.nanmax(obs_std)))
        deltaXm = np.nanmax(np.append(np.abs(np.nanmin(np.mean(obsData,axis=0))- \
                                             np.nanmin(np.mean(modelData,axis=0))) \
                             ,np.nanmax(obs_std)))
        deltaX = np.nanmax(np.append(deltaXp,deltaXm))
        xTickMin = np.floor(np.nanmin(np.mean(obsData,axis=0))-deltaX)
        xTickMax = np.ceil(np.nanmax(np.mean(obsData,axis=0))+deltaX)
        xTickInterval = np.round_((xTickMax-xTickMin)/3.0,0)
        #print the x-axis characteristics
        print('{:s}'.format(fldString+" x-axis traits..."))
        print('{:s}'.format("xTickMin ="+str(xTickMin)))
        print('{:s}'.format("xTickMax ="+str(xTickMax)))
        print('{:s}'.format("xTickInterval ="+str(xTickInterval)))
        #Setup the x-axis ticks and labels
        xticks=np.arange(xTickMin,xTickMax+xTickInterval,xTickInterval)
        xlabels=[]
        xlabels.extend(str(u).split('.')[0] for u in xticks.tolist())
        #Plot the observations (and uncertainty via errorbars)
        axs.errorbar(np.mean(obsData,axis=0), obsIndepVar, xerr=obs_std, capsize=2, \
                     label=obsLabel, **obs_marker_style)
        axs.errorbar(np.mean(modelData,axis=0), modelIndepVar, xerr=model_std, capsize=2, \
                     label=modelLabel, **model_marker_style)
        #axs.plot(np.mean(modelData,axis=0), modelIndepVar, \
        #         label=modelLabel, **model_marker_style)
        axs.set(xticks=xticks,xticklabels=xlabels,yticks=yticks,yticklabels=ylabels)
        #Format and beautify the axes ticks and limits
        axs.yaxis.set_minor_locator(AutoMinorLocator(4))
        axs.xaxis.set_minor_locator(AutoMinorLocator(4))
        axs.tick_params(direction='in',top=True,right=True,length=10, width=2, which='major')
        axs.tick_params(direction='in',top=True,right=True,length=5, width=1, which='minor')
        axs.tick_params(axis='both', which='major', labelsize=8)
        axs.tick_params(axis='both', which='minor', labelsize=6)
        axs.set_ylim(0.0,np.max(obsIndepVar+50))
        axs.set_xlim(xTickMin,xTickMax)
       
    def plotObsVsModelTimeSeriesAsSubplot(self,fig,axs,fldString, \
                                          obsData,obsIndepVar,obsLabel,obsLevs, \
                                          modelData,modelIndepVar,modelLabel,modelLevs):
        obs_marker_style = dict(linestyle='-')
        model_marker_style = dict(linestyle=':')
        for lev in range(obsData.shape[1]):
           axs.plot(obsIndepVar,obsData[:,lev],label = obsLabel+": "+str(obsLevs[0,lev]),**obs_marker_style)
        for lev in range(modelData.shape[1]):
           axs.plot(modelIndepVar,modelData[:,lev],label = modelLabel+": "+str(modelLevs[0,lev]),**model_marker_style)
 
    def plotSingleSourceTimeSeriesAsSubplot(self,fig,axs,fldString, \
                                            fldData,fldIndepVar,fldLabel,fldLevs):
        fld_marker_style = dict(linestyle='-')
        for lev in range(fldData.shape[1]):
           axs.plot(fldIndepVar,fldData[:,lev],label = fldLabel+": "+str(fldLevs[0,lev]),**fld_marker_style)

#####END OF the MMC_CLASS


### Readers for legacy MMC data

def read_ascii_header(f):
    """Read header from legacy MMC file, called by _read_ascii()"""
    head1 = f.readline()
    head2 = f.readline()
    head3 = f.readline()
    head4 = f.readline()
    head5 = f.readline()
    head6 = f.readline()
    head7 = f.readline()
    head8 = f.readline()
    head9 = f.readline()
    lab = head1[12:25].strip()
    print("lab: {:s}".format(lab))
    location = head2[12:25].strip()
    latitude = float(head3[12:25].strip())
    longitude = float(head4[12:25].strip())
    codename = head5[12:25].strip()
    print("codename: {:s}".format(codename))
    codetype = head6[12:25].strip()
    casename = head7[12:25].strip()
    benchmark = head8[12:25].strip()
    levels = int(head9[12:25].strip())
    print("levels: {:d}".format(levels))

