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Unit issue with MetPy's parcel_profile function

I have been working on programming to plot Skew_Ts from Wyoming's weather servers. The issue I am having is I get an error when attempting to run the parcel_profile function, it says it can not convert from dimensionless to hectopascals. The pressure array being fed into the function as well as the temperature and dewpoint data point have the appropriate units attached though. To add to my confusion, I have the exact same coding on another machine with the same library versions and it runs fine on that one. Am I missing an obvious problem? Code and relevant library versions are listed below:
import metpy as mp
from metpy.units import units
import metpy.calc as mpcalc
from siphon.simplewebservice.wyoming import WyomingUpperAir
from datetime import datetime
import pandas as pd
import numpy as np
final_time = datetime(2022, 1, 21, 12)
station = 'ABQ'
df = WyomingUpperAir.request_data(final_time, station)
data_dict = {"Press":"", "Temp": "", "Dew_Point": "", "Height":"",
"Mask": "", "Parcel": "", "Idx": "", "U": "", "V": ""}
data_dict['Press'] = df['pressure'].values * units(df.units['pressure'])
data_dict['Temp'] = df['temperature'].values * units(df.units['temperature'])
data_dict['Dew_Point'] = df['dewpoint'].values * units(df.units['dewpoint'])
data_dict['Height'] = df['height'].values * units(df.units['height'])
data_dict['U'] = df['u_wind'].values * units(df.units['u_wind'])
data_dict['V'] = df['v_wind'].values * units(df.units['v_wind'])
data_dict['Parcel'] = mpcalc.parcel_profile(data_dict['Press'],
data_dict['Temp'][0],
data_dict['Dew_Point'][0]).to('degC')
Error:
DimensionalityError Traceback (most recent call last)
C:\Users\####################.py in <module>
----> 1 data_dict['Parcel'] = mpcalc.parcel_profile(data_dict['Press'],
2 data_dict['Temp'][0],
3 data_dict['Dew_Point'][0]).to('degC')
~\anaconda3\envs\Met_World\lib\site-packages\metpy\xarray.py in wrapper(*args, **kwargs)
1214
1215 # Evaluate inner calculation
-> 1216 result = func(*bound_args.args, **bound_args.kwargs)
1217
1218 # Wrap output based on match and match_unit
~\anaconda3\envs\Met_World\lib\site-packages\metpy\units.py in wrapper(*args, **kwargs)
244 'that the function is being called properly.\n') + msg
245 raise ValueError(msg)
--> 246 return func(*args, **kwargs)
247
248 return wrapper
~\anaconda3\envs\Met_World\lib\site-packages\metpy\calc\thermo.py in parcel_profile(pressure, temperature, dewpoint)
737
738 """
--> 739 _, _, _, t_l, _, t_u = _parcel_profile_helper(pressure, temperature, dewpoint)
740 return concatenate((t_l, t_u))
741
~\anaconda3\envs\Met_World\lib\site-packages\metpy\calc\thermo.py in _parcel_profile_helper(pressure, temperature, dewpoint)
892
893 # If the pressure profile doesn't make it to the lcl, we can stop here
--> 894 if _greater_or_close(np.nanmin(pressure), press_lcl):
895 return (press_lower[:-1], press_lcl, units.Quantity(np.array([]), press_lower.units),
896 temp_lower[:-1], temp_lcl, units.Quantity(np.array([]), temp_lower.units))
~\anaconda3\envs\Met_World\lib\site-packages\metpy\calc\tools.py in _greater_or_close(a, value, **kwargs)
738
739 """
--> 740 return (a > value) | np.isclose(a, value, **kwargs)
741
742
~\anaconda3\envs\Met_World\lib\site-packages\pint\quantity.py in __array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1721 )
1722
-> 1723 return numpy_wrap("ufunc", ufunc, inputs, kwargs, types)
1724
1725 def __array_function__(self, func, types, args, kwargs):
~\anaconda3\envs\Met_World\lib\site-packages\pint\numpy_func.py in numpy_wrap(func_type, func, args, kwargs, types)
919 if name not in handled or any(is_upcast_type(t) for t in types):
920 return NotImplemented
--> 921 return handled[name](*args, **kwargs)
~\anaconda3\envs\Met_World\lib\site-packages\pint\numpy_func.py in implementation(*args, **kwargs)
