Re-add plot features (#93)

* returning this back because it got deleted

LAMDA/plot_features.py

@@ -0,0 +1,285 @@
+from datetime import datetime
+import numpy as np
+from textwrap import TextWrapper
+import matplotlib.pyplot as plt
+from emcpy.plots.plots import Scatter, Histogram, VerticalLine
+from emcpy.plots.map_plots import MapScatter
+from emcpy.plots import CreateMap, CreatePlot, VariableSpecs
+from emcpy.plots.map_tools import Domain, MapProjection
+import matplotlib
+matplotlib.use('agg')
+
+__all__ = ['get_obs_type', 'get_bins', 'calculate_stats',
+           'get_labels', 'varspecs_name']
+
+
+def qc_flag_colors(qcflag):
+    """
+    Dictionary of colors to use for specific QC flag numbers.
+    Args:
+        qcflag : (int) qc flag number
+    Returns:
+        color : (str) color based on qc flag number
+    """
+    qc_flag_colors = {
+        0: 'tab:green',
+        1: 'black',
+        2: 'tab:pink',
+        3: 'tab:red',
+        4: 'tab:orange',
+        5: 'tab:brown',
+        6: 'tab:olive',
+        7: 'skyblue',
+        8: 'tab:blue',
+        9: 'maroon',
+        10: 'tab:cyan',
+        11: 'slateblue',
+        12: 'navy',
+        13: 'tan',
+        14: 'gold',
+        15: 'darkcyan',
+        50: 'indigo',
+        51: 'tab:purple',
+        52: 'magenta',
+        53: 'cornflowerblue',
+        54: 'pink',
+        55: 'lightgrey',
+        56: 'darkgrey',
+        57: 'dimgrey'
+    }
+
+    return qc_flag_colors[qcflag]
+
+
+def get_obs_type(obs_id):
+    """
+    Grabs the full name of a specific observation.
+    Args:
+        obs_id: (list of ints) ID number(s) for conventional
+                diagnostic
+    Returns:
+        list of observation names. If ID in the list, it will return,
+        the proper name. If no observation ID, returns 'All Observations'.
+        If ID not in the list, returns list of string of the ID number.
+    """
+
+    obs_indicators = {
+        120: "Rawinsonde",
+        126: "RASS",
+        130: "Aircraft: AIREP and PIREP",
+        131: "Aircraft: AMDAR",
+        132: "Flight-Level Reconnaissance and Profile Dropsonde",
+        133: "Aircraft: MDCRS ACARS",
+        134: "Aircraft: TAMDAR",
+        135: "Aircraft: Canadian AMDAR",
+        153: "GPS-Integrated Precipitable Water",
+        180: ("Surface Marine w/ Station Pressure (Ship, Buoy, C-MAN, "
+              "Tide Guage)"),
+        181: "Surface Land w/ Station Pressure (Synoptic, METAR)",
+        182: "Splash-Level Dropsonde Over Ocean",
+        183: "Surface Marine or Land - Missing Station Pressure",
+        187: "Surface Land - Missing Station Pressure",
+        210: "Synthetic Tropical Cyclone",
+        220: "Rawinsonde",
+        221: "PIBAL",
+        224: "NEXRAD Vertical Azimuth Display",
+        228: "Wind Profiler: JMA",
+        229: "Wind Profiler: PIBAL",
+        230: "Aircraft: AIREP and PIREP",
+        231: "Aircraft: AMDAR",
+        232: "Flight-Level Reconnaissance and Profile Dropsonde",
+        233: "Aircraft: MDCRS ACARS",
+        234: "Aircraft: TAMDAR",
+        235: "Aircraft: Canadian AMDAR",
+        242: ("JMA IR (Longwave) and Visible Cloud Drift Below 850mb "
+              "(GMS, MTSAT, HIMAWARI)"),
+        243: ("EUMETSAT IR (Longwave) and Visible Cloud Drift Below "
+              " 850mb (METEOSAT)"),
+        244: "AVHRR/POES IR (Longwave) Cloud Drift",
+        245: "NESDIS IR (Longwave) Cloud Drift (All Levels)",
+        246: "NESDIS Imager Water Vapor (All Levels) - Cloud Top (GOES)",
+        250: ("JMA Imager Water Vapor (All Levels) - Cloud Top & Deep "
+              "Layer (GMS, MTSAT, HIMAWARI)"),
+        251: "NESDIS Visible Cloud Drift (All Levels) (GOES)",
+        252: ("JMA IR (Longwave) and Visible Cloud Drift Above 850mb "
+              "(GMS, MTSAT, HIMAWARI)"),
+        253: ("EUMETSAT IR (Longwave) and Visible Cloud Drift Above "
+              "850mb (METEOSAT)"),
+        254: ("EUMETSAT Imager Water Vapor (All Levels) - Cloud Top "
+              " & Deep Layer (METEOSAT)"),
+        257: ("MODIS/POES IR (Longwave) Cloud Drift (All Levels) "
+              "(AQUA, TERRA)"),
+        258: ("MODIS/POES Imager Water Vapor (All Levels) - Cloud Top "
+              "(AQUA, TERRA)"),
+        259: ("MODIS/POES Imager Water Vapor (All Levels) - Deep Layer "
+              "(AQUA, TERRA)"),
+        280: ("Surface Marine w/ Station Pressure (Ship, Buoy, C-MAN, "
+              "Tide Guage)"),
+        281: "Surface Land w/ Station Pressure (Synoptic, METAR)",
+        282: "ATLAS Buoy",
+        284: "Surface Marine or Land - Missing Station Pressure",
+        287: "Surface Land (METAR) - Missing Station Pressure",
+        289: "SUPEROBED (1.0 Lat/Lon) Scatterometer Winds over Ocean",
+        290: "Non-SUPEROBED Scatterometer Winds over Ocean"
+    }
+
+    descripts = list()
