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UDR_Python

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def astimezone_and_leap_second ( self , tz ) : dt , leap_second = self . utc_datetime_and_leap_second ( ) normalize = getattr ( tz , 'normalize' , None ) if self . shape and normalize is not None : dt = array ( [ normalize ( d . astimezone ( tz ) ) for d in dt ] ) elif self . shape : dt = array ( [ d . astimezone ( tz ) for d in dt ] ) elif normalize is not None : dt = normalize ( dt . astimezone ( tz ) ) else : dt = dt . astimezone ( tz ) return dt , leap_second
Convert to a Python datetime and leap second in a timezone .
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def utc_datetime_and_leap_second ( self ) : year , month , day , hour , minute , second = self . _utc_tuple ( _half_millisecond ) second , fraction = divmod ( second , 1.0 ) second = second . astype ( int ) leap_second = second // 60 second -= leap_second milli = ( fraction * 1000 ) . astype ( int ) * 1000 if self . shape : utcs = [ utc ] * self . shape [ 0 ] argsets = zip ( year , month , day , hour , minute , second , milli , utcs ) dt = array ( [ datetime ( * args ) for args in argsets ] ) else : dt = datetime ( year , month , day , hour , minute , second , milli , utc ) return dt , leap_second
Convert to a Python datetime in UTC plus a leap second value .
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def utc_strftime ( self , format ) : tup = self . _utc_tuple ( _half_second ) year , month , day , hour , minute , second = tup second = second . astype ( int ) zero = zeros_like ( year ) tup = ( year , month , day , hour , minute , second , zero , zero , zero ) if self . shape : return [ strftime ( format , item ) for item in zip ( * tup ) ] else : return strftime ( format , tup )
Format the UTC time using a Python date formatting string .
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def _utc_year ( self ) : d = self . _utc_float ( ) - 1721059.5 #d += offset C = 365 * 100 + 24 d -= 365 d += d // C - d // ( 4 * C ) d += 365 # Y = d / C * 100 # print(Y) K = 365 * 3 + 366 d -= ( d + K * 7 // 8 ) // K # d -= d // 1461.0 return d / 365.0
Return a fractional UTC year for convenience when plotting .
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def _utc_float ( self ) : tai = self . tai leap_dates = self . ts . leap_dates leap_offsets = self . ts . leap_offsets leap_reverse_dates = leap_dates + leap_offsets / DAY_S i = searchsorted ( leap_reverse_dates , tai , 'right' ) return tai - leap_offsets [ i ] / DAY_S
Return UTC as a floating point Julian date .
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def terra ( latitude , longitude , elevation , gast ) : zero = zeros_like ( gast ) sinphi = sin ( latitude ) cosphi = cos ( latitude ) c = 1.0 / sqrt ( cosphi * cosphi + sinphi * sinphi * one_minus_flattening_squared ) s = one_minus_flattening_squared * c ach = earth_radius_au * c + elevation ash = earth_radius_au * s + elevation # Compute local sidereal time factors at the observer's longitude. stlocl = 15.0 * DEG2RAD * gast + longitude sinst = sin ( stlocl ) cosst = cos ( stlocl ) # Compute position vector components in kilometers. ac = ach * cosphi acsst = ac * sinst accst = ac * cosst pos = array ( ( accst , acsst , zero + ash * sinphi ) ) # Compute velocity vector components in kilometers/sec. vel = ANGVEL * DAY_S * array ( ( - acsst , accst , zero ) ) return pos , vel
Compute the position and velocity of a terrestrial observer .
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def compute_limb_angle ( position_au , observer_au ) : # Compute the distance to the object and the distance to the observer. disobj = sqrt ( dots ( position_au , position_au ) ) disobs = sqrt ( dots ( observer_au , observer_au ) ) # Compute apparent angular radius of Earth's limb. aprad = arcsin ( minimum ( earth_radius_au / disobs , 1.0 ) ) # Compute zenith distance of Earth's limb. zdlim = pi - aprad # Compute zenith distance of observed object. coszd = dots ( position_au , observer_au ) / ( disobj * disobs ) coszd = clip ( coszd , - 1.0 , 1.0 ) zdobj = arccos ( coszd ) # Angle of object wrt limb is difference in zenith distances. limb_angle = ( zdlim - zdobj ) * RAD2DEG # Nadir angle of object as a fraction of angular radius of limb. nadir_angle = ( pi - zdobj ) / aprad return limb_angle , nadir_angle
Determine the angle of an object above or below the Earth s limb .
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def sidereal_time ( t ) : # Compute the Earth Rotation Angle. Time argument is UT1. theta = earth_rotation_angle ( t . ut1 ) # The equinox method. See Circular 179, Section 2.6.2. # Precession-in-RA terms in mean sidereal time taken from third # reference, eq. (42), with coefficients in arcseconds. t = ( t . tdb - T0 ) / 36525.0 st = ( 0.014506 + ( ( ( ( - 0.0000000368 * t - 0.000029956 ) * t - 0.00000044 ) * t + 1.3915817 ) * t + 4612.156534 ) * t ) # Form the Greenwich sidereal time. return ( st / 54000.0 + theta * 24.0 ) % 24.0
Compute Greenwich sidereal time at the given Time .
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def refraction ( alt_degrees , temperature_C , pressure_mbar ) : r = 0.016667 / tan ( ( alt_degrees + 7.31 / ( alt_degrees + 4.4 ) ) * DEG2RAD ) d = r * ( 0.28 * pressure_mbar / ( temperature_C + 273.0 ) ) return where ( ( - 1.0 <= alt_degrees ) & ( alt_degrees <= 89.9 ) , d , 0.0 )
Given an observed altitude return how much the image is refracted .
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def refract ( alt_degrees , temperature_C , pressure_mbar ) : alt = alt_degrees while True : alt1 = alt alt = alt_degrees + refraction ( alt , temperature_C , pressure_mbar ) converged = abs ( alt - alt1 ) <= 3.0e-5 if converged . all ( ) : break return alt
Given an unrefracted alt determine where it will appear in the sky .
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def compute_precession ( jd_tdb ) : eps0 = 84381.406 # 't' is time in TDB centuries. t = ( jd_tdb - T0 ) / 36525.0 # Numerical coefficients of psi_a, omega_a, and chi_a, along with # epsilon_0, the obliquity at J2000.0, are 4-angle formulation from # Capitaine et al. (2003), eqs. (4), (37), & (39). psia = ( ( ( ( - 0.0000000951 * t + 0.000132851 ) * t - 0.00114045 ) * t - 1.0790069 ) * t + 5038.481507 ) * t omegaa = ( ( ( ( + 0.0000003337 * t - 0.000000467 ) * t - 0.00772503 ) * t + 0.0512623 ) * t - 0.025754 ) * t + eps0 chia = ( ( ( ( - 0.0000000560 * t + 0.000170663 ) * t - 0.00121197 ) * t - 2.3814292 ) * t + 10.556403 ) * t eps0 = eps0 * ASEC2RAD psia = psia * ASEC2RAD omegaa = omegaa * ASEC2RAD chia = chia * ASEC2RAD sa = sin ( eps0 ) ca = cos ( eps0 ) sb = sin ( - psia ) cb = cos ( - psia ) sc = sin ( - omegaa ) cc = cos ( - omegaa ) sd = sin ( chia ) cd = cos ( chia ) # Compute elements of precession rotation matrix equivalent to # R3(chi_a) R1(-omega_a) R3(-psi_a) R1(epsilon_0). rot3 = array ( ( ( cd * cb - sb * sd * cc , cd * sb * ca + sd * cc * cb * ca - sa * sd * sc , cd * sb * sa + sd * cc * cb * sa + ca * sd * sc ) , ( - sd * cb - sb * cd * cc , - sd * sb * ca + cd * cc * cb * ca - sa * cd * sc , - sd * sb * sa + cd * cc * cb * sa + ca * cd * sc ) , ( sb * sc , - sc * cb * ca - sa * cc , - sc * cb * sa + cc * ca ) ) ) return rot3
Return the rotation matrices for precessing to an array of epochs .
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def compute_nutation ( t ) : oblm , oblt , eqeq , psi , eps = t . _earth_tilt cobm = cos ( oblm * DEG2RAD ) sobm = sin ( oblm * DEG2RAD ) cobt = cos ( oblt * DEG2RAD ) sobt = sin ( oblt * DEG2RAD ) cpsi = cos ( psi * ASEC2RAD ) spsi = sin ( psi * ASEC2RAD ) return array ( ( ( cpsi , - spsi * cobm , - spsi * sobm ) , ( spsi * cobt , cpsi * cobm * cobt + sobm * sobt , cpsi * sobm * cobt - cobm * sobt ) , ( spsi * sobt , cpsi * cobm * sobt - sobm * cobt , cpsi * sobm * sobt + cobm * cobt ) ) )
Generate the nutation rotations for Time t .
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def earth_tilt ( t ) : dp , de = t . _nutation_angles c_terms = equation_of_the_equinoxes_complimentary_terms ( t . tt ) / ASEC2RAD d_psi = dp * 1e-7 + t . psi_correction d_eps = de * 1e-7 + t . eps_correction mean_ob = mean_obliquity ( t . tdb ) true_ob = mean_ob + d_eps mean_ob /= 3600.0 true_ob /= 3600.0 eq_eq = d_psi * cos ( mean_ob * DEG2RAD ) + c_terms eq_eq /= 15.0 return mean_ob , true_ob , eq_eq , d_psi , d_eps
Return a tuple of information about the earth s axis and position .
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def mean_obliquity ( jd_tdb ) : # Compute time in Julian centuries from epoch J2000.0. t = ( jd_tdb - T0 ) / 36525.0 # Compute the mean obliquity in arcseconds. Use expression from the # reference's eq. (39) with obliquity at J2000.0 taken from eq. (37) # or Table 8. epsilon = ( ( ( ( - 0.0000000434 * t - 0.000000576 ) * t + 0.00200340 ) * t - 0.0001831 ) * t - 46.836769 ) * t + 84381.406 return epsilon
Return the mean obliquity of the ecliptic in arcseconds .