284 if input_units == "all_consistent":
285 # Match all input args/kwargs to same units
--> 286 stripped_args, stripped_kwargs = convert_to_consistent_units(
287 *args, pre_calc_units=first_input_units, **kwargs
288 )
~\anaconda3\envs\Met_World\lib\site-packages\pint\numpy_func.py in convert_to_consistent_units(pre_calc_units, *args, **kwargs)
105 """
106 return (
--> 107 tuple(convert_arg(arg, pre_calc_units=pre_calc_units) for arg in args),
108 {
109 key: convert_arg(arg, pre_calc_units=pre_calc_units)
~\anaconda3\envs\Met_World\lib\site-packages\pint\numpy_func.py in <genexpr>(.0)
105 """
106 return (
--> 107 tuple(convert_arg(arg, pre_calc_units=pre_calc_units) for arg in args),
108 {
109 key: convert_arg(arg, pre_calc_units=pre_calc_units)
~\anaconda3\envs\Met_World\lib\site-packages\pint\numpy_func.py in convert_arg(arg, pre_calc_units)
87 return arg
88 else:
---> 89 raise DimensionalityError("dimensionless", pre_calc_units)
90 elif _is_quantity(arg):
91 return arg.m
DimensionalityError: Cannot convert from 'dimensionless' to 'hectopascal'
Libraries used:
python 3.9.7
metpy 1.1.0
pandas 1.2.4
numpy 1.22.0
xarray 0.20.2

My first guess is that this is a problem with multiplying whatever e.g. df['u_wind'].values is returning by units. While it's a nicer syntax, the more robust way is to use the Quantity constructor:
data_dict['Press'] = units.Quantity(df['pressure'].values, units(df.units['pressure']))
You can shorten all of that, though, and use the Quantity() method by using MetPy's helper metpy.units.pandas_dataframe_to_unit_arrays:
data_dict = units.pandas_dataframe_to_unit_arrays(df)
If you want the column names you were originally using, you can change them with df.rename().

How to read ".edf" file in MNE-Python. I tried but showing the Following Error

import matplotlib
import pathlib
import mne
raw_path = pathlib.Path("data/")
raw_data = raw_path / 'Subject1' / 'S1.edf'
raw = mne.io.read_raw_edf(raw_path)
**Extracting EDF parameters from C:\research\data\Subject1\S1.edf...
EDF file detected**
---------------------------------------------------------------------------
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-28-daad488a71cf> in <module>
----> 1 raw = mne.io.read_raw_edf(raw_path)
~\Anaconda3\envs\mne\lib\site-packages\mne\io\edf\edf.py in read_raw_edf(input_fname, eog, misc, stim_channel, exclude, preload, verbose)
1218 raise NotImplementedError(
1219 'Only EDF files are supported by read_raw_edf, got %s' % (ext,))
-> 1220 return RawEDF(input_fname=input_fname, eog=eog, misc=misc,
1221 stim_channel=stim_channel, exclude=exclude, preload=preload,
1222 verbose=verbose)
<decorator-gen-197> in __init__(self, input_fname, eog, misc, stim_channel, exclude, preload, verbose)
~\Anaconda3\envs\mne\lib\site-packages\mne\io\edf\edf.py in __init__(self, input_fname, eog, misc, stim_channel, exclude, preload, verbose)
112 logger.info('Extracting EDF parameters from {}...'.format(input_fname))
113 input_fname = os.path.abspath(input_fname)
--> 114 info, edf_info, orig_units = _get_info(input_fname,
115 stim_channel, eog, misc,
116 exclude, preload)
~\Anaconda3\envs\mne\lib\site-packages\mne\io\edf\edf.py in _get_info(fname, stim_channel, eog, misc, exclude, preload)
356 misc = misc if misc is not None else []
357
--> 358 edf_info, orig_units = _read_header(fname, exclude)
359
360 # XXX: `tal_ch_names` to pass to `_check_stim_channel` should be computed
~\Anaconda3\envs\mne\lib\site-packages\mne\io\edf\edf.py in _read_header(fname, exclude)
343 logger.info('%s file detected' % ext.upper())
344 if ext in ('bdf', 'edf'):
--> 345 return _read_edf_header(fname, exclude)
346 elif ext == 'gdf':
347 return _read_gdf_header(fname, exclude), None
~\Anaconda3\envs\mne\lib\site-packages\mne\io\edf\edf.py in _read_edf_header(fname, exclude)
569 else:
570 meas_date = fid.read(8).decode('latin-1')
--> 571 day, month, year = [int(x) for x in meas_date.split('.')]