    fileheader = {
        'lab':lab,
        'location':location,
        'latitude':latitude,
        'longitude':longitude,
        'codename':codename,
        'codetype':codetype,
        'casename':casename,
        'benchmark':benchmark,
        'levels':levels,
    }

    return fileheader

def read_ascii_recordheader(f):
    """Read a record from legacy MMC file, called by _read_ascii()"""
    try:
        pos0=f.tell()
        head0 = f.readline()
        pos1=f.tell()
        if pos1==pos0:
            recordheader = {}
        else:
            head1 = f.readline()
            head2 = f.readline()
            head3 = f.readline()
            head4 = f.readline()
            head5 = f.readline()
            head6 = f.readline()
            head7 = f.readline()
            date  = head1[12:22]
            time  = head2[12:22]
            ustar = float(head3[26:36].strip())
            z0    = float(head4[26:36].strip())
            tskin = float(head5[26:36])
            hflux = float(head6[26:36])
            varlist = head7.split()

            varnames=[]
            varunits=[]

            for i in range(len(varlist)):
                if (i % 2) == 0:
                    varnames.append(varlist[i])
                if (i % 2) == 1:
                    varunits.append(varlist[i])

            recordheader = {
                'date':date,
                'time':time,
                'ustar':ustar,
                'z0':z0,
                'tskin':tskin,
                'hflux':hflux,
                'varnames':varnames,
                'varunits':varunits,
            }

    except:
        print("Error in readrecordheader... Check your datafile for bad records!!\n Lines read are")
        print("head1 = ",head1)
        print("head2 = ",head2)
        print("head3 = ",head3)
        print("head4 = ",head4)
        print("head5 = ",head5)
        print("head6 = ",head6)
        print("head7 = ",head7)

    return recordheader

def read_ascii_records(f,Nlevels):
    """Read specified number of records from legacy MMC file, called
    by _read_ascii().
    """
    record=[]
    for i in range(Nlevels):
        line = f.readline()
        #data = map(float,line.split())
        for data in map(float,line.split()):
            record.append(data)
        #print("len(data) = {:d}",len(data))
        #record.append(data)
        #print("len(record) = {:d}",len(record))
    recordarray=np.array(record).reshape(Nlevels,floor(len(record)/Nlevels))
    #print("recordarray.shape = ",recordarray.shape)
    return recordarray


### Utility functions for MMC class

def linearly_interpolate_nans(y):
    # Fit a linear regression to the non-nan y values

    # Create X matrix for linreg with an intercept and an index
    X = np.vstack((np.ones(len(y)), np.arange(len(y))))

    # Get the non-NaN values of X and y
    X_fit = X[:, ~np.isnan(y)]
    y_fit = y[~np.isnan(y)].reshape(-1, 1)

    # Estimate the coefficients of the linear regression
    beta = np.linalg.lstsq(X_fit.T, y_fit, rcond=None)[0]

    # Fill in all the nan values using the predicted coefficients
    y.flat[np.isnan(y)] = np.dot(X[:, np.isnan(y)].T, beta)
    return y

def running_mean(x, N):
    M = len(x)
    bad = np.where(np.isnan(x))
    B = bad[0].size
    if (B > 0) and (float(B)/float(M) <= 0.1):
       x = linearly_interpolate_nans(x)
    if (B > 0) and (float(B)/float(M) > 0.1):
       sys.exit("More than 10% data is NaN!")
    y = x
    cumsum = np.cumsum(np.insert(x, 0, 0))
    xavg = (cumsum[N:] - cumsum[:-N]) / N
    for i in range(0,floor(N/2)):
        xavg = np.insert(xavg,i,np.nanmean(y[i:i+N]))
    for i in range(M-floor(N/2)+1,M):
        xavg = np.append(xavg,np.nanmean(y[i-N:i]))
    return xavg

def running_mean2(x,N):
    xavg=[]
    M=len(x)
    for i in range(0,floor(N/2)):
        xavg.append(np.nanmean(x[i:i+N]))
    for i in range(floor(N/2),M-floor(N/2)):
        xavg.append(np.nanmean(x[i-floor(N/2):i+floor(N/2)]))
    for i in range(M-floor(N/2),M):
        xavg.append(np.nanmean(x[i-N:i]))
    return xavg