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def equation_of_the_equinoxes_complimentary_terms ( jd_tt ) : # Interval between fundamental epoch J2000.0 and current date. t = ( jd_tt - T0 ) / 36525.0 # Build array for intermediate results. shape = getattr ( jd_tt , 'shape' , ( ) ) fa = zeros ( ( 14 , ) if shape == ( ) else ( 14 , shape [ 0 ] ) ) # Mean Anomaly of the Moon. fa [ 0 ] = ( ( 485868.249036 + ( 715923.2178 + ( 31.8792 + ( 0.051635 + ( - 0.00024470 ) * t ) * t ) * t ) * t ) * ASEC2RAD + ( 1325.0 * t % 1.0 ) * tau ) # Mean Anomaly of the Sun. fa [ 1 ] = ( ( 1287104.793048 + ( 1292581.0481 + ( - 0.5532 + ( + 0.000136 + ( - 0.00001149 ) * t ) * t ) * t ) * t ) * ASEC2RAD + ( 99.0 * t % 1.0 ) * tau ) # Mean Longitude of the Moon minus Mean Longitude of the Ascending # Node of the Moon. fa [ 2 ] = ( ( 335779.526232 + ( 295262.8478 + ( - 12.7512 + ( - 0.001037 + ( 0.00000417 ) * t ) * t ) * t ) * t ) * ASEC2RAD + ( 1342.0 * t % 1.0 ) * tau ) # Mean Elongation of the Moon from the Sun. fa [ 3 ] = ( ( 1072260.703692 + ( 1105601.2090 + ( - 6.3706 + ( 0.006593 + ( - 0.00003169 ) * t ) * t ) * t ) * t ) * ASEC2RAD + ( 1236.0 * t % 1.0 ) * tau ) # Mean Longitude of the Ascending Node of the Moon. fa [ 4 ] = ( ( 450160.398036 + ( - 482890.5431 + ( 7.4722 + ( 0.007702 + ( - 0.00005939 ) * t ) * t ) * t ) * t ) * ASEC2RAD + ( - 5.0 * t % 1.0 ) * tau ) fa [ 5 ] = ( 4.402608842 + 2608.7903141574 * t ) fa [ 6 ] = ( 3.176146697 + 1021.3285546211 * t ) fa [ 7 ] = ( 1.753470314 + 628.3075849991 * t ) fa [ 8 ] = ( 6.203480913 + 334.0612426700 * t ) fa [ 9 ] = ( 0.599546497 + 52.9690962641 * t ) fa [ 10 ] = ( 0.874016757 + 21.3299104960 * t ) fa [ 11 ] = ( 5.481293872 + 7.4781598567 * t ) fa [ 12 ] = ( 5.311886287 + 3.8133035638 * t ) fa [ 13 ] = ( 0.024381750 + 0.00000538691 * t ) * t fa %= tau # Evaluate the complementary terms. a = ke0_t . dot ( fa ) s0 = se0_t_0 . dot ( sin ( a ) ) + se0_t_1 . dot ( cos ( a ) ) a = ke1 . dot ( fa ) s1 = se1_0 * sin ( a ) + se1_1 * cos ( a ) c_terms = s0 + s1 * t c_terms *= ASEC2RAD return c_terms
Compute the complementary terms of the equation of the equinoxes .
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def iau2000a ( jd_tt ) : # Interval between fundamental epoch J2000.0 and given date. t = ( jd_tt - T0 ) / 36525.0 # Compute fundamental arguments from Simon et al. (1994), in radians. a = fundamental_arguments ( t ) # ** Luni-solar nutation ** # Summation of luni-solar nutation series (in reverse order). arg = nals_t . dot ( a ) fmod ( arg , tau , out = arg ) sarg = sin ( arg ) carg = cos ( arg ) stsc = array ( ( sarg , t * sarg , carg ) ) . T ctcs = array ( ( carg , t * carg , sarg ) ) . T dpsi = tensordot ( stsc , lunisolar_longitude_coefficients ) deps = tensordot ( ctcs , lunisolar_obliquity_coefficients ) # Compute and add in planetary components. if getattr ( t , 'shape' , ( ) ) == ( ) : a = t * anomaly_coefficient + anomaly_constant else : a = ( outer ( anomaly_coefficient , t ) . T + anomaly_constant ) . T a [ - 1 ] *= t fmod ( a , tau , out = a ) arg = napl_t . dot ( a ) fmod ( arg , tau , out = arg ) sc = array ( ( sin ( arg ) , cos ( arg ) ) ) . T dpsi += tensordot ( sc , nutation_coefficients_longitude ) deps += tensordot ( sc , nutation_coefficients_obliquity ) return dpsi , deps
Compute Earth nutation based on the IAU 2000A nutation model .
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def iau2000b ( jd_tt ) : dpplan = - 0.000135 * 1e7 deplan = 0.000388 * 1e7 t = ( jd_tt - T0 ) / 36525.0 # TODO: can these be replaced with fa0 and f1? el = fmod ( 485868.249036 + t * 1717915923.2178 , ASEC360 ) * ASEC2RAD elp = fmod ( 1287104.79305 + t * 129596581.0481 , ASEC360 ) * ASEC2RAD f = fmod ( 335779.526232 + t * 1739527262.8478 , ASEC360 ) * ASEC2RAD d = fmod ( 1072260.70369 + t * 1602961601.2090 , ASEC360 ) * ASEC2RAD om = fmod ( 450160.398036 - t * 6962890.5431 , ASEC360 ) * ASEC2RAD a = array ( ( el , elp , f , d , om ) ) arg = nals_t [ : 77 ] . dot ( a ) fmod ( arg , tau , out = arg ) sarg = sin ( arg ) carg = cos ( arg ) stsc = array ( ( sarg , t * sarg , carg ) ) . T ctcs = array ( ( carg , t * carg , sarg ) ) . T dp = tensordot ( stsc , lunisolar_longitude_coefficients [ : 77 , ] ) de = tensordot ( ctcs , lunisolar_obliquity_coefficients [ : 77 , ] ) dpsi = dpplan + dp deps = deplan + de return dpsi , deps
Compute Earth nutation based on the faster IAU 2000B nutation model .
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def load_dataframe ( fobj , compression = 'gzip' ) : try : from pandas import read_fwf except ImportError : raise ImportError ( PANDAS_MESSAGE ) names , colspecs = zip ( ( 'hip' , ( 2 , 14 ) ) , ( 'magnitude' , ( 41 , 46 ) ) , ( 'ra_degrees' , ( 51 , 63 ) ) , ( 'dec_degrees' , ( 64 , 76 ) ) , ( 'parallax_mas' , ( 79 , 86 ) ) , # TODO: have Star load this ( 'ra_mas_per_year' , ( 87 , 95 ) ) , ( 'dec_mas_per_year' , ( 96 , 104 ) ) , ) df = read_fwf ( fobj , colspecs , names = names , compression = compression ) df = df . assign ( ra_hours = df [ 'ra_degrees' ] / 15.0 , epoch_year = 1991.25 , ) return df . set_index ( 'hip' )
Given an open file for hip_main . dat . gz return a parsed dataframe .
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def _altaz_rotation ( self , t ) : R_lon = rot_z ( - self . longitude . radians - t . gast * tau / 24.0 ) return einsum ( 'ij...,jk...,kl...->il...' , self . R_lat , R_lon , t . M )
Compute the rotation from the ICRF into the alt - az system .
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def _at ( self , t ) : pos , vel = terra ( self . latitude . radians , self . longitude . radians , self . elevation . au , t . gast ) pos = einsum ( 'ij...,j...->i...' , t . MT , pos ) vel = einsum ( 'ij...,j...->i...' , t . MT , vel ) if self . x : R = rot_y ( self . x * ASEC2RAD ) pos = einsum ( 'ij...,j...->i...' , R , pos ) if self . y : R = rot_x ( self . y * ASEC2RAD ) pos = einsum ( 'ij...,j...->i...' , R , pos ) # TODO: also rotate velocity return pos , vel , pos , None
Compute the GCRS position and velocity of this Topos at time t .
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def osculating_elements_of ( position , reference_frame = None ) : mu = GM_dict . get ( position . center , 0 ) + GM_dict . get ( position . target , 0 ) if reference_frame is not None : position_vec = Distance ( reference_frame . dot ( position . position . au ) ) velocity_vec = Velocity ( reference_frame . dot ( position . velocity . au_per_d ) ) else : position_vec = position . position velocity_vec = position . velocity return OsculatingElements ( position_vec , velocity_vec , position . t , mu )
Produce the osculating orbital elements for a position .
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def theta_GMST1982 ( jd_ut1 ) : t = ( jd_ut1 - T0 ) / 36525.0 g = 67310.54841 + ( 8640184.812866 + ( 0.093104 + ( - 6.2e-6 ) * t ) * t ) * t dg = 8640184.812866 + ( 0.093104 * 2.0 + ( - 6.2e-6 * 3.0 ) * t ) * t theta = ( jd_ut1 % 1.0 + g * _second % 1.0 ) * tau theta_dot = ( 1.0 + dg * _second / 36525.0 ) * tau return theta , theta_dot
Return the angle of Greenwich Mean Standard Time 1982 given the JD .
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def TEME_to_ITRF ( jd_ut1 , rTEME , vTEME , xp = 0.0 , yp = 0.0 ) : theta , theta_dot = theta_GMST1982 ( jd_ut1 ) zero = theta_dot * 0.0 angular_velocity = array ( [ zero , zero , - theta_dot ] ) R = rot_z ( - theta ) if len ( rTEME . shape ) == 1 : rPEF = ( R ) . dot ( rTEME ) vPEF = ( R ) . dot ( vTEME ) + cross ( angular_velocity , rPEF ) else : rPEF = einsum ( 'ij...,j...->i...' , R , rTEME ) vPEF = einsum ( 'ij...,j...->i...' , R , vTEME ) + cross ( angular_velocity , rPEF , 0 , 0 ) . T if xp == 0.0 and yp == 0.0 : rITRF = rPEF vITRF = vPEF else : W = ( rot_x ( yp ) ) . dot ( rot_y ( xp ) ) rITRF = ( W ) . dot ( rPEF ) vITRF = ( W ) . dot ( vPEF ) return rITRF , vITRF
Convert TEME position and velocity into standard ITRS coordinates .