572 year = year + 2000 if year < 85 else year + 1900
573
~\Anaconda3\envs\mne\lib\site-packages\mne\io\edf\edf.py in <listcomp>(.0)
569 else:
570 meas_date = fid.read(8).decode('latin-1')
--> 571 day, month, year = [int(x) for x in meas_date.split('.')]
572 year = year + 2000 if year < 85 else year + 1900
573
ValueError: invalid literal for int() with base 10: 'EyeLink '

(Memory) Problem when accessing Dask type arrays

I need to load some meteorological data to analyze several months but such data is stored in files that cover only one day so I need to acces many files at once.
I am following some pre-given instruction that told me to create a memory partition in my computer.
from datetime import datetime, timedelta
import dask.array as da
from dask.distributed import Client, LocalCluster
import xarray
try:
client
except NameError:
client = Client(n_workers=1, threads_per_worker=4, memory_limit='2GB')
else:
print("Client already exists")
After this, I create an array dates that goes from 1st June to 1st October and that is need in "files" to get the link to the meteorological data.
dates=[datetime(2019,6,1) + timedelta(days=i) for i in range(3*30)]
files= [date.strftime('http://mandeo.meteogalicia.es/thredds/dodsC/modelos/WRF_HIST/d03/%Y/%m/wrf_arw_det_history_d03_%Y%m%d_0000.nc4') for date in dates]
My issue starts when I try to unzip all that data as
multi = xarray.open_mfdataset(files, preprocess= lambda a : a.isel(time=slice(0,24)))
It raises the error:
KeyError Traceback (most recent call last)
~\Nueva carpeta\lib\site-packages\xarray\backends\file_manager.py in _acquire_with_cache_info(self, needs_lock)
197 try:
--> 198 file = self._cache[self._key]
199 except KeyError:
~\Nueva carpeta\lib\site-packages\xarray\backends\lru_cache.py in __getitem__(self, key)
52 with self._lock:
---> 53 value = self._cache[key]
54 self._cache.move_to_end(key)
KeyError: [<class 'netCDF4._netCDF4.Dataset'>, ('http://mandeo.meteogalicia.es/thredds/dodsC/modelos/WRF_HIST/d03/2019/06/wrf_arw_det_history_d03_20190626_0000.nc4',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False))]
During handling of the above exception, another exception occurred:
OSError Traceback (most recent call last)
<ipython-input-19-c3d0f4a8cc26> in <module>
----> 1 multi = xarray.open_mfdataset(files, preprocess= lambda a : a.isel(time=slice(0,24)))
~\Nueva carpeta\lib\site-packages\xarray\backends\api.py in open_mfdataset(paths, chunks, concat_dim, compat, preprocess, engine, lock, data_vars, coords, combine, autoclose, parallel, join, attrs_file, **kwargs)
916 getattr_ = getattr
917
--> 918 datasets = [open_(p, **open_kwargs) for p in paths]
919 file_objs = [getattr_(ds, "_file_obj") for ds in datasets]
920 if preprocess is not None:
~\Nueva carpeta\lib\site-packages\xarray\backends\api.py in <listcomp>(.0)
916 getattr_ = getattr
917
--> 918 datasets = [open_(p, **open_kwargs) for p in paths]
919 file_objs = [getattr_(ds, "_file_obj") for ds in datasets]
920 if preprocess is not None:
~\Nueva carpeta\lib\site-packages\xarray\backends\api.py in open_dataset(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables, backend_kwargs, use_cftime, decode_timedelta)
507 if engine == "netcdf4":
508 store = backends.NetCDF4DataStore.open(
--> 509 filename_or_obj, group=group, lock=lock, **backend_kwargs
510 )
511 elif engine == "scipy":
~\Nueva carpeta\lib\site-packages\xarray\backends\netCDF4_.py in open(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)
356 netCDF4.Dataset, filename, mode=mode, kwargs=kwargs
357 )
--> 358 return cls(manager, group=group, mode=mode, lock=lock, autoclose=autoclose)
359
360 def _acquire(self, needs_lock=True):
~\Nueva carpeta\lib\site-packages\xarray\backends\netCDF4_.py in __init__(self, manager, group, mode, lock, autoclose)
312 self._group = group
313 self._mode = mode
--> 314 self.format = self.ds.data_model
315 self._filename = self.ds.filepath()
316 self.is_remote = is_remote_uri(self._filename)
~\Nueva carpeta\lib\site-packages\xarray\backends\netCDF4_.py in ds(self)
365 #property
366 def ds(self):
--> 367 return self._acquire()
368
369 def open_store_variable(self, name, var):
~\Nueva carpeta\lib\site-packages\xarray\backends\netCDF4_.py in _acquire(self, needs_lock)
359
360 def _acquire(self, needs_lock=True):
--> 361 with self._manager.acquire_context(needs_lock) as root:
362 ds = _nc4_require_group(root, self._group, self._mode)
363 return ds
~\Nueva carpeta\lib\contextlib.py in __enter__(self)
110 del self.args, self.kwds, self.func
111 try:
--> 112 return next(self.gen)
113 except StopIteration:
114 raise RuntimeError("generator didn't yield") from None
~\Nueva carpeta\lib\site-packages\xarray\backends\file_manager.py in acquire_context(self, needs_lock)
184 def acquire_context(self, needs_lock=True):
185 """Context manager for acquiring a file."""
