Save data out as transparent .gif about metpy

[dopplershift]

I can see many uses for that. Unfortunately, that ends up being a limitation of matplotlib, so there's not much we can do; feel free to open an issue there. It is possible to use imagemagick to convert PNG files from matplotlib into the GIFs you're interested in.

[rankinstudio]

Here is a solution that works rather well converting them to transparent PNGs with PIL. These can be easily transported into Google Earth.

                       #SETUP PLOT TO SAVE WITHOUT ANY BORDERS OR INFO
                       fig = plt.figure(frameon=False)

                        cmap = ctables.registry.get_colortable('Carbone42')
                        plt.pcolormesh(xlocs, ylocs, data, cmap=cmap)
                        plt.gca().set_aspect('equal','datalim')

                        #SET LIMITS IF WANTED
                        plt.xlim(-160, 160)
                        plt.ylim(-160, 160)

                        #SETUP FIGURE TO HAVE JUST IMAGE DATA
                        a=fig.gca()
                        a.set_frame_on(False)
                        a.set_xticks([]); a.set_yticks([])
                        plt.axis('off')
                        plt.savefig('myimage.png', dpi=430, bbox_inches='tight', pad_inches = 0)

                        #******************* CONVERT TO TRANSPARENT *********************
                        # CONVERT ALL WHITE PIXELS TO TRANSPARENT
                        img = Image.open('myimage.png')
                        img = img.convert("RGBA")

                        pixdata = img.load()

                        for y in range(img.size[1]):
                            for x in range(img.size[0]):
                                if pixdata[x, y] == (255, 255, 255, 255):
                                    pixdata[x, y] = (255, 255, 255, 0)

                        img.save('myimage.png', "PNG")

[dopplershift]

Well if transparent PNGs are enough (before you specifically asked about GIF), then you can just set it to transparent using the matplotlib API:

fig.patch.set_alpha(0.0)
a.patch.set_alpha(0.0)

I just tried this out and it worked fine.

[rankinstudio]

Much easier. Thanks!

I did notice something else. On the level II example I think the functions of MetPy are assuming that the level II scans will always have all of the data it requests. I have noticed a few of the files I download throw errors. I think it may be because they are missing data on some of the radial scans. I have had to catch the following errors on occasion:

                    try:
                        name = each
                        fn = each.split('\\')[-1]
                        f = Level2File(name)
                        #f.sweeps[0][0]

                        # Pull data out of the file
                        sweep = 0

                        # First item in ray is header, which has azimuth angle
                        az = np.array([ray[0].az_angle for ray in f.sweeps[sweep]])

                        # 5th item is a dict mapping a var name (byte string) to a tuple of (header, data array)

                        ref_hdr = f.sweeps[sweep][0][4][b'REF'][0]
                        ref_range = np.arange(ref_hdr.num_gates) * ref_hdr.gate_width + ref_hdr.first_gate
                        ref = np.array([ray[4][b'REF'][1] for ray in f.sweeps[sweep]])

                        var_data = ref
                        var_range = ref_range

                        # Turn into an array, then mask
                        data = ma.array(var_data)
                        data[np.isnan(data)] = ma.masked

                        # Convert az,range to x,y
                        xlocs = var_range * np.sin(np.deg2rad(az[:, np.newaxis]))
                        ylocs = var_range * np.cos(np.deg2rad(az[:, np.newaxis]))

                        #******************* SETUP AND SAVE FIGURE ********************

                        fig = plt.figure(frameon=False)

                        #norm, cmap = ctables.registry.get_with_steps('NWSReflectivity', 16, 16)
                        cmap = ctables.registry.get_colortable('Carbone42')
                        plt.pcolormesh(xlocs, ylocs, data, cmap=cmap)
                        plt.gca().set_aspect('equal','datalim')

                        #SET LIMITS IF WANTED
                        plt.xlim(-160, 160)
                        plt.ylim(-160, 160)

                        # fig.tight_layout()
                        a=fig.gca()
                        a.set_frame_on(False)
                        a.set_xticks([]); a.set_yticks([])
                        fig.patch.set_alpha(0.0)
                        a.patch.set_alpha(0.0)
                        plt.axis('off')

                        plt.savefig(each + '.png', dpi=430, bbox_inches='tight', pad_inches = 0)
                        self.time['text'] = fn + ".png Saved"
                        plt.clf() #CLEAR FIGURE FROM MEMORY?
                        del fig #CLEAE FIGURE FROM MEMORY?

                    except (OSError,IndexError,AssertionError,TypeError,MemoryError):
                        print('ERROR ON FILE ' + each)
                        self.time['text'] = fn + ".png Converted"
                        continue

[dopplershift]

The level 2 code should be fault tolerant. Go ahead and open a new issue for that problem and post a link to the problematic file, if you can.

Ryan

On Jun 13, 2015, at 11:22, rankinstudio [email protected] wrote:

Much easier. Thanks!

I did notice something else. On the level II example I think the functions of MetPy are assuming that the level II scans will always have all of the data it requests. I have noticed a few of the files I download throw errors. I think it may be because they are missing data on some of the radial scans. I have had to catch the following errors on occasion:

                try:
                    name = each
                    fn = each.split('\\')[-1]
                    f = Level2File(name)
                    #f.sweeps[0][0]

                    # Pull data out of the file
                    sweep = 0

                    # First item in ray is header, which has azimuth angle
                    az = np.array([ray[0].az_angle for ray in f.sweeps[sweep]])

                    # 5th item is a dict mapping a var name (byte string) to a tuple of (header, data array)

                    ref_hdr = f.sweeps[sweep][0][4][b'REF'][0]
                    ref_range = np.arange(ref_hdr.num_gates) * ref_hdr.gate_width + ref_hdr.first_gate
                    ref = np.array([ray[4][b'REF'][1] for ray in f.sweeps[sweep]])

                    var_data = ref
                    var_range = ref_range

                    # Turn into an array, then mask
                    data = ma.array(var_data)
                    data[np.isnan(data)] = ma.masked

                    # Convert az,range to x,y
                    xlocs = var_range * np.sin(np.deg2rad(az[:, np.newaxis]))
                    ylocs = var_range * np.cos(np.deg2rad(az[:, np.newaxis]))

                    #******************* SETUP AND SAVE FIGURE ********************

                    fig = plt.figure(frameon=False)

                    #norm, cmap = ctables.registry.get_with_steps('NWSReflectivity', 16, 16)
                    cmap = ctables.registry.get_colortable('Carbone42')
                    plt.pcolormesh(xlocs, ylocs, data, cmap=cmap)
                    plt.gca().set_aspect('equal','datalim')

                    #SET LIMITS IF WANTED
                    plt.xlim(-160, 160)
                    plt.ylim(-160, 160)

                    # fig.tight_layout()
                    a=fig.gca()
                    a.set_frame_on(False)
                    a.set_xticks([]); a.set_yticks([])
                    fig.patch.set_alpha(0.0)
                    a.patch.set_alpha(0.0)
                    plt.axis('off')

                    plt.savefig(each + '.png', dpi=430, bbox_inches='tight', pad_inches = 0)
                    self.time['text'] = fn + ".png Saved"
                    plt.clf() #CLEAR FIGURE FROM MEMORY?
                    del fig #CLEAE FIGURE FROM MEMORY?

                except (OSError,IndexError,AssertionError,TypeError,MemoryError):
                    print('ERROR ON FILE ' + each)
                    self.time['text'] = fn + ".png Converted"
                    continue

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