mmcdata.py   This Python source defines the MMCData class   An instance of this class is a given set of 'observed' (via instrument or model) timeseries of U,V,W, and other state variables, along with associated location (lat/lon), time, and elevation characteristics of the data stream # legacy file format INSTITUTION:{institution:s}    LOCATION:{location:s}    LATITUDE:{latitude:10.4f}   LONGITUDE:{longitude:10.4f}    CODENAME:{codename:s}    CODETYPE:{codetype:s}    CASENAME:{casename:s}   BENCHMARK:{benchmark:s}      LEVELS:{levels:7d} DATE:{date:s}        TIME:{time:s} FRICTION VELOCITY [m/s] = {ustar:10.5f} SURFACE ROUGHNESS [m]   = {z0:10.5f} SKIN TEMPERATURE [K]    = {T0:10.5f} SURFACE FLUX [Km/s]     = {qwall:10.5f}              Z [m]           U [m/s]           V [m/s]           W [m/s]            TH [K]           P [mbar]    TKE [m^2/s^2]   TAU11 [m^2/s^2]   TAU12 [m^2/s^2]   TAU13 [m^2/s^2]   TAU22 [m^2/s^2]   TAU23 [m^2/s^2]   TAU33 [m^2/s^2]      HFLUX [Km/s] A given set of 'observed' (via instrument or model) timeseries of     U,V,W, and other state variables and any 'derived' (via calculation     methods) data like mean, perturbation, variance, correlations, etc...     that are attributes (could be defined or missing a value) in a given     MMCData instance Read ascii data in the legacy MMC format from `asciifile` or         pickled data in list form from `pklfile`. **kwargs can include         convert_ft_to_m=True, or specified_date="YYYY-MM-DD", e.g.         specified_date='2013-11-08' if necessary for specific legacy data          files. # first item is a dictionary with metadata Read entire legacy MMC file #is recordheader={} or Null value? if so break, otherwise read a record Updates dataset description, records, and dataDict # Re-cast fields as numpy arrays and add to 'dataDict' object attribute # Convert TTU/SWiFT height to meters from feet ;-( # Otherwise expect heights in meters as they should be #map TTU-sonic (unorth, vwest) coords to standard meteorology coordinates ### The follwing will yield correct results, but sneaky usage of arctan2 where first argument is defined as y-oriented ### self.dataDict['wdir']  = 180. + np.arctan2(self.dataDict['v'],self.dataDict['u'])*180./np.pi #Declare and initialize to 0 the *_mean arrays pickle the entire class instance return a multi-indexed pandas dataframe with standard         variables # the resulting dataframe has an integer multiindex formed by # range(num_samples) and range(num_levels) return an xarray dataset with standard variables #ds=xarray.decode_cf(xarray.Dataset(data_vars,coords)) #Remove the time dependence of heights by setting heights from each level as their temporal mean #def getDataSetRunningMean(self,windowLength,levels, start_datetime,stop_datetime): #print("setRunningMeans: k = {:d}".format(k)) #self.dataDict['wdir_mean'] = np.arctan2(self.dataDict['v_mean'],self.dataDict['u_mean'])*180./np.pi+180.0   #From Branko's original, but this seems incorrect... #       self.dataDict['shear_mean']=np.sqrt(np.square(self.uw_mean)+np.square(self.vw_mean)) # # Plotting functions (TODO: move to plotting.py) # #plt.show(block=False) #plt.show() #plt.pause(0.0001) #Set the Marker styles #Setup up the shared y-axis ticks and labels #Compute the standard deviation of obsField #Find the x-axis Min,Max, and Interval #print the x-axis characteristics #Setup the x-axis ticks and labels #Plot the observations (and uncertainty via errorbars) #axs.plot(np.mean(modelData,axis=0), modelIndepVar, \ #         label=modelLabel, **model_marker_style) #Format and beautify the axes ticks and limits #####END OF the MMC_CLASS ### Readers for legacy MMC data Read header from legacy MMC file, called by _read_ascii() Read a record from legacy MMC file, called by _read_ascii() Read specified number of records from legacy MMC file, called     by _read_ascii(). #data = map(float,line.split()) #print("len(data) = {:d}",len(data)) #record.append(data) #print("len(record) = {:d}",len(record)) #print("recordarray.shape = ",recordarray.shape) ### Utility functions for MMC class # Fit a linear regression to the non-nan y values # Create X matrix for linreg with an intercept and an index # Get the non-NaN values of X and y # Estimate the coefficients of the linear regression # Fill in all the nan values using the predicted coefficients