--> 186 file, cached = self._acquire_with_cache_info(needs_lock)
187 try:
188 yield file
~\Nueva carpeta\lib\site-packages\xarray\backends\file_manager.py in _acquire_with_cache_info(self, needs_lock)
202 kwargs = kwargs.copy()
203 kwargs["mode"] = self._mode
--> 204 file = self._opener(*self._args, **kwargs)
205 if self._mode == "w":
206 # ensure file doesn't get overriden when opened again
netCDF4\_netCDF4.pyx in netCDF4._netCDF4.Dataset.__init__()
netCDF4\_netCDF4.pyx in netCDF4._netCDF4._ensure_nc_success()
OSError: [Errno -37] NetCDF: Write to read only: b'http://mandeo.meteogalicia.es/thredds/dodsC/modelos/WRF_HIST/d03/2019/06/wrf_arw_det_history_d03_20190626_0000.nc4'
Does anyone know why this error occurs?

Bokeh Geoviews use Lat/Long or UTM?

I am trying to plot the Zillow dataset with Bokeh using Geoviews and Datashader but I am having the damnedest time getting it to work. I am able to plot the data on a Cartesian plane fine but when I attempt to overlay the data with a map I run into errors.
I have used code adapted from the census-hv example on the datashader github. I believe my problem is that it is looking for the coordinates to be in UTM not Lat/Long. Because the code works when I have my coordinates multiplied by a few thousand. The points are then put above the map in white space. If i attempt to plot the proper lat/long coordinates I get the following errors.
Can someone please point me in the direction of a map that uses Lat/Long
>>>props.head()
longitude latitude
0 -118.654084 34.144442
1 -118.625364 34.140430
2 -118.394633 33.989359
3 -118.437206 34.148863
4 -118.385816 34.194168
import pandas as pd
import holoviews as hv
import geoviews as gv
import datashader as ds
from bokeh.models import WMTSTileSource
from holoviews.operation.datashader import datashade, dynspread
hv.notebook_ex
tension('bokeh')
%%opts Overlay [width=900 height=525 xaxis=None yaxis=None]
geomap = gv.WMTS(WMTSTileSource(url=\
'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{Z}/{Y}/{X}.jpg'))
points = hv.Points(gv.Dataset(props, kdims=['longitude', 'latitude']))
# color_key = {'w':'aqua', 'b':'lime', 'a':'red', 'h':'fuchsia', 'o':'yellow' }
race = datashade(points, x_sampling=50, y_sampling=50,
element_type=gv.Image)
geomap * race
RETURNS ERROR:
WARNING:root:dynamic_operation: Exception raised in callable
'dynamic_operation' of type 'function'.
Invoked as dynamic_operation(height=400, scale=1.0, width=400, x_range=None, y_range=None)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
305 pass
306 else:
--> 307 return printer(obj)
308 # Finally look for special method names
309 method = get_real_method(obj, self.print_method)
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/ipython/display_hooks.py in pprint_display(obj)
255 if not ip.display_formatter.formatters['text/plain'].pprint:
256 return None
--> 257 return display(obj, raw=True)
258
259
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/ipython/display_hooks.py in display(obj, raw, **kwargs)
241 elif isinstance(obj, (HoloMap, DynamicMap)):
242 with option_state(obj):
--> 243 html = map_display(obj)
244 else:
245 return repr(obj) if raw else IPython.display.display(obj, **kwargs)
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/ipython/display_hooks.py in wrapped(element)
127 try:
128 html = fn(element,
--> 129 max_frames=OutputMagic.options['max_frames'])
130
131 # Only want to add to the archive for one display hook...