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def ITRF_position_velocity_error ( self , t ) : rTEME , vTEME , error = self . _position_and_velocity_TEME_km ( t ) rTEME /= AU_KM vTEME /= AU_KM vTEME *= DAY_S rITRF , vITRF = TEME_to_ITRF ( t . ut1 , rTEME , vTEME ) return rITRF , vITRF , error
Return the ITRF position velocity and error at time t .
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def _at ( self , t ) : rITRF , vITRF , error = self . ITRF_position_velocity_error ( t ) rGCRS , vGCRS = ITRF_to_GCRS2 ( t , rITRF , vITRF ) return rGCRS , vGCRS , rGCRS , error
Compute this satellite s GCRS position and velocity at time t .
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def morrison_and_stephenson_2004_table ( ) : import pandas as pd f = load . open ( 'http://eclipse.gsfc.nasa.gov/SEcat5/deltat.html' ) tables = pd . read_html ( f . read ( ) ) df = tables [ 0 ] return pd . DataFrame ( { 'year' : df [ 0 ] , 'delta_t' : df [ 1 ] } )
Table of smoothed Delta T values from Morrison and Stephenson 2004 .
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def angle_between ( u_vec , v_vec ) : u = length_of ( u_vec ) v = length_of ( v_vec ) num = v * u_vec - u * v_vec denom = v * u_vec + u * v_vec return 2 * arctan2 ( length_of ( num ) , length_of ( denom ) )
Given 2 vectors in v and u return the angle separating them .
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def _compute_vectors ( self ) : # Use 1 gigaparsec for stars whose parallax is zero. parallax = self . parallax_mas if parallax <= 0.0 : parallax = 1.0e-6 # Convert right ascension, declination, and parallax to position # vector in equatorial system with units of au. dist = 1.0 / sin ( parallax * 1.0e-3 * ASEC2RAD ) r = self . ra . radians d = self . dec . radians cra = cos ( r ) sra = sin ( r ) cdc = cos ( d ) sdc = sin ( d ) self . _position_au = array ( ( dist * cdc * cra , dist * cdc * sra , dist * sdc , ) ) # Compute Doppler factor, which accounts for change in light # travel time to star. k = 1.0 / ( 1.0 - self . radial_km_per_s / C * 1000.0 ) # Convert proper motion and radial velocity to orthogonal # components of motion with units of au/day. pmr = self . ra_mas_per_year / ( parallax * 365.25 ) * k pmd = self . dec_mas_per_year / ( parallax * 365.25 ) * k rvl = self . radial_km_per_s * DAY_S / self . au_km * k # Transform motion vector to equatorial system. self . _velocity_au_per_d = array ( ( - pmr * sra - pmd * sdc * cra + rvl * cdc * cra , pmr * cra - pmd * sdc * sra + rvl * cdc * sra , pmd * cdc + rvl * sdc , ) )
Compute the star s position as an ICRF position and velocity .
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def _to_array ( value ) : if isinstance ( value , ( tuple , list ) ) : return array ( value ) elif isinstance ( value , ( float , int ) ) : return np . float64 ( value ) else : return value
As a convenience turn Python lists and tuples into NumPy arrays .
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def _sexagesimalize_to_float ( value ) : sign = np . sign ( value ) n = abs ( value ) minutes , seconds = divmod ( n * 3600.0 , 60.0 ) units , minutes = divmod ( minutes , 60.0 ) return sign , units , minutes , seconds
Decompose value into units minutes and seconds .
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def _sexagesimalize_to_int ( value , places = 0 ) : sign = int ( np . sign ( value ) ) value = abs ( value ) power = 10 ** places n = int ( 7200 * power * value + 1 ) // 2 n , fraction = divmod ( n , power ) n , seconds = divmod ( n , 60 ) n , minutes = divmod ( n , 60 ) return sign , n , minutes , seconds , fraction
Decompose value into units minutes seconds and second fractions .
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def _hstr ( hours , places = 2 ) : if isnan ( hours ) : return 'nan' sgn , h , m , s , etc = _sexagesimalize_to_int ( hours , places ) sign = '-' if sgn < 0.0 else '' return '%s%02dh %02dm %02d.%0*ds' % ( sign , h , m , s , places , etc )
Convert floating point hours into a sexagesimal string .
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def _dstr ( degrees , places = 1 , signed = False ) : if isnan ( degrees ) : return 'nan' sgn , d , m , s , etc = _sexagesimalize_to_int ( degrees , places ) sign = '-' if sgn < 0.0 else '+' if signed else '' return '%s%02ddeg %02d\' %02d.%0*d"' % ( sign , d , m , s , places , etc )
r Convert floating point degrees into a sexagesimal string .
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def _interpret_angle ( name , angle_object , angle_float , unit = 'degrees' ) : if angle_object is not None : if isinstance ( angle_object , Angle ) : return angle_object . radians elif angle_float is not None : return _unsexagesimalize ( angle_float ) * _from_degrees raise ValueError ( 'you must either provide the {0}= parameter with' ' an Angle argument or supply the {0}_{1}= parameter' ' with a numeric argument' . format ( name , unit ) )
Return an angle in radians from one of two arguments .
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def _interpret_ltude ( value , name , psuffix , nsuffix ) : if not isinstance ( value , str ) : return Angle ( degrees = _unsexagesimalize ( value ) ) value = value . strip ( ) . upper ( ) if value . endswith ( psuffix ) : sign = + 1.0 elif value . endswith ( nsuffix ) : sign = - 1.0 else : raise ValueError ( 'your {0} string {1!r} does not end with either {2!r}' ' or {3!r}' . format ( name , value , psuffix , nsuffix ) ) try : value = float ( value [ : - 1 ] ) except ValueError : raise ValueError ( 'your {0} string {1!r} cannot be parsed as a floating' ' point number' . format ( name , value ) ) return Angle ( degrees = sign * value )
Interpret a string float or tuple as a latitude or longitude angle .
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def to ( self , unit ) : from astropy . units import au return ( self . au * au ) . to ( unit )
Convert this distance to the given AstroPy unit .
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def to ( self , unit ) : from astropy . units import au , d return ( self . au_per_d * au / d ) . to ( unit )
Convert this velocity to the given AstroPy unit .
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def hstr ( self , places = 2 , warn = True ) : if warn and self . preference != 'hours' : raise WrongUnitError ( 'hstr' ) if self . radians . size == 0 : return '<Angle []>' hours = self . _hours shape = getattr ( hours , 'shape' , ( ) ) if shape and shape != ( 1 , ) : return "{0} values from {1} to {2}" . format ( len ( hours ) , _hstr ( min ( hours ) , places ) , _hstr ( max ( hours ) , places ) , ) return _hstr ( hours , places )
Convert to a string like 12h 07m 30 . 00s .
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def dstr ( self , places = 1 , warn = True ) : if warn and self . preference != 'degrees' : raise WrongUnitError ( 'dstr' ) if self . radians . size == 0 : return '<Angle []>' degrees = self . _degrees signed = self . signed shape = getattr ( degrees , 'shape' , ( ) ) if shape and shape != ( 1 , ) : return "{0} values from {1} to {2}" . format ( len ( degrees ) , _dstr ( min ( degrees ) , places , signed ) , _dstr ( max ( degrees ) , places , signed ) , ) return _dstr ( degrees , places , signed )
Convert to a string like 181deg 52 \ 30 . 0 .
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def to ( self , unit ) : from astropy . units import rad return ( self . radians * rad ) . to ( unit ) # Or should this do: from astropy . coordinates import Angle from astropy . units import rad return Angle ( self . radians , rad ) . to ( unit )
Convert this angle to the given AstroPy unit .
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def separation_from ( self , another_icrf ) : p1 = self . position . au p2 = another_icrf . position . au u1 = p1 / length_of ( p1 ) u2 = p2 / length_of ( p2 ) if u2 . ndim > 1 : if u1 . ndim == 1 : u1 = u1 [ : , None ] elif u1 . ndim > 1 : u2 = u2 [ : , None ] c = dots ( u1 , u2 ) return Angle ( radians = arccos ( clip ( c , - 1.0 , 1.0 ) ) )
Return the angle between this position and another .
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def to_skycoord ( self , unit = None ) : from astropy . coordinates import SkyCoord from astropy . units import au x , y , z = self . position . au return SkyCoord ( representation = 'cartesian' , x = x , y = y , z = z , unit = au )
Convert this distance to an AstroPy SkyCoord object .
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def from_altaz ( self , alt = None , az = None , alt_degrees = None , az_degrees = None , distance = Distance ( au = 0.1 ) ) : # TODO: should this method live on another class? R = self . observer_data . altaz_rotation if self . observer_data else None if R is None : raise ValueError ( 'only a position generated by a topos() call' ' knows the orientation of the horizon' ' and can understand altitude and azimuth' ) alt = _interpret_angle ( 'alt' , alt , alt_degrees ) az = _interpret_angle ( 'az' , az , az_degrees ) r = distance . au p = from_polar ( r , alt , az ) p = einsum ( 'ji...,j...->i...' , R , p ) return Apparent ( p )
Generate an Apparent position from an altitude and azimuth .
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def observe ( self , body ) : p , v , t , light_time = body . _observe_from_bcrs ( self ) astrometric = Astrometric ( p , v , t , observer_data = self . observer_data ) astrometric . light_time = light_time return astrometric
Compute the Astrometric position of a body from this location .
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def subpoint ( self ) : if self . center != 399 : # TODO: should an __init__() check this? raise ValueError ( "you can only ask for the geographic subpoint" " of a position measured from Earth's center" ) t = self . t xyz_au = einsum ( 'ij...,j...->i...' , t . M , self . position . au ) lat , lon , elevation_m = reverse_terra ( xyz_au , t . gast ) # TODO. Move VectorFunction and Topos into this file, since the # three kinds of class work together: Topos is-a VF; VF.at() can # return a Geocentric position; and Geocentric.subpoint() should # return a Topos. I'm deferring the refactoring for now, to get # this new feature to users more quickly. from . toposlib import Topos return Topos ( latitude = Angle ( radians = lat ) , longitude = Angle ( radians = lon ) , elevation_m = elevation_m )
Return the latitude and longitude directly beneath this position .