+    if obs_id is None:
+        return ['All Observations']
+
+    else:
+        for ids in obs_id:
+            if ids in obs_indicators.keys():
+                descripts.append(obs_indicators[ids])
+
+            else:
+                descripts.append(str(ids))
+
+        return descripts
+
+
+def get_bins(eval_type, stats):
+    """
+    Calculates bins to use for histogram plot.
+    Args:
+        eval_type : (str) determines how to create bins
+                    whether 'diff' or 'magnitude'
+        stats : (dict) dictionary of stats from
+                `calculate_stats()`
+    Returns:
+        bins : (array-like)
+    """
+    binsize = stats['Std']/5
+
+    if eval_type == 'diff':
+        bins = np.arange(0-(4*stats['Std']),
+                         0+(4*stats['Std']),
+                         binsize)
+
+    else:
+        bins = np.arange(stats['Mean']-(4*stats['Std']),
+                         stats['Mean']+(4*stats['Std']),
+                         binsize)
+
+    return bins
+
+
+def calculate_stats(data):
+    """
+    Calculates n, mean, min, max,
+    standard deviation, and RMSE.
+    Returns dictionary with stats.
+    Args:
+        data : (array-like) array of data to calculate
+               stats
+    Returns:
+        stats : (dict) dictionary of stats
+    """
+
+    n = np.count_nonzero(~np.isnan(data))
+
+    mean = np.nanmean(data)
+    std = np.nanstd(data)
+    mx = np.nanmax(data)
+    mn = np.nanmin(data)
+
+    rmse = np.sqrt(np.nanmean(np.square(data)))
+
+    stats = {'Nobs': n,
+             'Min': np.round(mn, 3),
+             'Max': np.round(mx, 3),
+             'Mean': np.round(mean, 3),
+             'Std': np.round(std, 3),
+             'RMSE': np.round(rmse, 3)
+             }
+
+    return stats
+
+
+def get_labels(metadata):
+    """
+    Creates a dictionary of title, date title, and the save
+    file name using information from the metadata.
+    Args:
+        metadata : (dict) metadata dictionary created by diags.py
+    Returns:
+        labels : (dict) dictionary of labels
+    """
+
+    var = metadata['Satellite'] if 'Satellite' in metadata \
+        else metadata['Variable']
+
+    # Get title and save file name
+    if metadata['Anl Use Type'] is not None:
+        title = (f"{metadata['Obs Type']}: {var} - {metadata['Diag Type']}"
+                 f" - Data {metadata['Anl Use Type']}")
+
+        save_file = (f"{metadata['Date']:%Y%m%d%H}_{metadata['Obs Type']}_"
+                     f"{var}_{metadata['Diag Type']}_"
+                     f"{metadata['Anl Use Type']}_")
+
+    else:
+        title = f"{metadata['Obs Type']}: {var} - {metadata['Diag Type']}"
+        save_file = (f"{metadata['Date']:%Y%m%d%H}_{metadata['Obs Type']}_"
+                     f"{var}_{metadata['Diag Type']}_")
+
+    # Adds on specific obsid, channel, or layer info to title/save file
+    if metadata['Diag File Type'] == 'conventional':
+        title = title + '\n%s' % '\n'.join(metadata['ObsID Name'])
+        save_file = save_file + '%s' % '_'.join(
+            str(x) for x in metadata['ObsID'])
+
+    elif metadata['Diag File Type'] == 'radiance':
+        if metadata['Channels'] == 'All Channels':
+            title = title + '\nAll Channels'
+            save_file = save_file + '_All_Channels'
+
+        else:
+            title = title + '\nChannels: %s' % ', '.join(
+                str(x) for x in metadata['Channels'])
+            save_file = save_file + 'channels_%s' % '_'.join(
+                str(x) for x in metadata['Channels'])
+
+    else:
+        layer = metadata['Layer']
+        title = title + f'\nLayer: {layer}'
+        save_file = save_file + '%s' % layer
+
+    # Get date label
+    date_title = metadata['Date'].strftime("%d %b %Y %Hz")
+
+    labels = {
+        'title': title,
+        'date title': date_title,
+        'save file': save_file
+    }
+
+    return labels
+
+
+def varspecs_name(variable):
+    """
+    Grabs the specific variable name to utlitize emcpy's VariableSpecs.
+    Args:
+        variable : (str) variable name
+    Returns:
+        spec_variable : (str) name of spec variable
+    """
+    vardict = {
+            'temperature': ['air temperature', 'tmp', 'temp', 't'],
+            'specific humidity': ['q', 'spfh'],
+            'u': ['eastward wind', 'ugrd', 'zonal wind'],
+            'v': ['northward wind', 'vgrd', 'meridional wind'],
+            'wind speed': ['windspeed'],
+            'brightness temperature': ['bt'],
+            'integrated layer ozone in air': ['ozone', 'o3']
+        }
+
+    # makes lower case and replaces underscore with space
+    spec_variable = variable.lower().replace('_', ' ')
+
+    for key in vardict.keys():
+        spec_variable = key if spec_variable in vardict[key] \
+            else spec_variable
+
+    return spec_variable