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/ipython/display_hooks.py in map_display(vmap, max_frames)
196 return None
197
--> 198 return render(vmap)
199
200
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/ipython/display_hooks.py in render(obj, **kwargs)
57 if renderer.fig == 'pdf':
58 renderer = renderer.instance(fig='png')
---> 59 return renderer.html(obj, **kwargs)
60
61
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/plotting/renderer.py in html(self, obj, fmt, css, comm, **kwargs)
253 code to initialize a Comm, if the plot supplies one.
254 """
--> 255 plot, fmt = self._validate(obj, fmt)
256 figdata, _ = self(plot, fmt, **kwargs)
257 if css is None: css = self.css
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/plotting/renderer.py in _validate(self, obj, fmt)
189 if isinstance(obj, tuple(self.widgets.values())):
190 return obj, 'html'
--> 191 plot = self.get_plot(obj, renderer=self)
192
193 fig_formats = self.mode_formats['fig'][self.mode]
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/plotting/renderer.py in get_plot(self_or_cls, obj, renderer)
164 """
165 # Initialize DynamicMaps with first data item
--> 166 initialize_dynamic(obj)
167
168 if not isinstance(obj, Plot) and not displayable(obj):
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/plotting/util.py in initialize_dynamic(obj)
173 continue
174 if not len(dmap):
--> 175 dmap[dmap._initial_key()]
176
177
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/core/spaces.py in __getitem__(self, key)
942 # Not a cross product and nothing cached so compute element.
943 if cache is not None: return cache
--> 944 val = self._execute_callback(*tuple_key)
945 if data_slice:
946 val = self._dataslice(val, data_slice)
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/core/spaces.py in _execute_callback(self, *args)
791
792 with dynamicmap_memoization(self.callback, self.streams):
--> 793 retval = self.callback(*args, **kwargs)
794 return self._style(retval)
795
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/core/spaces.py in __call__(self, *args, **kwargs)
489 # Nothing to do for callbacks that accept no arguments
490 (inargs, inkwargs) = (args, kwargs)
--> 491 if not args and not kwargs: return self.callable()
492 inputs = [i for i in self.inputs if isinstance(i, DynamicMap)]
493 streams = []
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/core/overlay.py in dynamic_mul(*args, **kwargs)
27 from .spaces import Callable
28 def dynamic_mul(*args, **kwargs):
---> 29 element = other[args]
30 return self * element
31 callback = Callable(dynamic_mul, inputs=[self, other])
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/core/spaces.py in __getitem__(self, key)
942 # Not a cross product and nothing cached so compute element.
943 if cache is not None: return cache
--> 944 val = self._execute_callback(*tuple_key)
945 if data_slice:
946 val = self._dataslice(val, data_slice)
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/core/spaces.py in _execute_callback(self, *args)
791
792 with dynamicmap_memoization(self.callback, self.streams):
--> 793 retval = self.callback(*args, **kwargs)
794 return self._style(retval)
795
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/core/spaces.py in __call__(self, *args, **kwargs)
519
520 try:
--> 521 ret = self.callable(*args, **kwargs)
522 except:
523 posstr = ', '.join(['%r' % el for el in inargs]) if inargs else ''
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/util.py in dynamic_operation(*key, **kwargs)
101 self.p.kwargs.update(kwargs)
102 obj = map_obj[key] if isinstance(map_obj, HoloMap) else map_obj
--> 103 return self._process(obj, key)
104 else:
105 def dynamic_operation(*key, **kwargs):
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/util.py in _process(self, element, key)
87 kwargs = {k: v for k, v in self.p.kwargs.items()
88 if k in self.p.operation.params()}
---> 89 return self.p.operation.process_element(element, key, **kwargs)
90 else:
91 return self.p.operation(element, **self.p.kwargs)
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/core/operation.py in process_element(self, element, key, **params)
133 """
134 self.p = param.ParamOverrides(self, params)
--> 135 return self._process(element, key)
136
137
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/operation/datashader.py in _process(self, element, key)
357
358 def _process(self, element, key=None):
--> 359 agg = aggregate._process(self, element, key)
360 shaded = shade._process(self, agg, key)
361 return shaded
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/operation/datashader.py in _process(self, element, key)
226 agg = getattr(cvs, glyph)(data, x, y, self.p.aggregator)
227 if agg.ndim == 2:
--> 228 return self.p.element_type(agg, **params)
229 else:
230 return NdOverlay({c: self.p.element_type(agg.sel(**{column: c}),
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/geoviews/element/geo.py in __init__(self, data, **kwargs)
81 elif crs:
82 kwargs['crs'] = crs
---> 83 super(_Element, self).__init__(data, **kwargs)
84
85
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/element/raster.py in __init__(self, data, bounds, extents, xdensity, ydensity, **params)
242 if bounds is None:
243 xvals = self.dimension_values(0, False)
--> 244 l, r, xdensity, _ = util.bound_range(xvals, xdensity)
245 yvals = self.dimension_values(1, False)
246 b, t, ydensity, _ = util.bound_range(yvals, ydensity)
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/holoviews/core/util.py in bound_range(vals, density)
1373 using significant digits reported by sys.float_info.dig.