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def _plot_stars ( catalog , observer , project , ax , mag1 , mag2 , margin = 1.25 ) : art = [ ] # from astropy import wcs # w = wcs.WCS(naxis=2) # w.wcs.crpix = [-234.75, 8.3393] # w.wcs.cdelt = np.array([-0.066667, 0.066667]) # w.wcs.crval = [0, -90] # w.wcs.ctype = ["RA---AIR", "DEC--AIR"] # w.wcs.set_pv([(2, 1, 45.0)]) # import matplotlib.pyplot as plt # plt.subplot(projection=wcs) # #plt.imshow(hdu.data, vmin=-2.e-5, vmax=2.e-4, origin='lower') # plt.grid(color='white', ls='solid') # plt.xlabel('Galactic Longitude') # plt.ylabel('Galactic Latitude') xmin , xmax = ax . get_xlim ( ) ymin , ymax = ax . get_xlim ( ) lim = max ( abs ( xmin ) , abs ( xmax ) , abs ( ymin ) , abs ( ymax ) ) * margin lims = ( - lim , lim ) ax . set_xlim ( lims ) ax . set_ylim ( lims ) ax . set_aspect ( 'equal' ) o = observer [ 0 ] # Dim stars: points of with varying gray levels. c = catalog c = c [ c [ 'magnitude' ] > mag1 ] c = c [ c [ 'magnitude' ] <= mag2 ] #print('Second star group:', len(c)) c = c . sort_values ( 'magnitude' , ascending = False ) s = Star ( ra_hours = c . ra_hours , dec_degrees = c . dec_degrees ) spos = o . observe ( s ) x , y = project ( spos ) m = ( mag2 - c [ 'magnitude' ] ) / ( mag2 - mag1 ) # Note that "gray_r" is white for 0.0 and black for 1.0 art . append ( ax . scatter ( x , y , s = 1.0 , c = 1 - 0.8 * m , cmap = 'gray_r' , vmin = 0.0 , vmax = 1.0 , ) ) # Bright stars: black circles of varying radius, surrounded by a # white gap in case stars are touching. Draw the brightest stars # first to stop them from completely occluding smaller companions. def mag_to_radius ( m ) : return ( mag1 - m ) * scale + 1.0 c = catalog c = c [ c [ 'magnitude' ] <= mag1 ] c = c . sort_values ( 'magnitude' , ascending = True ) #print('First star group:', len(c)) s = Star ( ra_hours = c . ra_hours , dec_degrees = c . dec_degrees ) spos = o . observe ( s ) x , y = project ( spos ) scale = 1.5 radius = mag_to_radius ( c [ 'magnitude' ] ) x2 = np . repeat ( x , 2 ) y2 = np . repeat ( y , 2 ) radius2 = ( radius [ : , None ] + ( 3.0 , 0.0 ) ) . flatten ( ) c2 = ( 'w' , 'k' ) c2 = ( 'k' , 'w' ) art . append ( ax . scatter ( x2 , y2 , s = radius2 ** 2.0 , c = c2 ) ) return art , mag_to_radius
Experiment in progress hence the underscore ; expect changes .
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def phase_angle ( ephemeris , body , t ) : earth = ephemeris [ 'earth' ] sun = ephemeris [ 'sun' ] body = ephemeris [ body ] pe = earth . at ( t ) . observe ( body ) pe . position . au *= - 1 # rotate 180 degrees to point back at Earth t2 = t . ts . tt_jd ( t . tt - pe . light_time ) ps = body . at ( t2 ) . observe ( sun ) return pe . separation_from ( ps )
Compute the phase angle of a body viewed from Earth .
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def fraction_illuminated ( ephemeris , body , t ) : a = phase_angle ( ephemeris , body , t ) . radians return 0.5 * ( 1.0 + cos ( a ) )
Compute the illuminated fraction of a body viewed from Earth .
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def find_discrete ( start_time , end_time , f , epsilon = EPSILON , num = 12 ) : ts = start_time . ts jd0 = start_time . tt jd1 = end_time . tt if jd0 >= jd1 : raise ValueError ( 'your start_time {0} is later than your end_time {1}' . format ( start_time , end_time ) ) periods = ( jd1 - jd0 ) / f . rough_period if periods < 1.0 : periods = 1.0 jd = linspace ( jd0 , jd1 , periods * num // 1.0 ) end_mask = linspace ( 0.0 , 1.0 , num ) start_mask = end_mask [ : : - 1 ] o = multiply . outer while True : t = ts . tt_jd ( jd ) y = f ( t ) indices = flatnonzero ( diff ( y ) ) if not len ( indices ) : return indices , y [ 0 : 0 ] starts = jd . take ( indices ) ends = jd . take ( indices + 1 ) # Since we start with equal intervals, they all should fall # below epsilon at around the same time; so for efficiency we # only test the first pair. if ends [ 0 ] - starts [ 0 ] <= epsilon : break jd = o ( starts , start_mask ) . flatten ( ) + o ( ends , end_mask ) . flatten ( ) return ts . tt_jd ( ends ) , y . take ( indices + 1 )
Find the times when a function changes value .
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def seasons ( ephemeris ) : earth = ephemeris [ 'earth' ] sun = ephemeris [ 'sun' ] def season_at ( t ) : """Return season 0 (Spring) through 3 (Winter) at time `t`.""" t . _nutation_angles = iau2000b ( t . tt ) e = earth . at ( t ) _ , slon , _ = e . observe ( sun ) . apparent ( ) . ecliptic_latlon ( 'date' ) return ( slon . radians // ( tau / 4 ) % 4 ) . astype ( int ) season_at . rough_period = 90.0 return season_at
Build a function of time that returns the quarter of the year .
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def sunrise_sunset ( ephemeris , topos ) : sun = ephemeris [ 'sun' ] topos_at = ( ephemeris [ 'earth' ] + topos ) . at def is_sun_up_at ( t ) : """Return `True` if the sun has risen by time `t`.""" t . _nutation_angles = iau2000b ( t . tt ) return topos_at ( t ) . observe ( sun ) . apparent ( ) . altaz ( ) [ 0 ] . degrees > - 0.8333 is_sun_up_at . rough_period = 0.5 # twice a day return is_sun_up_at
Build a function of time that returns whether the sun is up .
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def moon_phases ( ephemeris ) : earth = ephemeris [ 'earth' ] moon = ephemeris [ 'moon' ] sun = ephemeris [ 'sun' ] def moon_phase_at ( t ) : """Return the phase of the moon 0 through 3 at time `t`.""" t . _nutation_angles = iau2000b ( t . tt ) e = earth . at ( t ) _ , mlon , _ = e . observe ( moon ) . apparent ( ) . ecliptic_latlon ( 'date' ) _ , slon , _ = e . observe ( sun ) . apparent ( ) . ecliptic_latlon ( 'date' ) return ( ( mlon . radians - slon . radians ) // ( tau / 4 ) % 4 ) . astype ( int ) moon_phase_at . rough_period = 7.0 # one lunar phase per week return moon_phase_at
Build a function of time that returns the moon phase 0 through 3 .
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def _derive_stereographic ( ) : from sympy import symbols , atan2 , acos , rot_axis1 , rot_axis3 , Matrix x_c , y_c , z_c , x , y , z = symbols ( 'x_c y_c z_c x y z' ) # The angles we'll need to rotate through. around_z = atan2 ( x_c , y_c ) around_x = acos ( - z_c ) # Apply rotations to produce an "o" = output vector. v = Matrix ( [ x , y , z ] ) xo , yo , zo = rot_axis1 ( around_x ) * rot_axis3 ( - around_z ) * v # Which we then use the stereographic projection to produce the # final "p" = plotting coordinates. xp = xo / ( 1 - zo ) yp = yo / ( 1 - zo ) return xp , yp
Compute the formulae to cut - and - paste into the routine below .
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def classifier_factory ( clf ) : required_methods = [ 'fit' , 'score' , 'predict' ] for method in required_methods : if not hasattr ( clf , method ) : raise TypeError ( '"{}" is not in clf. Did you pass a ' 'classifier instance?' . format ( method ) ) optional_methods = [ 'predict_proba' ] for method in optional_methods : if not hasattr ( clf , method ) : warnings . warn ( '{} not in clf. Some plots may ' 'not be possible to generate.' . format ( method ) ) additional_methods = { 'plot_learning_curve' : plot_learning_curve , 'plot_confusion_matrix' : plot_confusion_matrix_with_cv , 'plot_roc_curve' : plot_roc_curve_with_cv , 'plot_ks_statistic' : plot_ks_statistic_with_cv , 'plot_precision_recall_curve' : plot_precision_recall_curve_with_cv , 'plot_feature_importances' : plot_feature_importances } for key , fn in six . iteritems ( additional_methods ) : if hasattr ( clf , key ) : warnings . warn ( '"{}" method already in clf. ' 'Overriding anyway. This may ' 'result in unintended behavior.' . format ( key ) ) setattr ( clf , key , types . MethodType ( fn , clf ) ) return clf
Embeds scikit - plot instance methods in an sklearn classifier .
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def plot_confusion_matrix_with_cv ( clf , X , y , labels = None , true_labels = None , pred_labels = None , title = None , normalize = False , hide_zeros = False , x_tick_rotation = 0 , do_cv = True , cv = None , shuffle = True , random_state = None , ax = None , figsize = None , cmap = 'Blues' , title_fontsize = "large" , text_fontsize = "medium" ) : y = np . array ( y ) if not do_cv : y_pred = clf . predict ( X ) y_true = y else : if cv is None : cv = StratifiedKFold ( shuffle = shuffle , random_state = random_state ) elif isinstance ( cv , int ) : cv = StratifiedKFold ( n_splits = cv , shuffle = shuffle , random_state = random_state ) else : pass clf_clone = clone ( clf ) preds_list = [ ] trues_list = [ ] for train_index , test_index in cv . split ( X , y ) : X_train , X_test = X [ train_index ] , X [ test_index ] y_train , y_test = y [ train_index ] , y [ test_index ] clf_clone . fit ( X_train , y_train ) preds = clf_clone . predict ( X_test ) preds_list . append ( preds ) trues_list . append ( y_test ) y_pred = np . concatenate ( preds_list ) y_true = np . concatenate ( trues_list ) ax = plotters . plot_confusion_matrix ( y_true = y_true , y_pred = y_pred , labels = labels , true_labels = true_labels , pred_labels = pred_labels , title = title , normalize = normalize , hide_zeros = hide_zeros , x_tick_rotation = x_tick_rotation , ax = ax , figsize = figsize , cmap = cmap , title_fontsize = title_fontsize , text_fontsize = text_fontsize ) return ax
Generates the confusion matrix for a given classifier and dataset .
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def plot_ks_statistic_with_cv ( clf , X , y , title = 'KS Statistic Plot' , do_cv = True , cv = None , shuffle = True , random_state = None , ax = None , figsize = None , title_fontsize = "large" , text_fontsize = "medium" ) : y = np . array ( y ) if not hasattr ( clf , 'predict_proba' ) : raise TypeError ( '"predict_proba" method not in classifier. ' 'Cannot calculate ROC Curve.' ) if not do_cv : probas = clf . predict_proba ( X ) y_true = y else : if cv is None : cv = StratifiedKFold ( shuffle = shuffle , random_state = random_state ) elif isinstance ( cv , int ) : cv = StratifiedKFold ( n_splits = cv , shuffle = shuffle , random_state = random_state ) else : pass clf_clone = clone ( clf ) preds_list = [ ] trues_list = [ ] for train_index , test_index in cv . split ( X , y ) : X_train , X_test = X [ train_index ] , X [ test_index ] y_train , y_test = y [ train_index ] , y [ test_index ] clf_clone . fit ( X_train , y_train ) preds = clf_clone . predict_proba ( X_test ) preds_list . append ( preds ) trues_list . append ( y_test ) probas = np . concatenate ( preds_list , axis = 0 ) y_true = np . concatenate ( trues_list ) ax = plotters . plot_ks_statistic ( y_true , probas , title = title , ax = ax , figsize = figsize , title_fontsize = title_fontsize , text_fontsize = text_fontsize ) return ax
Generates the KS Statistic plot for a given classifier and dataset .
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def plot_confusion_matrix ( y_true , y_pred , labels = None , true_labels = None , pred_labels = None , title = None , normalize = False , hide_zeros = False , x_tick_rotation = 0 , ax = None , figsize = None , cmap = 'Blues' , title_fontsize = "large" , text_fontsize = "medium" ) : if ax is None : fig , ax = plt . subplots ( 1 , 1 , figsize = figsize ) cm = confusion_matrix ( y_true , y_pred , labels = labels ) if labels is None : classes = unique_labels ( y_true , y_pred ) else : classes = np . asarray ( labels ) if normalize : cm = cm . astype ( 'float' ) / cm . sum ( axis = 1 ) [ : , np . newaxis ] cm = np . around ( cm , decimals = 2 ) cm [ np . isnan ( cm ) ] = 0.0 if true_labels is None : true_classes = classes else : validate_labels ( classes , true_labels , "true_labels" ) true_label_indexes = np . in1d ( classes , true_labels ) true_classes = classes [ true_label_indexes ] cm = cm [ true_label_indexes ] if pred_labels is None : pred_classes = classes else : validate_labels ( classes , pred_labels , "pred_labels" ) pred_label_indexes = np . in1d ( classes , pred_labels ) pred_classes = classes [ pred_label_indexes ] cm = cm [ : , pred_label_indexes ] if title : ax . set_title ( title , fontsize = title_fontsize ) elif normalize : ax . set_title ( 'Normalized Confusion Matrix' , fontsize = title_fontsize ) else : ax . set_title ( 'Confusion Matrix' , fontsize = title_fontsize ) image = ax . imshow ( cm , interpolation = 'nearest' , cmap = plt . cm . get_cmap ( cmap ) ) plt . colorbar ( mappable = image ) x_tick_marks = np . arange ( len ( pred_classes ) ) y_tick_marks = np . arange ( len ( true_classes ) ) ax . set_xticks ( x_tick_marks ) ax . set_xticklabels ( pred_classes , fontsize = text_fontsize , rotation = x_tick_rotation ) ax . set_yticks ( y_tick_marks ) ax . set_yticklabels ( true_classes , fontsize = text_fontsize ) thresh = cm . max ( ) / 2. for i , j in itertools . product ( range ( cm . shape [ 0 ] ) , range ( cm . shape [ 1 ] ) ) : if not ( hide_zeros and cm [ i , j ] == 0 ) : ax . text ( j , i , cm [ i , j ] , horizontalalignment = "center" , verticalalignment = "center" , fontsize = text_fontsize , color = "white" if cm [ i , j ] > thresh else "black" ) ax . set_ylabel ( 'True label' , fontsize = text_fontsize ) ax . set_xlabel ( 'Predicted label' , fontsize = text_fontsize ) ax . grid ( 'off' ) return ax
Generates confusion matrix plot from predictions and true labels
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def plot_feature_importances ( clf , title = 'Feature Importance' , feature_names = None , max_num_features = 20 , order = 'descending' , x_tick_rotation = 0 , ax = None , figsize = None , title_fontsize = "large" , text_fontsize = "medium" ) : if not hasattr ( clf , 'feature_importances_' ) : raise TypeError ( '"feature_importances_" attribute not in classifier. ' 'Cannot plot feature importances.' ) importances = clf . feature_importances_ if hasattr ( clf , 'estimators_' ) and isinstance ( clf . estimators_ , list ) and hasattr ( clf . estimators_ [ 0 ] , 'feature_importances_' ) : std = np . std ( [ tree . feature_importances_ for tree in clf . estimators_ ] , axis = 0 ) else : std = None if order == 'descending' : indices = np . argsort ( importances ) [ : : - 1 ] elif order == 'ascending' : indices = np . argsort ( importances ) elif order is None : indices = np . array ( range ( len ( importances ) ) ) else : raise ValueError ( 'Invalid argument {} for "order"' . format ( order ) ) if ax is None : fig , ax = plt . subplots ( 1 , 1 , figsize = figsize ) if feature_names is None : feature_names = indices else : feature_names = np . array ( feature_names ) [ indices ] max_num_features = min ( max_num_features , len ( importances ) ) ax . set_title ( title , fontsize = title_fontsize ) if std is not None : ax . bar ( range ( max_num_features ) , importances [ indices ] [ : max_num_features ] , color = 'r' , yerr = std [ indices ] [ : max_num_features ] , align = 'center' ) else : ax . bar ( range ( max_num_features ) , importances [ indices ] [ : max_num_features ] , color = 'r' , align = 'center' ) ax . set_xticks ( range ( max_num_features ) ) ax . set_xticklabels ( feature_names [ : max_num_features ] , rotation = x_tick_rotation ) ax . set_xlim ( [ - 1 , max_num_features ] ) ax . tick_params ( labelsize = text_fontsize ) return ax
Generates a plot of a classifier s feature importances .
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def plot_silhouette ( clf , X , title = 'Silhouette Analysis' , metric = 'euclidean' , copy = True , ax = None , figsize = None , cmap = 'nipy_spectral' , title_fontsize = "large" , text_fontsize = "medium" ) : if copy : clf = clone ( clf ) cluster_labels = clf . fit_predict ( X ) n_clusters = len ( set ( cluster_labels ) ) silhouette_avg = silhouette_score ( X , cluster_labels , metric = metric ) sample_silhouette_values = silhouette_samples ( X , cluster_labels , metric = metric ) if ax is None : fig , ax = plt . subplots ( 1 , 1 , figsize = figsize ) ax . set_title ( title , fontsize = title_fontsize ) ax . set_xlim ( [ - 0.1 , 1 ] ) ax . set_ylim ( [ 0 , len ( X ) + ( n_clusters + 1 ) * 10 + 10 ] ) ax . set_xlabel ( 'Silhouette coefficient values' , fontsize = text_fontsize ) ax . set_ylabel ( 'Cluster label' , fontsize = text_fontsize ) y_lower = 10 for i in range ( n_clusters ) : ith_cluster_silhouette_values = sample_silhouette_values [ cluster_labels == i ] ith_cluster_silhouette_values . sort ( ) size_cluster_i = ith_cluster_silhouette_values . shape [ 0 ] y_upper = y_lower + size_cluster_i color = plt . cm . get_cmap ( cmap ) ( float ( i ) / n_clusters ) ax . fill_betweenx ( np . arange ( y_lower , y_upper ) , 0 , ith_cluster_silhouette_values , facecolor = color , edgecolor = color , alpha = 0.7 ) ax . text ( - 0.05 , y_lower + 0.5 * size_cluster_i , str ( i ) , fontsize = text_fontsize ) y_lower = y_upper + 10 ax . axvline ( x = silhouette_avg , color = "red" , linestyle = "--" , label = 'Silhouette score: {0:0.3f}' . format ( silhouette_avg ) ) ax . set_yticks ( [ ] ) # Clear the y-axis labels / ticks ax . set_xticks ( np . arange ( - 0.1 , 1.0 , 0.2 ) ) ax . tick_params ( labelsize = text_fontsize ) ax . legend ( loc = 'best' , fontsize = text_fontsize ) return ax
Plots silhouette analysis of clusters using fit_predict .
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def _clone_and_score_clusterer ( clf , X , n_clusters ) : start = time . time ( ) clf = clone ( clf ) setattr ( clf , 'n_clusters' , n_clusters ) return clf . fit ( X ) . score ( X ) , time . time ( ) - start
Clones and scores clusterer instance .