1374 """
-> 1375 low, high = vals.min(), vals.max()
1376 invert = False
1377 if vals[0] > vals[1]:
/home/mcamp/anaconda3/envs/py3.6/lib/python3.6/site-packages/numpy/core/_methods.py in _amin(a, axis, out, keepdims)
27
28 def _amin(a, axis=None, out=None, keepdims=False):
---> 29 return umr_minimum(a, axis, None, out, keepdims)
30
31 def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
ValueError: zero-size array to reduction operation minimum which has no identity
Out[54]:
b':DynamicMap []'

I think the problem here is two-fold, first of all since the coordinates are latitudes and longitudes and you specify xsampling/ysampling values of 50 the datashaded image ends up with a tiny or zero shape, which causes this error. My suggestion would be to cast the coordinates to Google Mercator first. In future this PR will let you do so very simply by calling this:
import cartopy.crs as ccrs
projected = gv.operation.project(points, projection=ccrs.GOOGLE_MERCATOR)
...
To do this manually for now you can use the cartopy projection directly:
coords = ccrs.GOOGLE_MERCATOR.transform_points(ccrs.PlateCarree(), lons, lats)
projected = gv.Points(coords, crs=ccrs.GOOGLE_MERCATOR)
...

pymc3: Disaster example with deterministic switchpoint function

I'm trying to reproduce coal mining example with deterministic function for switchpoint instead of using theano's switch function. Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pymc3
import numpy as np
import theano.tensor as t
import theano
data = np.hstack((np.random.poisson(15,1000),np.random.poisson(2,100)))
plt.plot(data)
#theano.compile.ops.as_op(itypes=[t.lscalar, t.dscalar,t.dscalar],otypes=[t.dvector])
def rate1(sw,mu1,mu2):
n = len(data)
out = np.empty(n)
out[:sw] = mu1
out[sw:] = mu2
return out
with pymc3.Model() as dis:
switchpoint = pymc3.DiscreteUniform('switchpoint',lower=0, upper=len(data)-1)
mu1 = pymc3.Exponential('mu1', lam=1.)
mu2 = pymc3.Exponential('mu2',lam=1.)
disasters=pymc3.Poisson('disasters', mu=rate1, observed = data)
But this code rise an error:
--------------------------------------------------------------------------- KeyError Traceback (most recent call
last) c:\program files\git\theano\theano\tensor\type.py in
dtype_specs(self)
266 'complex64': (complex, 'theano_complex64', 'NPY_COMPLEX64')
--> 267 }[self.dtype]
268 except KeyError:
KeyError: 'object'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call
last) c:\program files\git\theano\theano\tensor\basic.py in
constant_or_value(x, rtype, name, ndim, dtype)
407 rval = rtype(
--> 408 TensorType(dtype=x_.dtype, broadcastable=bcastable),
409 x_.copy(),
c:\program files\git\theano\theano\tensor\type.py in init(self,
dtype, broadcastable, name, sparse_grad)
49 self.broadcastable = tuple(bool(b) for b in broadcastable)
---> 50 self.dtype_specs() # error checking is done there
51 self.name = name
c:\program files\git\theano\theano\tensor\type.py in dtype_specs(self)
269 raise TypeError("Unsupported dtype for %s: %s"
--> 270 % (self.class.name, self.dtype))
271
TypeError: Unsupported dtype for TensorType: object
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call
last) c:\program files\git\theano\theano\tensor\basic.py in
as_tensor_variable(x, name, ndim)
201 try:
--> 202 return constant(x, name=name, ndim=ndim)
203 except TypeError:
c:\program files\git\theano\theano\tensor\basic.py in constant(x,
name, ndim, dtype)
421 ret = constant_or_value(x, rtype=TensorConstant, name=name, ndim=ndim,
--> 422 dtype=dtype)
423
c:\program files\git\theano\theano\tensor\basic.py in
constant_or_value(x, rtype, name, ndim, dtype)
416 except Exception:
--> 417 raise TypeError("Could not convert %s to TensorType" % x, type(x))
418
TypeError: ('Could not convert FromFunctionOp{rate1} to TensorType',
)
During handling of the above exception, another exception occurred:
AsTensorError Traceback (most recent call
last) in ()
14 mu2 = pymc3.Exponential('mu2',lam=1.)