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def plot_learning_curve ( clf , X , y , title = 'Learning Curve' , cv = None , shuffle = False , random_state = None , train_sizes = None , n_jobs = 1 , scoring = None , ax = None , figsize = None , title_fontsize = "large" , text_fontsize = "medium" ) : if ax is None : fig , ax = plt . subplots ( 1 , 1 , figsize = figsize ) if train_sizes is None : train_sizes = np . linspace ( .1 , 1.0 , 5 ) ax . set_title ( title , fontsize = title_fontsize ) ax . set_xlabel ( "Training examples" , fontsize = text_fontsize ) ax . set_ylabel ( "Score" , fontsize = text_fontsize ) train_sizes , train_scores , test_scores = learning_curve ( clf , X , y , cv = cv , n_jobs = n_jobs , train_sizes = train_sizes , scoring = scoring , shuffle = shuffle , random_state = random_state ) train_scores_mean = np . mean ( train_scores , axis = 1 ) train_scores_std = np . std ( train_scores , axis = 1 ) test_scores_mean = np . mean ( test_scores , axis = 1 ) test_scores_std = np . std ( test_scores , axis = 1 ) ax . grid ( ) ax . fill_between ( train_sizes , train_scores_mean - train_scores_std , train_scores_mean + train_scores_std , alpha = 0.1 , color = "r" ) ax . fill_between ( train_sizes , test_scores_mean - test_scores_std , test_scores_mean + test_scores_std , alpha = 0.1 , color = "g" ) ax . plot ( train_sizes , train_scores_mean , 'o-' , color = "r" , label = "Training score" ) ax . plot ( train_sizes , test_scores_mean , 'o-' , color = "g" , label = "Cross-validation score" ) ax . tick_params ( labelsize = text_fontsize ) ax . legend ( loc = "best" , fontsize = text_fontsize ) return ax
Generates a plot of the train and test learning curves for a classifier .
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def plot_calibration_curve ( y_true , probas_list , clf_names = None , n_bins = 10 , title = 'Calibration plots (Reliability Curves)' , ax = None , figsize = None , cmap = 'nipy_spectral' , title_fontsize = "large" , text_fontsize = "medium" ) : y_true = np . asarray ( y_true ) if not isinstance ( probas_list , list ) : raise ValueError ( '`probas_list` does not contain a list.' ) classes = np . unique ( y_true ) if len ( classes ) > 2 : raise ValueError ( 'plot_calibration_curve only ' 'works for binary classification' ) if clf_names is None : clf_names = [ 'Classifier {}' . format ( x + 1 ) for x in range ( len ( probas_list ) ) ] if len ( clf_names ) != len ( probas_list ) : raise ValueError ( 'Length {} of `clf_names` does not match length {} of' ' `probas_list`' . format ( len ( clf_names ) , len ( probas_list ) ) ) if ax is None : fig , ax = plt . subplots ( 1 , 1 , figsize = figsize ) ax . plot ( [ 0 , 1 ] , [ 0 , 1 ] , "k:" , label = "Perfectly calibrated" ) for i , probas in enumerate ( probas_list ) : probas = np . asarray ( probas ) if probas . ndim > 2 : raise ValueError ( 'Index {} in probas_list has invalid ' 'shape {}' . format ( i , probas . shape ) ) if probas . ndim == 2 : probas = probas [ : , 1 ] if probas . shape != y_true . shape : raise ValueError ( 'Index {} in probas_list has invalid ' 'shape {}' . format ( i , probas . shape ) ) probas = ( probas - probas . min ( ) ) / ( probas . max ( ) - probas . min ( ) ) fraction_of_positives , mean_predicted_value = calibration_curve ( y_true , probas , n_bins = n_bins ) color = plt . cm . get_cmap ( cmap ) ( float ( i ) / len ( probas_list ) ) ax . plot ( mean_predicted_value , fraction_of_positives , 's-' , label = clf_names [ i ] , color = color ) ax . set_title ( title , fontsize = title_fontsize ) ax . set_xlabel ( 'Mean predicted value' , fontsize = text_fontsize ) ax . set_ylabel ( 'Fraction of positives' , fontsize = text_fontsize ) ax . set_ylim ( [ - 0.05 , 1.05 ] ) ax . legend ( loc = 'lower right' ) return ax
Plots calibration curves for a set of classifier probability estimates .
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def clustering_factory ( clf ) : required_methods = [ 'fit' , 'fit_predict' ] for method in required_methods : if not hasattr ( clf , method ) : raise TypeError ( '"{}" is not in clf. Did you ' 'pass a clusterer instance?' . format ( method ) ) additional_methods = { 'plot_silhouette' : plot_silhouette , 'plot_elbow_curve' : plot_elbow_curve } for key , fn in six . iteritems ( additional_methods ) : if hasattr ( clf , key ) : warnings . warn ( '"{}" method already in clf. ' 'Overriding anyway. This may ' 'result in unintended behavior.' . format ( key ) ) setattr ( clf , key , types . MethodType ( fn , clf ) ) return clf
Embeds scikit - plot plotting methods in an sklearn clusterer instance .
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def validate_labels ( known_classes , passed_labels , argument_name ) : known_classes = np . array ( known_classes ) passed_labels = np . array ( passed_labels ) unique_labels , unique_indexes = np . unique ( passed_labels , return_index = True ) if len ( passed_labels ) != len ( unique_labels ) : indexes = np . arange ( 0 , len ( passed_labels ) ) duplicate_indexes = indexes [ ~ np . in1d ( indexes , unique_indexes ) ] duplicate_labels = [ str ( x ) for x in passed_labels [ duplicate_indexes ] ] msg = "The following duplicate labels were passed into {0}: {1}" . format ( argument_name , ", " . join ( duplicate_labels ) ) raise ValueError ( msg ) passed_labels_absent = ~ np . in1d ( passed_labels , known_classes ) if np . any ( passed_labels_absent ) : absent_labels = [ str ( x ) for x in passed_labels [ passed_labels_absent ] ] msg = ( "The following labels " "were passed into {0}, " "but were not found in " "labels: {1}" ) . format ( argument_name , ", " . join ( absent_labels ) ) raise ValueError ( msg ) return
Validates the labels passed into the true_labels or pred_labels arguments in the plot_confusion_matrix function .
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def cumulative_gain_curve ( y_true , y_score , pos_label = None ) : y_true , y_score = np . asarray ( y_true ) , np . asarray ( y_score ) # ensure binary classification if pos_label is not specified classes = np . unique ( y_true ) if ( pos_label is None and not ( np . array_equal ( classes , [ 0 , 1 ] ) or np . array_equal ( classes , [ - 1 , 1 ] ) or np . array_equal ( classes , [ 0 ] ) or np . array_equal ( classes , [ - 1 ] ) or np . array_equal ( classes , [ 1 ] ) ) ) : raise ValueError ( "Data is not binary and pos_label is not specified" ) elif pos_label is None : pos_label = 1. # make y_true a boolean vector y_true = ( y_true == pos_label ) sorted_indices = np . argsort ( y_score ) [ : : - 1 ] y_true = y_true [ sorted_indices ] gains = np . cumsum ( y_true ) percentages = np . arange ( start = 1 , stop = len ( y_true ) + 1 ) gains = gains / float ( np . sum ( y_true ) ) percentages = percentages / float ( len ( y_true ) ) gains = np . insert ( gains , 0 , [ 0 ] ) percentages = np . insert ( percentages , 0 , [ 0 ] ) return percentages , gains
This function generates the points necessary to plot the Cumulative Gain
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def getargspec ( func ) : # noqa: E731 type: (Any) -> Any if inspect . ismethod ( func ) : func = func . __func__ parts = 0 , ( ) # noqa: E731 type: Tuple[int, Tuple[unicode, ...]] if type ( func ) is partial : keywords = func . keywords if keywords is None : keywords = { } parts = len ( func . args ) , keywords . keys ( ) func = func . func if not inspect . isfunction ( func ) : raise TypeError ( '%r is not a Python function' % func ) args , varargs , varkw = inspect . getargs ( func . __code__ ) func_defaults = func . __defaults__ if func_defaults is None : func_defaults = [ ] else : func_defaults = list ( func_defaults ) if parts [ 0 ] : args = args [ parts [ 0 ] : ] if parts [ 1 ] : for arg in parts [ 1 ] : i = args . index ( arg ) - len ( args ) # type: ignore del args [ i ] try : del func_defaults [ i ] except IndexError : pass return inspect . ArgSpec ( args , varargs , varkw , func_defaults )
Used because getargspec for python 2 . 7 does not accept functools . partial which is the type for pytest fixtures .
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def querystring ( self ) : return { key : value for ( key , value ) in self . qs . items ( ) if key . startswith ( self . MANAGED_KEYS ) or self . _get_key_values ( 'filter[' ) }
Return original querystring but containing only managed keys
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def filters ( self ) : results = [ ] filters = self . qs . get ( 'filter' ) if filters is not None : try : results . extend ( json . loads ( filters ) ) except ( ValueError , TypeError ) : raise InvalidFilters ( "Parse error" ) if self . _get_key_values ( 'filter[' ) : results . extend ( self . _simple_filters ( self . _get_key_values ( 'filter[' ) ) ) return results
Return filters from query string .
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def pagination ( self ) : # check values type result = self . _get_key_values ( 'page' ) for key , value in result . items ( ) : if key not in ( 'number' , 'size' ) : raise BadRequest ( "{} is not a valid parameter of pagination" . format ( key ) , source = { 'parameter' : 'page' } ) try : int ( value ) except ValueError : raise BadRequest ( "Parse error" , source = { 'parameter' : 'page[{}]' . format ( key ) } ) if current_app . config . get ( 'ALLOW_DISABLE_PAGINATION' , True ) is False and int ( result . get ( 'size' , 1 ) ) == 0 : raise BadRequest ( "You are not allowed to disable pagination" , source = { 'parameter' : 'page[size]' } ) if current_app . config . get ( 'MAX_PAGE_SIZE' ) is not None and 'size' in result : if int ( result [ 'size' ] ) > current_app . config [ 'MAX_PAGE_SIZE' ] : raise BadRequest ( "Maximum page size is {}" . format ( current_app . config [ 'MAX_PAGE_SIZE' ] ) , source = { 'parameter' : 'page[size]' } ) return result
Return all page parameters as a dict .
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def fields ( self ) : result = self . _get_key_values ( 'fields' ) for key , value in result . items ( ) : if not isinstance ( value , list ) : result [ key ] = [ value ] for key , value in result . items ( ) : schema = get_schema_from_type ( key ) for obj in value : if obj not in schema . _declared_fields : raise InvalidField ( "{} has no attribute {}" . format ( schema . __name__ , obj ) ) return result
Return fields wanted by client .