15 #rate1 = pymc3.switch(switchpoint >= np.arange(len(data)), mu1,mu2)
---> 16 disasters=pymc3.Poisson('disasters', mu=rate1, observed = data)
C:\Users\User\Anaconda3\lib\site-packages\pymc3\distributions\distribution.py
in new(cls, name, *args, **kwargs)
19 if isinstance(name, str):
20 data = kwargs.pop('observed', None)
---> 21 dist = cls.dist(*args, **kwargs)
22 return model.Var(name, dist, data)
23 elif name is None:
C:\Users\User\Anaconda3\lib\site-packages\pymc3\distributions\distribution.py
in dist(cls, *args, **kwargs)
32 def dist(cls, *args, **kwargs):
33 dist = object.new(cls)
---> 34 dist.init(*args, **kwargs)
35 return dist
36
C:\Users\User\Anaconda3\lib\site-packages\pymc3\distributions\discrete.py
in init(self, mu, *args, **kwargs)
185 super(Poisson, self).init(*args, **kwargs)
186 self.mu = mu
--> 187 self.mode = floor(mu).astype('int32')
188
189 def random(self, point=None, size=None, repeat=None):
c:\program files\git\theano\theano\gof\op.py in call(self,
*inputs, **kwargs)
598 """
599 return_list = kwargs.pop('return_list', False)
--> 600 node = self.make_node(*inputs, **kwargs)
601
602 if config.compute_test_value != 'off':
c:\program files\git\theano\theano\tensor\elemwise.py in
make_node(self, *inputs)
540 using DimShuffle.
541 """
--> 542 inputs = list(map(as_tensor_variable, inputs))
543 shadow = self.scalar_op.make_node(
544 *[get_scalar_type(dtype=i.type.dtype).make_variable()
c:\program files\git\theano\theano\tensor\basic.py in
as_tensor_variable(x, name, ndim)
206 except Exception:
207 str_x = repr(x)
--> 208 raise AsTensorError("Cannot convert %s to TensorType" % str_x, type(x))
209
210 # this has a different name, because _as_tensor_variable is the
AsTensorError: ('Cannot convert FromFunctionOp{rate1} to TensorType',
)
How i handle this?
The second thing - when i'm using the pymc3.switch function like this:
with pymc3.Model() as dis:
switchpoint = pymc3.DiscreteUniform('switchpoint',lower=0, upper=len(data)-1)
mu1 = pymc3.Exponential('mu1', lam=1.)
mu2 = pymc3.Exponential('mu2',lam=1.)
rate1 = pymc3.switch(switchpoint >= np.arange(len(data)), mu1,mu2)
disasters=pymc3.Poisson('disasters', mu=rate1, observed = data)
And next try to sample:
with dis:
step1 = pymc3.NUTS([mu1, mu2])
step2 = pymc3.Metropolis([switchpoint])
trace = pymc3.sample(10000, step = [step1,step2])
I get an error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
c:\program files\git\theano\theano\compile\function_module.py in __call__(self, *args, **kwargs)
858 try:
--> 859 outputs = self.fn()
860 except Exception:
TypeError: expected type_num 9 (NPY_INT64) got 7
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-4-3247d908f897> in <module>()
2 step1 = pymc3.NUTS([mu1, mu2])
3 step2 = pymc3.Metropolis([switchpoint])
----> 4 trace = pymc3.sample(10000, step = [step1,step2])
C:\Users\User\Anaconda3\lib\site-packages\pymc3\sampling.py in sample(draws, step, start, trace, chain, njobs, tune, progressbar, model, random_seed)
153 sample_args = [draws, step, start, trace, chain,
154 tune, progressbar, model, random_seed]
--> 155 return sample_func(*sample_args)
156
157
C:\Users\User\Anaconda3\lib\site-packages\pymc3\sampling.py in _sample(draws, step, start, trace, chain, tune, progressbar, model, random_seed)
162 progress = progress_bar(draws)
163 try:
--> 164 for i, strace in enumerate(sampling):
165 if progressbar:
166 progress.update(i)
C:\Users\User\Anaconda3\lib\site-packages\pymc3\sampling.py in _iter_sample(draws, step, start, trace, chain, tune, model, random_seed)
244 if i == tune:
245 step = stop_tuning(step)
--> 246 point = step.step(point)
247 strace.record(point)
248 yield strace
C:\Users\User\Anaconda3\lib\site-packages\pymc3\step_methods\compound.py in step(self, point)
11 def step(self, point):
12 for method in self.methods:
---> 13 point = method.step(point)
14 return point
C:\Users\User\Anaconda3\lib\site-packages\pymc3\step_methods\arraystep.py in step(self, point)
116 bij = DictToArrayBijection(self.ordering, point)
117
--> 118 apoint = self.astep(bij.map(point))
119 return bij.rmap(apoint)
120
C:\Users\User\Anaconda3\lib\site-packages\pymc3\step_methods\metropolis.py in astep(self, q0)
123
124
--> 125 q_new = metrop_select(self.delta_logp(q,q0), q, q0)
126
127 if q_new is q:
c:\program files\git\theano\theano\compile\function_module.py in __call__(self, *args, **kwargs)
869 node=self.fn.nodes[self.fn.position_of_error],
870 thunk=thunk,
--> 871 storage_map=getattr(self.fn, 'storage_map', None))
872 else:
873 # old-style linkers raise their own exceptions
c:\program files\git\theano\theano\gof\link.py in raise_with_op(node, thunk, exc_info, storage_map)
312 # extra long error message in that case.
313 pass
--> 314 reraise(exc_type, exc_value, exc_trace)
315
316
C:\Users\User\Anaconda3\lib\site-packages\six.py in reraise(tp, value, tb)
656 value = tp()
657 if value.__traceback__ is not tb:
--> 658 raise value.with_traceback(tb)
659 raise value
660
c:\program files\git\theano\theano\compile\function_module.py in __call__(self, *args, **kwargs)
857 t0_fn = time.time()
858 try:
--> 859 outputs = self.fn()
860 except Exception:
861 if hasattr(self.fn, 'position_of_error'):
TypeError: expected type_num 9 (NPY_INT64) got 7
Apply node that caused the error: Elemwise{Composite{Switch(GE(i0, i1), i2, i3)}}(InplaceDimShuffle{x}.0, TensorConstant{[ 0 1..1098 1099]}, InplaceDimShuffle{x}.0, InplaceDimShuffle{x}.0)
Toposort index: 11
Inputs types: [TensorType(int64, (True,)), TensorType(int32, vector), TensorType(float64, (True,)), TensorType(float64, (True,))]
Inputs shapes: [(1,), (1100,), (1,), (1,)]
Inputs strides: [(4,), (4,), (8,), (8,)]
Inputs values: [array([549]), 'not shown', array([ 1.07762995]), array([ 1.01502801])]
Outputs clients: [[Elemwise{eq,no_inplace}(Elemwise{Composite{Switch(GE(i0, i1), i2, i3)}}.0, TensorConstant{(1,) of 0}), Elemwise{Composite{Switch(GE(i0, i1), ((Switch(i2, i3, (i4 * log(i0))) - i5) - i0), i3)}}[(0, 0)](Elemwise{Composite{Switch(GE(i0, i1), i2, i3)}}.0, TensorConstant{(1,) of 0}, InplaceDimShuffle{x}.0, TensorConstant{(1,) of -inf}, TensorConstant{[ 13. 13... 0. 1.]}, TensorConstant{[ 22.55216... ]})]]
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.
Being simple analyst, should i learn all this stuff about theano to being able to work with my statistical problems? Is a new mcmc sampler with gradient feature is only one thing that should motivates me to switch from pymc2 to pymc3?

For your first question, it looks like you're trying to pass a theano function as a variable. You need to call the function with the other variables as arguments, which will then return a theano variable. Try changing your line to
disasters=pymc3.Poisson('disasters', mu=rate1(switchpoint, mu1, mu2), observed = data)
I couldn't reproduce the error in your second part; the sampling worked just fine for me.