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def sorting ( self ) : if self . qs . get ( 'sort' ) : sorting_results = [ ] for sort_field in self . qs [ 'sort' ] . split ( ',' ) : field = sort_field . replace ( '-' , '' ) if field not in self . schema . _declared_fields : raise InvalidSort ( "{} has no attribute {}" . format ( self . schema . __name__ , field ) ) if field in get_relationships ( self . schema ) : raise InvalidSort ( "You can't sort on {} because it is a relationship field" . format ( field ) ) field = get_model_field ( self . schema , field ) order = 'desc' if sort_field . startswith ( '-' ) else 'asc' sorting_results . append ( { 'field' : field , 'order' : order } ) return sorting_results return [ ]
Return fields to sort by including sort name for SQLAlchemy and row sort parameter for other ORMs
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def include ( self ) : include_param = self . qs . get ( 'include' , [ ] ) if current_app . config . get ( 'MAX_INCLUDE_DEPTH' ) is not None : for include_path in include_param : if len ( include_path . split ( '.' ) ) > current_app . config [ 'MAX_INCLUDE_DEPTH' ] : raise InvalidInclude ( "You can't use include through more than {} relationships" . format ( current_app . config [ 'MAX_INCLUDE_DEPTH' ] ) ) return include_param . split ( ',' ) if include_param else [ ]
Return fields to include
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def to_dict ( self ) : error_dict = { } for field in ( 'status' , 'source' , 'title' , 'detail' , 'id' , 'code' , 'links' , 'meta' ) : if getattr ( self , field , None ) : error_dict . update ( { field : getattr ( self , field ) } ) return error_dict
Return values of each fields of an jsonapi error
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def dispatch_request ( self , * args , * * kwargs ) : method = getattr ( self , request . method . lower ( ) , None ) if method is None and request . method == 'HEAD' : method = getattr ( self , 'get' , None ) assert method is not None , 'Unimplemented method {}' . format ( request . method ) headers = { 'Content-Type' : 'application/vnd.api+json' } response = method ( * args , * * kwargs ) if isinstance ( response , Response ) : response . headers . add ( 'Content-Type' , 'application/vnd.api+json' ) return response if not isinstance ( response , tuple ) : if isinstance ( response , dict ) : response . update ( { 'jsonapi' : { 'version' : '1.0' } } ) return make_response ( json . dumps ( response , cls = JSONEncoder ) , 200 , headers ) try : data , status_code , headers = response headers . update ( { 'Content-Type' : 'application/vnd.api+json' } ) except ValueError : pass try : data , status_code = response except ValueError : pass if isinstance ( data , dict ) : data . update ( { 'jsonapi' : { 'version' : '1.0' } } ) return make_response ( json . dumps ( data , cls = JSONEncoder ) , status_code , headers )
Logic of how to handle a request
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def get ( self , * args , * * kwargs ) : self . before_get ( args , kwargs ) qs = QSManager ( request . args , self . schema ) objects_count , objects = self . get_collection ( qs , kwargs ) schema_kwargs = getattr ( self , 'get_schema_kwargs' , dict ( ) ) schema_kwargs . update ( { 'many' : True } ) self . before_marshmallow ( args , kwargs ) schema = compute_schema ( self . schema , schema_kwargs , qs , qs . include ) result = schema . dump ( objects ) . data view_kwargs = request . view_args if getattr ( self , 'view_kwargs' , None ) is True else dict ( ) add_pagination_links ( result , objects_count , qs , url_for ( self . view , _external = True , * * view_kwargs ) ) result . update ( { 'meta' : { 'count' : objects_count } } ) final_result = self . after_get ( result ) return final_result
Retrieve a collection of objects
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def post ( self , * args , * * kwargs ) : json_data = request . get_json ( ) or { } qs = QSManager ( request . args , self . schema ) schema = compute_schema ( self . schema , getattr ( self , 'post_schema_kwargs' , dict ( ) ) , qs , qs . include ) try : data , errors = schema . load ( json_data ) except IncorrectTypeError as e : errors = e . messages for error in errors [ 'errors' ] : error [ 'status' ] = '409' error [ 'title' ] = "Incorrect type" return errors , 409 except ValidationError as e : errors = e . messages for message in errors [ 'errors' ] : message [ 'status' ] = '422' message [ 'title' ] = "Validation error" return errors , 422 if errors : for error in errors [ 'errors' ] : error [ 'status' ] = "422" error [ 'title' ] = "Validation error" return errors , 422 self . before_post ( args , kwargs , data = data ) obj = self . create_object ( data , kwargs ) result = schema . dump ( obj ) . data if result [ 'data' ] . get ( 'links' , { } ) . get ( 'self' ) : final_result = ( result , 201 , { 'Location' : result [ 'data' ] [ 'links' ] [ 'self' ] } ) else : final_result = ( result , 201 ) result = self . after_post ( final_result ) return result
Create an object
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def get ( self , * args , * * kwargs ) : self . before_get ( args , kwargs ) qs = QSManager ( request . args , self . schema ) obj = self . get_object ( kwargs , qs ) self . before_marshmallow ( args , kwargs ) schema = compute_schema ( self . schema , getattr ( self , 'get_schema_kwargs' , dict ( ) ) , qs , qs . include ) result = schema . dump ( obj ) . data final_result = self . after_get ( result ) return final_result
Get object details
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def patch ( self , * args , * * kwargs ) : json_data = request . get_json ( ) or { } qs = QSManager ( request . args , self . schema ) schema_kwargs = getattr ( self , 'patch_schema_kwargs' , dict ( ) ) schema_kwargs . update ( { 'partial' : True } ) self . before_marshmallow ( args , kwargs ) schema = compute_schema ( self . schema , schema_kwargs , qs , qs . include ) try : data , errors = schema . load ( json_data ) except IncorrectTypeError as e : errors = e . messages for error in errors [ 'errors' ] : error [ 'status' ] = '409' error [ 'title' ] = "Incorrect type" return errors , 409 except ValidationError as e : errors = e . messages for message in errors [ 'errors' ] : message [ 'status' ] = '422' message [ 'title' ] = "Validation error" return errors , 422 if errors : for error in errors [ 'errors' ] : error [ 'status' ] = "422" error [ 'title' ] = "Validation error" return errors , 422 if 'id' not in json_data [ 'data' ] : raise BadRequest ( 'Missing id in "data" node' , source = { 'pointer' : '/data/id' } ) if ( str ( json_data [ 'data' ] [ 'id' ] ) != str ( kwargs [ getattr ( self . _data_layer , 'url_field' , 'id' ) ] ) ) : raise BadRequest ( 'Value of id does not match the resource identifier in url' , source = { 'pointer' : '/data/id' } ) self . before_patch ( args , kwargs , data = data ) obj = self . update_object ( data , qs , kwargs ) result = schema . dump ( obj ) . data final_result = self . after_patch ( result ) return final_result
Update an object
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def delete ( self , * args , * * kwargs ) : self . before_delete ( args , kwargs ) self . delete_object ( kwargs ) result = { 'meta' : { 'message' : 'Object successfully deleted' } } final_result = self . after_delete ( result ) return final_result
Delete an object
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def get ( self , * args , * * kwargs ) : self . before_get ( args , kwargs ) relationship_field , model_relationship_field , related_type_ , related_id_field = self . _get_relationship_data ( ) obj , data = self . _data_layer . get_relationship ( model_relationship_field , related_type_ , related_id_field , kwargs ) result = { 'links' : { 'self' : request . path , 'related' : self . schema . _declared_fields [ relationship_field ] . get_related_url ( obj ) } , 'data' : data } qs = QSManager ( request . args , self . schema ) if qs . include : schema = compute_schema ( self . schema , dict ( ) , qs , qs . include ) serialized_obj = schema . dump ( obj ) result [ 'included' ] = serialized_obj . data . get ( 'included' , dict ( ) ) final_result = self . after_get ( result ) return final_result
Get a relationship details
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def patch ( self , * args , * * kwargs ) : json_data = request . get_json ( ) or { } relationship_field , model_relationship_field , related_type_ , related_id_field = self . _get_relationship_data ( ) if 'data' not in json_data : raise BadRequest ( 'You must provide data with a "data" route node' , source = { 'pointer' : '/data' } ) if isinstance ( json_data [ 'data' ] , dict ) : if 'type' not in json_data [ 'data' ] : raise BadRequest ( 'Missing type in "data" node' , source = { 'pointer' : '/data/type' } ) if 'id' not in json_data [ 'data' ] : raise BadRequest ( 'Missing id in "data" node' , source = { 'pointer' : '/data/id' } ) if json_data [ 'data' ] [ 'type' ] != related_type_ : raise InvalidType ( 'The type field does not match the resource type' , source = { 'pointer' : '/data/type' } ) if isinstance ( json_data [ 'data' ] , list ) : for obj in json_data [ 'data' ] : if 'type' not in obj : raise BadRequest ( 'Missing type in "data" node' , source = { 'pointer' : '/data/type' } ) if 'id' not in obj : raise BadRequest ( 'Missing id in "data" node' , source = { 'pointer' : '/data/id' } ) if obj [ 'type' ] != related_type_ : raise InvalidType ( 'The type provided does not match the resource type' , source = { 'pointer' : '/data/type' } ) self . before_patch ( args , kwargs , json_data = json_data ) obj_ , updated = self . _data_layer . update_relationship ( json_data , model_relationship_field , related_id_field , kwargs ) status_code = 200 result = { 'meta' : { 'message' : 'Relationship successfully updated' } } if updated is False : result = '' status_code = 204 final_result = self . after_patch ( result , status_code ) return final_result
Update a relationship
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def _get_relationship_data ( self ) : relationship_field = request . path . split ( '/' ) [ - 1 ] . replace ( '-' , '_' ) if relationship_field not in get_relationships ( self . schema ) : raise RelationNotFound ( "{} has no attribute {}" . format ( self . schema . __name__ , relationship_field ) ) related_type_ = self . schema . _declared_fields [ relationship_field ] . type_ related_id_field = self . schema . _declared_fields [ relationship_field ] . id_field model_relationship_field = get_model_field ( self . schema , relationship_field ) return relationship_field , model_relationship_field , related_type_ , related_id_field
Get useful data for relationship management
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def compute_schema ( schema_cls , default_kwargs , qs , include ) : # manage include_data parameter of the schema schema_kwargs = default_kwargs schema_kwargs [ 'include_data' ] = tuple ( ) # collect sub-related_includes related_includes = { } if include : for include_path in include : field = include_path . split ( '.' ) [ 0 ] if field not in schema_cls . _declared_fields : raise InvalidInclude ( "{} has no attribute {}" . format ( schema_cls . __name__ , field ) ) elif not isinstance ( schema_cls . _declared_fields [ field ] , Relationship ) : raise InvalidInclude ( "{} is not a relationship attribute of {}" . format ( field , schema_cls . __name__ ) ) schema_kwargs [ 'include_data' ] += ( field , ) if field not in related_includes : related_includes [ field ] = [ ] if '.' in include_path : related_includes [ field ] += [ '.' . join ( include_path . split ( '.' ) [ 1 : ] ) ] # make sure id field is in only parameter unless marshamllow will raise an Exception if schema_kwargs . get ( 'only' ) is not None and 'id' not in schema_kwargs [ 'only' ] : schema_kwargs [ 'only' ] += ( 'id' , ) # create base schema instance schema = schema_cls ( * * schema_kwargs ) # manage sparse fieldsets if schema . opts . type_ in qs . fields : tmp_only = set ( schema . declared_fields . keys ( ) ) & set ( qs . fields [ schema . opts . type_ ] ) if schema . only : tmp_only &= set ( schema . only ) schema . only = tuple ( tmp_only ) # make sure again that id field is in only parameter unless marshamllow will raise an Exception if schema . only is not None and 'id' not in schema . only : schema . only += ( 'id' , ) # manage compound documents if include : for include_path in include : field = include_path . split ( '.' ) [ 0 ] relation_field = schema . declared_fields [ field ] related_schema_cls = schema . declared_fields [ field ] . __dict__ [ '_Relationship__schema' ] related_schema_kwargs = { } if 'context' in default_kwargs : related_schema_kwargs [ 'context' ] = default_kwargs [ 'context' ] if isinstance ( related_schema_cls , SchemaABC ) : related_schema_kwargs [ 'many' ] = related_schema_cls . many related_schema_cls = related_schema_cls . __class__ if isinstance ( related_schema_cls , str ) : related_schema_cls = class_registry . get_class ( related_schema_cls ) related_schema = compute_schema ( related_schema_cls , related_schema_kwargs , qs , related_includes [ field ] or None ) relation_field . __dict__ [ '_Relationship__schema' ] = related_schema return schema
Compute a schema around compound documents and sparse fieldsets
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def get_model_field ( schema , field ) : if schema . _declared_fields . get ( field ) is None : raise Exception ( "{} has no attribute {}" . format ( schema . __name__ , field ) ) if schema . _declared_fields [ field ] . attribute is not None : return schema . _declared_fields [ field ] . attribute return field
Get the model field of a schema field
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def get_nested_fields ( schema , model_field = False ) : nested_fields = [ ] for ( key , value ) in schema . _declared_fields . items ( ) : if isinstance ( value , List ) and isinstance ( value . container , Nested ) : nested_fields . append ( key ) elif isinstance ( value , Nested ) : nested_fields . append ( key ) if model_field is True : nested_fields = [ get_model_field ( schema , key ) for key in nested_fields ] return nested_fields
Return nested fields of a schema to support a join
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def get_relationships ( schema , model_field = False ) : relationships = [ key for ( key , value ) in schema . _declared_fields . items ( ) if isinstance ( value , Relationship ) ] if model_field is True : relationships = [ get_model_field ( schema , key ) for key in relationships ] return relationships
Return relationship fields of a schema
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def get_schema_from_type ( resource_type ) : for cls_name , cls in class_registry . _registry . items ( ) : try : if cls [ 0 ] . opts . type_ == resource_type : return cls [ 0 ] except Exception : pass raise Exception ( "Couldn't find schema for type: {}" . format ( resource_type ) )
Retrieve a schema from the registry by his type
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def get_schema_field ( schema , field ) : schema_fields_to_model = { key : get_model_field ( schema , key ) for ( key , value ) in schema . _declared_fields . items ( ) } for key , value in schema_fields_to_model . items ( ) : if value == field : return key raise Exception ( "Couldn't find schema field from {}" . format ( field ) )
Get the schema field of a model field
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def bound_rewritable_methods ( self , methods ) : for key , value in methods . items ( ) : if key in self . REWRITABLE_METHODS : setattr ( self , key , types . MethodType ( value , self ) )
Bound additional methods to current instance
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def create_filters ( model , filter_info , resource ) : filters = [ ] for filter_ in filter_info : filters . append ( Node ( model , filter_ , resource , resource . schema ) . resolve ( ) ) return filters
Apply filters from filters information to base query
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def resolve ( self ) : if 'or' not in self . filter_ and 'and' not in self . filter_ and 'not' not in self . filter_ : value = self . value if isinstance ( value , dict ) : value = Node ( self . related_model , value , self . resource , self . related_schema ) . resolve ( ) if '__' in self . filter_ . get ( 'name' , '' ) : value = { self . filter_ [ 'name' ] . split ( '__' ) [ 1 ] : value } if isinstance ( value , dict ) : return getattr ( self . column , self . operator ) ( * * value ) else : return getattr ( self . column , self . operator ) ( value ) if 'or' in self . filter_ : return or_ ( Node ( self . model , filt , self . resource , self . schema ) . resolve ( ) for filt in self . filter_ [ 'or' ] ) if 'and' in self . filter_ : return and_ ( Node ( self . model , filt , self . resource , self . schema ) . resolve ( ) for filt in self . filter_ [ 'and' ] ) if 'not' in self . filter_ : return not_ ( Node ( self . model , self . filter_ [ 'not' ] , self . resource , self . schema ) . resolve ( ) )
Create filter for a particular node of the filter tree
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def name ( self ) : name = self . filter_ . get ( 'name' ) if name is None : raise InvalidFilters ( "Can't find name of a filter" ) if '__' in name : name = name . split ( '__' ) [ 0 ] if name not in self . schema . _declared_fields : raise InvalidFilters ( "{} has no attribute {}" . format ( self . schema . __name__ , name ) ) return name
Return the name of the node or raise a BadRequest exception
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def column ( self ) : field = self . name model_field = get_model_field ( self . schema , field ) try : return getattr ( self . model , model_field ) except AttributeError : raise InvalidFilters ( "{} has no attribute {}" . format ( self . model . __name__ , model_field ) )
Get the column object
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def operator ( self ) : operators = ( self . op , self . op + '_' , '__' + self . op + '__' ) for op in operators : if hasattr ( self . column , op ) : return op raise InvalidFilters ( "{} has no operator {}" . format ( self . column . key , self . op ) )
Get the function operator from his name
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def value ( self ) : if self . filter_ . get ( 'field' ) is not None : try : result = getattr ( self . model , self . filter_ [ 'field' ] ) except AttributeError : raise InvalidFilters ( "{} has no attribute {}" . format ( self . model . __name__ , self . filter_ [ 'field' ] ) ) else : return result else : if 'val' not in self . filter_ : raise InvalidFilters ( "Can't find value or field in a filter" ) return self . filter_ [ 'val' ]
Get the value to filter on
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def related_model ( self ) : relationship_field = self . name if relationship_field not in get_relationships ( self . schema ) : raise InvalidFilters ( "{} has no relationship attribute {}" . format ( self . schema . __name__ , relationship_field ) ) return getattr ( self . model , get_model_field ( self . schema , relationship_field ) ) . property . mapper . class_
Get the related model of a relationship field
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def related_schema ( self ) : relationship_field = self . name if relationship_field not in get_relationships ( self . schema ) : raise InvalidFilters ( "{} has no relationship attribute {}" . format ( self . schema . __name__ , relationship_field ) ) return self . schema . _declared_fields [ relationship_field ] . schema . __class__
Get the related schema of a relationship field
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def init_app ( self , app = None , blueprint = None , additional_blueprints = None ) : if app is not None : self . app = app if blueprint is not None : self . blueprint = blueprint for resource in self . resources : self . route ( resource [ 'resource' ] , resource [ 'view' ] , * resource [ 'urls' ] , url_rule_options = resource [ 'url_rule_options' ] ) if self . blueprint is not None : self . app . register_blueprint ( self . blueprint ) if additional_blueprints is not None : for blueprint in additional_blueprints : self . app . register_blueprint ( blueprint ) self . app . config . setdefault ( 'PAGE_SIZE' , 30 )
Update flask application with our api
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def route ( self , resource , view , * urls , * * kwargs ) : resource . view = view url_rule_options = kwargs . get ( 'url_rule_options' ) or dict ( ) view_func = resource . as_view ( view ) if 'blueprint' in kwargs : resource . view = '.' . join ( [ kwargs [ 'blueprint' ] . name , resource . view ] ) for url in urls : kwargs [ 'blueprint' ] . add_url_rule ( url , view_func = view_func , * * url_rule_options ) elif self . blueprint is not None : resource . view = '.' . join ( [ self . blueprint . name , resource . view ] ) for url in urls : self . blueprint . add_url_rule ( url , view_func = view_func , * * url_rule_options ) elif self . app is not None : for url in urls : self . app . add_url_rule ( url , view_func = view_func , * * url_rule_options ) else : self . resources . append ( { 'resource' : resource , 'view' : view , 'urls' : urls , 'url_rule_options' : url_rule_options } ) self . resource_registry . append ( resource )
Create an api view .
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def oauth_manager ( self , oauth_manager ) : @ self . app . before_request def before_request ( ) : endpoint = request . endpoint resource = self . app . view_functions [ endpoint ] . view_class if not getattr ( resource , 'disable_oauth' ) : scopes = request . args . get ( 'scopes' ) if getattr ( resource , 'schema' ) : scopes = [ self . build_scope ( resource , request . method ) ] elif scopes : scopes = scopes . split ( ',' ) if scopes : scopes = scopes . split ( ',' ) valid , req = oauth_manager . verify_request ( scopes ) for func in oauth_manager . _after_request_funcs : valid , req = func ( valid , req ) if not valid : if oauth_manager . _invalid_response : return oauth_manager . _invalid_response ( req ) return abort ( 401 ) request . oauth = req
Use the oauth manager to enable oauth for API
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